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A level-crossing ADC, which includes a 1-bit DAC, a rail-to-rail continuous-time comparator and a 3-stage continuous-time comparator, has been simulated in 0.18 μm CMOS process with 0.8 V supply voltage. Interpolation between samples has been used to add more samples to the original signal and reconstruct ADC’s output signal. The ADC has SNDR of 47.2 dB (with polynomial interpolation), ENOB of 7.5-bit and power consumption of 460 nW for 1 kHz sinusoidal input signal with common-mode voltage of 400 mV and amplitude of 800 m . [ABSTRACT FROM AUTHOR]

This paper proposes a method for visualizing three-dimensional non-uniformly sampled data from multibeam echosounder systems (MBESs), aimed at addressing the requirements of monitoring complex and dynamic underwater flow fields. To tackle the challenges associated with spatially non-uniform sampling, the proposed method employs linear interpolation along the radial direction and arc length weighted interpolation in the beam direction. This approach ensures consistent resolution of three-dimensional data across the same dimension. Additionally, an opacity transfer function is generated to enhance the visualization performance of the ray casting algorithm. This function leverages data values and gradient information, including the first and second directional derivatives, to suppress the rendering of background and non-interest regions while emphasizing target areas and boundary features. The simulation and experimental results demonstrate that, compared to conventional two-dimensional beam images and three-dimensional images, the proposed algorithm provides a more intuitive and accurate representation of three-dimensional data, offering significant support for the observation and analysis of spatial flow field characteristics. [ABSTRACT FROM AUTHOR]

In order to solve the problems of misdetection of similar behaviors and low accuracy for detecting local body behaviors in the spatio-temporal action detection of abnormal human behavior, based on the self-made uncivilized behavior spatio-temporal action detection dataset (UBSAD), a method that integrates non-uniform sampling and feature enhancement is proposed. Firstly, this method incorporates the video swin transformer (VST) as the backbone network in the spatio-temporal feature extraction stage to capture long-term temporal dependencies in videos, and enhance the network’s global information learning capability. Additionally, a ringed residual VST block replaces the standard VST block in the final stage of the backbone network, enlarging the difference between target area and background area. Combined with the multi-head self-attention mechanism, the feature extraction of the target area is strengthened. Furthermore, during the video frame collection stage, a unique non-uniform sampling method is proposed to adjust the input data distribution according to task requirements, allowing the model to obtain action change information in a hierarchical manner, effectively improving the network’s attention to detailed features of similar behaviors. Finally, after the feature extraction network, a new cascaded pooling three-dimensional spatial pyramid feature enhancement module incorporating shallow features is embedded to further enhance feature applicability at various scales, reduce the loss of detailed motion information during the feature extraction process, reduce the interference of background information, and achieve the effect of feature enhancement. Experimental results show that the method achieves mAP of 71.93% and 83.09% respectively on the UBSAD dataset and the public dataset UCF101-24. They are 7.39 percentage points and 1.22 percentage points higher than those of using the baseline network VST as the spatio-temporal feature extraction model, demonstrating the method’s effectiveness in accurately detecting behavior.

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

During the structural health monitoring of bridges, it has been observed that the vibration data collected can sometimes be randomly lost or sampled non-uniformly. This leads to a low signal-to-noise ratio in the spectral functions of the measured data, making it difficult to identify weak modes. To address this issue, a framework for operational modal identification is proposed in this study. It utilizes the fast Bayesian fast Fourier transform (FFT) method to estimate the modal parameters of highway bridges considering the non-uniform monitoring data. The initial frequency parameters for the fast Bayesian FFT approach are automatically determined using the proposed autoregressive (AR) power spectral density (PSD)-guided peak picking method. This overcomes the challenge of capturing initial frequencies related to weakly contributed modes. Additionally, the bandwidth parameter for each mode is determined using the modal assurance criterion (MAC) of the first left singular vectors of PSD matrices. Furthermore, when analyzing non-uniform vibration data, it is recommended to use the non-uniform FFT (NUFFT) for calculating PSD functions in order to improve identification accuracy. The proposed method is validated using acceleration data from both a numerical model and a real-world bridge. The results demonstrate that the identification uncertainty of modal parameters increases with higher non-uniform levels. [ABSTRACT FROM AUTHOR]

