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.

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: 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: 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: 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.

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

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: 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: 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: 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.

Description: Recently, several high-resolution parameter estimation algorithms have been developed to exploit the structure of strictly second-order (SO) non-circular (NC) signals. They achieve a higher estimation accuracy and can resolve up to twice as many signal sources compared to the traditional methods for arbitrary signals. As a benchmark for these NC methods, we derive the closed-form deterministic $R$ -D NC Cramér-Rao bound (NC CRB) for the multi-dimensional parameter estimation of strictly non-circular (rectilinear) signal sources in this paper. Assuming a separable centro-symmetric $R$ -D array, we show that in some special cases, the deterministic $R$ -D NC CRB reduces to the existing deterministic $R$ -D CRB for arbitrary signals. This suggests that no gain from strictly non-circular sources (NC gain) can be achieved under the deterministic data assumption in these cases. For more general scenarios, finding an analytical expression of the NC gain for an arbitrary number of sources is very challenging. Thus, in this paper, we simplify the derived NC CRB and the existing CRB for the special case of two closely-spaced strictly non-circular sources captured by a uniform linear array (ULA). Subsequently, we use these simplified CRB expressions to analytically compute the maximum achievable asymptotic NC gain for the considered two source case. The resulting expression only depends on the various physical parameters and we find the conditions that provide the largest NC gain. Our analysis is supported by extensive simulation results.

Description: As communication systems scale up in bandwidth, the limited resolution in high-speed analog-to-digital converters (ADCs) is a key challenge in realizing low-cost “mostly digital” transceiver architectures. This motivates a systematic effort to understand the limits of such architectures under the severe quantization constraints imposed by the use of low-precision ADCs. In particular, we investigate a canonical problem of blind carrier phase and frequency synchronization with coarse phase quantization in this paper. We develop a Bayesian approach to blind phase estimation, jointly modeling the unknown data, unknown phase and the quantization nonlinearity. We highlight the crucial role of dither, implemented via a mixed signal architecture with a digitally controlled phase shift prior to the ADC. We show the efficacy of random dither, and then improve upon its performance with a simple feedback control policy that is close to optimal in terms of rapidly reducing the mean squared error of phase estimation. This initial blind phase acquisition stage is followed by feedback-based phase/frequency tracking using an Extended Kalman Filter. Performance evaluations for a QPSK system show that excellent bit error rate (BER) performance, close to that of an unquantized system, is achieved by the use of 8 phase bins (implementable using 4 one-bit ADCs operating on linear combinations of in-phase and quadrature components).

Description: State estimation is studied for a special class of flag Hidden Markov Models (HMMs), which comprise 1) an arbitrary finite-state underlying Markov chain and 2) a structured observation process wherein a subset of states emit distinct flags with some probability while other states are unmeasured. For flag HMMs, an explicit computation of the probability of error for the maximum-likelihood filter and smoother is developed. Also, the form of the optimal filter is further characterized in terms of the time since the last flag, and this result is used to further simplify the error-probability computation. Some preliminary graph-theoretic insights into the error probability and its computation are discussed. Finally, these algebraic and structural results are leveraged to address sensor placement in two examples, including one on activity-monitoring in a home environment that is drawn from field data. These examples indicate that low error-probability filtering and smoothing can be achieved with relatively few sensors.

Description: We demonstrate an omnidirectional laser from a 3-D Bragg microcavity, which is fabricated from a cholesteric liquid crystal that is doped with reactive mesogen. The wavelength of the laser is found to be determined by the position of the photonic bandgap that was formed within the 3-D Bragg microcavity. Compared with the laser from pure cholesteric liquid crystal microcavity, a rigid polymer network is formed within the microcavity due to the doped reactive mesogen. The polymer network successfully enhances the resistance ability of the 3-D Bragg microcavity to higher pump power by 1.5 times, thus leading to a more stable laser based on it. This laser may have potential use in optics, displays, and other photonic devices.

Description: A copper-nanowire (CuNWs)-based saturable absorber (SA) is utilized as a new Q-switcher for pulsed operations in visible regions. The plasmon resonance effect of CuNWs contributes to inducing the saturable absorption property. By incorporating the CuNWs-based SA into a red praseodymium $Pr^{3+}$ - doped ZBLAN fiber laser cavity, the red light shifts from a continuous wave (CW) state to a stable pulsed operation state. When it changes the injected pump power, the pulse repetition rate can be varied from 239.8 to 312.4 kHz. The narrowest pulse duration of this laser is 394 ns. Moreover, the maximum output power under a $Q$ - switching operation is 9.6 mW. The results indicate that a CuNWs-based SA is an available Q-switcher for the red laser, revealing the potential of metal nanomaterials for short-pulse generation in visible regions.

