ALBERT

Description: Commercial, industrial, and military aerospace designs are increasingly deploying MEMS micro inertial measurement unit (MIMU) for motion control, automation, and positioning applications, such as the unmanned aerial vehicle (UAV), robot, and smart phone. On the one hand, MIMU has the merit of low cost, small size, low-power consumption, and high shock resistance, but on the other hand, low-cost MIMU is affected by systematic error caused by the instability of the drift, scaling factors, and axes misalignment, which may lead to large errors in the position and attitude’s determination from time to time. That means calibration before use is an effective way to improve the practical precision of MIMU. However, many customers have no precise turntable to calibrate the MIMU before they use it. To address these problems, this paper presents an easy self-calibration method to implement calibration of the MIMU on a common table only with an inclined surface, no precise turntable is needed. The calibration method is based on the following principles. First, the module of the output vector of the orthogonal configured three-axis accelerometers is equal to unit gravity. Second, when IMU rotates to a known gesture with a stable axis, the angles can be calculated through integration. Third, when the accelerometers’ parameters are calculated, it can act as a level datum. Furthermore, the accelerometers on the inclined surface are used to determine the rotating heading datum. Finally, after a series static positions test and rotating test, the parameters can be extracted and estimated. To demonstrate the success and the convenience of the proposed method, comparison experiments with the precision turntable have been made on an ADI’s MIMU. The calibration results show that the accuracy and precision of this method is quite equivalent with the turntable-based calibration, and the scale factors error with an order of magnitude always equal or less than $10^{-5}$ . The observed static and dynamic yaw maximum angular error in a certain period is <0.8°, the pitch maximum angular error is <0.5°, and the roll maximum angular error is <0.3°.

Description: We examine a distributed detection problem in a wireless sensor network, where sensor nodes collaborate to detect a Gaussian signal with an unknown change of power, i.e., a scale parameter. Due to power/bandwidth constraints, we consider the case where each sensor quantizes its observation into a binary digit. The binary data are then transmitted through error-prone wireless links to a fusion center, where a generalized likelihood ratio test (GLRT) detector is employed to perform a global decision. We study the design of a binary quantizer based on an asymptotic analysis of the GLRT. Interestingly, the quantization threshold of the quantizer is independent of the unknown scale parameter. Numerical results are included to illustrate the performance of the proposed quantizer and GLRT in binary symmetric channels (BSCs).

Description: More and more hybrid electric vehicles are driven since they offer such advantages as energy savings and better active safety performance. Hybrid vehicles have two or more power driving systems and frequently switch working condition, so controlling stability is very important. In this work, a two-stage Kalman algorithm method is used to fuse data in hybrid vehicle stability testing. First, the RT3102 navigation system and Dewetron system are introduced. Second, a modeling of data fusion is proposed based on the Kalman filter. Then, this modeling is simulated and tested on a sample vehicle, using Carsim and Simulink software to test the results. The results showed the merits of this modeling.

Description: Low-energy technologies in the Internet of Things (IoTs) era are still unable to provide the reliability needed by the industrial world, particularly in terms of the wireless operation that pervasive deployments demand. While the industrial wireless performance has achieved an acceptable degree in communications, it is no easy task to determine an efficient energy-dimensioning of the device in order to meet the application requirements. This is especially true in the face of the uncertainty inherent in energy harvesting. Thus, it is of utmost importance to model and dimension the energy consumption of the IoT applications at the pre-deployment or pre-production stages, especially when considering critical factors, such as reduced cost, life-time, and available energy. This paper presents a comprehensive model for the power consumption of wireless sensor nodes. The model takes a system-level perspective to account for all energy expenditures: communications, acquisition and processing. Furthermore, it is based only on parameters that can empirically be quantified once the platform (i.e., technology) and the application (i.e., operating conditions) are defined. This results in a new framework for studying and analyzing the energy life-cycles in applications, and it is suitable for determining in advance the specific weight of application parameters, as well as for understanding the tolerance margins and tradeoffs in the system.

Description: With the development of Internet of Things (IoT), a large amount of machine-to-machine (M2M) devices produce the data from a huge number of M2M communications. A massive access congestion control scheme in M2M communication is important in a wireless M2M network, where a wireless M2M network is constituted by a large number of wireless M2M devices. In this paper, we developed a spiderweb-based massive access management protocol for M2M wireless networks. The wireless M2M network topology considered, in this paper, is a spiderweb-based topology, which belongs to a regular topology. The spiderweb-based topology is split into a number of sub-spiderweb, while each sub-spiderweb can be seen as a small reservoir. The main function of the small reservoir is to temporarily store the massive access if the congestion problem is occurred due to the massive assess has been suddenly happened from the M2M devices. When the congestion problem is alleviated gradually, the temporarily-stored massive assess can be digested from these small reservoirs. The contribution of the proposed scheme is to reduce the packet loss rate and prolong the wireless M2M network lifetime. In addition, a dynamically adjustment of the different sub-spiderweb size is developed based on the amount of different data traffics. Simulation result finally illustrates that the proposed mechanism significantly reduces the packet loss rate and keeps the maximum network lifetime.

Description: The paper presents the methodology including the deployment of a structure comprising of C-clamp magnetic stripes and a self-calibration scheme using a four-coil design to resolve the issues of the interference and misplacement problems existing in the flexible nonintrusive current sensor tag for the current detection of household two-wire power lines. Experimental results show that the stripe structure can effectively screen out the magnetic flux originating from the nearby power line for the reduction of sensing error caused by the electromagnetic field interference and the calibration scheme containing the correction of horizontal misplacement and the analysis of power cable thickness via the four-coil design can provide the information of sensor location for accurate current sensing. Less than 4% of sensing error indicates that the proposed methodology can indeed facilitate the flexible current sensor tag for practical use.

Description: Rapidly emerging batteryless sensors are creating tremendous opportunities for truly wearable sensors for activity recognition. However, data streams from such sensors are characterized by sparsity and noise, which make activity recognition a challenging task. In this paper, we study the feasibility of passive computational RFID sensors for ambulatory monitoring. In particular, we focus on recognizing transfers out of beds or chairs and walking. Ideally, all these activities need to be monitored by movement sensor alarm systems to alert caregivers to provide supervision during the ambulation of older people in hospitals and nursing homes to prevent a fall. Our novel approach to partition continuous sensor data on natural activity boundaries and to identify transfers out of beds or chairs and walking as transitions between sequences of movements overcomes issues posed by the sparsity and the noise. We demonstrate through in-depth experiments the high performance (F-score > 93%) and the responsiveness of our approach.

Description: This paper proposes an approach to design multiple-input multiple-output radar waveforms that are orthogonal on both the transmitter and receiver. The proposed method jointly utilizes the direct sequence spread spectrum coding and orthogonal frequency division multiplexing (OFDM) chirp signaling techniques. We name it spread spectrum-coded OFDM chirp waveform diversity design. The performance of the designed waveforms is analyzed by examining the ambiguity function and correlation function. The influences of the spread spectrum code choice and the OFDM chirp parameters are also investigated. It is verified that the proposed design scheme can ensure these waveforms stay orthogonal on the receiver and have large time-bandwidth product which is beneficial to separate closely spaced targets.

Description: Studies have presented that the driver vigilance level has serious implication in the causation of road accidents. This paper focuses on integrating both the vehicle-based control behavior and physiological state to predict the driver vigilance index which is evaluated by using a smartwatch. The vehicle control behavior can be observed from the steering wheel movement. Our study utilized the smartwatch motion sensors to study the steering wheel behavior. Meanwhile, physiological state of driver reflects the driver capability of safety alert driving which is estimated by photoplethysmogram (PPG) and respiration signals in this paper. The PPG sensor is integrated in a sport wristband with a Bluetooth low energy module, transmitted the PPG signals to smartwatch in real time. The steering angle is derived by the reading from smartwatch built-in accelerometer and gyroscope sensors. On the other hand, the respiration is derived using the PPG peak baseline method. In order to utterly investigate the sleepiness-induced factors, the time, spectral, and phase space domain features are calculated. Considering the smartwatch processing capability, mutual-information technique is applied to designate the ten most descriptive features. Then, the extracted descriptive features are serve as parameters to a classifier to determine the driver aptitude status. The features are analyzed for their correlation with the subjective Koralinska sleepiness scale and through recorded video observations. The experimental results reveal that our system is capable of estimating driver hypervigilance at average of 96.5% accuracy rate by evaluating on both driving behavior and driver physiological state, provided a novel and low-cost implementation.

Description: A cavitating two-phase flow of water in a pipe with area shrinkage was experimentally investigated, acquiring at high sampling rate pressure signals and images of the cavitating flow field. The time series of the pressure fluctuations was analyzed in terms of power spectral density and related to the cavitation regimes. Furthermore, the fluctuations of the pressure measurements were also decomposed using the wavelet transform to analyze the frequency distribution of the signals energy with respect to the flow behavior. The energy content at each frequency band of the acquire signals is well related to cavitation flow-field behavior. Moreover, the artificial neural network and the least squares support vector machine (LS-SVM) were implemented to identify the cavitation regime, using, as inputs, the power spectral density distributions of the pressure fluctuations, and some features of the decomposed signals, as the wavelet energy for each decomposition level and wavelet entropy. Results indicate the most accurate model to be used in the cavitation regime identification, underlining the enhanced capability of LS-SVM trained with the input data set based on the wavelet decomposition features.

Description: In this paper, we present a novel silicon-on-insulator (SOI) complementary metal–oxide–semiconductor (CMOS) microelectromechanical-system thermal wall shear stress sensor based on a tungsten hot-wire and a single thermopile. Devices were fabricated using a commercial 1- $mu text{m}$ SOI-CMOS process followed by a deep reactive ion etching back-etch step to release a silicon dioxide membrane, which mechanically supports and thermally isolates heating and sensing elements. The sensors show an electrothermal transduction efficiency of $50~mu text{W}$ /°C, and a very small zero flow offset. Calibration for wall shear stress measurement in air in the range of 0–0.48 Pa was performed using a suction type, 2-D flow wind tunnel. The sensors were found to be extremely sensitive, up to 4 V/Pa for low wall shear stress values. Furthermore, we demonstrate the superior signal-to-noise ratio (up to five times higher) of a single thermopile readout configuration compared with a double thermopile readout configuration (embedded for comparison purposes within the same device). Finally, we verify that the output of the sensor is proportional to the cube root of the wall shear stress and we propose an accurate semiempirical formula for its modeling.

Description: We propose an optical fiber hydrostatic pressure sensor based on micro-cavities generated by the fiber fuse effect. The presented sensor is manufactured through the recycling of optical fiber destroyed by the fiber fuse effect, being, therefore, a cost-effective solution, when compared with other similar micro-cavity-based solutions. The developed sensor was characterized for pressures up to 20 kPa, showing a linear sensitivity coefficient of $0.47pm 0.03~{rm nm}cdot {rm kPa}^{-1}$ , for pressure values below 8 kPa. Furthermore, we propose a new theoretical model to describe the behavior of the microcavities embedded in optical fibers. This allows us to solve the discrepancies, already identified by other authors, between the experimental results and the ones attained with the flat mirrors Fabry-Perot model. By this way, we were able to describe the sensor response, within the full dynamic range.

