ALBERT

Description: Recent advances in smart devices have sustained them as a better alternative for the design of human–machine interaction (HMI), because they are equipped with accelerometer sensor, gyroscope sensor, and an advanced operating system. This paper presents a continuous hand gestures recognition technique that is capable of continuous recognition of hand gestures using three-axis accelerometer and gyroscope sensors in a smart device. To reduce the influence of unstableness of a hand making the gesture and compress the data, a gesture coding algorithm is developed. An automatic gesture spotting algorithm is developed to detect the start and end points of meaningful gesture segments. Finally, a gesture is recognized by comparing the gesture code with gesture database using dynamic time warping algorithm. In addition, a prototype system is developed to recognize the continuous hand gestures-based HMI. With the smartphone, the user is able to perform the predefined gestures and control smart appliances using the Samsung AllShare protocol.

Description: As nanometer technology advances, conventional optical proximity correction (OPC) that minimizes the edge placement error (EPE) at the nominal process condition alone often leads to poor process windows. To improve the mask printability across various process corners, process-window OPC optimizes EPE for multiple process corners, but often suffers long runtime, due to repeated lithographic simulations. This paper presents an efficient process variation (PV)-aware mask optimization framework, namely PVOPC, to simultaneously minimize EPE and PV band with fast convergence. The PVOPC framework includes EPE-sensitivity-driven dynamic fragmentation, PV-aware EPE modeling, and correction with three new EPE-converging techniques and a systematic subresolution-assisted feature insertion algorithm. Experimental results show that our approach efficiently achieves high-quality EPE and PV band results.

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Description: Binning after volume production is a widely accepted technique to classify fabricated integrated circuits (ICs) into different clusters depending on different degrees of specification compliance. This allows the manufacturer to sell nonoptimal devices at lower rates, so adapting to customer’s quality-price requirements. The binning procedure can be carried out by measuring every single circuit performances, but this approach is costly and time-consuming. On the contrary, if alternate measurements are used to characterize the bins, the procedure is considerably enhanced. In such a case, the specification bin boundaries become arbitrary shape regions due to the highly nonlinear mappings between the specifications space and the alternate measurements space. The binning strategy proposed in this paper functions with the same efficiency regardless of these shapes. The digital encoding of the bins in the alternate measurements space using octrees is the key idea of the proposal. The strategy has two phases: 1) the training phase and 2) the binning phase. In the training phase, the specification bins are encoded using octrees. This first phase requires sufficient samples of each class to generate the octree under realistic variations, but it only needs to be performed once. The binning phase corresponds to the actual production binning of the fabricated ICs. This is achieved by evaluating the alternate measurements in the previously generated octree. The binning phase is fast due to the inherent sparsity of the octree data structure. In order to illustrate the proposal, the method has been applied to a band-pass Butterworth filter considering three specification bins as a proof of concept. Successful simulation results are reported showing considerable advantages as compared to a support vector machine (SVM)-based classifier. Similar bin misclassifications are obtained with both methods, 1.68% using octrees and 1.83% using SVM, while binning time is $5times $ times faster using octrees than using the SVM-based classifier.

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Description: The small-signal vibration model of Tb x Dy 1– x Fe y plate was developed by mechanical-electrical analogy method. Based on the model, we demonstrated the reasonableness of measuring the small-signal magnetomechanical behaviors by a laser Doppler vibrometer. The strain coefficient of the Tb x Dy 1– x Fe y plates was measured as a function of frequency and bias field ( $H_{mathrm{ dc}})$ , and Young’s modulus, mechanical quality factor ( $Q_{m})$ , relative permeability, and magnetomechanical coupling coefficient were investigated as a function of $H_{mathrm{ dc}}$ . Many novel characteristics were observed under the drive of a small-signal field (7.96 A/m). The change tendency of the strain coefficient at resonance differs from that at low frequency, and the resonant strain coefficients are significantly high (>85 nm/A) in a wide range of bias field from 4.78 to 55.72 kA/m. Both the negative- $Delta E$ and positive- $Delta E$ effects are observed, and the negative- $Delta E$ effect in the low field range is also remarkable. In particular, $Q_{m}$ sharply decreases from the initial value of 104 to a minimum value of 11.4 and, then, increases slowly, and the ratio of the maximum variation of $Q_{m}$ over $H_{mathrm{ dc}}$ to the minimum value of $Q_{m}$ exceeds $sim 812.3$ %. This is an important systematic investigation on the small-signal dynamic magnetomechanical behavior of Tb x Dy 1– x Fe y , and the results are highly beneficial to the designing of magnetostrictive devices.

Description: The presence of particles, which can intrude into the air bearing, is one of the most common factors in the failure of hard disk drives (HDDs). Previous studies have investigated particles trajectories with the assumption of ideal trapping or reflecting boundary conditions in air-filled drives. However, only the colliding particle with insufficient energy to escape the potential well will be trapped by the surface. In this paper, considering the particle-surface energy during the collision, the trapping criterion of the incident normal critical velocity ( $V_{text {ni}}^{mathrm {ast }})$ for Al 2 O 3 particles is developed as the boundary conditions for different colliding surfaces inside a 2.5 in drive. Then, trapping status for Al 2 O 3 particles and particles trajectories inside the drive are simulated by using the commercial computational fluid dynamics solver FLUENT with user-defined functions. The results reveal that the particles will travel longer distances until trapped by HDD components when considering the trapping criterion. In addition, smaller particles will more likely degrade the head–disk interface reliability, since they easily stick on the disk surface.

Description: Benefiting from its simple switching scheme (only a bidirectional current source), high-speed and low-power spin-transfer torque (STT) has been regarded as one of the most promising switching mechanisms for a magnetic tunnel junction (MTJ)-based non-volatile memory and logic circuits. However, it suffers from a number of reliability issues like write error induced by its intrinsic stochasticity, process variation, and so on. In order to reduce the write error rate, the mainstream solution is to enlarge the write pulse duration at the expense of write energy dissipation. Some self-terminated write circuits have been proposed to avoid the wasted write energy. But the hardware cost of these write circuits is especially large. In this paper, a novel cost-efficient self-terminated write circuit is proposed using two simple built-in sensing circuits. The proposed write circuit is simulated with a physics-based STT-MTJ compact model and a commercial CMOS 40 nm design kit. The simulation result shows about 35% reduction of circuit area and 10% lower energy consumption in comparison with that in prior work. In addition, the Error-Free write operation under process variation of both the CMOS transistor and the STT-MTJ is achieved due to its large sense margin ( $sim 320$ mV).

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Description: Domain wall nanomagnet (DWNM)-based devices have been extensively studied as a promising alternative to the conventional CMOS technology in both the memory and logic implementations due to their non-volatility, near-zero standby power, and high integration density characteristics. In this paper, we leverage a physics-based model of a DWNM device to design a highly scalable current-mode majority gate to achieve a novel one bit full-adder (FA) circuit. The modeled DWNM specifications are calibrated with the experimentally measured data. The functionality of the proposed DWNM-based FA (DWNM-FA) is verified using a SPICE circuit simulator. The detailed analysis and the calculations have been performed to realize the proposed DWNM-FA delay and power consumption corresponding to the various induced input currents at different operating temperatures. The power-delay product of DWNM-FA is examined to tune the operation within the optimum induced input current region to obtain desired power-delay requirements over a range of 200 $mu text{A}$ to 1 mA at temperatures from 298 to 378 K. Finally, the comparison results exhibit 52% and 49% area improvement as well as 41% and 31% improvement in device count complexity over CMOS-based and magnetic tunnel junction-based FA designs, respectively.

Description: Bulk CoCr 2 O 4 undergoes a transition from paramagnetic to long-range ferrimagnetic phase at $T_{c}$ (94 K) to a long-range and/or short-range spiral order at $T_{s}$ ( $sim 24$ K), and finally shows a lock-in-transition below 15 K. The spiral component induces an electric polarization and also a spontaneous magnetization for which it is said to be multiferroic. Reducing the size of a CoCr 2 O 4 multiferroic material to $sim 50$ nm by a coprecipitation method, we obtain a pure cubic phase with space group, Fd3m and lattice parameter (8.334 ± 0.003 °A). A rich sequence of magnetic transitions are examined by measuring temperature and field-dependent magnetization and diffused neutron scattering (DNS) using polarized neutron at different temperatures. While paramagnetic to ferrimagnetic transition is enhanced from 97 K in bulk to 99 K at 0.5 kOe field, followed by a decrease in lock-in-transition ( $T_{L}$ ) from 15 K in bulk to 8 K, spiral ordering temperature does not show a significant change. A strong disagreement between paramagnetic moment obtained from the fitting of $chi ^{-1}=({T}/{C})+({1}/{chi _{o}})-({b}/{T-theta })$ and ferrimagnetic moment obtained from the $M$ versus $H$ loop taken at 2 K, nonsaturated magnetization at 50–100 kOe field, two order of magnitude higher coercivity ( $H_{c}$ ), and splitting of ac susceptibly confirm the core–shell structure of the particles. Furthermore, a magnetic scattering analysis clearly shows that while the paramagnetic to ferrimagnetic transition is continuous, the spiral ordering is sharp, short range, and commensurate in contrast to incommensurate spiral order observed single crystal of CoCr 2 O 4 .

Description: The finite-element analysis for the simulation of magnetic fields in electrical machines leads to an index-1 differential-algebraic equation (DAE) (as opposed to a conventional ordinary differential equation), because the electrical conductivity can be zero in certain regions. First, we construct a DAE-compatible time integration scheme which is energy-balanced, meaning that in a linear system, the input stored and lost powers sum exactly to zero. Second, we use a method based on the energy balance to compute torque. We show that the energy balance method approaches the virtual work principle applied at remeshing layer, as the time step is refined. A similar result also holds if the rotation of the rotor is implemented by Nitsche’s method, which is an instance of the so-called mortar methods.