Description: Estimating low-rank positive-semidefinite (PSD) matrices from symmetric rank-one measurements is of great importance in many applications, such as high-dimensional data processing, quantum state tomography, and phase retrieval. When the rank is known a priori , this problem can be regarded as solving a system of quadratic equations of a low-dimensional subspace. The authors develop a fast iterative algorithm based on an adaptation of the Kaczmarz method, which is traditionally used for solving overdetermined linear systems. In particular, the authors characterize the dynamics of the algorithm when the measurement vectors are composed of standard Gaussian entries in the online setting. Numerical simulations demonstrate the compelling performance of the proposed algorithm.

Description: In this paper, we consider the problem of multi-parameter estimation in the presence of compound Gaussian clutter for cognitive radar by the variational Bayesian method. The advantage of variational Bayesian is that the estimation of multi-variate parameters is decomposed to problems of estimation of univariate parameters by variational approximation, thus enabling analytically tractable approximate posterior densities in complex statistical models consisting of observed data, unknown parameters, and hidden variables. We derive the asymptotic Bayesian Cramer–Rao bounds and demonstrate by numerical simulations that the proposed approach leads to improved estimation accuracy than the expectation maximization method and the exact Bayesian method in the case of non-Gaussian nonlinear signal models and small data sample size.

Description: Spatiotemporal signal reconstruction from samples randomly gathered in a multidimensional space with uncertainty is a crucial problem for a variety of applications. Such a problem generalizes the reconstruction of a deterministic signal and that of a stationary random process in one dimension, which was first addressed by Whittaker, Kotelnikov, and Shannon. In this work we analyze multidimensional random sampling with uncertainties jointly accounting for signal properties (signal spectrum and spatial correlation) and for sampling properties (inhomogeneous sample spatial distribution, sample availability, and non-ideal knowledge of sample positions). The reconstructed signal spectrum and the signal reconstruction accuracy are derived as a function of signal and sampling properties. It is shown that some of these properties expand the signal spectrum while others modify the spectrum without expansion. The signal reconstruction accuracy is first determined in a general case and then specialized for cases of practical interests. The optimal interpolator function is derived and asymptotic results are obtained to show the impact of sampling non-idealities. The analysis is corroborated by verifying that previously known results can be obtained as special cases of the general one and by means of a case study accounting for various settings of signal and sample properties.

Description: We study strategies for enhanced secrecy using cooperative jamming in secure communication systems with limited rate feedback. A Gaussian multiple-input multiple-output (MIMO) wiretap channel with a jamming helper is considered. The transmitter and helper both require channel state information (CSI), which is quantized at the receiver and fed back through two sum-rate-limited feedback channels. The quantization errors result in reduced beamforming gain from the transmitter, as well as interference leakage from the helper. First, under the assumption that the eavesdropper's CSI is completely unknown, we derive a lower bound on the average main channel rate and find the feedback bit allocation that maximizes the jamming power under a constraint on the bound. For the case where statistical CSI for the eavesdropper's channel is available, we derive a lower bound on the average secrecy rate, and we optimize the bound to find a suitable bit allocation and the transmit powers allocated to the transmitter and helper. For the case where the transmitter and helper have the same number of antennas, we obtain a closed-form solution for the optimal bit allocation. Simulations verify the theoretical analysis and demonstrate the significant performance gain that results with intelligent feedback bit allocation and power control.

Description: As a good way to represent target backscatter measured by high-frequency synthetic aperture radar (SAR) systems, the attributed scattering center (ASC) model is able to provide concise and physically relevant features of a complex target and has played an important role in model-based automatic target recognition (ATR). However, most existing ASC feature extraction methods suffer from imprecise image segmentation or high computational cost, which greatly encumber their practical applications. To tackle this problem, we present a novel ASC feature extraction algorithm for SAR targets based on Lévy random fields in a nonparametric Bayesian framework. Specifically, Lévy random fields, yielding a natural sparse representation of the unknown ASC model, are introduced to construct prior distributions, which lead to the specification of a joint prior distribution for the number of ASCs and the ASC associated parameters. Meanwhile, the problem may be formulated as a sparse representation problem, with regularization induced through the Lévy random field prior. We also develop a reversible jump Markov chain Monte Carlo (RJ-MCMC) method to enable relatively fast posterior inference. Experimental results confirm the effectiveness and efficiency of the proposed algorithm.