Description: The monitoring and control of crops in precision agriculture sometimes requires a high collection frequency of information (e.g., temperature, humidity, and salinity) due to the variability in crops. Data acquisition and transmission are generally achieved thanks to wireless sensor networks. However, sensor nodes have limited resources. Thus, it is necessary to adapt the increase in sampling frequency for different crops, under application constraints (reliability, packet delay, and lifetime duration). In this paper, we propose to properly tune IEEE 802.15.4 MAC parameters ( macMinBE and macMaxCSMABackoffs ) and the sampling frequency of deployed sensor nodes. An analytical model of network performance is derived and used to perform the tuning of these tradeoff parameters. Simulation analysis shows that our scheme provides an efficient increase of sampling frequency of sensor nodes while satisfying application requirements.

Description: Sensors based on the magnetoelectric (ME) effect have the potential to be genuine alternatives for measuring bio-magnetic signals. Unfortunately, the sensor structure usually inhibits the problem that several non-magnetic types of noise couple mechanically into the sensor: in this contribution, we will focus on undesired acoustic coupling. Therefore, an adaptive cancellation approach based on a computationally efficient gradient estimation algorithm with a pseudo-optimally control scheme is proposed. The approach is using a microphone as a noise reference sensor and is implemented in real time. An evaluation in terms of measurements is performed inside a magnetically shielded chamber. For a particular scenario, which is characterized by double excitation, an algorithm with binary control-scheme improves the signal-to-noise ratio (SNR) only by around 4dB. If the proposed control scheme is used instead, an improvement of the SNR of around 13dB is achieved.

Description: Target detection is an important issue in the unattended ground sensors. In this paper, inspired by the idea of subspace-based direction of arrival estimation algorithms, a new target detection algorithm called subspace-based target detection (SBTD) is proposed to detect moving targets. The SBTD employs the SNR of the acoustic signals to decide whether moving targets are exiting or not. Although the SBTD has good detection performance, its cost maybe a little high for unattended sensors with low-cost hardware and long-term monitoring. To relieve the cost, we propose the hierarchical detection scheme and develop a two-stage detection method based on the SBTD for target detection in the wild, in which the first stage detection algorithm is chosen from current detection algorithms, while the second stage detection algorithm employs the SBTD. Experiments are conducted to verify the proposed detection method through acoustic signals gathered by the micro-electro-mechanical systems (MEMS) microphone array in the wild. Results show that the detector constructed by our two-stage detection method cannot only estimate the SNR of the acoustic signals but also can reduce the false alarm rate significantly with the detection rate almost unchanged in comparison with the detector chosen by its first-stage detection algorithm. The results indicate that a better detection performance is achieved in terms of the receiver operator characteristic curves.

Description: Acoustic localization is an essential technique in speech capturing, speech enhancement, video conferencing, and human–robot interaction. However, in practical situations, localization has to be performed in abominable environments, where the presence of reverberation and noise degrades the performance of available position estimates. Besides, the designed systems should be adaptive to locomotion of targets with low computational complexity. In the context, this paper introduces a robust hierarchical acoustic localization method via time-delay compensation (TDC) and interaural matching filter (IMF). Firstly, interaural time-delay (ITD) and interaural level difference (ILD), which are cues involved in first two layers, respectively, are yielded by TDC all at once. Then, a novel feature named IMF, which can eliminate the difference between binaural signals, is proposed in the third layer. The final decision making is based on a Bayesian rule. The relationships among the three layers are that the former layer provides candidate directions for later ones such that the searching space becomes gradually smaller to reduce matching time. Experiments using both a public database and a real scenario verify that TDC and IMF are robust for acoustic localization, and hierarchical system has less consumption time.

Description: Canonical correlation analysis (CCA) is a widely used data analysis tool that allows to assess the correlation between two distinct sets of signals. It computes optimal linear combinations of the signals in both sets such that the resulting signals are maximally correlated. The weight vectors defining these optimal linear combinations are referred to as “principal CCA directions”. In addition to this particular type of data analysis, CCA is also often used as a blind source separation (BSS) technique, i.e., under certain assumptions, the principal CCA directions have certain demixing properties. In this paper, we propose a distributed CCA (DCCA) algorithm that can operate in wireless sensor networks (WSNs) with a fully connected or a tree topology. The algorithm estimates the $Q$ principal CCA directions from the sensor signal observations collected by the different nodes in the WSN and extracts the corresponding sources. These network-wide principal CCA directions are estimated in a time-recursive fashion without explicitly constructing the corresponding network-wide correlation matrices, i.e., without the need for data centralization. Instead, each node locally computes smaller CCA problems and only transmits compressed sensor signal observations (of dimension $Q$ ), which significantly reduces the bit rate over the wireless links of the WSN. We prove convergence and optimality of the DCCA algorithm, and we demonstrate its performance by means of numerical simulations in a blind source separation scenario.

Description: In this paper, a comprehensive comparison analysis in terms of outage probability and average symbol error ratio (SER) is presented for cooperative cognitive multiple-input and multiple-output (CC-MIMO) multiuser systems with amplify-and-forward (AF) protocol. Specially, we consider two scenarios where the CC-MIMO multiuser systems have the perfect and imperfect channel state information (CSI). The CC-MIMO multiuser systems consist of one multi-antenna source, one single-antenna relay, and multiple multi-antenna destinations. At the secondary source and destinations, the maximal ratio transmission (MRT) and maximal ratio combining (MRC) are employed, respectively. For such CC-MIMO multiuser systems, we first obtain the exact closed-form expressions of outage probability under the two cases where the CC-MIMO multiuser systems have the perfect and imperfect CSI. Then, to reduce the implementation complexity, the tight lower bounds of outage probability and average SER are derived. Finally, to obtain insight, by using the high signal-to-noise ratio (SNR) approximation, the asymptotic estimations of outage probability are achieved. The numerical results show that the derivations are agreed with the simulations, which validate our derivations. At the same time, the results show that, for the systems without perfect CSI, the achievable diversity order reduces to one, regardless of the number of antennas at the cognitive source and destinations as well as the number of the cognitive destinations. Nevertheless, these key parameters affect the coding gain of the CC-MIMO multiuser systems. When the systems have the perfect CSI (or without feedback delay), the achievable diversity gain is determined by the minimum between the number of source’s antennas and the product of the number of destinations and the number of destination’s antennas. For the effect of PU’s parameters, our results indicate that primary systems only affect the coding gain but not the diversity gain.

Description: Currently deep learning has made great breakthroughs in visual and speech processing, mainly because it draws lessons from the hierarchical mode that brain deals with images and speech. In the field of NLP, a topic model is one of the important ways for modeling documents. Topic models are built on a generative model that clearly does not match the way humans write. In this paper, we propose Event Model, which is unsupervised and based on the language processing mechanism of neurolinguistics, to model documents. In Event Model, documents are descriptions of concrete or abstract events seen, heard, or sensed by people and words are objects in the events. Event Model has two stages: word learning and dimensionality reduction. Word learning is to learn semantics of words based on deep learning. Dimensionality reduction is the process that representing a document as a low dimensional vector by a linear mode that is completely different from topic models. Event Model achieves state-of-the-art results on document retrieval tasks.

Description: Internet of Things (IoTs) is a technology to connect machines, objects, and people with electronic devices. Many researchers have focused their works on architecture design from different perspectives. The deployment of IoT applications contains a variety of things that are attached with end-point devices, such as sensors, actuators, and radio frequency identification tags to perform sensing, controlling, and/or identifying things. The GS1 EPCglobal Architecture and IEEE 1451 standards are adopted as the cornerstone of our IoT architecture framework. In this paper, we focus on how to resolve the heterogeneity and dynamic characteristics of this variety of things. A uniform expression mechanism based on electronic product coding (EPC) scheme is introduced. The practical facilities are presented and examples of how to generate EPC scheme for transducers are illustrated. By integrating the IEEE 1451 compatible transducers, based on transducer electronic data sheet, to the identify layer of the extended GS1 EPCglobal Architecture, we can process more versatile data of different things in IoT applications. The uniform EPC scheme of heterogeneous things in the identify layer will make a consistent expression of things and compatible with the upper layer. This mechanism plays a fundamental role to support transducer capability in GS1 EPCglobal Architecture.

Description: Hierarchical routing in wireless sensor networks (WSNs) is a very important topic that has been attracting the research community in the last decade. Typical hierarchical routing is called clustering routing, in which the network is divided into multiple clusters. Recently, some types of atypical hierarchical routing arise, including chain-based, tree-based, grid-based routing, and area-based routing. There are several survey papers that present and compare the hierarchical routing protocols from various perspectives, but a survey on atypical hierarchical routing is still missing. This paper makes a first attempt to provide a comprehensive review on atypical hierarchical routing. We offer a classification of atypical hierarchical routing of WSNs, and give detailed analysis of different logical topologies. The most representative atypical hierarchical routing protocols are described, discussed, and qualitatively compared. In particular, the advantages and disadvantages of different atypical hierarchical routing protocols are analyzed with respect to their significant performances and application scenarios. Finally, we put forward some open issues concerning the design of hierarchical WSNs. This survey aims to provide useful guidance for system designers on how to evaluate and select appropriate logical topologies and hierarchical routing protocols for specific applications.

Description: A novel fiber-optic relative humidity (RH) sensor with thermal compensation based on a fiber Bragg grating (FBG) is presented. Changes in the RH are transduced to the FBG properties by an ORMOCER coating. The cross sensitivity to temperature is compensated by inscribing the Bragg grating in a highly birefringent fiber and using the individual Bragg peaks of each polarization direction for separation (discrimination) of temperature and humidity influences.

Description: This paper focuses on the nondestructive dielectric measurement for thin dielectric material using open-ended coaxial probe. The probe calibration procedure requires only a measurement of a half-space air and three open standard kits. The measured reflection coefficient for thin sample, which is backed by metal plate, is taken with a vector network analyzer up to 7 GHz and the reflection coefficient is converted to relative dielectric constant and tangent loss via closed form capacitance model and simple calibration process.

Description: We present a physical model to study the dark current and noise characteristics of quantum-dot infrared photodetectors (QDIPs). This model considers both carrier’s thermoexcitaion and carrier’s field-assisted tunneling, which are the main origin of dark current and noise in QDIPs. We use this model to analyze the effect of different structural parameters, such as QDs lateral size and density of QD, and environmental parameters, such as bias voltage and operating temperature on the dark current and the noise of QDIP. The theoretical results obtained from our model are in excellent agreement with reported experimental data. Therefore, the validity of the model is proven for these reasons. This model can be extended to other quantum structure detectors to evaluate the noise and dark current characteristics.

Description: There has been much research on shrinkage methods for real-valued covariance matrices and their inverses (precision matrices). In spectral analysis of $p$ -vector-valued time series, complex-valued spectral matrices and precision matrices arise, and good shrinkage methods are often required, most notably when the estimated complex-valued spectral matrix is singular. As an improvement on the Ledoit-Wolf (LW) type of spectral matrix estimator we use random matrix theory to derive a Rao-Blackwell estimator for a spectral matrix, its inverse being a Rao–Blackwellized estimator for the spectral precision matrix. A random matrix method has previously been proposed for complex-valued precision matrices. It was implemented by very costly simulations. We formulate a fast, completely analytic approach. Moreover, we derive a way of selecting an important parameter using predictive risk methodology. We show that both the Rao–Blackwell estimator and the random matrix estimator of the precision matrix can substantially outperform the inverse of the LW estimator in a time series setting. Our new methodology is applied to EEG-derived time series data where it is seen to work well and deliver substantial improvements for precision matrix estimation.