Description: Investigations about induction sensors, electromagnetic launchers, shields, transformers, and power line-induced currents address increased number of low-frequency research and industrial applications. In general, a magneto-quasi-static (MQS) approximation is considered for the solutions of low-frequency problems in electromagnetics. This approximation leads to a diffusion process when displacement currents are neglected. However, keeping the displacement currents, Maxwell’s equations are valid at low frequencies. In this manner, the finite difference time domain (FDTD) method must be modified by the slowing down propagation velocity at low-frequency regime. In this paper, important and crucial points of the MQS approximation and its application in the FDTD method are clarified in the sense of analytical and numerical aspects. A material scaling technique of dielectric permittivity for the QS FDTD application is analyzed within comprehensive investigations. Furthermore, a criterion for choosing a proper value of scaling parameter will be revealed. Finally, effects of proper and improper values of the scaling parameter are presented with validated analytical and numerical results.

Description: The ability to manipulate the relative magnetization alignment between ferromagnetic source and drain electrodes attached to a molecule or small quantum dot is a prerequisite for a number of spintronic device applications. The influence of electrode shape and field orientation on pairwise magnetization reversal mechanisms in nanogap and point-contact structures is investigated here using micromagnetic simulations. A favorable device geometry and setup are identified for enabling planar, monodomain source and drain electrodes with a magnetization alignment that may be controllably switched between a parallel and antiparallel configuration.

Description: In typical inductive power transfer (IPT) applications, soft magnetic materials are employed in both transmitter and receiver coils as low reluctance paths in order to improve the inductive coupling and support the guidance of magnetic flux. Considering their critical role in the overall performance, this paper investigates the impact of magnetic material properties on the basic features of inductive powering systems. To this end, a group of Ni–Zn and Ni–Cu–Zn spinel ferrite samples with a wide value range of magnetic properties is prepared. The materials are characterized in terms of their structural and magnetic properties, and they are used to build various substrate assemblies for the IPT transmitter (Tx) coil. Conducting Al and Cu sheets and biasing permanent magnets are combined with ferrite disks to simulate common Tx coil designs. This allows the identification of the correlation between the material properties, especially power losses, and the measured power transfer efficiency and shielding effectiveness.

Description: This paper presents a method to estimate steady wind and a maneuvering strategy for loitering under a strong, steady wind. The wind estimation uses Global Positioning System velocity only through a novel filter design without airspeed measurement. The wind estimate is then used to guide the aircraft to crab in a direction perpendicular to the wind, thereby avoiding large changes in flight variables if an orbit-type maneuver is attempted. The proposed method is demonstrated through flight tests.

Description: We propose biased estimators to find the direction of arrival of emitters present in the mainlobe of a spinning antenna-based electronic intelligence system. The proposed estimators were constructed by using Bayesian techniques and by performing a linear transformation and an affine transformation on the maximum likelihood estimator. From a Monte Carlo simulation and experimental results, we demonstrate that the proposed estimators outperform the limit set by the popular performance benchmark, the Cramer-Rao lower bound.

Description: Objective: We present the framework for wearable joint rehabilitation assessment following musculoskeletal injury. We propose a multimodal sensing (i.e., contact based and airborne measurement of joint acoustic emission) system for at-home monitoring. Methods: We used three types of microphones—electret, MEMS, and piezoelectric film microphones—to obtain joint sounds in healthy collegiate athletes during unloaded flexion/extension, and we evaluated the robustness of each microphone's measurements via: 1) signal quality and 2) within-day consistency. Results: First, air microphones acquired higher quality signals than contact microphones (signal-to-noise-and-interference ratio of 11.7 and 12.4 dB for electret and MEMS, respectively, versus 8.4 dB for piezoelectric). Furthermore, air microphones measured similar acoustic signatures on the skin and 5 cm off the skin (∼4.5× smaller amplitude). Second, the main acoustic event during repetitive motions occurred at consistent joint angles (intra-class correlation coefficient ICC(1, 1) = 0.94 and ICC(1, k) = 0.99). Additionally, we found that this angular location was similar between right and left legs, with asymmetry observed in only a few individuals. Conclusion: We recommend using air microphones for wearable joint sound sensing; for practical implementation of contact microphones in a wearable device, interface noise must be reduced. Importantly, we show that airborne signals can be measured consistently and that healthy left and right knees often produce a similar pattern in acoustic emissions. Significance: These proposed methods have the potential for enabling knee joint acoustics measurement outside the clinic/lab and permitting long-term monitoring of knee health for patients rehabilitating an acute knee joint injury.

Description: A new method was proposed for chirp signal detection and estimation built on the frame-based fast Fourier transform (FFT). The proposed method uses the peak frequency difference between FFT frames to detect a chirp signal and estimate chirp rate. This approach differs from conventional methods and is easy to implement. It generates more accurate chirp rate estimation especially under a low signal-to-noise ratio. Simulation and experimental data are used to verify the proposed methods.

Description: As telehealth applications emerge, the need for accurate and reliable biosignal quality indices has increased. One typical modality used in remote patient monitoring is the electrocardiogram (ECG), which is inherently susceptible to several different noise sources, including environmental (e.g., powerline interference), experimental (e.g., movement artifacts), and physiological (e.g., muscle and breathing artifacts). Accurate measurement of ECG quality can allow for automated decision support systems to make intelligent decisions about patient conditions. This is particularly true for in-home monitoring applications, where the patient is mobile and the ECG signal can be severely corrupted by movement artifacts. In this paper, we propose an innovative ECG quality index based on the so-called modulation spectral signal representation. The representation quantifies the rate of change of ECG spectral components, which are shown to be different from the rate of change of typical ECG noise sources. The proposed modulation spectral-based quality index, MS-QI, was tested on 1) synthetic ECG signals corrupted by varying levels of noise, 2) single-lead recorded data using the Hexoskin garment during three activity levels (sitting, walking, running), 3) 12-lead recorded data using conventional ECG machines (Computing in Cardiology 2011 dataset), and 4) two-lead ambulatory ECG recorded from arrhythmia patients (MIT-BIH Arrhythmia Database). Experimental results showed the proposed index outperforming two conventional benchmark quality measures, particularly in the scenarios involving recorded data in real-world environments.

Description: Platelet-rich plasma (PRP) is a volume of autologous plasma that has a higher platelet concentration above baseline. It has already been approved as a new therapeutic modality and investigated in clinics, such as bone repair and regeneration, and oral surgery, with low cost-effectiveness ratio. At present, PRP is mostly prepared using a centrifuge. However, this method has several shortcomings, such as long preparation time (30 min), complexity in operation, and contamination of red blood cells (RBCs). In this paper, a new PRP preparation approach was proposed and tested. Ultrasound waves (4.5 MHz) generated from piezoelectric ceramics can establish standing waves inside a syringe filled with the whole blood. Subsequently, RBCs would accumulate at the locations of pressure nodes in response to acoustic radiation force, and the formed clusters would have a high speed of sedimentation. It is found that the PRP prepared by the proposed device can achieve higher platelet concentration and less RBCs contamination than a commercial centrifugal device, but similar growth factor (i.e., PDGF-ββ). In addition, the sedimentation process under centrifugation and sonication was simulated using the Mason–Weaver equation and compared with each other to illustrate the differences between these two technologies and to optimize the design in the future. Altogether, ultrasound method is an effective method of PRP preparation with comparable outcomes as the commercially available centrifugal products.

Description: The quasimaximum likelihood (QML) estimator of polynomial-phase signals (PPSs) is based on the maximization of the short-time Fourier transform and suffers from aliasing when signals are sampled below the Nyquist sampling rate. In this paper, a phase unwrapping procedure has been proposed as an additional step in the QML to estimate parameters of such signals. Statistical study has shown excellent performance of the proposed approach.

Description: A new wireless sensor was designed, fabricated, and applied for in situ monitoring of tensile force at a wound site. The sensor was comprised of a thin strip of magnetoelastic material with its two ends connected to suture threads for securing the sensor across a wound repair site. Since the sensor was remotely interrogated by applying an ac magnetic field and capturing the resulting magnetic field, it did not require direct wire connections to an external device or internal battery for long-term use. Due to its magnetoelastic property, the application of a tensile force changed the magnetic permeability of the sensor, altering the amplitude of the measured magnetic field. This study presents two sensor designs: one for high and one for low-force ranges. A sensor was fabricated by directly adhering the magnetoelastic strip to the suture. This sensor showed good sensitivity at low force, but its response saturated at about 1.5 N. To monitor high tensile force, the magnetoelastic strip was attached to a metal strip for load sharing. The suture thread was attached to the both ends of the metal strip so only a fraction of the applied force was directed to the sensor, allowing it to exhibit good sensitivity even at 44.5 N. The sensor was applied to two ex vivo models: a sutured section of porcine skin and a whitetail deer Achilles tendon. The results demonstrate the potential for in vivo force monitoring at a wound repair site.