Description: Recent research work on magneto-optical micro- structure devices in terahertz (THz) regime has been reviewed. Some magneto-optical materials responding at THz frequency range were introduced. Based on these magneto-optical materials, MO microstructures devices were reviewed, including magnetic photonic crystals, magneto-plasmonics, and magneto-metasurface all in the submillimetre scale. These devices can realize several functions of isolating, modulation, sensing, and directional beam scanning. Moreover, the necessary conditions of forming THz nonreciprocal transmission in magneto-optical microstructure devices were concluded, and the tunability of these devices was also analysed, which strongly depends on the magneto-optical property of material and the symmetry of structure. The unique magneto-optical effects make it play an irreplaceable role in the high performance THz applications of communication, imaging, and sensing systems.

Description: Dynamic hand gesture recognition is a crucial but challenging task in the pattern recognition and computer vision communities. In this paper, we propose a novel feature vector which is suitable for representing dynamic hand gestures, and presents a satisfactory solution to recognizing dynamic hand gestures with a Leap Motion controller (LMC) only. These have not been reported in other papers. The feature vector with depth information is computed and fed into the Hidden Conditional Neural Field (HCNF) classifier to recognize dynamic hand gestures. The systematic framework of the proposed method includes two main steps: feature extraction and classification with the HCNF classifier. The proposed method is evaluated on two dynamic hand gesture datasets with frames acquired with a LMC. The recognition accuracy is 89.5% for the LeapMotion-Gesture3D dataset and 95.0% for the Handicraft-Gesture dataset. Experimental results show that the proposed method is suitable for certain dynamic hand gesture recognition tasks.

Description: We consider optimal precoder design for multiuser multiple-input multiple-output broadcasting channels in single-carrier systems. Instead of linear detection, we assume that the advanced nonlinear channel shortening detectors are utilized at the receivers. Such a scenario is challenging for precoder design as the uplink–downlink duality is inapplicable. The target of our linear precoder design is to maximize the sum of the achievable information rate (sum-AIR), with AIR of each user being explicitly derived. We analyze such a precoder design in general, and provide an efficient per-user based optimization algorithm for the design of block-diagonalization precoder.

Description: This paper proposes an efficient time-domain mixed potential integral equation (TD-MPIE) technique to analyze the penetrated transient electromagnetic field into a perforated rectangular metallic enclosure loaded with conducting objects. To this end, the surface equivalent principle is applied, and the fields in the interior and the exterior regions of the enclosure are formulated by using the time-domain cavity and free space Green's functions, respectively. Then, the governing TD-MPIE is derived by enforcing the continuity of the tangential magnetic and electric fields on the surface of the apertures and conducting loads inside the shield, respectively. The resultant time-domain equation is then solved by using the marching-on-in time scheme. Here, the spatial integrations and temporal convolutions are carried out analytically which eliminates the need to complicated numerical integration routines and remove the late time instability. In addition, an accelerated algorithm is proposed to scale down the computational complexity from $O(N_{rm mod } N_t^2 N_s^2)$ down to $O(N_{{rm mod}} N_t (log ^2 N_t + 2N_s))$ where $N_{{rm mod}}$ , $N_{t}$ and $N_{s}$ denote the total number of necessary modes inside the cavity, the number of time steps, and the spatial unknowns, respectively. The accuracy of the proposed technique is validated by comparing the simulated results with the well-known CST Microwave Studio time-domain solver, as well as the frequency-domain solution, and the measurements.

Description: Coupling between closely spaced wideband analog radio frequency integrated circuits can cause degradation in device performance. Methods for reducing the coupling between packages were tested, with a strong focus on maintaining manufacturability and minimizing any increase in coupling between structures within the package. Methods include the application of magnetic absorbing material or resistive sheets to the package surface and the inclusion of conductive vias in the package walls. Package modifications were tested from 5–40 GHz through both full-wave simulation and physical measurement. The best tradeoff in performance and manufacturability below 20 GHz was found using 250-Ω/sq. resistive sheets connected to the return plane with vias in the package corners, while above 20 GHz, the best tradeoff was found by covering the package with magnetic absorbing materials. The magnetic absorbing material can be embedded directly in the package polymer itself, allowing easy manufacture.