Description: In this paper, the state estimation problem for discrete-time linear systems influenced by multiplicative and time-correlated additive measurement noises is considered where the multiplicative noises are zero-mean white noise sequences, and the time-correlated additive noise is described by a linear system model with white noise. An optimal linear estimator for the system under consideration is proposed, which does not require computing the inverse of state transition matrix. The proposed estimator has a recursive structure, and has time-independent computation and storage load. Computer simulations are carried out to demonstrate the performance of the proposed estimator. The simulation results show the superiority of the proposed estimator.

Description: In this paper, we start with the standard support vector machine (SVM) formulation and extend it by considering a general SVM formulation with normalized margin. This results in a unified convex framework that allows many different variations in the formulation with very diverse numerical performance. The proposed unified framework can capture the existing methods, i.e., standard soft-margin SVM, $ell_{1}$ -SVM, and SVMs with standardization, feature selection, scaling, and many more SVMs, as special cases. Furthermore, our proposed framework can not only provide us with more insights on different SVMs from the “energy” and “penalty” point of views, which help us understand the connections and differences between them in a unified way, but also enable us to propose more SVMs that outperform the existing ones under some scenarios.

Description: We consider multiple-antenna signal detection of primary user transmission signals by a secondary user receiver in cognitive radio networks. The optimal detector is analyzed for the scenario where the number of primary user signals is no less than the number of receive antennas at the secondary user. We first derive exact expressions for the moments of the generalized likelihood ratio test (GLRT) statistic, yielding approximations for the false alarm and detection probabilities. We then show that the normalized GLRT statistic converges in distribution to a Gaussian random variable when the number of antennas and observations grow large at the same rate. Further, using results from large random matrix theory, we derive expressions to compute the detection probability without explicit knowledge of the channel, and then particularize these expressions for two scenarios of practical interest: 1) a single primary user sending spatially multiplexed signals, and 2) multiple spatially distributed primary users. Our analytical results are finally used to obtain simple design rules for the signal detection threshold.

Description: Wireless sensor networks (WSNs) are a key part of the Internet of Things vision which aims at bridging together the physical and the digital worlds in several application domains. In the building automation field, WSNs are widely adopted for energy optimization, safety and security purposes, and could greatly benefit from existing information already available in preexisting building information models (BIMs). Such BIMs are normally developed during the building design phase and reused continuously during the construction and operation phases of the building life-cycle. In current deployments, however, due to lack of interoperability such information (e.g., walls geometry, materials, and so on) must often be recollected and reinputed by WSN commissioning specialists. Open development and planning tools and methodologies can play a key role in fostering interoperability and convergence of BIM and WSN systems. This paper aims at demonstrating how increased interoperability between WSN development-support tools and BIM systems could provide advantages to developers, integrators, domain specialist, and BIM users. The methodology is validated by applying a newly proposed tool exploiting BIM interoperability to support the planning of the topology of a WSN based on ultrawideband technologies. The proposed approach is evaluated through a small-scale experimentation held in a historical building in Torino (Italy).

Description: Community detection in a complex network is an important problem of much interest in recent years. In general, a community detection algorithm chooses an objective function and captures the communities of the network by optimizing the objective function, and then, one uses various heuristics to solve the optimization problem to extract the interesting communities for the user. In this article, we demonstrate the procedure to transform a graph into points of a metric space and develop the methods of community detection with the help of a metric defined for a pair of points. We have also studied and analyzed the community structure of the network therein. The results obtained with our approach are very competitive with most of the well-known algorithms in the literature, and this is justified over the large collection of datasets. On the other hand, it can be observed that time taken by our algorithm is quite less compared to other methods and justifies the theoretical findings.

Description: We propose to exploit filter stopband for high sensitivity radio frequency (RF) interferometer operation by utilizing reflection scattering parameters. Combined with passband filter operation, the modified RF interferometer effectively expands its frequency coverage. A simple model is described to analyze and predict interferometer performance. A high-pass filter and a low-pass filter are designed and built to demonstrate the interferometer operation as well as to verify the model over a frequency of 1–4 GHz. Lossy materials are shown to significantly degrade filter sensitivity enhancement effects due to reduced group delay and lower RF fields. Further work is needed to address the issue.

Description: A three-step iterative method with fifth-order convergence as a new modification of Newton’s method was presented. This method is for finding multiple roots of nonlinear equation with unknown multiplicity m whose multiplicity m is the highest multiplicity. Its order of convergence is analyzed and proved. Results for some numerical examples show the efficiency of the new method.

Description: This paper presents an optimized low-complexity and high-throughput MIMO signal detector core for detecting spatially multiplexed data streams. The core architecture supports various layer configurations up to 4, while achieving near-optimal performance, and configurable modulation constellations up to 256-QAM on each layer. The core is capable of operating as a soft-input soft-output log-likelihood ratio (LLR) MIMO detector which can be used in the context of iterative detection and decoding. High area-efficiency is achieved via algorithmic and architectural optimizations performed at two levels. First, distance computations and slicing operations for an optimal 2-layer maximum a posteriori MIMO detector are optimized to eliminate use of multipliers and reduce the overhead of slicing in the presence of soft-input LLRs. We show that distances can be easily computed using elementary addition operations, while optimal slicing is done via efficient comparisons with soft decision boundaries, resulting in a simple feed-forward pipelined architecture. Second, to support more layers, an efficient channel decomposition scheme is presented that reduces the detection of multiple layers into multiple 2-layer detection subproblems, which map onto the 2-layer core with a slight modification using a distance accumulation stage and a post-LLR processing stage. Various architectures are accordingly developed to achieve a desired detection throughput and run-time reconfigurability by time-multiplexing of one or more component cores. The proposed core is applied also to design an optimal multiuser MIMO detector for LTE. The core occupies an area of 1.58 MGE and achieves a throughput of 733 Mbps for 256-QAM when synthesized in 90-nm CMOS.

Description: In this paper, we present a radar sensor system for real-time blast furnace burden surface imaging inside a fully operative blast furnace, called BLASTDAR, the blast furnace radar. The designed frequency-modulated continuous-wave (FMCW) radar sensor array operates in the frequency band around 77 GHz and consists of several nonuniformly spaced receive and transmit antennas, making it a multiple-input multiple-output radar system with large aperture. Mechanical steering is replaced by digital array processing techniques. Off-the-shelf automotive-qualified multichannel monolithic microwave integrated circuits are used. By means of this configuration, a virtual antenna array with 256 elements was developed that guarantees the desired angular resolution of better than 3°, and a range resolution of about 15 cm. Based on the single-channel FMCW signal model, this paper will derive a multichannel signal model in combination with a digital beamforming approach and further advanced signal processing algorithms. The implementation of a simulation tool covering the whole design process is shown. Based on these simulation results, a system configuration is chosen and the obtained setup is defined and presented. A description of the manufactured cost-efficient radio frequency and baseband boards together with the housing design shows the practical implementation of the sensor. For the system calibration, two different methods are listed and compared regarding their performance. Verification measurements confirm the predicted performance of the developed sensor. Several measurements inside a fully operational blast furnace demonstrate the proper long-term functionality of the system, to the best of our knowledge, for the first time worldwide. It is in continuous operation since about two years in blast furnace #5 of voestalpine Stahl GmbH, Linz.

Description: Indoor lighting systems with wireless sensor-equipped luminaires are considered as an infrastructure for offering location-based illumination control. Wireless occupancy and light sensors transmit sensing information to a lighting controller, which then adapts artificial illumination from luminaires to user occupancy and daylight changes. Such wireless sensor transmissions can be used to collect received signal strength indicator (RSSI) values at a user device. We propose to use the RSSI measurements at the user device, in combination with accelerometer sensor data, to determine user location zone when a user seeks personalized light control. Based on its location zone, the user is granted control of associated luminaires. The performance of the proposed system and positioning method is evaluated in a large indoor lighting open-plan office model.

Description: In this paper, a Pd–TiO 2 nanofiber structure with photoactivation capability for H 2 gas sensing is made using the electrospinning procedure. The solution which was stacked into the syringe for electrospinning Pd–TiO 2 nanofibers consisted of titanium tetraisopropoxide, acetic acid, ethanol, polyvinyl pyrrolidone, and different amounts of Pd solution. The X-ray diffraction, field emission scanning microscopy, transmission electron microscopy, and spectrophotometry techniques were employed to analyze the crystalline structure and surface morphology of the nanofibers. Moreover, Fe 2 O 3 and HNO 3 were also used as additional additives and the effect of additives in the absorption spectrum shift towards the visible light spectrum was investigated. About 95-nm red shift toward visible light from 370 to 465 nm for TiO 2 /Pd/N/Fe 2 O 3 was observed in comparison with the pure TiO 2 nanofibers. By using additives and visible light irradiation, the operating temperature lowered from 290 °C to 130 °C and the response increased from 11 to 368. At an operating temperature of 150 °C, the response time also reduced from 25 to 0.9 s and recovery times reduced from 40 to 2 s. The response dropped only by $sim 30$ %, 12%, and 5% after nine months for the TiO 2 , TiO 2 /Pd, and TiO 2 /Pd/N/Fe 2 O 3 samples, respectively.

Description: Over the years, several QRS complex detection algorithms have been proposed with different features, but the remaining problem is their implementation in low-cost portable platforms for real-time applications, where hardware resources are limited, still providing the accuracy level required for medical applications. The proposed algorithm copes at the same time with both requirements: 1) accuracy and 2) low resource consumption. In this paper, a real-time QRS complex detector is proposed. This algorithm is based on a differentiation at the pre-processing stage combined with a dynamic threshold to detect R peaks. The thresholding stage is based on a finite-state machine, which modifies the threshold value according to the evolution of the signal and the previously detected peak. It has been evaluated on several databases, including the standard ones, thus resulting sensitivities and positive predictivities better than 99.3%. In order to analyze the computational complexity of the algorithm, it has been compared with the well-known Pan and Tompkins’ algorithm. As a result, the proposed detector achieves a reduction in processing time of almost 50% by using only the 25% of hardware resources (memory, adders, and multipliers).

Description: 1-D oxide nanostructures-based metal-insulator–metal structures represent potential gas sensor devices, owing to their vertical electron transport feature. In this paper, we demonstrate that for achieving optimum gas sensing by a TiO 2 nanotube (NT) array in vertical mode, tuning of NTs (1-D) surface area as well as carrier transport path length by tailoring the NT length can be a valuable approach. For anodization times of 1, 4, 8, 12, and 16 h, the corresponding NT length was found to be 280–320, 506–514, 1730–1790, 2000–2200, and 2380–2420 nm, respectively, with almost no variation in tube diameters or separations. The carrier concentrations of the NTs were found to be decreasing with increasing tube length. The vertical device structure, employing NT arrays of different lengths as the sensing layer, was investigated in the temperature range of 50 °C–250 °C for sensing acetone, as a test gas/vapor, in the concentration range of 10–1000 ppm. The response magnitude of the sensor was increased with increased NT length, possibly owing to the availability of larger amounts of gas interaction sites due to higher surface area at increased length. The response time and recovery time of the developed sensor also increased with increasing tube length and became excessively sluggish after the critical tube length exceeded 2250 nm owing to slower adsorption/desorption and diffusion kinetics.

Description: One of the key studies in the cognitive radio networks is maximizing some important metrics such as transmission/energy efficiencies or throughput with respect to network parameters such as sensing and transmission time. There is always a tradeoff between sensing time and transmission time. While longer sensing times results in higher detection accuracy, it loses transmission opportunities and hence decreases transmission efficiency. Many works have been done to find the best transmission and sensing time to maximize the efficiencies such as transmission and energy, but the interference and hence the probability of collision was not defined and formulated correctly. In this paper, we consider the trilateral tradeoff between sensing, transmission, and contention times. To do this, the interference between primary user (PU) and secondary user (SU) is formulated. We consider interference due to both imperfect sensing and PU-return. Furthermore, the interference among SUs has been also formulated. These metrics are used in defining transmission efficiency and probability of collision as a constraint.