Description: This study presents a precise way to detect the third ( $S_{3}$ ) heart sound, which is recognized as an important indication of heart failure, based on nonlinear single decomposition and time–frequency localization. The detection of the $S_{3}$ is obscured due to its significantly low energy and frequency. Even more, the detected $S_{3}$ may be misunderstood as an abnormal second heart sound with a fixed split, which was not addressed in the literature. To detect such $S_{3}$ , the Hilbert vibration decomposition method is applied to decompose the heart sound into a certain number of subcomponents while intactly preserving the phase information. Thus, the time information of all of the decomposed components are unchanged, which further expedites the identification and localization of any module/section of a signal properly. Next, the proposed localization step is applied to the decomposed subcomponents by using smoothed pseudo Wigner–Ville distribution followed by the reassignment method. Finally, based on the positional information, the $S_{3}$ is distinguished and confirmed by measuring time delays between the $S_{2}$ and $S_{3}$ . In total, 82 sets of cardiac cycles collected from different databases including Texas Heart Institute database are examined for evaluation of the proposed method. The result analysis shows that the proposed method can detect the $S_{3}$ correctly, even when the - ormalized temporal energy of $S_{3}$ is larger than 0.16, and the frequency of those is larger than 34 Hz. In a performance analysis, the proposed method demonstrates that the accuracy rate of $S_{3}$ detection is as high as 93.9%, which is significantly higher compared with the other methods. Such findings prove the robustness of the proposed idea for detecting substantially low-energized $S_{3}$ .

Description: Electromagnetic (EM) tracking systems are highly susceptible to field distortion. The interference can cause measurement errors up to a few centimeters in clinical environments, which limits the reliability of these systems. Unless corrected for, this measurement error imperils the success of clinical procedures. It is therefore fundamental to dynamically calibrate EM tracking systems and compensate for measurement error caused by field distorting objects commonly present in clinical environments. We propose to combine a motion model with observations of redundant EM sensors and compensate for field distortions in real time. We employ a simultaneous localization and mapping technique to accurately estimate the pose of the tracked instrument while creating the field distortion map. We conducted experiments with six degrees-of-freedom motions in the presence of field distorting objects in research and clinical environments. We applied our approach to improve the EM tracking accuracy and compared our results to a conventional sensor fusion technique. Using our approach, the maximum tracking error was reduced by 67% for position measurements and by 64% for orientation measurements. Currently, clinical applications of EM trackers are hampered by the adverse distortion effects. Our approach introduces a novel method for dynamic field distortion compensation, independent from preoperative calibrations or external tracking devices, and enables reliable EM navigation for potential applications.

Description: A feasibility study on a new technique capable of monitoring localized sweat rate is explored in this paper. Wearable devices commonly used in clinical practice for sweat sampling (i.e., Macroducts) were positioned on the body of an athlete whose sweat rate was then monitored during cycling sessions. The position at which the sweat fills the Macroduct was indicated by a contrasting marker and captured via a series of time-stamped photos or a video recording of the device during an exercise period. Given that the time of each captured image/frame is known (either through time stamp on photos or the constant frame rate of the video capture), it was, therefore, possible to estimate the sweat flow rate through a simple calibration model. The importance of gathering such valuable information is described, together with the results from a number of exercise trials to investigate the viability of this approach.

Description: Objective: A novel high-precision approach [lifetime-decomposition measurement (LTDM)] for the assessment of the glomerular filtration rate (GFR) based on clearance measurements of exogenous filtration marker. Methods: The time-correlated single photon counting (TCSPC) acquisition in combination with a new decomposition method allows the separation of signal and background from transcutaneous measurements of GFR. Results: The performance of LTDM is compared versus the commercially available NIC-kidney patch-based system for transcutaneous GFR measurement. Measurements are performed in awake Sprague Dawley (SD) rats. Using the standard concentration required for the NIC-kidney system [7-mg/100-g body weight (b.w.) FITC-Sinistrin] as reference, the mean difference (bias) of the elimination curves GFR between LTDM and NIC-kidney was 4.8%. On the same animal and same day, the capability of LTDM to measure GFR with a FITC-Sinistrin dose reduced by a factor of 200 (35-μg/100-g b.w.) was tested as well. The mean differences (half lives with low dose using LTDM compared with those using first, the NIC-Kidney system and its standard concentration, and second, LTDM with the same concentration as for the NIC-Kidney system) were 3.4% and 4.5%, respectively. Conclusion: We demonstrate that with the LTDM strategy substantial reductions in marker concentrations are possible at the same level of accuracy. Significance: LTDM aims to resolve the issue of the currently necessary large doses of fluorescence tracer required for transcutaneous GFR measurement. Due to substantially less influences from autofluorescence and artifacts, the proposed method outperforms other existing techniques for accurate percutaneous organ function measurement.

Description: Classic brain–machine interface (BMI) approaches decode neural signals from the brain responsible for achieving specific motor movements, which subsequently command prosthetic devices. Brain activities adaptively change during the control of the neuroprosthesis in BMIs, where the alteration of the preferred direction and the modulation of the gain depth are observed. The static neural tuning models have been limited by fixed codes, resulting in a decay of decoding performance over the course of the movement and subsequent instability in motor performance. To achieve stable performance, we propose a dual sequential Monte Carlo adaptive point process method, which models and decodes the gradually changing modulation depth of individual neuron over the course of a movement. We use multichannel neural spike trains from the primary motor cortex of a monkey trained to perform a target pursuit task using a joystick. Our results show that our computational approach successfully tracks the neural modulation depth over time with better goodness-of-fit than classic static neural tuning models, resulting in smaller errors between the true kinematics and the estimations in both simulated and real data. Our novel decoding approach suggests that the brain may employ such strategies to achieve stable motor output, i.e., plastic neural tuning is a feature of neural systems. BMI users may benefit from this adaptive algorithm to achieve more complex and controlled movement outcomes.

Description: With shrinking process technology, decreasing supply voltage, and increasing clock frequency, noise reduction becomes more and more crucial to the success of modern mixed-signal system-on-chip design. To eliminate the switching noise due to crosstalk coupling between analog and digital signals, it is essential to fully separate the routing paths of analog and digital nets when generating mixed-signal layouts. Different from previous works which cannot fully separate analog and digital routing paths, this paper presents a novel hierarchical deterministic mixed-signal layout synthesis approach with the separation of analog and digital signal paths for switching noise elimination. Experimental results based on a third-order $ {Sigma Delta }$ modulator show that the proposed approach can result in various layouts with separated analog and digital signal paths while achieving better signal-to-noise and distortion ratio, and overall performance specifications.

Description: Efficient performance modeling of today’s analog and mixed-signal circuits is an important yet challenging task, due to the high-dimensional variation space and expensive circuit simulation. In this paper, we propose a novel performance modeling algorithm that is referred to as Bayesian model fusion (BMF) to address this challenge. The key idea of BMF is to borrow the information collected from an early stage (e.g., schematic level) to facilitate efficient performance modeling at a late stage (e.g., post layout). Such a goal is achieved by statistically modeling the performance correlation between early and late stages through Bayesian inference. Furthermore, to make the proposed BMF method of practical utility, four implementation issues, including: 1) prior mapping; 2) missing prior knowledge; 3) fast solver; and 4) prior and hyper-parameter selection, are carefully considered in this paper. Two circuit examples designed in a commercial 32 nm CMOS silicon on insulator process demonstrate that the proposed BMF method achieves up to $9times $ runtime speed-up over the traditional modeling technique without surrendering any accuracy.

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Description: Layout-dependent effects (LDEs) have become a critical issue in modern analog and mixed-signal circuit designs. The three major sources of LDEs, well proximity, length of oxide diffusion, and oxide-to-oxide spacing, significantly affect the threshold voltage and mobility of devices in advanced technology nodes. In this paper, we propose the first work to consider the three major sources of LDEs during analog placement. We first transform the three LDE models into nonlinear analytical placement models. Then an LDE-aware analytical analog placement algorithm is presented to mitigate the influence of the LDEs while improving circuit performance. Experimental results show that our placement algorithm can effectively and efficiently reduce the LDE-induced variations and improve circuit performance.

Description: Reconfigurable digital filter is being widely used in applications such as communication and signal processing. Its performance, power consumption, and logic resource utilization are the major factors to be taken into consideration when designing the filters. This paper proposes a concise canonic signed digit coefficient grouping method aiming at reducing the number of common subexpressions (CSs). Further, we statistically analyze every CS occurance for numerous sorts of the finite-impulse response (FIR) filters and obtain characterization of the distribution behavior for all the possible CS patterns in a 16-bit coefficient. Thus, a novel processing element structure is proposed to form a medium-grain array for computationally efficient realization of reconfigurable FIR filter. The experiment results suggest such design implementations typically achieve 21% reduction in silicon area, 20% decrease in power consumption, and 14% improvement in operation speed in comparison to other conventional FIR architectures.

Description: Digital microfluidic biochips (DMFBs) are gaining increasing attention with promising applications for automating and miniaturizing laboratory procedures in biochemistry. In DMFBs, cross-contamination of droplets with different biomolecules is a major issue, which causes significant errors in bioassays. Washing operations are introduced to clean the cross-contamination spots. However, existing works have oversimplified assumptions on the washing behavior, which either assume infinite washing capacity, or ignore the routing conflicts between functional and washing droplets. This paper proposes the first integrated functional and washing droplet routing flow, which considers practical issues including the finite washing capacity constraint, and the routing conflicts between functional and washing droplets. Washing droplets of different sizes are also proposed to wash the congested cross-contamination spots. Effectiveness of the proposed method is validated by real-life biochemical applications.