Electrocardiographic signals (ECG) are ubiquitous, which justifies the research of their optimal storage and transmission. However, proposals for non-uniform signal sampling must take into account the priority of diagnostic data accuracy and record integrity, as well as robustness to noise and interference. In this study, two novel methods are introduced, each utilizing a distinct neural network architecture for optimizing non-uniform sampling of ECG signal. A transformer model refines each time point selection through an iterative process using gradient descent optimization, with the goal of minimizing the mean squared error between the original and resampled signals. It adaptively modifies time points, which improves the alignment between both signals. In contrast, the Temporal Convolutional Network model trains on the original signal, and gradient descent optimization is utilized to improve the selection of time points. Evaluation of both strategies' efficacy is performed by calculating signal distances at lower and higher sampling rates. First, a collection of synthetic data points that resembled the P-QRS-T wave was used to train the model. Then, the ECG-ID database for real data analysis was used. Filtering to remove baseline wander followed by evaluation and testing were carried out in the real patient data. The results, in particular MSE = 0.0005, RMSE = 0.0216, and Pearson's CC = 0.9904 for 120 sps in the case of the transformer patient data model, provide viable paths for maintaining the precision and dependability of ECG-based diagnostic systems at much lower sampling rate. Outcomes indicate that both techniques are effective at improving the fidelity between the original and modified ECG signals. [ABSTRACT FROM AUTHOR]

In this paper, a new non-uniform differential sample and hold circuit is proposed using low-distortion sampling switches for biomedical signal processing applications. The proposed design can be used in the biomedical low-frequency range with low-power consumption which makes the proposed design a good candidate for bio-signal sampling purposes. The body biasing technique is employed to reduce the distortion of the sampling switches. Moreover, to reduce the required sampled-data storage capacity, the signal slope rate detection is used to predict the input signal frequency and variations in order to adjust the proper sampling rate of the proposed non-uniform sampling circuit. To realize the proposed design, a capacitive digital-to-analog converter is used to sample the slope changes of signals at two sampling frequencies of 1000 and 100 Hz. The circuit simulation results using 0.18 μm CMOS technology parameters at 1 V supply voltage, indicate the saving in power consumption value up to 57.5% in comparison with other conventional designs. Additionally, the output signal of the proposed circuit can be reconstructed with a percentage root-mean-square difference factor of as low as 2.1%, a mean square error of 0.0025, and a signal-to-noise ratio value of 79.34 dB. [ABSTRACT FROM AUTHOR]

Copyright of Coal Geology & Exploration is the property of Xian Research Institute of China Coal Research Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objectives: In order to withstand the deep water pressure, the cylindrical structure and the coni⁃ cal structure are often used as the submarine pressure hull. The section roundness of the submarine pressure hull needs to be measured during construction and repair. Due to the limitation of environment conditions and the fusion of different survey methods to obtain data, the survey points are often non-uniform. This case would result in the deviation between the results of classical least square circle fitting method and the actual situation, especially to the maximum deflection which is used as the judgment condition of round⁃ ness. Therefore, it is necessary to adopt a method to eliminate the influence of non-uniform distribution of the survey points. Methods: A non-uniform sampling weighted least squares circle fitting method and its weighting rules are proposed based on the classical Pratt circle fitting method. There are three steps in the proposed method. First, the survey points are fitted by the classical Pratt method. Then, the weight of point is calculated according to the corresponding center angle. Finally, the survey points with weight are fitted. Results: Numerical experiments on standard ellipse sampling analysis are carried out. The results show that the maximum deflection is more affected than the fitted center and radius by the non-uniformity of the sampling points. Because the roundness evaluation judgment of the submarine pressure hull is based on the maximum deflection, the fitting results of the proposed method are more accurate and reliable than the classical circle fitting method. Conclusions: The proposed method can eliminate the influence of nonuniform sampling on circle fitting better, and has good accuracy and engineering practical value [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of asynchronous estimation in the presence of packet losses for the randomly sampling nonlinear system. Packet losses occur at the control input and at the measurement side. Firstly, the synchronization of the asynchronous sampling system is realized by weighting the state of the adjacent state update points. Secondly, the projection theorem is used to estimate the system state at the sampling time. Due to modeling errors and unmodeled dynamics, obtaining an accurate dynamic model is challenging. Therefore, observation inference based on interpolation techniques is proposed to solve the asynchronous estimation problem. Furthermore, the algorithm is extended to multi-sensor systems to obtain a distributed fusion estimator. Finally, simulation experiments are conducted to validate the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Efficient and robust analytical methods are needed to improve the identification and subsequent regulation of new psychoactive substances (NPS). NMR spectroscopy is a unique method able to determine the structure of small molecules such as NPS even in mixtures. However, high‐field NMR analysis is associated with expensive purchase and maintenance costs. For more than a decade, compact NMR spectrometers have changed this paradigm. It was recently shown that a dedicated analytical workflow combining compact NMR and databases could identify the molecular structure of NPS, in spite of the lower spectral dispersion and sensitivity of compact spectrometers. This approach relies on 1H‐13C HSQC to both recognize NPS and elucidate the structure of unknown substances. Still, its performance is limited by the need to compromise between resolution and experiment time. Here, we show that this strategy can be significantly improved by implementing non‐uniform sampling (NUS) to improve spectral resolution in the 13C dimension of HSQC at no cost in terms of experiment time. Gains in the range of 3 to 4 in resolution are achieved for pure NPS and for a mixture. Finally, 2D HSQC with NUS was applied to improve the identification of NPS with the assistance of databases. The resulting method appears as a useful tool for the characterization of NPS in mixtures, which is essential for forensic laboratories. [ABSTRACT FROM AUTHOR]