Description: A formula is derived for the plane wave shielding effectiveness of a spacecraft Faraday cage with a penetrating cable. The potentially disastrous effects of space radiation, corrosion, and thermal cycling are described.

Description: This paper presents the results of a radiated electromagnetic field due to lightning strike to a tall tower sitting on a mountainous terrain when considering both the first and subsequent return strokes. A cone-shaped ground with finite conductivity represents the mountainous terrain; and a cylindrical two-dimensional finite-difference time-domain (FDTD) scheme is used to determine the lightning-generated electromagnetic fields. The return stroke channel is modeled using the antenna theory model with fixed inductive loading which is appropriately incorporated into the FDTD algorithm. Simulation results are presented for different values of the cone angle and height, and for different values of the tower height. It is shown that when the first and subsequent return strokes hit a tower on a cone-shaped ground, the radiated electric and magnetic fields experience an enhancement in their amplitudes as compared to the case where the tower sits on a flat ground. Also, the current reflections from the tower base, influencing the current distribution along the tower and radiated electromagnetic field waveforms, depend on the cone angle and height, as well as the tower height.

Description: The optical and electrical properties of organic solar cells with metallic Ag incorporating into the PEDOT:PSS layer in the case of oblique incidence are numerically investigated based on in house finite-difference frequency-domain code. It is found that the light absorption decreases with the increase of incident angle, and the absorption enhancement varies slightly due to the contribution of the broadband surface plasmon (SP)-mode. Since the absorption reduces at large incident angle, the short-circuit current undoubtedly decreases, thus fill factor and power conversion efficiency reduce accordingly. However, there is little variety of open-circuit voltage, which indicates that open-circuit voltage is independent of incident angle. Our results can be used as a reference for better understanding of the optical and electrical properties of organic solar cells with oblique incidence.

Description: Through co-design of a dual SiGe transimpedance amplifier and an integrated silicon photonic circuit, we realized for the first time an ultra-compact and low-power silicon single-polarization coherent receiver operating at 40 GBd. A bit-error rate of $〈3.8times 10^{{{-3}}}$ was obtained for an optical signal-to-noise ratio of 14 dB for QPSK modulation (80 Gb/s), and 26.5 dB for 16-QAM (160 Gb/s). We also demonstrate robust performance of the receiver over temperature and wavelength.

Description: The future technology migration in access networks compels the development of key innovative transmitters operating at 10 Gb/s around 1550 nm and capable of transmitting data in extended reach passive optical networks (>60 km). A laser modulated directly appears to be a low cost and a simple solution to address these needs. However, the extinction ratio and the distance of transmission are limited by frequency chirping inherent to high bit rate modulation at 1550 nm. In this letter, we demonstrate the monolithic integration of a directly modulated lasers and a ring resonator (RR) and show the possibility to engineer the RR with specific frequency modulation efficiency. The ring is used as an optical eye reshaper, which enables the increase of the extinction ratio above 9 dB. Transmissions up to 65 km with 5.8 dBm of modulated optical power coupled into the fiber are demonstrated, proving the feasibility of our transmitter for the next generation passive optical network stage 2.

Description: We propose an imaging receiver (ImR) using angle diversity detectors for indoor space division multiplexing-based visible light communication (SDM-VLC) systems. Compared with a conventional ImR which utilizes vertically oriented detectors, the proposed imaging angle diversity receiver (ImADR) enjoys two main advantages, including a wider field-of-view (FOV) and higher optical gain. Both theoretical analysis and Monte Carlo simulations are carried out to evaluate the performance of the proposed ImADR in an indoor four-channel SDM-VLC system. Analytical results show that, for a target bit error rate of $10^{mathrm { {-3}}}$ , the proposed ImADR-based SDM-VLC system achieves 44% reduction in the transmit optical power and 130% coverage improvement than the system using a conventional ImR.

Description: Compared with Mach–Zehnder modulators, silicon microring modulators have the important advantages of smaller footprint and significantly lower energy per bit but suffer the disadvantages of inherent wavelength dependence and a modulation bandwidth that is limited by the resonator bandwidth. Here, we address the latter disadvantage of using an integrated silicon microring modulator with spectrally efficient modulation with 16-quadrature amplitude modulation on direct detection orthogonal frequency division multiplexing (OFDM). We demonstrate a line rate of 128-Gb/s and a net data rate of 107.6-Gb/s OFDM signal modulation using this tunable microring modulator. The 6.6- and 10-km single-mode fiber transmission of the modulated signal is presented. The measured bit error rates of back-to-back, 6.6-km, and 10-km fiber transmission are well below the forward error correction threshold.