Description: It is known that bus-oriented escape routing and area routing are necessary in a high-speed printed circuit board (PCB) design. In this paper, given a set of global routed buses in a high-speed PCB design, it is assumed that the routed nets in a single bus are represented as a bus-oriented net between two escaped boundary pins. Based on the construction of a virtual wall between two circuit components, the connection transformation of the given bus-oriented nets inside a closed region and the construction of a covering graph for the represented intervals, an iterative modified left-edge algorithm is proposed to minimize the number of the assigned layers and assign all the bus-oriented nets onto the available layers. Compared with Tsai’s algorithm, the experimental results show that our proposed algorithm reduces 15.0% of the layer number and 21.9% of CPU time for six tested examples on the average, respectively. Compared with Chin’s algorithm, the experimental results show that our proposed algorithm use less CPU time to reduce 15.0% of the layer number for six tested examples on the average.

Description: Provides a listing of the editors, board members, and current staff for this issue of the publication.

Description: Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Description: Applications executed on multicore embedded systems interact with system software [such as the operating system (OS)] and hardware, leading to widely varying thermal profiles which accelerate some aging mechanisms, reducing the lifetime reliability. Effectively managing the temperature therefore requires: 1) autonomous detection of changes in application workload and 2) appropriate selection of control levers to manage thermal profiles of these workloads. In this paper, we propose a technique for workload change detection using density ratio-based statistical divergence between overlapping sliding windows of CPU performance statistics. This is integrated in a runtime approach for thermal management, which uses reinforcement learning to select workload-specific thermal control levers by sampling on-board thermal sensors. Identified control levers override the OSs native thread allocation decision and scale hardware voltage–frequency to improve average temperature, peak temperature, and thermal cycling. The proposed approach is validated through its implementation as a hierarchical runtime manager for Linux, with heuristic-based thread affinity selected from the upper hierarchy to reduce thermal cycling and learning-based voltage–frequency selected from the lower hierarchy to reduce average and peak temperatures. Experiments conducted with mobile, embedded, and high performance applications on ARM-based embedded systems demonstrate that the proposed approach increases workload change detection accuracy by an average ${3.4times }$ , reducing the average temperature by 4 °C–25 °C, peak temperature by 6 °C–24 °C, and thermal cycling by 7%–35% over state-of-the-art approaches.

Description: This paper proposes a three-tier algorithmic framework as the basis for the flexible design of data-driven structural health monitoring (SHM) systems. The three major functions of the SHM system, including data normalization, feature extraction, and hypothesis testing (HT), are mapped to the three layers of the framework. The first tier of the framework is devoted to data normalization. Machine learning (ML) methods are adopted to normalize available data sets by binning data sets to similar environmental and operational conditions (EOCs) of the system. Specifically, affinity propagation clustering is used to delineate data into groups of similar EOC. Once data are normalized by EOC, the second tier of the framework extracts features from the data to serve as condition parameters (CPs) for damage assessment. To ascertain the health state of the structure, the third tier of the framework is devoted to statistical analysis of the CP through HT. An intrinsic goal of the study is to explore the modularity of the three tier framework as a means of offering SHM system designers opportunity to explore and test different computational block sets at each layer to maximize the detection capability of the SHM system. Various realizations of the three-tier modular framework are presented and applied to acceleration and EOC data collected from an operational 3-kW wind turbine. In total, 354 data sets are collected from the turbine, including tower lateral accelerations in two orthogonal directions at six heights, wind speed and wind direction; 317 of the data sets correspond to the wind turbine in a healthy state and 37 with the wind turbine in a damage state. Using quantitative metrics derived from receiver operating characteristic (ROC) curves, the damage classification capabilities of the framework are validated and shown to accurately identify intentionally introduced damage in the turbine.

Description: Reinforced concrete infrastructure assets built during the last century are required to stay in-service beyond their intended design lives, which requires techniques that allow the effects of deterioration and overloading to be monitored to ensure these assets are still fit for purpose. One such indicator of distress in a reinforced concrete structure is crack movement; models have been proposed that allow the internal stresses within a structure to be estimated if crack width and slip can be measured. This paper introduces a technique that uses digital image correlation (DIC) to measure not only crack width but also crack slip. The effect of curvature on this measurement technique is discussed and a method for minimizing the errors due to curvature is presented. The technique is used to measure crack width and slip in reinforced concrete beams with both small crack slip and significant crack slip. The results suggest that the technique can be used to measure crack movement in beams once the effects of curvature are accounted for. Future research in this area will focus on development of this technique for use in the field.

Description: Ultrasonic guided waves are an attractive tool for structural health monitoring due to their capability to rapidly assess large regions of a structure. Yet, most guided wave based methods for detecting, locating, and classifying structural damage rely on our ability to accurately predict guided wave behavior. Characterizing and predicting guided wave behavior is difficult, particularly in mechanically complex materials such as fiber-reinforced composites. In this paper, we address this challenge through a sparse wavenumber analysis framework. Sparse wavenumber analysis integrates physics-based models, signal processing algorithms for compressive sensing, and a small number of local measurements to predict global wave behavior. We implement sparse wavenumber analysis for three wave systems: standing waves on a string, Lamb waves in an isotropic plate, and guided waves in a unidirectional, anisotropic plate. Through the use of simulation and experimental data, we show that sparse wavenumber analysis can accurately recover the sparse representations (i.e., the eigenmodes) of each system and then use these representations to predict global wave behavior. For the anisotropic plate, we accurately predict 149765 experimental time-domain measurements from only 36 local measurements.

Description: Guided wave sensors are widely used in a number of industries and have found particular application in the oil and gas industry for the inspection of pipework. Traditionally this type of sensor was used for one-off inspections, but in recent years there has been a move towards permanent installation of the sensor. This has enabled highly repeatable readings of the same section of pipe, potentially allowing improvements in defect detection and classification. This paper proposes a novel approach using independent component analysis to decompose repeat guided wave signals into constituent independent components. This separates the defect from coherent noise caused by changing environmental conditions, improving detectability. This paper demonstrates independent component analysis applied to guided wave signals from a range of industrial inspection scenarios. The analysis is performed on test data from pipe loops that have been subject to multiple temperature cycles both in undamaged and damaged states. In addition to processing data from experimental damaged conditions, simulated damage signals have been added to “undamaged” experimental data, so enabling multiple different damage scenarios to be investigated. The algorithm has also been used to process guided wave signals from finite element simulations of a pipe with distributed shallow general corrosion, within which there is a patch of severe corrosion. In all these scenarios, the independent component analysis algorithm was able to extract the defect signal, rejecting coherent noise.

Description: Integrated structural health monitoring and damage prognosis (SHM-DP) methodologies, coupled with sensor-based nondestructive evaluation (NDE) techniques, are becoming increasingly important for the near-real-time condition assessment (i.e., SHM) and future performance predictions (i.e., DP) of aging mechanical systems, civil structures, and infrastructure networks, as well as automotive, naval, and aerospace vehicles. A successful SHM-DP strategy, capable of identifying all critical damage mechanisms while accounting for all relevant sources of uncertainty, can be used as an advanced tool to effectively and optimally manage the life-cycle of the monitored system, recursively forecast its remaining useful life (RUL), and ultimately reduce the overall ownership cost through dynamic reliability-based inspection and maintenance (RBIM) plans, system downtime minimization, catastrophic failure prevention, and potential RUL extension. In this perspective, fatigue damage propagation is one of the most critical and unpredictable deterioration processes for a large variety of structural and mechanical systems that are subjected repeatedly to cyclic and/or random operational loading during their service life. Within this limited scope, the authors developed a comprehensive NDE-based SHM-DP framework for recursively predicting the time-varying system reliability and the remaining fatigue life (RFL) of monitored systems subjected to deterioration by multi-site fatigue damage propagation. This paper provides a brief overview of the proposed framework and then uses a set of experimental fatigue test data to perform a thorough statistical performance assessment of the developed methodology at the local reliability component level (i.e., single damage mechanism and single damage location) including NDE detectability and measurement uncertainty as well as both load and model parameter uncertainty.

Description: Accurate and reliable damage characterization (i.e., damage detection, localization, and evaluation of extent) in civil structures and infrastructure is an important objective of structural health monitoring (SHM). Highly accurate and reliable characterization of damage at early stages requires continuous or quasi-continuous direct sensing of the critical parameters. Direct sensing requires deploying dense arrays of sensors, to enhance the probability that damage will result in signals that can be directly acquired by the sensors. However, coverage by dense arrays of sensors over the large areas that are of relevance represents an enormous challenge for current technologies. Large area electronics (LAE) is an emerging technology that can enable the formation of dense sensor arrays spanning large areas (several square meters) on flexible substrates. This paper explores the requirements and technology for a sensing sheet for SHM based on LAE and crystalline silicon CMOS integrated circuits (ICs). The sensing sheet contains a dense array of thin-film full-bridge resistive strain sensors, along with the electronics for strain readout, full-system self-powering, and communication. Research on several stages is presented for translating the sensing sheet to practical SHM applications. This includes experimental characterization of an individual sensor’s response when exposed to cracks in concrete and steel; theoretical and experimental performance evaluation of various geometrical parameters of the sensing sheet; and development of the electronics necessary for sensor readout, power management, and sensor-data communication. The concept of direct sensing has been experimentally validated, and the potential of a sensing sheet to provide direct sensing and successful damage characterization has been evaluated in the laboratory setting. A prototype of the sensing sheet has also been successfully developed and independently characterized in the laboratory, meeting the- required specifications. Thus, a sensing sheet for SHM applications shows promise both in terms of practicality and effectiveness.