The ability to non‐uniformly weight the input space is desirable for many applications, and has been explored for space‐filling approaches. Increased interests in linking models, such as in a digital twinning framework, increases the need for sampling emulators where they are most likely to be evaluated. In particular, we apply non‐uniform sampling methods for the construction of aerodynamic databases. This paper combines non‐uniform weighting with active learning for Gaussian Processes (GPs) to develop a closed‐form solution to a non‐uniform active learning criterion. We accomplish this by utilizing a kernel density estimator as the weight function. We demonstrate the need and efficacy of this approach with an atmospheric entry example that accounts for both model uncertainty as well as the practical state space of the vehicle, as determined by forward modeling within the active learning loop. [ABSTRACT FROM AUTHOR]

The phase noise, commonly known as jitter, in Phase-Locked Loops (PLLs) is conventionally perceived as a stochastic process, necessitating a degree of tolerance in downstream circuits such as Analog-to-Digital Converters (ADCs). This paper addresses this issue within the context of a Time-to-Digital Converter (TDC)-based All-Digital Phase-Locked Loop (ADPLL) responsible for generating the sampling clock for ADCs. The ADPLL uses a low-quality, high-noise oscillator while remaining area- and power-efficient. Any jitter introduced in the ADPLL’s clock signal directly causes non-uniform sampling by the ADC. We propose a background all-digital algorithm to address this challenge. The least-square jitter estimation algorithm uses the TDC phase information to detect instantaneous sampling error and a Taylor-based correction is used to perform ADC post-correction. We evaluate the efficacy of these algorithms through extensive simulations, considering a realistic system setup. The overarching system performance is quantified using SNDR as the primary performance metric, demonstrating enhancements of up to 33 dB across the designated operational frequency range when contrasted with uncorrected ADC outputs.

In this paper, we explore a nonlinear interactive network system comprising nodalized flapping-wing micro air vehicles (FMAVs) to address the distributed H ∞ state estimation problem associated with FMAVs. We enhance the model by introducing an information fusion function, leading to an information-fusionized estimator model. This model ensures both estimation accuracy and the completeness of FMAV topological information within a unified framework. To facilitate the analysis, each FMAV's received signal is individually sampled using independent and time-varying samplers. Transforming the received signals into equivalent bounded time-varying delays through the input delay method yields a more manageable and analyzable time-varying nonlinear network error system. Subsequently, we construct a Lyapunov–Krasovskii functional (LKF) and integrate it with the refined Wirtinger and relaxed integral inequalities to derive design conditions for the FMAVs' distributed H ∞ state estimator, minimizing conservatism. Finally, we validate the effectiveness and superiority of the designed estimator through simulations. [ABSTRACT FROM AUTHOR]

Electrocardiographic signals (ECG) are ubiquitous, which justifies the research of their optimal storage and transmission. However, proposals for non-uniform signal sampling must take into account the priority of diagnostic data accuracy and record integrity, as well as robustness to noise and interference. In this study, two novel methods are introduced, each utilizing a distinct neural network architecture for optimizing non-uniform sampling of ECG signal. A transformer model refines each time point selection through an iterative process using gradient descent optimization, with the goal of minimizing the mean squared error between the original and resampled signals. It adaptively modifies time points, which improves the alignment between both signals. In contrast, the Temporal Convolutional Network model trains on the original signal, and gradient descent optimization is utilized to improve the selection of time points. Evaluation of both strategies’ efficacy is performed by calculating signal distances at lower and higher sampling rates. First, a collection of synthetic data points that resembled the P-QRS-T wave was used to train the model. Then, the ECG-ID database for real data analysis was used. Filtering to remove baseline wander followed by evaluation and testing were carried out in the real patient data. The results, in particular MSE = 0.0005, RMSE = 0.0216, and Pearson’s CC = 0.9904 for 120 sps in the case of the transformer patient data model, provide viable paths for maintaining the precision and dependability of ECG-based diagnostic systems at much lower sampling rate. Outcomes indicate that both techniques are effective at improving the fidelity between the original and modified ECG signals.