Description: We report on the experimental fabrication and functioning of an optical fiber sensing probe operating on the principle of a localized surface plasmon resonance (LSPR) at near infrared (NIR) wavelengths, around the 2- $mu text{m}$ window. As indium–tin oxide exhibits an absorption peak at NIR wavelengths, we have synthesized and characterized ITO nanoparticles with a doping concentration of 9:1 of In:Sn and attached these onto the unclad core of the fiber. Corresponding to the absorption peak of the bare nanoparticles, the fiber probe displays an LSPR peak at 1950 nm, which falls in the signature band of many bacterial contaminants. The probe is further used for the sensing of analytes of refractive indices varying from 1.335 to 1.375. The results reveal that the average peak absorbance of the probe changes by 3.46 absorbance units for a unit change in refractive index.

Description: Photonic band crystal semiconductor lasers generating gain-switched picosecond pulses in the 1060-nm spectral range are demonstrated. The unique ultra-broad laser waveguide allows the simultaneous delivery of high output power and high beam quality, enabling the generation of optical pulses with peak brightness up to 130 MWcm $^{-2}$ sr $^{-1}$ . The spectral dynamics are investigated by external filtering of the laser emission. The effects of the filter bandwidth and central wavelength on the resulting optical pulse characteristics are identified. By properly chosen filter parameters, it is found that the higher order relaxation oscillation part of the optical pulse is completely suppressed even at the highest injected pulse currents, also resulting in a pulse width reduction down to less than 21.5 ps.

Description: We propose and demonstrate a polarization beam splitter (PBS) based on a tapered directional coupler (DC). The tapered DC structure has the advantage of being insensitive to variations of the coupling length and the local coupling coefficient, and thus can significantly increase the bandwidth and polarization extinction ratio (ER). The fabricated PBS with one waveguide width tapered from 0.55 to 0.41 $mu text{m}$ and another tapered reversely in a length of 29 $mu text{m}$ shows a polarization ER above 16 dB and an insertion loss less than 0.4 dB over a wavelength range of 100 nm. The fabrication tolerance of the proposed PBS is also analyzed to be ±50 nm, ensuring the compatibility with the standard 180-nm complementary metal–oxide–semiconductor process.

Description: Gain enhancement of single-mode Cr-doped crystalline core fiber (SMCDCCF) with longer fiber length fabricated by online laser-heated pedestal growth (LHPG) technique is demonstrated. In comparison with the SMCDCCF fabricated without online growth, the online technique enables real-time monitoring and controlling small molten zone in the LHPG to achieve longer length with better uniformity and smaller core diameter of the SMCDCCFs. The SMCDCCF exhibits a length of 10.6 cm, a core diameter of 25 $mu text{m}$ , and a $V$ -value of 2.40, which confirms the LP 01 single-mode operation by the far-field pattern measurement. A 3.9-dB gross gain and a 1.9-dB net gain of the SMCDCCF at the wavelength of 1400 nm were obtained. The gross and net gains are the highest yet reported of the SMCDCCFs. Further development on higher gain of the SMCDCCF may be functioned the SMCDCCF as a broadband fiber amplifier for use in the next-generation fiber transmission systems.

Description: This letter focuses on a spatial modulation (SM)-based multiple input single output optical wireless communication system. In the system, both input-dependent noise and finite alphabet are employed. Based on information theory, the theoretical expressions of the mutual information and its lower bound are derived. By maximizing the minimum distance, an optimization problem for precoding design is formulated. Then, an interior point method-based iteration algorithm is proposed to solve the problem. Numerical results show that the performance of the proposed SM scheme outperforms that of the conventional, choosing the best-gain channel scheme. Moreover, the proposed precoding scheme can provide better system performance than the scheme without precoding.