Description: The inverse problem of synthetic aperture imaging radiometers (SAIRs) has been demonstrated to be not well posed. The regularization methods are crucial for providing unique and stable solutions in the reconstruction of radiometric brightness temperature (BT) maps. Different to deterministic ones, a new approach is presented by referring to the rule of Bayesian inference, providing a probability model of regularized constraints to combat the ill-posedness of finite-dimensional discrete inverse problems. In addition, the SAIR inverse problem can be converted into the probability estimation of the reconstructed BT. Furthermore, in application to both uniformly and nonuniformly spaced arrays, our method can obtain the optimal solution adaptively and avoid the dilemma of choosing the optimal regularization parameter. Finally, simulation results illustrating the effectiveness and performance of the proposed method are provided.

Description: A range-cell-focusing algorithm is proposed in order to improve the quality of the target image. In a synthetic aperture radar (SAR) imaging system, the range resolution depends on the frequency bandwidth and determines the ability to distinguish between targets that are very close to each other. In cases where the resolution and the SNR from the environment are not adequate, targets cannot be accurately visualized. In order to successively classify targets that are close, we are combining an enhanced-multiple-signal-classification spectrum as a weighting function to reproduce the raw data. The proposed algorithm improves classification and separation for close targets while suppressing artifacts in the final images. The targets of interest are stationary point scatterers. The results are obtained from both simulated and experimental data to demonstrate that the proposed algorithm provides better performance than a conventional SAR imaging algorithm, the range migration algorithm.

Description: This letter presents a multiscale edge detection method for multilook polarimetric synthetic aperture radar (PolSAR) images based on the nonsubsampled contourlet transform (NSCT). The NSCT can provide flexible multiscale and directional decomposition. In the multiscale decomposition, the coefficients of the nonsubsampled pyramid in the NSCT are calculated via maximizing the polarimetric contrast between the adjacent subband levels, instead of using the difference of the adjacent subbands as used in the additive noise model. By this way, we make the NSCT applicable to PolSAR data and multiband data. Then, the edges are detected in the NSCT domain based on a fusion of the directional subband coefficients at different scales. Experimental results with both simulated and real PolSAR data show that the present approach is robust to noise and the extracted edges are complete and continuous.

Description: A method for defining the spatial resolution of a Global Navigation Satellite System reflectometry delay–Doppler map (DDM) and of any derived geophysical product is proposed. An effective spatial resolution is derived as a function of measurement geometry and delay–Doppler (DD) interval, and as a more appropriate representation of resolution than the geometric resolution previously used in the literature. The definition more accurately accounts for variations in the scattered power across different pixels of the DDM and more accurately includes the power spreading effect caused by the Woodward ambiguity function. The dependence of the effective resolution on incidence angle, receiver altitude, and DD interval is analyzed and compared with the dependence of the geometric resolution with similar parameters.

Description: Laser scanner-captured 3-D point cloud data analysis is becoming more commonly used for remote sensing and plant science applications. Because of nonrigidity and complexity, reconstructing a 3-D model of a plant is extremely challenging. Existing algorithms often fail to find correct correspondences for plantlike thin structures. We address the problem of finding 3-D junction points in plant point cloud data as a first step of this correspondence matching process. Temporarily, we transform the 3-D problem into 2-D by performing appropriate coordinate transformations to the neighborhood of each 3-D point. Our proposed method has two steps. First, a statistical dip test of multimodality is performed to detect the nonlinearity of the local 2D structure. Then, each branch is approximated by sequential random-sample-consensus line fitting and a Euclidean clustering technique. The straight line parameters of each branch are extracted using total-least-squares estimation. Finally, the straight line equations are solved to determine if they intersect in the local neighborhood. Such junction points are good candidates for subsequent correspondence algorithms. Using these detected junction points, we formulate a correspondence algorithm as a subgraph matching problem and show that, without using traditional descriptor similarity-based matching, good correspondences can be obtained by simply considering geodesic distances among graph nodes. Experiments on synthetic and real ( Arabidopsis plant) data show that the proposed method outperforms the state of the art.

Description: Repeat-track analysis is commonly utilized to generate elevation change time series from satellite radar altimetry over ice sheets. It requires surface gradient (SG) correction due primarily to orbital drifts and radar-related empirical corrections caused by radar scatters from ice surface and potential subsurface. In this letter, two approaches, namely, the use of a digital elevation model (DEM) and the modified repeat-track analysis, which uses the accumulated Envisat altimetry profiles, are applied to correct the SG over both Greenland ice sheet (GrIS) and Antarctic ice sheet (AIS). By comparing the root mean square (rms) of elevation change time series after SG correction, the percentage of data (rms< 1 m) obtained by using modified repeat-track analysis is found to be 85% and 88% for the GrIS and AIS, respectively, as opposed to 45% and 44% if the DEM method is used. Furthermore, three cases are studied to assess empirical corrections for elevation retrieved from both ice-1 and ice-2 algorithms over the AIS. We conclude that the modified repeat-track analysis is more effective to remove topographic induced error. For the ice-2 algorithm, waveform shape parameters are needed in addition to applying corrections from changes in backscatter coefficients. The trend of elevation changes from the ice-1 algorithm with only backscatter analysis agrees with that from the ice-2 algorithm with corrections from backscatter coefficient changes and waveform shape parameters. This study could provide a potential data processing recipe for generating improved satellite radar altimetry elevation time series over ice sheets.

Description: Extracting ships from complex backgrounds is the bottleneck of ship detection in high-resolution optical satellite images. In this letter, we propose a nearly closed-form ship rotated bounding box space used for ship detection and design a method to generate a small number of highly potential candidates based on this space. We first analyze the possibility of accurately covering all ships by labeling rotated bounding boxes. Moreover, to reduce search space, we construct a nearly closed-form ship rotated bounding box space. Then, by scoring for each latent candidate in the space using a two-cascaded linear model followed by binary linear programming, we select a small number of highly potential candidates. Moreover, we also propose a fast version of our method. Experiments on our data set validate the effectiveness of our method and the efficiency of its fast version, which achieves a close detection rate in near real time.

Description: In this letter, a two-stage method for airport detection on remote sensing images is proposed. In the first stage, a new algorithm composed of several line-based processing steps is used for extraction of candidate airport regions. In the second stage, the scale-invariant feature transformation and Fisher vector coding are used for efficient representation of the airport and nonairport regions and support vector machines employed for classification. In order to evaluate the performance of the proposed method, extensive experiments are conducted on airports around the world with different layouts. The measures used in the evaluation are accuracy, sensitivity, and specificity. The proposed method achieved an accuracy of 94.6%, which was benchmarked with two previous methods to prove its superiority.

Description: High-resolution wide-swath synthetic aperture radar (SAR) systems are very attractive for the observation of dynamic processes on the Earth's surface, but they require the downlink of a huge volume of data. In order to comply with azimuth ambiguity requirements, in fact, a pulse repetition frequency much higher than the required processed Doppler bandwidth is often desirable. The volume of downlinked data, however, can be drastically reduced by performing Doppler filtering and decimation on board. A finite-impulse-response filter with a relatively small number of taps suffices to suppress the additional ambiguous components and to recover the original impulse response. This strategy is of special relevance for staggered SAR systems, which are typically characterized by a high oversampling factor. The proposed data reduction technique is also baseline for Tandem-L, where onboard Doppler filtering, resampling, and decimation will be jointly implemented.

Description: In this letter, we generate anisotropic bicontinuous media with different vertical and horizontal correlation functions. With the computer-generated bicontinuous medium, we then use numerical solutions of Maxwell equations in 3-dimensions (NMM3D) to calculate the anisotropic effective permittivities and the effective propagation constants of V and H polarizations. The copolarization phase difference (CPD) of VV and HH is then derived. The CPDs have recently been applied to the retrieval of snow water equivalent, snow depth, and anisotropy. The NMM3D simulation results are also compared with the results of the strong permittivity fluctuations in the low frequency limit and compared against the Maxwell–Garnett mixing formula.

Description: The IEEE GRS Society is grateful for the support given by the organizations listed and invites applications for Institutional Listings from other firms interested in the field of geoscience and remote sensing.

Description: The mucosal microanatomy of the large intestine is characterized by the presence of crypts of Lieberkühn, which is associated predominantly with goblet cells. Such cellular-level intestinal microstructures undergo morphological changes during the progression of bowel diseases, such as colon cancer or ulcerative colitis. As an indicator of gastric cancers, intestinal metaplasia in the large intestine is characterized by the appearance of goblet cells in gastric epithelium, and therefore, visualization of intestinal microstructure changes in cross-sectional view, particularly in vivo , in a high-speed fashion would assist early disease diagnosis and its treatment. In this paper, we investigated the capability of micro-optical coherence tomography $(mutext{OCT})$ for high-speed cellular-level crypt and goblet cell structures imaging ex vivo and in vivo . The adopted $mutext{OCT}$ system achieved a resolution of 2.0 $mutext{m}$ in both the lateral and axial directions in air. Ex vivo and video-rate in vivo images acquired in 3-D at respective imaging rates of 20 and 60 frames/s are presented and compared with the histology images. Imaging results show that the detailed microstructures, such as the crypt lumen and the goblet cells, could be clearly identified and are also comparable with those in histology images. Such comparisons also indicate that high-resolution $mutext{OCT}$ could be a powerful tool to perform “optical biopsy” in colorectal tissue. This is the first work, to the best of our knowledge, on cellular-level structure imaging in intestinal mucosa using spectral-- omain OCT.