Description: To reduce the energy cost of underwater acoustic sensor networks (UWSNs), the duty cycle (i.e., periodic wake-up and sleep) concept has been used in several medium access control (MAC) protocols. Although these protocols are energy efficient, they sacrifice bandwidth utilization, which leads to lower transmission rate. In order to solve this problem, asynchronous duty cycle with network-coding Asynchronous Duty Cycle with Network-Coding MAC (ADCNC-MAC) is proposed. It contains initialization of the MAC protocol phase and data transmission phase. In the first phase, we use an asynchronous duty cycle to find a rendezvous time for exchanging data. A strategy to select network coder nodes is presented to confirm the number of network coder nodes and distribution in the network coder layer. In the data transmission phase, the network coder nodes transmit using the proposed network-coding-based algorithm and a higher volume of packet will be transmitted to the Sink with the same number of transmissions. Simulation results show that ADCNC-MAC achieves higher power efficiency, improves packet delivery ratio (PDR), and network throughput.

Description: We study a tandem of agents who make decisions about an underlying binary hypothesis, where the distribution of the agent observations under each hypothesis comes from an uncertainty class defined by a 2-alternating capacity. We investigate both decentralized detection rules, where agents collaborate to minimize the error probability of the final agent, and social learning rules, where each agent minimizes its own local minimax error probability. We then extend our results to the infinite tandem network, and derive necessary and sufficient conditions on the uncertainty classes for the minimax error probability to converge to zero when agents know their positions in the tandem. On the other hand, when agents do not know their positions in the network, we study the cases where agents collaborate to minimize the asymptotic minimax error probability, and where agents seek to minimize their worst-case minimax error probability (over all possible positions in the tandem). We show that asymptotic learning of the true hypothesis is no longer possible in these cases, and derive characterizations for the minimax error performance.

Description: Various blind synchronization methods built on the maximum likelihood (ML) principle have been proposed, where the addressed scenarios include additive white Gaussian noise (AWGN), single-path fading, and multipath fading channels. We consider ML blind synchronization over wide-sense stationary uncorrelated scattering (WSSUS) channels. Different from existing studies, we exploit a more complete signal correlation function and find the carrier frequency offset estimate to be the solution of a quartic equation, rather than the phase angle of a complex number. As the truly ML synchronizer (dubbed MLE) is very complicated, we also derive a reduced-complexity alternative (dubbed RCE). It is found that the RCE yields indistinguishable performance from the MLE, at a somewhat lower complexity than an existing rival. We also present an in-depth theoretical analysis and comparison of the performance of various methods. Simulations show that the proposed methods yield rather robust performance in modeling errors of the fading rate and the channel power-delay profile (PDP).

Description: In this paper, a 2-D air holes in silicon slab photonic crystal-based structure with line defect has been designed and simulated for detection of high glucose concentration in urine from 0–15 to 10 gm/dl in the wavelength range of 1530–1565 nm. High glucose concentration in urine is referred as glycosuria. The proposed sensor structure is modeled and simulated in time domain by using Massachusetts Institute of Technology (MIT) electromagnetic equation propagation simulation tool. Finite-difference time-domain method has been used for the analysis. Band structure has been computed and eigen frequencies have been extracted for each k-point for the designed sensor structure by using (MIT photonic bands) simulation tool. The changes in the normalized output power levels, quality ( $Q$ )-factor, and resonant frequency have been observed for different concentrations of glucose present in urine. The calculated value of $Q$ -factor obtained is $Q=23,575$ . This paper has been done for normal urine and glycosuric urine. It is clear from the simulated graphs of transmission spectrum and band structure that for minute changes in the refractive index, the transmitted output power level range varies from 0.2298 to −0.091 dB and the resonant frequency range varies from 0.229259–0.22914 (in units of c/a) and hence it acts as a sensor for detection for glycosuria. Our designed sensor has achieved sensitivity of 638 nm /RIU.

Description: This paper reports a simple fluorescence-based tapered fiber optic probe for fluoride ion having a detection range of $2.08 times 10^{mathrm { {-6}}}$ – $2.005 times 10^{mathrm { {-4}}}$ M. The performance of the tapered probe is evaluated with respect to the probes that consist of combinations of bare uncladded multimode optical fibers. The effect of fluorescence quenching of a natural dye curcumin in the presence of fluoride ion is used in the implementation of the probes. The probe effectively uses multiple mechanisms for the excitation and collection of fluorescence from the medium enabling higher sensitivity compared with conventional spectrophotometry especially at very low concentrations of fluoride.

Description: In this paper, the arrayed flexible pH sensor and glucose biosensor modified by magnetic beads and graphene were proposed. The ruthenium dioxide (RuO 2 ) sensing films were deposited by radio frequency sputtering system and the screen-printed technique was used to construct the silver conducting wires and insulation layer of the RuO 2 electrodes. In order to enhance performance of the pH sensor and glucose biosensor, the microfluidic device had been utilized and developed. In the measurement processes, the different pH and glucose solutions were investigated in various flow rates. According to the experimental results, the average sensitivity of the pH sensor was enhanced from 52.280 to 57.981 mV/pH and the average sensitivity of the RuO 2 /graphene/magnetic bead-GO x -Nafion glucose biosensor was enhanced from 10.628 to 13.541 mV/mM. With regard to the wireless sensing measurements, the wireless sensing system which complied the ZigBee standard was employed to transmit the signals of the pH or glucose measurements in this investigation, and the system consisted of the Xbee device, Arduino Mega 2560, readout circuit, pH or glucose biosensor and computer. According to the experimental results, the average sensitivity and linearity of the pH sensor were 51.063 mV/pH and 0.988, respectively, and the average sensitivity and linearity of the RuO 2 /graphene/magnetic bead-GO x -Nafion glucose biosensor were 11.005 mV/mM and 0.995, respectively.

Description: This paper considers the throughput maximization of a secondary user (SU) in a realistic cognitive radio (CR) network where the battery suffers from constant energy leakage. We investigate two different CR scenarios where the primary user (PU) switches between idle and active states in a time-slotted manner. In the first scenario, the SU knows the exact state of the PU at the beginning of each time slot, whereas in the second scenario, the SU attempts to detect state of the PU by spectrum sensing. For both scenarios, we determine the maximum throughput of the SU with consideration of battery leakage of the SU and interference constraint of the PU. The optimal solutions of transmitting power and sensing duration are achieved by using golden section search method and a simplified brute-force search method. Finally, the theoretical analysis is verified through the Monte Carlo simulations.

Description: A rotameter is a variable-area-type flow rate measuring instrument in which the position of a metallic float in a transparent conical tube is taken as a flow rate indicator. It has the disadvantage that it is a local indicating type instrument and special type of transducer is used for its remote indication. In this paper, a noncontact flow rate measurement technique using Hall probe sensor and rotameter is designed, developed, and tested. In this design, a float carrying a thin circular permanent magnet is used, and a Hall probe sensor placed outside the rotameter tube has been used to sense the variation of magnetic field of the magnet with the variation of float position. A signal conditioner unit has been used to convert the Hall probe sensor output into 1–5 V dc signal. This dc signal output of the signal conditioner has been sent to a PC-based flow indicator through optoisolator and analog input channel of a data acquisition system (DAS) card. The PC-based flow indicator has been designed using the Lab Tech Note Book Pro software and the PC-based supervisory control and DAS. A theoretical equation has been derived to explain the operation of the system. The performance of the system has been tested experimentally, and the experimental results are reported in this paper. A very good repeatability and linearity of results has been observed.

Description: Traditional wet or gel impedimetric electrodes for neuro-physiological signal (e.g., electroencephalography and electrocardiography) monitoring are usable for a short duration, as the performance of electrodes deteriorates rapidly when exposed to the environment. Dry impedimetric electrode is a promising alternative tool for long duration monitoring, however suffers from high interfacing impedance. This paper describes a novel dry interfacing electrode utilizing patterned vertical carbon nanotube (pvCNT) for impedimetric sensing. The electrodes were fabricated on circular stainless steel foil substrates (thickness = 2 mil) that are laser cut to circular discs (Ø = 10 mm). Pattern on the substrate was developed with a custom shadow mask while sputter coating the substrate with Al 2 O 3 and iron. Electrically conductive multiwalled CNTs were then grown vertically in pillar formation ( $100~mu text{m}^{}$ each side of square footprint) with various interpillar spacing (50, 100, and $200~mu text{m}$ for various masks). The heights of the CNT pillars were between 1 and 1.5 mm. The impedances of the electrodes were 1.92, 3.11, and $8.15~Omega $ for 50-, 100-, and 200- $mu text{m}$ spacing, respectively. A comparative in vitro study with commercial wet and gel electrodes showed pvCNT electrode has lower interfacial impedance, comparable signal capture quality, and ability to be used for stimulation. Long duration study showed minimal impedance degradation for pvCNT electrodes over a week. The results demonstrate pvCNT is a promising dry electrode for impedimetric sensing and stimulation of neurophysiological signals over - prolonged duration.

Description: In the case of an unavoidable frontal collision between a vehicle and a pedestrian, the activation of a protection system can reduce the severity of injuries caused by the collision. However, to fulfill this objective, it is necessary to determine when the shock will occur. To overcome this problem, we place inside the front bumper a metallic sheet that emits an electric field. This field is oriented toward the front of the vehicle by means of various guard screens. The intrusion into the emitted electric field induces a capacitive coupling with the sensor. Which will enable the electronic device to detect the presence of the pedestrian at a distance of 1 m. To avoid slow drifts in time, the change in capacity is measured every millisecond. The last hundred acquisitions are considered as a history. This history is compared with the signature of a pedestrian who was established in the laboratory. When the pedestrian is about a thirty centimeters of the vehicle and the shock is inevitable, the protection systems are activated. When the vehicle is at very low speed, this device can be used as parking assistance.

Description: In this paper, the performance of cloud radio access networks (CRANs) where spatially distributed remote radio heads (RRHs) aid the macro base station (MBS) in transmission is analysed. In order to reflect a realistic scenario, the MBS and the RRHs are assumed to be equipped with multiple antennas and distributed according to a Poisson point process. Both, the MBS and the RRHs, are assumed to employ maximal ratio transmission (MRT) or transmit antenna selection (TAS). Considering downlink transmission, the outage performance of three schemes is studied; first is the selection transmission (ST) scheme, in which the MBS or the RRH with the best channel is selected for transmission. In the second scheme, all the RRHs participate (ARP) and transmit the signal to the user, whereas in the third scheme, a minimal number of RRHs, to attain a desired data-rate, participate in transmission (MRP). Exact closed-form expression for the outage probability is derived for the ST scheme. For the ARP and MRP schemes, analytical approximations of the outage probability are derived which are tight at high signal-to-noise ratios. In addition, for the MRP scheme, the minimal number of RRHs required to meet a target data rate is also calculated which can be useful in characterizing the system complexity. Furthermore, the derived expressions are validated through numerical simulation. It is shown that the average diversity gains of these schemes are independent of the intensity/number of RRHs and only depend on the number of antennas on the MBS. Furthermore, the ARP scheme outperforms the ST scheme when the MBS/RRHs transmit with maximum power. However, in case of a sum power constraint and equal power allocation, the ST scheme outperforms the ARP scheme.