Description: We consider the problem of selecting an optimal mask for an image manifold, i.e., choosing a subset of the pixels of the image that preserves the manifold’s geometric structure present in the original data. Such masking implements a form of compressive sensing through emerging imaging sensor platforms for which the power expense grows with the number of pixels acquired. Our goal is for the manifold learned from masked images to resemble its full image counterpart as closely as possible. More precisely, we show that one can indeed accurately learn an image manifold without having to consider a large majority of the image pixels. In doing so, we consider two masking methods that preserve the local and global geometric structure of the manifold, respectively. In each case, the process of finding the optimal masking pattern can be cast as a binary integer program, which is computationally expensive but can be approximated by a fast greedy algorithm. Numerical experiments show that the relevant manifold structure is preserved through the data-dependent masking process, even for modest mask sizes.

Description: A Bayesian framework is proposed to define flexible coupling models for joint tensor decompositions of multiple datasets. Under this framework, a natural formulation of the data fusion problem is to cast it in terms of a joint maximum a posteriori (MAP) estimator. Data-driven scenarios of joint posterior distributions are provided, including general Gaussian priors and non Gaussian coupling priors. We present and discuss implementation issues of algorithms used to obtain the joint MAP estimator. We also show how this framework can be adapted to tackle the problem of joint decompositions of large datasets. In the case of a conditional Gaussian coupling with a linear transformation, we give theoretical bounds on the data fusion performance using the Bayesian Cramér–Rao bound. Simulations are reported for hybrid coupling models ranging from simple additive Gaussian models to Gamma-type models with positive variables and to the coupling of data sets which are inherently of different size due to different resolution of the measurement devices.

Description: The objective of generalized sampling expansion (GSE) is the reconstruction of an unknown, continuously defined function $fleft(tright)$ from samples of the responses from $M$ linear time-invariant (LTI) systems that are each sampled using the $1/M$ th Nyquist rate. In this paper, we investigate the GSE for lowpass and bandpass signals with multiple sampling rates in the fractional Fourier transform (FRFT) domain. First, we propose an improvement of Papoulis’ GSE, which has multiple sampling rates in the FRFT domain. Based on the proposed GSE, we derive the periodic nonuniform sampling scheme and the derivative interpolation method by designing different fractional filters and selecting specific sampling rates. In addition, the Papoulis GSE and the previous GSE associated with FRFT are shown to be special instances of our results. Second, we address the problem of the GSE of fractional bandpass signals. A new GSE for fractional bandpass signals with equal sampling rates is derived. We show that the restriction of an even number of channels in the GSE for fractional bandpass signals is unnecessary, and perfect signal reconstruction is possible for any arbitrary number of channels. Further, we develop the GSE for a fractional bandpass signal with multiple sampling rates. Lastly, we discuss the application of the proposed method in the context of single-image super-resolution reconstruction based on GSE. Illustrations and simulations are presented to verify the validity and effectiveness of the proposed results.

Description: We consider a sensor scheduling problem where the sensors have multiple choices of communication channel to send their local measurements to a remote state estimator for state estimation. Specifically, the sensors can transmit high-precision data packets over an expensive channel or low-precision data packets, which are quantized in several bits, over some cheap channels. The expensive channel, though being able to deliver more accurate data which leads to good estimation quality at the remote estimator, can only be used scarcely due to its high cost (e.g., high energy consumption). On the other hand, the cheap channel, though having a small cost, delivers less accurate data which inevitably deteriorates the remote estimation quality. In this work we propose a new framework in which the sensors switch between the two channels to achieve a better tradeoff among the communication cost, the estimation performance and the computational complexity, where the two-channel case can be easily extended to a multiple-channel case. We propose an opportunistic sensor schedule which reduces the communication cost by randomly switching among the expensive and cheap channels, and in the meantime maintains low computational complexity while introducing data quantization into the estimation problem. We present a minimum mean square error (MMSE) estimator in a closed-form under the proposed opportunistic sensor schedule. We also formulate an optimization problem to search the best opportunistic schedule with a linear quantizer. Furthermore, we show that the MMSE estimator in the limiting case becomes the standard Kalman filter.

Description: The stability problems of the least mean fourth (LMF) algorithm put a limitation on its tracking capability. The paper investigates the possibility of solving this problem via stabilization of the algorithm. The analysis is done for a Markov plant. It is found that the available stable normalized LMF (NLMF) algorithm has a tracking limitation for high signal-to-noise ratio. The paper presents a new stable NLMF algorithm that is free of this limitation. Mean-square stability of the algorithm is proved. Expressions are derived for the minimum steady-state mean square deviation (MSD) and the corresponding convergence time. The new algorithm outperforms the available stable NLMF algorithm in both the transient and steady states. The new algorithm is also compared with the NLMS algorithm when the adaptation parameter of each algorithm is set to the value that minimizes its steady-state MSD. For large initial MSD, the algorithm outperforms the NLMS algorithm, even for Gaussian noise. For small initial MSD, the algorithm outperforms the NLMS algorithm for sub-Gaussian noise, while the situation is opposite for Gaussian noise. Analytical results are supported by simulations.