Description: Influence of different high-index oxide overlayers on the performance of a fiber optic SPR sensor coated with bimetallic layer of aluminum (Al)/copper (Cu) is reported. The oxides considered for the analysis are Al 2 O 3 , Sc 2 O 3 , Lu 2 O 3, GeO 2 , SnO 2 , TeO 2 , MgO, ITO, and ZnO, which on-coated over Al/Cu bimetallic layer causes an enhancement in electric field intensity at the oxide–analyte interface and increases the red shift in the resonance wavelength with the increase in the refractive index of the analyte solution. In addition, the figure of merit is also drastically improved. All the performance parameters are found to be different for different oxides. On the basis of comparison, the best possible oxide along with Al/Cu bimetallic layer and its requisite thickness is predicted. The sensor with Al/Cu/TeO 2 is found to possess the best performance parameters. A fiber optic SPR sensor with coatings of Al/Cu/TeO 2 layers over unclad core of the fiber is fabricated and its performance parameters are compared with the theoretically obtained values. Further, the sensitivity (5.98 µm/RIU) and the figure of merit (35.95) for 1.33 refractive index of the analyte are found to be better than those of previously reported sensors to the best of our knowledge.

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Description: GaN p-i-n ultraviolet avalanche photodiodes (UV-APDs) were fabricated from epitaxial structures grown on low-dislocation-density free-standing GaN substrates to form $4times 4$ UV-APD arrays with a device size of $75times 75~mu text{m}^{{ {2}}}$ . The devices in the UV-APD array showed a uniform and reliable distribution of breakdown voltage ( $V_{text {BR}}$ ) and leakage current density. The average $V_{text {BR}}$ of the 16 devices in one of the UV-APD arrays was 96±0.6 V, and the average dark current density ( $J_{R_{}{text {Dark}}}$ ) and photocurrent density ( $J_{R_{}{text {Photo}}}$ ) were measured to be (6.5±1.8) $times 10^{-7}$ and (5.7±1.1) $times 10^{{text {-6}}}$ A/cm $^{{{2}}}$ at the reverse bias voltage of $V_{R}=48$ V (50% of the average onset point of $V_{text {BR}}$ ), respectively. The reliable device performance was confirmed by performing multiple reverse bias $I$ – $V$ scans for the selected devices in the UV-APD array. We also observed the significantly enhanced spectral resp- nsivity from the 142 to 5485 mA/W due to the strong carrier impact ionization at high reverse bias.

Description: The combination of a thin dielectric grating with a thin metal film is shown to reveal the existence of multiple plasmons, some are short range and others long range both represented by resonant dips in the reflectivity. Usually the plasmons come in pairs, where one is excited at the substrate metal interface while the other at the grating metal interface. One of these dips is sensitive to the refractive index (RI) of the medium adjacent to the metal surface while the other to that near the grating interfaces. Using an optimum design it is possible to obtain high sensitivity to the RI variations of one of the adjacent media but not to the other, hence a self-referenced biosensor can be built using this design. Two configurations are shown to reveal unique features in the angular mode: 1) the possibility of using both angular and intensity sensitivity to detect variations in the RI of the analyte, 2) the possibility of using the excited multiple sharp plasmons that cause multiple resonances (dips) in the reflectivity, where part of these resonances are red-shifted due to variations in the RI of the analyte, while the others are blue shifted. Hence, by measuring the shift of one with respect to the other the angular sensitivity is improved, 3) multiple dips can be used for reference, and 4) high figure of merit is obtained. The thin dielectric grating is shown to have two roles, one to provide the momentum matching whereas the other is to act effectively as a dielectric layer underneath the metal film to enable the excitation of both the long and short range surface plasmons.

Description: An optimized broadband source emitting from 1064 to 1600 nm was specially designed for coherent anti-Stokes Raman scattering spectroscopy. This source is based on the use of a ytterbium-doped photonic crystal fiber with a large core in which a supercontinuum is generated from a signal wave at 1064 nm regenerated by ytterbium ions pumping. A particularly flat spectrum with high spectral power density and perfectly synchronized spectral components is obtained.

Description: We experimentally demonstrate switchable dual-wavelength mode-locking of thulium-doped fiber laser (TDFL), using single-wall carbon nanotubes as saturable absorber. Due to the cavity birefringence-induced comb filter, switchable mode-locking can be individually realized for the proposed TDFL among three wavelengths of 1947, 1945, and 1943 nm, with almost the same 3-dB spectral bandwidth of 2.2 nm, repetition rate of 13.6 MHz, and pulsewidth of 1.8 ps. Furthermore, after finely adjusting the intra-cavity birefringence, we are able to demonstrate switchable dual-wavelength mode-locking at either 1947/1945 or 1945/1943 nm. The optical spectra of dual-wavelength mode-locking have almost the same characteristics and can maintain stable operation for a long period.

Description: A capillary covered silica hollow core fiber (HCF) has been designed and tested for the measurement of displacement based on antiresonant reflecting optical waveguide. A section of the silica HCF was inserted into a silver coated capillary. A Fabry–Perot resonator can be formed in the silica cladding. The leaky mode of the guided light can be achieved at resonant wavelengths of the Fabry–Perot resonator, which results in lossy dips in the transmission spectrum. The transmission power of the dip is sensitive with the displacement of the capillary since the effective reflectivity of the Fabry–Perot resonator is affected by the location between the capillary and the silica HCF. The experimental results show that the sensitivity of up to 0.578 dB/μm is achieved, and the proposed sensor is insensitive with the temperature.

Description: We report high-responsivity GaN/InGaN heterojunction phototransistors (HPTs) grown on sapphire substrates. Under the ultraviolet (UV) photon illumination from the front side of the wafer, an HPT shows broad photoresponse spectrum with the short-wavelength cutoff wavelength well beyond $lambda = 280$ nm, and the UV-to-visible-band rejection ratio is $> 8 times 10^{3}$ . The responsivity ( $R_{lambda }$ ) of HPT is greater than 8 A/W at $lambda = 373$ nm, and is greater than 3 A/W at $lambda = 280$ nm as the device is biased at $V_{mathrm{ CE}}=10$ V. As the HPT is biased at the near breakdown voltage ( $V_{mathrm{ CE}}> 35$ V), the responsivity performance was enhanced due to the carrier multiplication, resulting in $R_{lambda }> 100$ A/W at $V_{mathrm{ CE}}= 40$ V for $P_{mathrm{ opt}}=1.73~mu text{W}$ /cm 2 at $lambda = 373$ nm. These results demonstrate that GaN/InGaN HPTs can achieve low light detection with a broadband photon response in the near-UV-to-deep-UV spectral ranges.

Description: The S-transform (ST), as a time–frequency analysis tool, has been widely used, but the amplitude preserving property is a little poor near the boundary of the selected discrete signal. The reason lies that the summation of the product between the analytical window and the comprehensive window over the sliding step deviates from unity near the boundary in the discrete cases. In order to hold the amplitude preserving property for the discrete signal recovery analysis, an amplitude preserving S-transform (APST) is proposed based on a novel analytical window selection. First, lots of numerical tests are used to analyze the shortcomings of the ST near the boundary for the selected discrete signal and demonstrate the effectiveness and the validity of the proposed APST using the novel analytical window. After that, the proposed APST is used for seismic data attenuation compensation, during which the attenuation function is estimated based on the minimum phase assumption using a statistical variable-step hyperbolic smoothing method. Numerical examples on synthetic and field data demonstrate the validity of the proposed method using the seismogram and time–frequency spectrum comparisons. Besides, the proposed APST can be easily extended into a generalized ST which is more flexible compared with the ST, and it can also be used in seismology, remote sensing, and other related discrete signal analysis fields.

Description: The multiplexing and the amplification of 2- $mu text{m}$ vortex beams are experimentally verified in a Ho:YAG crystal rod amplifier. Spatially multiplexed vortex beams are studied and the amplification results are presented and discussed. The integrity of the launched vortex beams is well maintained through the amplification process. Further discussions are provided to increase the gain as well as for power scaling. Due to the nature of Ho:YAG material and the geometry of the rod amplifier, our system shows the potential of working as a power amplifier for vortex beams.

Description: This paper addresses fundamental scaling issues that hinder phase retrieval (PR) in high dimensions. We show that, if the measurement matrix can be put into a generalized block-diagonal form, a large PR problem can be solved on separate blocks, at the cost of a few extra global measurements to merge the partial results. We illustrate this principle using two distinct PR methods, and discuss different design trade-offs. Experimental results indicate that this block-based PR framework can reduce computational cost and memory requirements by several orders of magnitude.

Description: Objective: This work evaluates current 3-D image registration tools on clinically acquired abdominal computed tomography (CT) scans. Methods: Thirteen abdominal organs were manually labeled on a set of 100 CT images, and the 100 labeled images (i.e., atlases) were pairwise registered based on intensity information with six registration tools (FSL, ANTS-CC, ANTS-QUICK-MI, IRTK, NIFTYREG, and DEEDS). The Dice similarity coefficient (DSC), mean surface distance, and Hausdorff distance were calculated on the registered organs individually. Permutation tests and indifference-zone ranking were performed to examine the statistical and practical significance, respectively. Results: The results suggest that DEEDS yielded the best registration performance. However, due to the overall low DSC values, and substantial portion of low-performing outliers, great care must be taken when image registration is used for local interpretation of abdominal CT. Conclusion: There is substantial room for improvement in image registration for abdominal CT. Significance: All data and source code are available so that innovations in registration can be directly compared with the current generation of tools without excessive duplication of effort.

Description: Wireless networks face the challenge of increasing energy consumption while satisfying the unprecedented demand for higher data rates. Energy-efficient transmission has been regarded as a key technology for the next-generation wireless system. Meanwhile, to reduce the cost, in practice, a base station usually has less radio chains than the antennas, which makes antenna selection an appealing transmission strategy. This letter addresses the problem of joint optimization of energy-efficient beamforming and antenna selection for downlink multiuser systems. The nonconvexity arising from both the nonlinear fractional programming and the $ell _{0}$ -(quasi)norm presents the main difficulty in solving the joint optimization problem. Nevertheless, we develop an effective algorithm to address this problem. Numerical results are given to validate the effectiveness and the performance of the developed algorithm.