Description: This paper proposes a dynamic resource allocation scheme to exploit the mixed timescale channel state information (CSI) knowledge structure in a multi-antenna base station-assisted device-to-device (D2D) network. The short-term multi-antenna beamforming control at each transmit device is adaptive to the local real-time CSI. The long-term routing and flow control is adaptive to the global topology and the long-term global CSI statistics of the D2D network. The design objective is to maximize a network utility function subject to the average transmit power constraint, the flow balance constraints and the instantaneous physical layer capacity constraints. The mixed timescale problem can be decomposed into a short-term beamforming control problem and a long-term flow and routing control problem. Using the stochastic cutting plane, we propose a low complexity, self-learning algorithm, which converges to the global optimal solution without explicit knowledge of the channel statistics. Simulation illustrates performance gains with several baselines.

Description: This paper considers the estimation of multi-scale multi-lag (MSML) channels. The MSML channel model is a good representation for wideband communication channels, such as underwater acoustic communication and radar. This model is characterized by a limited number of paths, each parameterized by a delay, Doppler scale, and attenuation factor. Herein, it is shown that an OFDM signal after passing through the MSML channel exhibits a low rank representation. This feature can be exploited to improve the channel estimation. By characterizing the received signal, it is shown that the MSML channel estimation problem can be adapted to a structured spectral estimation problem. The challenge is that the unknown frequencies are very close to each other due to the small values of Doppler scales. This feature can be employed to show that the data matrix is approximately low-rank. By exploiting structural features of the received signal, the Prony algorithm is modified to estimate the Doppler scales (close frequencies), delays and channel gains. Two strategies using convex and no-convex regularizers to remove noise from the corrupted signal are proposed. These algorithms are iterative based on the alternating direction method of multipliers. A bound on the reconstruction of the noiseless received signal provides guidance on the selection of the relaxation parameter in the optimizations. The performance of the proposed estimation strategies are investigated via numerical simulations, and it is shown that the proposed non-convex method offers up to 7 dB improvement in low SNR and the convex method offers up to 5 dB improvement in high SNR over prior methods for the MSML channel estimation.

Description: The possibility of having information access anytime and anywhere has caused a huge increase of the popularity of wireless networks. Requirements of users and owners have been ever-increasing. However, concerns about the potential health impact of exposure to radio frequency (RF) sources have arisen and are getting accounted for in wireless network planning. In addition to adequate coverage and reduced human exposure, the installation cost of the wireless network is also an important criterion in the planning process. In this paper, a hybrid algorithm is used to optimize indoor wireless network planning while satisfying three demands: maximum coverage, minimal full installation cost (cabling, cable gutters, drilling holes, labor, etc.), and minimal human exposure. For the first time, wireless indoor networks are being optimized based on these advanced and realistic conditions. The algorithm is investigated for three scenarios and for different configurations. The impact of different exposure requirements and cost scenarios is assessed.

Description: In this paper, power allocation in distributed multiple-input multiple-output radar is investigated for range-only target tracking such that the determinant of Bayesian Fisher information matrix (B-FIM) is maximized. First, the B-FIM is derived for a signal model that incorporates the propagation path loss, the target reflectivity, the transmitted power, and the target prior density. Then, we model the problem as a cooperative game and exploit the solution concept of the Shapley value to distribute a given power budget among all transmitting radars for target tracking. In numerical examples, it is shown that uniform power allocation is not in general optimal. We illustrate the effects of the radar network geometry configuration, target prior density and number of antenna upon the power allocation results, and further demonstrate the superior performance of the proposed optimal power allocation scheme via Monte Carlo simulations.

Description: A fiber Bragg grating (FBG) accelerometer based on a push–pull elastic cylinder structure is demonstrated. First, the model based on the uniform cylinder structure is analyzed and the optimized accelerometer parameters are given. Then, by designing a radius-varying cylinder structure, the FBG strain becomes larger than the cylinder strain, which leads to enhanced sensitivity amplification for a small accelerometer size and relatively high resonant frequency. Meanwhile, the influence of the transverse force on the accelerometer is theoretically analyzed. These results indicate that the transverse-induced FBG deformation is very big so that a strict transverse constrain is needed. The formula of the strain magnification is derived and the design rules of the strain magnification are given. After structure optimization according to the rules, the FBG strain increases to 1.5 times, the sensitivity increases to 1.82 times, whereas the resonant frequency reduces to 0.9 times compared with the parameters of accelerometer based on uniform cylinder structure. Finally, the accelerometer size reduces to $Phi {20~{rm mm}times 34~{rm mm}}$ , the sensitivity increases to 623 pm/g, and the resonant frequency reduces to 449 Hz.

Description: The proliferation of radio frequency (RF) communication technology in biomedical signal transmission is frequently flustered by electromagnetic interference. Even though the flexibility and mobility of RF-based communication have much attraction, the radiation brings damage to hospital equipments and even harm to humans. In this letter, we propose a novel scheme for transmission of electroencephalography (EEG) biomedical signal using a visible light communication (VLC) link. The data transmission is performed in line of sight (LOS) condition using ON–OFF keying nonreturn-to-zero modulation by utilizing all three components, red, green, and blue, of RGB LED. Experiments are carried out for transmitting EEG signals using the VLC link. The transmitter consists of RGB LEDs, and at the receiver side, three photodiodes with red, green, and blue color filters are installed. The experimental results show excellent reliability and accuracy of the proposed scheme.

Description: Physical transceiver implementations for wireless communication systems usually suffer from transmit-radio frequency (Tx-RF) and receiver-RF (Rx-RF) impairments. In this paper, we aim to design efficient coordinated beamforming for multicell multiuser multi-antenna systems by fully taking into account the residual transceiver impairments. Our design objectives include both spectral efficiency and energy efficiency. In particular, we first derive the closed-form expression of the mean square error (MSE) which includes the impact of transceiver impairments. Based on that, we propose an alternating optimization algorithm to solve the coordinated multicell beamforming problems with the goal of minimizing the worst user MSE, and the sum MSE. Then, by exploiting the relationship between the minimum mean square error (MMSE) and the achievable rate, we develop a new algorithm to address the sum rate maximization problem. This approach is further generalized to solve the more intractable energy efficiency optimization problem. We prove that all the proposed iterative algorithms guarantee to converge to a stationary point. Numerical results show that our proposed schemes achieve a better performance than conventional coordinated beamforming algorithms that were designed ignoring the transceiver impairments.

Description: Distributed processing over networks relies on in-network processing and cooperation among neighboring agents. Cooperation is beneficial when agents share a common objective. However, in many applications, agents may belong to different clusters that pursue different objectives. Then, indiscriminate cooperation will lead to undesired results. In this paper, we propose an adaptive clustering and learning scheme that allows agents to learn which neighbors they should cooperate with and which other neighbors they should ignore. In doing so, the resulting algorithm enables the agents to identify their clusters and to attain improved learning and estimation accuracy over networks. We carry out a detailed mean-square analysis and assess the error probabilities of Types I and II, i.e., false alarm and misdetection, for the clustering mechanism. Among other results, we establish that these probabilities decay exponentially with the step-sizes so that the probability of correct clustering can be made arbitrarily close to one.

Description: Robust Chinese remainder theorem (CRT) has been recently investigated for both integers and real numbers, where the folding integers are accurately recovered from erroneous remainders. In this paper, we consider the CRT problem for real numbers with noisy remainders that follow wrapped Gaussian distributions. We propose the maximum-likelihood estimation (MLE) based CRT when the remainder noises may not necessarily have the same variances. Furthermore, we present a fast algorithm for the MLE based CRT algorithm that only needs to search for the solution among $L$ elements, where $L$ is the number of remainders. Then, a necessary and sufficient condition on the remainder errors for the MLE CRT to be robust is obtained, which is weaker than the existing result. Finally, we compare the performances of the newly proposed algorithm and the existing algorithm in terms of both theoretical analysis and numerical simulations. The results demonstrate that the proposed algorithm not only has a better performance especially when the remainders have different error levels/variances, but also has a much lower computational complexity.

Description: Sparse signal restoration is usually formulated as the minimization of a quadratic cost function $Vert { mbi { y}} - { mbi { A}} { mbi { x}} Vert_{2}^{2}$ where $ { mbi { A}} $ is a dictionary and $ { mbi { x}} $ is an unknown sparse vector. It is well-known that imposing an $ell _{0}$ constraint leads to an NP-hard minimization problem. The convex relaxation approach has received considerable attention, where the $ell _{0}$ -norm is replaced by the $ell _{1}$ -norm. Among the many effective $ell _{1}$ solvers, the homotopy algorithm minimizes $Vert { mbi { y}} - { mbi { A}} { mbi { x}} Vert_{2}^{2}+lambda Vert { mbi { x}} Vert _{1}$ with respect to $ { mbi { x}} $ for a continuum of $lambda $ ’s. It is inspired by the piecewise regularity of the $ell _{1}$ -regularization path, also referred to as the homotopy path. In this paper, we address the minimization problem $Vert { mbi { y}} - { mbi { A}} { mbi { x}} Vert_{2}^{2}+lambda Vert { mbi { x}} Vert _{0}$ for a continuum of $lambda $ ’s and propose two heuristic search algorithms for $ell _{0}$ -homotopy. Continuation Single Best Replacement is a forward–backward greedy strategy extending the Single Best Replacement algorithm, previously proposed for $ell _{0}$ -minimization at a given $lambda $ . The adaptive search of the $lambda $ -values is inspired by $ell _{1}$ -homotopy. $ell _{0}$ Regularization Path Descent is a more complex algorithm exploiting the structural properties of the $ell _{0}$ -regularization path, which is piecewise constant with respect to $lambda $ . Both algorithms are empirically evaluated for difficult inverse problems involving ill-conditioned dictionaries. Finally, we show that they can be easily coupled with usual methods of model order selection.

Description: In recent work, robust Principal Components Analysis (PCA) has been posed as a problem of recovering a low-rank matrix ${bf L}$ and a sparse matrix ${bf S}$ from their sum, ${bf M}:= {bf L} + {bf S}$ and a provably exact convex optimization solution called PCP has been proposed. This work studies the following problem. Suppose that we have partial knowledge about the column space of the low rank matrix ${bf L}$ . Can we use this information to improve the PCP solution, i.e., allow recovery under weaker assumptions? We propose here a simple but useful modification of the PCP idea, called modified-PCP, that allows us to use this knowledge. We derive its correctness result which shows that, when the available subspace knowledge is accurate, modified-PCP indeed requires significantly weaker incoherence assumptions than PCP. Extensive simulations are also used to illustrate this. Comparisons with PCP and other existing work are shown for a stylized real application as well. Finally, we explain how this problem naturally occurs in many applications involving time series data, i.e., in what is called the online or recursive robust PCA problem. A corollary for this case is also given.

Description: Phased array is widely used in radar systems with its beam steering fixed in one direction for all ranges. Therefore, the range of a target cannot be determined within a single pulse when range ambiguity exists. In this paper, an unambiguous approach for joint range and angle estimation is devised for multiple-input multiple-output (MIMO) radar with frequency diverse array (FDA). Unlike the traditional phased array, FDA is capable of employing a small frequency increment across the array elements. Because of the frequency increment, the transmit steering vector of the FDA-MIMO radar is a function of both range and angle. As a result, the FDA-MIMO radar is able to utilize degrees-of-freedom in the range-angle domains to jointly determine the range and angle parameters of the target. In addition, the Cramér–Rao bounds for range and angle are derived, and the coupling between these two parameters is analyzed. Numerical results are presented to validate the effectiveness of the proposed approach.