Description: Twisted particle filters are a class of sequential Monte Carlo methods recently introduced by Whiteley and Lee to improve the efficiency of marginal likelihood estimation in state-space models. The purpose of this article is to extend the twisted particle filtering methodology, establish accessible theoretical results which convey its rationale, and provide a demonstration of its practical performance within particle Markov chain Monte Carlo for estimating static model parameters. We derive twisted particle filters that incorporate systematic or multinomial resampling and information from historical particle states, and a transparent proof which identifies the optimal algorithm for marginal likelihood estimation. We demonstrate how to approximate the optimal algorithm for nonlinear state-space models with Gaussian noise and we apply such approximations to two examples: a range and bearing tracking problem and an indoor positioning problem with Bluetooth signal strength measurements. We demonstrate improvements over standard algorithms in terms of variance of marginal likelihood estimates and Markov chain autocorrelation for given CPU time, and improved tracking performance using estimated parameters.

Description: This paper deals with a novel generalization of classical blind source separation (BSS) in two directions. First, relaxing the constraint that the latent sources must be statistically independent. This generalization is well-known and sometimes termed independent subspace analysis (ISA). Second, jointly analyzing several ISA problems, where the link is due to statistical dependence among corresponding sources in different mixtures. When the data are one-dimensional, i.e., multiple classical BSS problems, this model, known as independent vector analysis (IVA), has already been studied. In this paper, we combine IVA with ISA and term this new model joint independent subspace analysis (JISA). We provide full performance analysis of JISA, including closed-form expressions for minimal mean square error (MSE), Fisher information and Cramér–Rao lower bound, in the separation of Gaussian data. The derived MSE applies also for non-Gaussian data, when only second-order statistics are used. We generalize previously known results on IVA, including its ability to uniquely resolve instantaneous mixtures of real Gaussian stationary data, and having the same arbitrary permutation at all mixtures. Numerical experiments validate our theoretical results and show the gain with respect to two competing approaches that either use a finer block partition or a different norm.

Description: This paper addresses the design of adaptive subspace matched filter (ASMF) detectors in the presence of a mismatch in the steering vector. These detectors are coined as adaptive in reference to the step of utilizing an estimate of the clutter covariance matrix using training data of signal-free observations. To estimate the clutter covariance matrix, we employ regularized covariance estimators that, by construction, force the eigenvalues of the covariance estimates to be greater than a positive scalar $rho $ . While this feature is likely to increase the bias of the covariance estimate, it presents the advantage of improving its conditioning, thus making the regularization suitable for handling high-dimensional regimes. In this paper, we consider the setting of the regularization parameter and the threshold for ASMF detectors in both Gaussian and compound Gaussian clutters. In order to allow for a proper selection of these parameters, it is essential to analyze the false alarm and detection probabilities. For tractability, such a task is carried out under the asymptotic regime in which the number of observations and their dimensions grow simultaneously large, thereby allowing us to leverage existing results from random matrix theory. Simulation results are provided in order to illustrate the relevance of the proposed design strategy and to compare the performances of the proposed ASMF detectors versus adaptive normalized matched filter (ANMF) detectors under mismatch scenarios.

Description: Cloud storage offers a popular environment for users to store and share content. Yet, content sharing leads to multiple downloads of the same content when users synchronize devices. These downloads contribute to bandwidth waste and increase server workloads. To what extent does this occur? Would network caches alleviate the problem? The authors address these questions by investigating the traffic generated by Dropbox, a popular cloud storage service. Using data collected from four networks, they show that a large fraction (57-70 percent) of downloads generated by Dropbox users is associated with content shared among multiple devices. Moreover, despite optimizations such as the LAN Sync protocol, up to 25 percent of Dropbox download traffic comes from content replicas. The authors then evaluate an alternative synchronization architecture that uses caches to offload storage servers from such downloads. Their experiments show that the approach cost-effectively avoids most repetitive downloads, benefiting service providers, the network, and end users.