Description: Gallium-nitride (GaN)-based light-emitting diodes (LEDs) are highly efficient sources for general purpose illumination. Visible light communications (VLC) uses these sources to supplement existing wireless communications by offering a large, licence-free region of optical spectrum. Here, we report on progress in the development of micro-scale GaN LEDs (micro-LEDs), optimized for VLC. These blue-emitting micro-LEDs are shown to have very high electrical-to-optical modulation bandwidths, exceeding 800 MHz. The data transmission capabilities of the micro-LEDs are illustrated by demonstrations using ON–OFF-keying, pulse-amplitude modulation, and orthogonal frequency division multiplexing modulation schemes to transmit data over free space at the rates of 1.7, 3.4, and 5 Gb/s, respectively.

Description: Goal : In K-edge tomographic imaging with photon counting detectors, the energy window width of photon counting detectors significantly affects the signal-to-noise ratio (SNR) of measured intensity data and the contrast-to-noise ratio (CNR) of reconstructed images. In this paper, we present an optimization method to determine an optimal window width around a K-edge for optimal SNR and CNR. Methods : An objective function is designed to describe SNR of the projection data based on the Poisson distribution of detected X-ray photons. Then, a univariate optimization method is applied to obtain an X-ray energy window width. Results : Numerical simulations are performed to evaluate the proposed method, and the results show that the optimal energy window width obtained from the proposed method produces not only optimal SNR data in the projection domain but also optimal CNR values in the image domain. Conclusion : The proposed method in the projection domain can determine an optimal energy window width for X-ray photon counting imaging, and achieve optimality in both projection and image domains. Significance : Our study provides a practical way to determine the optimal energy window width of photon counting detectors, which helps improve contrast resolution for X-ray K-edge tomographic imaging.

Description: Objective: The propagation of electrophysiological activity measured by multichannel devices could have significant clinical implications. Gastric slow waves normally propagate along longitudinal paths that are evident in recordings of serosal potentials and transcutaneous magnetic fields. We employed a realistic model of gastric slow wave activity to simulate the transabdominal magnetogastrogram (MGG) recorded in a multichannel biomagnetometer and to determine characteristics of electrophysiological propagation from MGG measurements. Methods: Using MGG simulations of slow wave sources in a realistic abdomen (both superficial and deep sources) and in a horizontally-layered volume conductor, we compared two analytic methods (second-order blind identification, SOBI and surface current density, SCD) that allow quantitative characterization of slow wave propagation. We also evaluated the performance of the methods with simulated experimental noise. The methods were also validated in an experimental animal model. Results: Mean square errors in position estimates were within 2 cm of the correct position, and average propagation velocities within 2 mm/s of the actual velocities. SOBI propagation analysis outperformed the SCD method for dipoles in the superficial and horizontal layer models with and without additive noise. The SCD method gave better estimates for deep sources, but did not handle additive noise as well as SOBI. Conclusion: SOBI-MGG and SCD-MGG were used to quantify slow wave propagation in a realistic abdomen model of gastric electrical activity. Significance: These methods could be generalized to any propagating electrophysiological activity detected by multichannel sensor arrays.

Description:   Goal : This study aims at a systematic assessment of five computational models of a birdcage coil for magnetic resonance imaging (MRI) with respect to accuracy and computational cost. Methods : The models were implemented using the same geometrical model and numerical algorithm, but different driving methods (i.e., coil “defeaturing”). The defeatured models were labeled as: specific ( S2 ), generic ( G32 , G16 ), and hybrid ( H16, $hbox{H16}_{{rm fr}text{-}{rm forced}}$ ). The accuracy of the models was evaluated using the “symmetric mean absolute percentage error” (“SMAPE”), by comparison with measurements in terms of frequency response, as well as electric ( $|{vec E}|$ ) and magnetic ( $| {vec B} |$ ) field magnitude. Results : All the models computed the $| {vec B} |$ within 35% of the measurements, only the S2 , G32, and H16 were able to accurately model the $|{vec E}|$ inside the phantom with a maximum SMAPE of 16%. Outside the phantom, only the S2 showed a SMAPE lower than 11%. Conclusions : Results showed that assessing the accuracy of $| {vec B} |$ based only on comparison along the central longitudinal line of the coil can be misleading. Generic or hybrid coils — when properly modeling the currents along the rings/rungs — were sufficient to accur- tely reproduce the fields inside a phantom while a specific model was needed to accurately model $|{vec E}|$ in the space between coil and phantom. Significance : Computational modeling of birdcage body coils is extensively used in the evaluation of radiofrequency-induced heating during MRI. Experimental validation of numerical models is needed to determine if a model is an accurate representation of a physical coil.

Description: Objective: The objective of this research was to develop a bioimpedance platform for monitoring fluid volume in residual limbs of people with trans-tibial limb loss using prostheses. Methods: A customized multifrequency current stimulus profile was sent to thin flat electrodes positioned on the thigh and distal residual limb. The applied current signal and sensed voltage signals from four pairs of electrodes located on the anterior and posterior surfaces were demodulated into resistive and reactive components. An established electrical model (Cole) and segmental limb geometry model were used to convert results to extracellular and intracellular fluid volumes. Bench tests and testing on amputee participants were conducted to optimize the stimulus profile and electrode design and layout. Results: The proximal current injection electrode needed to be at least 25 cm from the proximal voltage sensing electrode. A thin layer of hydrogel needed to be present during testing to ensure good electrical coupling. Using a burst duration of 2.0 ms, intermission interval of 100 μs, and sampling delay of 10 μs at each of 24 frequencies except 5 kHz, which required a 200-μs sampling delay, the system achieved a sampling rate of 19.7 Hz. Conclusion: The designed bioimpedance platform allowed system settings and electrode layouts and positions to be optimized for amputee limb fluid volume measurement. Significance: The system will be useful toward identifying and ranking prosthetic design features and participant characteristics that impact residual limb fluid volume.

Description: Surface electromyography (sEMG) has been the predominant method for sensing electrical activity for a number of applications involving muscle–computer interfaces, including myoelectric control of prostheses and rehabilitation robots. Ultrasound imaging for sensing mechanical deformation of functional muscle compartments can overcome several limitations of sEMG, including the inability to differentiate between deep contiguous muscle compartments, low signal-to-noise ratio, and lack of a robust graded signal. The objective of this study was to evaluate the feasibility of real-time graded control using a computationally efficient method to differentiate between complex hand motions based on ultrasound imaging of forearm muscles. Dynamic ultrasound images of the forearm muscles were obtained from six able-bodied volunteers and analyzed to map muscle activity based on the deformation of the contracting muscles during different hand motions. Each participant performed 15 different hand motions, including digit flexion, different grips (i.e., power grasp and pinch grip), and grips in combination with wrist pronation. During the training phase, we generated a database of activity patterns corresponding to different hand motions for each participant. During the testing phase, novel activity patterns were classified using a nearest neighbor classification algorithm based on that database. The average classification accuracy was 91%. Real-time image-based control of a virtual hand showed an average classification accuracy of 92%. Our results demonstrate the feasibility of using ultrasound imaging as a robust muscle–computer interface. Potential clinical applications include control of multiarticulated prosthetic hands, stroke rehabilitation, and fundamental investigations of motor control and biomechanics.

Description: Presents the introductory editorial for this issue of the publication.

Description: We have been developing an automated cardiovascular drug infusion system for simultaneous control of arterial pressure (AP), cardiac output (CO), and left atrial pressure (P LA ) in decompensated heart failure (HF). In our prototype system, CO and P LA were measured invasively through thoracotomy. Furthermore, the control logic inevitably required use of inotropes to improve hemodynamics, which was not in line with clinical HF guidelines. The goal of this study was to solve these problems and develop a clinically feasible system. We integrated to the system minimally invasive monitors of CO and pulmonary capillary wedge pressure (PCWP, surrogates for P LA ) that we developed recently. We also redesigned the control logic to reduce the use of inotrope. We applied the newly developed system to nine dogs with decompensated HF. Once activated, our system started to control the infusion of vasodilator and diuretics in all the animals. Inotrope was not infused in three animals, and infused at minimal doses in six animals that were intolerant of vasodilator infusion alone. Within 50 min, our system controlled AP, CO, and PCWP to their respective targets accurately. Pulmonary artery catheterization confirmed optimization of hemodynamics (AP, from 98 ± 4 to 74 ± 11 mmHg; CO, from 2.2 ± 0.5 to 2.9 ± 0.3 L·min −1 ·m −2 ; PCWP, from 27.0 ± 6.6 to 13.8 ± 3.0 mmHg). In a minimally invasive setting while reducing the use of inotrope, our system succeeded in automatically optimizing the overall hemodynamics in canine models of HF. The present results pave the way for clinical application of our automated drug infusion system.

Description: Image registration is a key problem in a variety of applications, such as computer vision, medical image processing, pattern recognition, etc., while the application of registration is limited by time consumption and the accuracy in the case of large pose differences. Aimed at these two kinds of problems, we propose a fast rotation-free feature-based rigid registration method based on our proposed accelerated-NSIFT and GMM registration-based parallel optimization (PO-GMMREG). Our method is accelerated by using the GPU/CUDA programming and preserving only the location information without constructing the descriptor of each interest point, while its robustness to missing correspondences and outliers is improved by converting the interest point matching to Gaussian mixture model alignment. The accuracy in the case of large pose differences is settled by our proposed PO-GMMREG algorithm by constructing a set of initial transformations. Experimental results demonstrate that our proposed algorithm can fast rigidly register 3-D medical images and is reliable for aligning 3-D scans even when they exhibit a poor initialization.