Description: In this paper, we propose a novel single-group multicasting relay beamforming scheme. We assume a source that transmits common messages via multiple amplify-and-forward relays to multiple destinations. To increase the number of degrees of freedom in the beamforming design, the relays process two received signals jointly and transmit the Alamouti space-time block code over two different beams. Furthermore, in contrast to the existing relay multicasting scheme of the literature, we take into account the direct links from the source to the destinations. We aim to maximize the lowest received quality-of-service by choosing the proper relay weights and the ideal distribution of the power resources in the network. To solve the corresponding optimization problem, we propose an iterative algorithm which solves sequences of convex approximations of the original non-convex optimization problem. Simulation results demonstrate significant performance improvements of the proposed methods as compared with the existing relay multicasting scheme of the literature and an algorithm based on the popular semidefinite relaxation technique.

Description: In this paper, we propose a new class of iteratively re-weighted least squares (IRLS) for sparse recovery problems. The proposed methods are inspired by constrained maximum-likelihood estimation under a Gaussian scale mixture (GSM) distribution assumption. In the noise-free setting, we provide sufficient conditions ensuring the convergence of the sequences generated by these algorithms to the set of fixed points of the maps that rule their dynamics and derive conditions verifiable a posteriori for the convergence to a sparse solution. We further prove that these algorithms are quadratically fast in a neighborhood of a sparse solution. We show through numerical experiments that the proposed methods outperform classical IRLS for $ell_{tau}$ -minimization with $tauin(0,1]$ in terms of speed and of sparsity-undersampling tradeoff and are robust even in presence of noise. The simplicity and the theoretical guarantees provided in this paper make this class of algorithms an attractive solution for sparse recovery problems.

Description: We consider the problem of approximating optimal in the Minimum Mean Squared Error (MMSE) sense nonlinear filters in a discrete time setting, exploiting properties of stochastically convergent state process approximations. More specifically, we consider a class of nonlinear, partially observable stochastic systems, comprised by a (possibly nonstationary) hidden stochastic process (the state), observed through another conditionally Gaussian stochastic process (the observations). Under general assumptions, we show that, given an approximating process which, for each time step, is stochastically convergent to the state process, an approximate filtering operator can be defined, which converges to the true optimal nonlinear filter of the state in a strong and well defined sense. In particular, the convergence is compact in time and uniform in a completely characterized set of probability measure almost unity. The results presented in this paper can form a common basis for the analysis and characterization of a number of popular but heuristic approaches for approximating optimal nonlinear filters, such as approximate grid based techniques.

Description: A standard assumption for consistent estimation in the errors-in-variables setting is persistency of excitation of the noise-free input signal. We relax this assumption by considering data from multiple experiments. Consistency is obtained asymptotically as the number of experiments tends to infinity. The main theoretical and algorithmic difficulties are related to the growing number of to-be-estimated initial conditions. The method proposed in the paper is based on analytic elimination of the initial conditions and optimization over the remaining parameters. The resulting estimator is consistent; however, achieving asymptotically efficiency is an open problem.

Description: Bayesian filtering aims at estimating sequentially a hidden process from an observed one. In particular, sequential Monte Carlo (SMC) techniques propagate in time weighted trajectories which represent the posterior probability density function (pdf) of the hidden process given the available observations. On the other hand, conditional Monte Carlo (CMC) is a variance reduction technique which replaces the estimator of a moment of interest by its conditional expectation given another variable. In this paper, we show that up to some adaptations, one can make use of the time recursive nature of SMC algorithms in order to propose natural temporal CMC estimators of some point estimates of the hidden process, which outperform the associated crude Monte Carlo (MC) estimator whatever the number of samples. We next show that our Bayesian CMC estimators can be computed exactly, or approximated efficiently, in some hidden Markov chain (HMC) models; in some jump Markov state-space systems (JMSS); as well as in multitarget filtering. Finally our algorithms are validated via simulations.

Description: In this paper, cooperative sensor localization using asynchronous time-of-arrival measurements is investigated. It is well known that localization performance in wireless networks using time-based ranging or pseudo-ranging methods is greatly affected by the accuracy of the timing synchronization between the nodes involved in the estimation. Commonly, the original estimation problem is broken down into two subproblems, the synchronization problem and the localization problem, in what is known as a two-step approach. However, in this paper, the joint synchronization and localization problem is considered and examined for use in cooperative networks. It is discussed that the cooperation between the source nodes eliminates the need for high anchor node densities and improves localization performance significantly. Furthermore, the Cramér-Rao lower bounds (CRLB) and the maximum likelihood (ML) estimator are derived. It is shown that the ML estimator is highly nonlinear and nonconvex and must, therefore, be solved by using computationally complex algorithms. In order to reduce the complexity of the estimation, a novel semidefinite programming (SDP) relaxation method is developed by relaxing the original nonconvex ML problem, in such a way as to reformulate the estimation problem as a convex problem. The performance of the proposed SDP method is shown through computer simulations to nearly equal that of the ML estimator. The approach is also applied to the noncooperative case where it is found to be superior in performance than the previously proposed suboptimal estimators. Finally, complexity analyses are included for the estimators under consideration.

Description: In this paper, we present three improvements to a three-point third order variant of Newton’s method derived from the Simpson rule. The first one is a fifth order method using the same number of functional evaluations as the third order method, the second one is a four-point 10th order method and the last one is a five-point 20th order method. In terms of computational point of view, our methods require four evaluations (one function and three first derivatives) to get fifth order, five evaluations (two functions and three derivatives) to get 10th order and six evaluations (three functions and three derivatives) to get 20th order. Hence, these methods have efficiency indexes of 1.495, 1.585 and 1.648, respectively which are better than the efficiency index of 1.316 of the third order method. We test the methods through some numerical experiments which show that the 20th order method is very efficient.

Description: Robust small target detection of low signal-to-noise ratio (SNR) is very important in infrared search and track applications for self-defense or attacks. Due to the complex background, current algorithms have some unsolved issues with false alarm rate. In order to reduce the false alarm rate, an infrared small target detection algorithm based on saliency detection and support vector machine was proposed. Firstly, we detect salient regions that may contain targets with phase spectrum Fourier transform (PFT) approach. Then, target recognition was performed in the salient regions. Experimental results show the proposed algorithm has ideal robustness and efficiency for real infrared small target detection applications.

Description: In dynamic propagation environments, beamforming algorithms may suffer from strong interference, steering vector mismatches, a low convergence speed and a high computational complexity. Reduced-rank signal processing techniques provide a way to address the problems mentioned above. This paper presents a low-complexity robust data-dependent dimensionality reduction based on an iterative optimization with steering vector perturbation (IOVP) algorithm for reduced-rank beamforming and steering vector estimation. The proposed robust optimization procedure jointly adjusts the parameters of a rank reduction matrix and an adaptive beamformer. The optimized rank reduction matrix projects the received signal vector onto a subspace with lower dimension. The beamformer/steering vector optimization is then performed in a reduced dimension subspace. We devise efficient stochastic gradient and recursive least-squares algorithms for implementing the proposed robust IOVP design. The proposed robust IOVP beamforming algorithms result in a faster convergence speed and an improved performance. Simulation results show that the proposed IOVP algorithms outperform some existing full-rank and reduced-rank algorithms with a comparable complexity.

Description: Recently, wireless sensor networks (WSNs) have drawn great interest due to their outstanding monitoring and management potential in medical, environmental and industrial applications. Most of the applications that employ WSNs demand all of the sensor nodes to run on a common time scale, a requirement that highlights the importance of clock synchronization. The clock synchronization problem in WSNs is inherently related to parameter estimation. The accuracy of clock synchronization algorithms depends essentially on the statistical properties of the parameter estimation algorithms. Recently, studies dedicated to the estimation of synchronization parameters, such as clock offset and skew, have begun to emerge in the literature. The aim of this article is to provide an overview of the state-of-the-art clock synchronization algorithms for WSNs from a statistical signal processing point of view. This article focuses on describing the key features of the class of clock synchronization algorithms that exploit the traditional two-way message (signal) exchange mechanism. Upon introducing the two-way message exchange mechanism, the main clock offset estimation algorithms for pairwise synchronization of sensor nodes are first reviewed, and their performance is compared. The class of fully-distributed clock offset estimation algorithms for network-wide synchronization is then surveyed. The paper concludes with a list of open research problems pertaining to clock synchronization of WSNs.

Description: In this paper, we propose a monocular multiframe high dynamic range (HDR) monocular vision system to improve the imaging quality of traditional CMOS/charge-coupled device (CCD)-based vision system for advanced driver assistance systems (ADASs). Conventional CMOS/CCD image sensors are confined to limited dynamic range that it impairs the imaging quality under undesirable environments for ADAS (e.g., strong contrast of bright and darkness, strong sunlight, headlights at night, and so on). Contrary to current HDR video solutions relying on expensive specially designed sensors, we implement a multiframe HDR algorithm to enable one common CMOS/CCD sensor capturing HDR video. Key parts of the realized HDR vision system are: 1) circular exposure control; 2) latent image calculation; and 3) exposure fusion. We have successfully realized a prototype of monocular HDR vision system and mounted it on our SetCar platform. The effectiveness of this technique is demonstrated by our experimental results, while its bottleneck is the processing time. By exploring the capability of the proposed method in the future, a low-cost HDR vision system can be achieved for ADAS.

Description: In this paper, a fiber-optic surface plasmon resonance (SPR) biosensor is presented, in which a sheet of graphene acting as a sensing layer is coated around the gold film. A theoretical study of the proposed fiber-optic biosensor has been carried out by applying four-layer modal, which shows that by incorporating a graphene sensing layer, the sensitivity of the proposed SPR fiber biosensor can be greatly enhanced than the conventional gold film SPR fiber sensors. The relationship between resonance wavelengths and sensitivity of the proposed graphene sensing layer-based SPR fiber biosensor with the number of sensing layer has also been studied.

Description: We report the development of cantilever-based cold cathode ionization pressure sensor that works on the principle of dielectric breakdown of gaseous medium. The high electric field ( $E_{m}=1$ MV/m) developed at the corners of the microtip produced dielectric breakdown of the medium, sufficient to produce ionization current at a relatively low bias voltage $V_{b} 〈10$ V. With increase in pressure, the availability of molecules in the vicinity of the micro-tip increased which when ionized by the application of $V_{b}$ , produced enhanced ionization current through the device. The devices with cantilever lengths $L=50$ , 150, 250, 350, and $450~mu text{m}$ were designed with interelectrode spacing ( ${d}_{textrm {int}} )$ of 1250 nm, which was further reduced by the application of suitable $V_{b}$ to enhance the sensor performance. The device with $L=450~mu text{m}$ provides the widest dynamic range of $10^{mathrm {mathbf {-4}}}-10^{mathrm {mathbf {2}}}$ mbar and the highest sensitivity of 2.44 $mu text{A}$ /mbar with power consumption of $1.32times 10^{-1}~mu text{W}$ at 10 V, which is $10^{4}$ - times lower than previously reported device. The device offers a service life of 60 cycles assuming a tolerance of 10% in device performance.

Description: In this paper, we consider the problem of parameter estimation over sensor networks in the presence of quantized data and directed communication links. We propose a two-stage distributed algorithm aiming at achieving the centralized sample mean estimate in a distributed manner. Different from the existing algorithms, a running average technique is utilized in the proposed algorithm to smear out the randomness caused by the probabilistic quantization scheme. With the running average technique, it is shown that the centralized sample mean estimate can be achieved both in the mean square and almost sure senses, which is not observed in the standard consensus algorithms. In addition, the rates of convergence are given to quantify the mean square and almost sure performances. Finally, simulation results are presented to illustrate the effectiveness of the proposed algorithm and highlight the improvements by using running average technique.

Description: This paper studies a pilot-assisted physical layer data fusion technique known as Distributed Co-Phasing (DCP). In this two-phase scheme, the sensors first estimate the channel to the fusion center (FC) using pilots sent by the latter; and then they simultaneously transmit their common data by pre-rotating them by the estimated channel phase, thereby achieving physical layer data fusion. First, by analyzing the symmetric mutual information of the system, it is shown that the use of higher order constellations (HOC) can improve the throughput of DCP compared to the binary signaling considered heretofore. Using an HOC in the DCP setting requires the estimation of the composite DCP channel at the FC for data decoding. To this end, two blind algorithms are proposed: 1) power method, and 2) modified $K$ -means algorithm. The latter algorithm is shown to be computationally efficient and converges significantly faster than the conventional $K$ -means algorithm. Analytical expressions for the probability of error are derived, and it is found that even at moderate to low SNRs, the modified $K$ -means algorithm achieves a probability of error comparable to that achievable with a perfect channel estimate at the FC, while requiring no pilot symbols to be transmitted from the sensor nodes. Also, the problem of signal corruption due to imperfect DCP is investigated, and constellation shaping to minimize the probability of signal corruption is proposed and analyzed. The analysis is validated, and the promising performance of DCP for energy-efficient physical layer data fusion is illustrated, using Monte Carlo simulations.

Description: A simple noncontact displacement microfiber sensor using adiabatic U-shaped tapered fiber is proposed and demonstrated. The microfiber is fabricated using a systematic fiber flame brushing technique, where the fiber waist diameter is proportional to the duration of the heating cycles. The sensor is capable of measuring a wide displacement distance up to 12 mm. A sensitivity of 0.2 dB/mm is recorded at a minimum tapered diameter of 8 $mu text{m}$ . In comparison with the previous works of using Fresnel reflection method, the results of our proposed method show significant improvement in sensing range, which is indicated by distinct inclination of the loss slope. The microfiber probe shows a promise for a sensitive sensing at low development cost.

Description: In this paper we propose a fast and efficient Jacobi-like approach named JET (Joint Eigenvalue decomposition based on Triangular matrices) for the Joint EigenValue Decomposition (JEVD) of a set of real or complex non-defective matrices based on the LU factorization of the matrix of eigenvectors. Contrarily to classical Jacobi-like JEVD methods, the iterative procedure of the JET approach can be reduced to the search for only one of the two triangular matrices involved in the factorization of the matrix of eigenvectors, hence decreasing the numerical complexity. Two variants of the JET technique, namely JET-U and JET-O, which correspond to the optimization of two different cost functions are described in detail and these are extended to the complex case. Numerical simulations show that in many practical cases the JET approach provides more accurate estimation of the matrix of eigenvectors than its competitors and that the lowest numerical complexity is consistently achieved by the JET-U algorithm. In addition, we illustrate in the ICA context the interest of being able to solve efficiently the (non-orthogonal) JEVD problem. More particularly, based on our JET-U algorithm, we propose a more robust version of an existing ICA method, named MICAR-U. The identifiability of the latter is studied and proved under some conditions. Computer results given in the context of brain interfaces show the better ability of MICAR-U to denoise simulated electrocortical data compared to classical ICA techniques for low signal to noise ratio values.

Description: A new design for successive interference cancellation (SIC) detection for multiple-input multiple-output systems is introduced, and it is developed on the basis of the method of normal equations commonly used to solve the linear least squares problem. On the basis of this design, optimal-ordered and suboptimal-ordered SIC detection algorithms are derived. It is shown that the proposed optimal-ordered SIC detection algorithm offers a complexity reduction ratio of 1.11–1.25 compared to the fastest known optimal-ordered SIC detection algorithm for intermediate and large numbers of antennas and in terms of the average complexity. On the other hand, the proposed suboptimal-ordered SIC detection algorithm requires a lower complexity than the proposed optimal-ordered one and provides a bit-error-rate performance close to that of the optimal-ordered one and better than those of the other suboptimal-ordered SIC detection algorithms.

Description: We consider the problem of signal recovery on graphs. Graphs model data with complex structure as signals on a graph. Graph signal recovery recovers one or multiple smooth graph signals from noisy, corrupted, or incomplete measurements. We formulate graph signal recovery as an optimization problem, for which we provide a general solution through the alternating direction methods of multipliers. We show how signal inpainting, matrix completion, robust principal component analysis, and anomaly detection all relate to graph signal recovery and provide corresponding specific solutions and theoretical analysis. We validate the proposed methods on real-world recovery problems, including online blog classification, bridge condition identification, temperature estimation, recommender system for jokes, and expert opinion combination of online blog classification.

Description: In this paper, we investigate the network power minimization problem for the multicast cloud radio access network (Cloud-RAN) with imperfect channel state information (CSI). The key observation is that network power minimization can be achieved by adaptively selecting active remote radio heads (RRHs) via controlling the group-sparsity structure of the beamforming vector. However, this yields a non-convex combinatorial optimization problem, for which we propose a three-stage robust group sparse beamforming algorithm. In the first stage, a quadratic variational formulation of the weighted mixed $ell_1/ell_2$ -norm is proposed to induce the group-sparsity structure in the aggregated beamforming vector, which indicates those RRHs that can be switched off. A perturbed alternating optimization algorithm is then proposed to solve the resultant non-convex group-sparsity inducing optimization problem by exploiting its convex substructures. In the second stage, we propose a PhaseLift technique based algorithm to solve the feasibility problem with a given active RRH set, which helps determine the active RRHs. Finally, the semidefinite relaxation (SDR) technique is adopted to determine the robust multicast beamformers. Simulation results will demonstrate the convergence of the perturbed alternating optimization algorithm, as well as, the effectiveness of the proposed algorithm to minimize the network power consumption for multicast Cloud-RAN.

Description: Many machine learning frameworks, such as resource-allocating networks, kernel-based methods, Gaussian processes, and radial-basis-function networks, require a sparsification scheme in order to address the online learning paradigm. For this purpose, several online sparsification criteria have been proposed to restrict the model definition on a subset of samples. The most known criterion is the (linear) approximation criterion, which discards any sample that can be well represented by the already contributing samples, an operation with excessive computational complexity. Several computationally efficient sparsification criteria have been introduced in the literature with the distance and the coherence criteria. This paper provides a unified framework that connects these sparsification criteria in terms of approximating samples, by establishing theoretical bounds on the approximation errors. Furthermore, the error of approximating any pattern is investigated, by proposing upper bounds on the approximation error for each of the aforementioned sparsification criteria. Two classes of fundamental patterns are described in detail, the centroid (i.e., empirical mean) and the principal axes in the kernel principal component analysis. Experimental results show the relevance of the theoretical results established in this paper.

Description: Network coding is an emerging technique known to improve the network performance in many aspects. In Vehicular Ad-hoc Networks (VANET), the bandwidth is considered to be one of the most important network resources. In this paper, we propose a network coding technique to improve the bandwidth utilization for non-safety applications in VANET. In a scenario where there are two sources broadcasting the data into the same area at the same time, the relay will use the network coding technique to decrease the number of rebroadcasting events and the consumption of the bandwidth, However, a fundamental problem for the relay when it receives a packet, is whether to wait for a coding opportunity and save the bandwidth or send the packet directly and reduce the delay. In order to address such tradeoff, we introduce two versions of our protocol, namely buffer size control scheme (BSCS) and time control scheme (TCS); by both versions we aim to control the delay that is experienced by the packet at each hop, while achieving better bandwidth utilization.Up to 38 % improvement in the bandwidth utilization has been recorded, and both schemes have shown a considerable amount of control on the imposed delay.

Description: In this paper, wireless sensing in the presence of complex electromagnetic media created by combinations of reinforcing bars and concrete is investigated. The wireless displacement sensing system, primarily designed for use in structural health monitoring (SHM), is composed of a comb-like nested split-ring resonator (NSRR) probe and a transceiver antenna. Although each complex medium scenario is predicted to have a detrimental effect on sensing in principle, it is demonstrated that the proposed sensor geometry is able to operate fairly well in all scenarios except one. In these scenarios that mimic real-life SHM, it is shown that this sensor exhibits a high displacement resolution of 1 $mu text{m}$ , a good sensitivity of 7 MHz/mm in average, and a high dynamic range extending over 20 mm. For the most disruptive scenario of placing concrete immediately behind NSRR, a solution based on employing a separator behind the probe is proposed to overcome the handicaps introduced by the medium. In order to obtain a one-to-one mapping from the measured frequency shift to the displacement, a numerical fit is proposed and used. The effects of several complex medium scenarios on this fit are discussed. These results indicate that the proposed sensing scheme works well in real-life SHM applications.

Description: Due to electromagnetic silence, passive tracking systems for emitter targets usually produce track segments (i.e., tracklets) rather than an entire trajectory of the target. Therefore, a multistage method for emitter target tracking is proposed in this paper. In the stage of tracklet generation, the Gaussian mixture-probability hypothesis density tracker with adaptive estimation of target birth intensity is applied to generate reliable tracklets of the emitter targets. After that, in the stage of tracklet association, the multipoint motion information and emitter signal information are integrated to compute the similarities between the tracklets. The affinity propagation algorithm, which does not impose the constraint of one-to-one correspondence, is then used to cluster the tracklets. In the stage of association refining, the clustering result is adjusted to refine the final trajectories according to the spatial-temporal constraint of the tracklets. The simulation results show that the proposed method is robust and performs well.

Description: Entomologists need to observe and monitor insects in their natural habitats for various reasons. One of the most common techniques in use today is to attach low-cost harmonic sensor or transponder to insects and to track them using an associated harmonic radar. Existing harmonic transponders based on monopoles, loop-dipoles, and Minkowski loops have been successfully attached to small low-flying insects. However, they offer significant constraints from a mounting perspective, weight and size considerations, aerodynamic drag, and the elevation of the insect center of gravity. This paper addresses these issues by presenting novel design methodologies for millimeter wave harmonic sensors and radar. The sensors address the challenges of ease of design and fabrication, robustness, conversion efficiency, miniaturization, antenna entangling, and attachment problems that are generally encountered with other transponders.

Description: This paper presents an ultra-low power DC–DC converter to be embedded into body sensor nodes. The proposed DC–DC converter is used to step-up the voltage at the output of a micro-thermoelectric generator up to a usable supply voltage between 0.42 and 1.05 V. Measurements have been conducted on ten available samples. The resulting mean value of the minimum input voltage is 96 mV. At the minimum input voltage, the power consumption is only 0.23 $mu text{W}$ . The operating temperature ranges from −40 °C to 100 °C.

Description: Various jamming techniques have been developed to prevent interferometric synthetic aperture radar from effective detection and observation. In this paper, a thorough analysis of the jamming effects on correlation and interferometric phase is provided. To derive the jamming result, a general signal model for the interference is first presented and the corresponding imaging results are produced through the range-Doppler algorithm. Then, the impacts of the interference on correlation are analyzed. The non-center located jammer decreases the correlation seriously due to the low correlation of the interference. However, the center located jammer clearly increases the correlation when the input jamming-to-signal ratio is large enough. Finally, the jammed interferometric phases for different jammer positions are discussed. It shows that the non-center located jammer results in large phase errors, while for the center located jammer, the interferometric phase approaches a constant. The effects of interference are demonstrated by simulated data based on the TerraSAR system.