Description: Spare parts support services have received increasing management attention due to the growing number of critical systems in many business sectors. In this paper, we examine an integrated system design approach to customize spare parts support services based on response time with inventory pooling strategies. To provide customized services that meet user requirements for spare part response time, we depart from the traditional spare parts management and develop a systematic approach to design service parts support services based on axiomatic design theory. In particular, we focus on pricing discrimination decisions in service parts contracts for two-tier users under a mechanism design framework. Distinguishing between users of next-day and same-day contracts for service parts operations, we further evaluate the effect of various inventory pool structures with reserve strategies through a simulation model for the objective of cost minimization. These analytical results of this new approach provide guidance for managers in customizing spare parts support services with the holistic consideration of pricing scheme, response time, and inventory policy.

Description: Images coded at low bit rates in real-world applications usually suffer from significant compression noise, which significantly degrades the visual quality. Traditional denoising methods are not suitable for the content-dependent compression noise, which usually assume that noise is independent and with identical distribution. In this paper, we propose a unified framework of content-adaptive estimation and reduction for compression noise via low-rank decomposition of similar image patches. We first formulate the framework of compression noise reduction based upon low-rank decomposition. Compression noises are removed by soft thresholding the singular values in singular value decomposition of every group of similar image patches. For each group of similar patches, the thresholds are adaptively determined according to compression noise levels and singular values. We analyze the relationship of image statistical characteristics in spatial and transform domains, and estimate compression noise level for every group of similar patches from statistics in both domains jointly with quantization steps. Finally, quantization constraint is applied to estimated images to avoid over-smoothing. Extensive experimental results show that the proposed method not only improves the quality of compressed images obviously for post-processing, but are also helpful for computer vision tasks as a pre-processing method.

Description: We propose a method for estimating the image and video noises of different types: white Gaussian (signal-independent), mixed Poissonian–Gaussian (signal-dependent), or processed (non-white). Our method also estimates the noise level function (NLF) of these types. We do so by classifying image patches based on their intensity and variance in order to find homogeneous regions that best represent the noise. We assume that the noise variance is a piecewise linear function of intensity in each intensity class. To find noise representative regions, noisy (signal-free) patches are first nominated in each intensity class. Next, clusters of connected patches are weighted, where the weights are calculated based on the degree of similarity to the noise model. The highest ranked cluster defines the peak noise variance, and other selected clusters are used to approximate the NLF. The more information we incorporate, such as temporal data and camera settings, the more reliable the estimation becomes. To account for the processed noise, (i.e., remaining after in-camera processing), we consider the ratio of low-to-high-frequency energies. We address noise variations along video signals using a temporal stabilization of the estimated noise. Objective and subjective simulations demonstrate that the proposed method outperforms other noise estimation techniques, both in accuracy and speed.

Description: In this paper, we address the problem of object retrieval by hyperlinking the reference data set at subimage level. One of the main challenges in object retrieval involves small objects on cluttered backgrounds, where the similarity between the querying object and a relevant image can be heavily affected by the background. To address this problem, we propose an efficient object retrieval technique by hyperlinking the visual entities among the reference data set. In particular, a two-step framework is proposed: subimage-level hyperlinking and hyperlink-aware reranking. For hyperlinking, we propose a scalable object mining technique using Thread-of-Features, which is designed for mining subimage-level objects. For reranking, the initial search results are reranked with a hyperlink-aware transition matrix encoding subimage-level connectivity. Through this framework, small objects can be retrieved effectively. Moreover, our method introduces only a tiny computation overhead to online processing, due to the sparse transition matrix. The proposed technique is featured by the novel perspective (object hyperlinking) for visual search, as well as the object hyperlinking technique. We demonstrate the effectiveness and efficiency of our hyperlinking and retrieval methods by experimenting upon several object-retrieval data sets.

Description: Feature pooling in a majority of sparse coding-based tracking algorithms computes final feature vectors only by low-order statistics or extreme responses of sparse codes. The high-order statistics and the correlations between responses to different dictionary items are neglected. We present a more generalized feature pooling method for visual tracking by utilizing the probabilistic function to model the statistical distribution of sparse codes. Since immediate matching between two distributions usually requires high computational costs, we introduce the Fisher vector to derive a more compact and discriminative representation for sparse codes of the visual target. We encode target patches by local coordinate coding, utilize Gaussian mixture model to compute Fisher vectors, and finally train semi-supervised linear kernel classifiers for visual tracking. In order to handle the drifting problem during the tracking process, these classifiers are updated online with current tracking results. The experimental results on two challenging tracking benchmarks demonstrate that the proposed approach achieves a better performance than the state-of-the-art tracking algorithms.

Description: This paper presents a robust information theoretic (RIT) model to reduce the uncertainties, i.e., missing and noisy labels, in general discriminative data representation tasks. The fundamental pursuit of our model is to simultaneously learn a transformation function and a discriminative classifier that maximize the mutual information of data and their labels in the latent space. In this general paradigm, we, respectively, discuss three types of the RIT implementations with linear subspace embedding, deep transformation, and structured sparse learning. In practice, the RIT and deep RIT are exploited to solve the image categorization task whose performances will be verified on various benchmark data sets. The structured sparse RIT is further applied to a medical image analysis task for brain magnetic resonance image segmentation that allows group-level feature selections on the brain tissues.

Description: Single image super-resolution (SR) algorithms based on joint dictionaries and sparse representations of image patches have received significant attention in the literature and deliver the state-of-the-art results. Recently, Gaussian mixture models (GMMs) have emerged as favored prior for natural image patches in various image restoration problems. In this paper, we approach the single image SR problem by using a joint GMM learnt from concatenated vectors of high and low resolution patches sampled from a large database of pairs of high resolution and the corresponding low resolution images. Covariance matrices of the learnt Gaussian models capture the inherent correlations between high and low resolution patches, which are utilized for inferring high resolution patches from given low resolution patches. The proposed joint GMM method can be interpreted as the GMM analogue of joint dictionary-based algorithms for single image SR. We study the performance of the proposed joint GMM method by comparing with various competing algorithms for single image SR. Our experiments on various natural images demonstrate the competitive performance obtained by the proposed method at low computational cost.

Description: During the past few years, there have been various kinds of content-aware image retargeting operators proposed for image resizing. However, the lack of effective objective retargeting quality assessment metrics limits the further development of image retargeting techniques. Different from traditional image quality assessment (IQA) metrics, the quality degradation during image retargeting is caused by artificial retargeting modifications, and the difficulty for image retargeting quality assessment (IRQA) lies in the alternation of the image resolution and content, which makes it impossible to directly evaluate the quality degradation like traditional IQA. In this paper, we interpret the image retargeting in a unified framework of resampling grid generation and forward resampling. We show that the geometric change estimation is an efficient way to clarify the relationship between the images. We formulate the geometric change estimation as a backward registration problem with Markov random field and provide an effective solution. The geometric change aims to provide the evidence about how the original image is resized into the target image. Under the guidance of the geometric change, we develop a novel aspect ratio similarity (ARS) metric to evaluate the visual quality of retargeted images by exploiting the local block changes with a visual importance pooling strategy. Experimental results on the publicly available MIT RetargetMe and CUHK data sets demonstrate that the proposed ARS can predict more accurate visual quality of retargeted images compared with the state-of-the-art IRQA metrics.

Description: Multicarrier waveforms bring several major advantages over single carrier waveforms in radar systems: frequency diversity, waveform diversity, short time on target and the possibility to optimize the transmitted waveforms, to mention a few. Interesting waveform designs utilizing diversity have been proposed in the literature already. In this paper, we develop a generalized model that can accommodate a wide variety of design options, both existing and novel ones, in an easy and intuitive way. The developed matrix equations for transmitter and receiver allow for implementing different waveforms simply by filling in the elements to corresponding matrices accordingly. Moreover, intuitive agile generation of waveforms in simulation environments and in practice is facilitated. Waveform optimization examples are provided using the derived model. A Mutual Information based criterion is employed to formulate the optimization problems which are solved analytically. Novel multicarrier spread spectrum waveforms are proposed and generated using the derived model. The radar performance of one of these waveforms is investigated through simulations. It is demonstrated that it can outperform well-known existing multicarrier waveforms. It is also shown that such waveform can lower the peak-to-average-power ratio due to the spreading operation, which is a benefit for the front end designs of the transmitter and receiver.

Description: In this paper, the secrecy performance of finite-sized cooperative cyclic prefixed single carrier systems with multiple eavesdroppers and unreliable wireless backhaul connections across multiple transmitters is investigated. For nonidentical frequency-selective fading channels between the relay and destination nodes, secrecy performance metrics including the secrecy outage probability, ergodic secrecy rate, and probability of nonzero achievable secrecy rate are derived. Furthermore, the existence of performance limits on the secrecy outage probability and probability of non-zero achievable secrecy rate are verified for various backhaul scenarios. These limits are found to be exclusively determined by the backhaul reliability. For imperfect backhaul connections, it is found that the diversity gain promised by cooperative cyclic prefixed single-carrier systems cannot be achieved in the conventional asymptotic high signal-to-noise ratio region. Link-level simulations are conducted to verify the derived impact of backhaul reliability on the secrecy performance.