Description: This paper demonstrates the measurement of respiration waveform during sleep with a noncontact radar sensor. Instead of measuring only the respiration rate, the methods that allow monitoring the absolute respiration displacement were studied. Absolute respiration displacement can in theory be measured with a quadrature microwave Doppler radar sensor and using the nonlinear demodulation as the channel combining method. However, in this paper, relative respiration displacement measures were used as a reference. This is the first time that longer data sets have been analyzed successfully with the nonlinear demodulation method. This paper consists of whole-night recordings of three patients in an uncontrolled environment. The reference respiration data were obtained from a full polysomnography recorded simultaneously. The feasibility of the nonlinear demodulation in a real-life setting has been unclear. However, this paper shows that it is successful most of the time. The coverage of successfully demodulated radar data was $sim 58$ %–78%. The use of the nonlinear demodulation is not possible in the following cases: 1) if the chest wall displacement is too small compared with the wavelength of the radar; 2) if the radar data do not form an arc-like shape in the $IQ$ -plot; or 3) if there are large movement artifacts present in the data. Both in academic literature and in commercial radar devices, the data are processed based on the presumption that it forms either an arc or a line in the $IQ$ -plot. Our measurements show that the presumption is not always valid.

Description: This paper presents a demodulation algorithm based on the synchronous integrator circuit for the MEMS tuning fork gyroscope utilizing the digital processing technology. The synchronous integral demodulator (SID) is adopted to demodulate the drive-detection signal and sense-axis output signal separately. Combining with automatic gain control and phase-locked loop technology, the closed-loop control of drive mode has been implemented, and high precision output of the gyroscope has been achieved. The simulation results have verified the effectiveness of the synchronous integrator circuit, which is in good agreement with the theoretical analysis. Compared with other demodulation algorithms, the SID has the advantages of the lower noise level, a better ability to attenuate the harmonics, and the best hardware efficiency. The frequency characteristic of the synchronous integrator is also analyzed, which is vital to the bandwidth of gyroscope. The print circuit board based on field-programmable gate array digital circuit is manufactured and the corresponding experiment is carried out. The experimental results show that the SID algorithm of digital control system for the gyroscope developed in our laboratory has achieved a good performance. The bias instability of tested gyroscope is measured to be 0.2°/h with the angle random walk of 0.14°/ $surd text{h}$ and the nonlinearity of the scale factor is <60 ppm with the measurement range of ±100°/s.

Description: We report the observed bifurcation in extraordinary electroconductance (EEC) sensor response to direct reverse bias based on measurement lead location as well as a dramatic enhancement in responsivity achieved via a modification of the shunt geometry. A maximum percent change in the four-point resistance of 130856% was achieved under a direct reverse bias of −1 V using an enhanced shunt design, a 325 fold increase over the conventional EEC square shunt design. This result was accompanied by an observed bifurcation in sensor response, driven by a rotation of the four-point measurement leads.

Description: This paper introduces joint neighbor discovery (ND) and coarse time-of-arrival (ToA) estimation in wireless sensor networks (WSNs) via orthogonal frequency-division multiple access. In the proposed technique, each sensor node exploits at least one orthogonal sub-carrier as its allocated signature, to respond the ND and ToA estimation requests transmitted by target nodes. The target node utilizes the orthogonality across sub-carriers to detect the transmitted signatures and their corresponding delays. This technique is energy efficient as it avoids multiple transmissions and receptions inherent in traditional ND protocols and ToA estimation techniques in WSN. Moreover, in this technique, network initiation process does not require channel information or time synchronization across sensor nodes. The performance of the proposed method is studied by evaluating the probabilities of false alarm and miss detection of the ND. In addition, ToA estimation error is calculated theoretically and via simulations. Moreover, the impact of available bandwidth on the performance and energy efficiency of ND and ToA estimation are investigated. Simulation results confirm the energy efficiency and the feasibility of the proposed method even at low signal-to-noise ratio regimes and in multi-path and frequency selective channels.

Description: This paper presents a new method for sensing and the quantification of the number of solid microparticles using surface acoustic wave (SAW) devices. In contrast to the standard mass loaded delay line approach, microcavities with varying geometrical shapes and sizes are formed between SAW interdigitated transducer pairs. The system operation relies on the resonance condition occurring inside the microcavity through the coupling of Rayleigh waves to the sample, and the output phase angle is used for obtaining measurement results. It is shown through measurements that it is possible to interact with polystyrene solid microbeads trapped inside the microcavity and extract information about the size of the sample. Furthermore, the number of microbeads placed in a single file along the microcavity width can be quantified using this platform. Experimental results are compared and verified with finite-element method simulations. In essence, this novel approach resulted in a platform capable of analyzing sample volumes less than 10 pL in a non-invasive manner. For size differentiation, experimental phase shifts of 0.14° ± 0.05°, 0.81° ± 0.26°, and 3.54° ± 0.49° were obtained in rectangular microcavities for 10, 15, and 20 $mu text{m}$ microbeads, respectively. On the other hand, a distribution of phase shifts as 0.51° ± 0.19°, 0.98° ±0.12°, and 1.34° ± 0.15° are obtained for counting one, two, or three microbeads, respectively. The proposed system was designed, simulated, fabricated, and tested successfully.

Description: The radix- $2^{k}$ algorithm plays a crucial role in the pipelined implementation of fast Fourier transform (FFT). This paper presents a fixed-point analysis and hardware evaluation of radix- $2^{k}$ FFT under the framework of the single-path delay feedback (SDF) and multi-path delay commutator (MDC) pipelined structure. The investigation is carried out with variable operating word-lengths to ensure the generality. Furthermore, the main streams to fulfill FFT coefficients weighting, namely, the approach using complex multipliers and the one adopting memoryless CORDIC units, are both considered in the analysis. Based on these derivations, a joint optimization of radix- $2^{k}$ algorithm and operating word-length is discussed to achieve a reasonable trade-off between computational accuracy and hardware expenditure. Simulations and experiments indicates that the derived SQNR is reliable to unfold the quantization effects of fixed-point radix- $2^{k}$ FFT. In addition, the proposed joint optimization strategy is capable of providing better solutions to implement the radix- $2^{k}$ FFT processor efficiently.

Description: In this paper, the quickest change detection problem is studied in two-state hidden Markov models (HMM), where the vector parameter $theta$ of the HMM changes from $theta_{0}$ to $theta_{1}$ at some unknown time, and one wants to detect the true change as quickly as possible while controlling the false alarm rate. It turns out that the generalized likelihood ratio (GLR) scheme, while theoretically straightforward, is generally computationally infeasible for the HMM. To develop efficient but computationally simple schemes for the HMM, we first discuss a subtlety in the recursive form of the generalized likelihood ratio (GLR) scheme for the HMM. Then we show that the recursive CUSUM scheme proposed in Fuh (Ann. Statist., 2003) can be regarded as a quasi-GLR scheme for pseudo post-change hypotheses with certain dependence structure between pre- and postchange observations. Next, we extend the quasi-GLR idea to propose recursive score schemes in the scenario when the postchange parameter $theta_{1}$ of the HMM involves a real-valued nuisance parameter. Finally, the Kullback-Leibler (KL) divergence plays an essential role in the quickest change detection problem and many other fields, however it is rather challenging to numerically compute it in HMMs. Here we develop a non-Monte Carlo method that computes the KL divergence of two-state HMMs via the underlying invariant probability measure, which is characterized by the Fredholm integral equation. Numerical study demonstrates an unusual property of the KL divergence for HMM that implies the severe effects of misspecifying the postchange parameter for the HMM.

Description: Long-Term Evolution (LTE) was implemented to fulfill and satisfy users’ needs as well as their demands for an improvised, fast and efficient Quality of service (QoS). A minimal aggregate of waiting time in return would give users a better Quality of experience (QoE). Real-time service packet scheduling is an important process in allocating resources to users. An efficient packet scheduling scheme will be able to cater fairly and efficiently to its users in the LTE network. Hence, studies are performed focusing on real-time traffic which includes video as well as Voice over Internet Protocol (VoIP) transmissions. In this work, the existing exponential rule (EXP rule) is utilized to benchmark our proposed packet scheduling techniques so that we are able to further evaluate the scheduling performance. In response to the increasing likelihood of losing packets in the EXP rule’s algorithm and maximizing the throughput rate, several schemes have been experimented with. The proposed schemes include 1) simplified EXP rule (sEXP Rule), 2) modified EXP rule (mEXP Rule), 3) EXP rule with maximum throughput (MT) (EXP_MT Rule), and 4) enhanced EXP rule with MT (E2M). By adding MT as a weight to the EXP rule, the throughput is maximized, thus providing higher throughput rates for real-time and non-real-time traffic. The simulation results show that the sEXP rule has a better performance in throughput, packet loss rate (PLR), and spectral efficiency for video traffic. Aside from this, our proposed E2M rule performs better than the benchmark EXP rule and outperforms the other proposed schemes, as well. It is observed that the E2M rule has better QoS support for real-time transmission in terms of delay, packet loss, throughput and spectral efficiency, within the LTE network. Hence, our proposed E2M rule is an enhancement of the benchmark EXP rule, which is commonly used in LTE packet scheduling.

Description: Convex optimization is a powerful tool for resource allocation and signal processing in wireless networks. As the network density is expected to drastically increase in order to accommodate the exponentially growing mobile data traffic, performance optimization problems are entering a new era characterized by a high dimension and/or a large number of constraints, which poses significant design and computational challenges. In this paper, we present a novel two-stage approach to solve large-scale convex optimization problems for dense wireless cooperative networks, which can effectively detect infeasibility and enjoy modeling flexibility. In the proposed approach, the original large-scale convex problem is transformed into a standard cone programming form in the first stage via matrix stuffing, which only needs to copy the problem parameters such as channel state information (CSI) and quality-of-service (QoS) requirements to the prestored structure of the standard form. The capability of yielding infeasibility certificates and enabling parallel computing is achieved by solving the homogeneous self-dual embedding of the primal-dual pair of the standard form. In the solving stage, the operator splitting method, namely, the alternating direction method of multipliers (ADMM), is adopted to solve the large-scale homogeneous self-dual embedding. Compared with second-order methods, ADMM can solve large-scale problems in parallel with modest accuracy within a reasonable amount of time. Simulation results will demonstrate the speedup, scalability, and reliability of the proposed framework compared with the state-of-the-art modeling frameworks and solvers.

Description: This paper describes printed resonant structures on a flexible substrate for humidity detection. The devices consist of a screen-printed spiral inductor working as radio frequency identification (RFID) antenna and two different inkjet-printed planar capacitive structures forming LC resonators. The sensitive material is directly the chosen substrate: a polyimide whose electrical permittivity is dependent on the moisture content. Therefore, no extra sensitive layer is required to functionalize these structures. In particular, two different approaches can be followed to use it. On one hand, the resonant structure can be used as a threshold tag including an RFID chip. The tags will be detected or not by an RFID reader depending on the humidity. On the other hand, these structures can be directly used as quantitative humidity sensors where the moisture content is associated with the resonant frequency of the structure.

Description: A novel signal-processing algorithm based on phase tracking in frequency domain of optical fiber Fabry-Perot interferometric spectrum is described. The concept was demonstrated with three fiber Fabry-Perot interferometers for relative humidity measurement. Experimental results show that the proposed method can yield optical path difference measurement with high resolution, especially in the lower range of absolute humidity under 2000 ppm.