1H-detected solid-state NMR spectroscopy has been becoming increasingly popular for the characterization of protein structure, dynamics, and function. Recently, we showed that higher-dimensionality solid-state NMR spectroscopy can aid resonance assignments in large micro-crystalline protein targets to combat ambiguity (Klein et al., Proc. Natl. Acad. Sci. U.S.A. 2022). However, assignments represent both, a time-limiting factor and one of the major practical disadvantages within solid-state NMR studies compared to other structural-biology techniques from a very general perspective. Here, we show that 5D solid-state NMR spectroscopy is not only justified for high-molecular-weight targets but will also be a realistic and practicable method to streamline resonance assignment in small to medium-sized protein targets, which such methodology might not have been expected to be of advantage for. Using a combination of non-uniform sampling and the signal separating algorithm for spectral reconstruction on a deuterated and proton back-exchanged micro-crystalline protein at fast magic-angle spinning, direct amide-to-amide correlations in five dimensions are obtained with competitive sensitivity compatible with common hardware and measurement time commitments. The self-sufficient backbone walks enable efficient assignment with very high confidence and can be combined with higher-dimensionality sidechain-to-backbone correlations from protonated preparations into minimal sets of experiments to be acquired for simultaneous backbone and sidechain assignment. The strategies present themselves as potent alternatives for efficient assignment compared to the traditional assignment approaches in 3D, avoiding user misassignments derived from ambiguity or loss of overview and facilitating automation. This will ease future access to NMR-based characterization for the typical solid-state NMR targets at fast MAS. [ABSTRACT FROM AUTHOR]

Multi-parameter modulated signal can improve the anti-interception ability yet makes the range extraction of the target inconvenient. To solve the problem, a novel scaling transformation combined with the non-uniform short-time fractional Fourier transformation is proposed to effectively acquire the target range information. Unlike the traditional methods, the influence of the varied carrier frequency is firstly mitigated by the proposed scaling transformation. Then, the error caused by the period-varying is relieved by the sampling conversion in the digital domain. Further, the corresponding dictionary is constructed to accomplish the ranging information searching and extracting. Simulation results show that the proposed method can mitigate the impact of multiple varied parameters on signal processing. The detection error could be reduced by 67.9% after using the proposed method. • The non-uniform short-time fractional Fourier transformation is applied to deal with ranging problem of the. • A novel scaling transformation is proposed to mitigate the influence of the varied frequency. • Uniform sampling and the non-uniform sampling are investigated to reduce the ranging error caused by the varied period. • The dictionary is constructed in the sparse domain for the multi-parameter modulated signal to extract the ranging information. [ABSTRACT FROM AUTHOR]

The accelerated acquisition of multidimensional NMR spectra using sparse non-uniform sampling (NUS) has been widely adopted in recent years. The key concept in NUS is that a major part of the data is omitted during measurement, and then reconstructed using, for example, compressed sensing (CS) methods. CS requires spectra to be compressible, that is, they should contain relatively few "significant" points. The more compressible the spectrum, the fewer experimental NUS points needed in order for it to be accurately reconstructed. In this paper we show that the CS processing of similar spectra can be enhanced by reconstructing only the differences between them. Accurate reconstruction can be obtained at lower sampling levels as the difference is sparser than the spectrum itself. In many situations this method is superior to "conventional" compressed sensing. We exemplify the concept of "difference CS" with one such case—the study of alpha-synuclein binding to liposomes and its dependence on temperature. To obtain information on temperature-dependent transitions between different states, we need to acquire several dozen spectra at various temperatures, with and without the presence of liposomes. Our detailed investigation reveals that changes in the binding modes of the alpha-synuclein ensemble are not only temperature-dependent but also show non-linear behavior in their transitions. Our proposed CS processing approach dramatically reduces the number of NUS points required and thus significantly shortens the experimental time. [ABSTRACT FROM AUTHOR]

RRAM devices have recently seen wide-spread adoption into applications such as neural networks and storage elements since their inherent non-volatility and multi-bit-capability renders them a possible candidate for mitigating the von-Neumann bottleneck. Researchers often face difficulties when developing edge devices, since dealing with sensors detecting parameters such as humidity or temperature often requires large and power-consuming ADCs. We propose a possible mitigation, namely using a RRAM device in combination with a comparator circuit to form a basic block for threshold detection. This can be expanded towards programmable non-uniform sampling ADCs, significantly reducing both area and power consumption since significantly smaller bit-resolutions are required. We demonstrate how a comparator circuit designed in 130 nm technology can be reprogrammed by programming the incorporated RRAM device. Our proposed building block consumes 83 µW. [ABSTRACT FROM AUTHOR]

Time-lapse video is a technique whereby each successive film frame is captured such a slow rate that it can be played back. When it replayed at a normal speed, time appear to be faster and thus lapsing. Times lapse technique is a pretty powerful technique to video document extremely slowly moving object. A differential time-lapse technique will allow us to get rid of the unnecessary data from the regular video, in order to save the time for visualization and storage space capacity. If Time-lapse techniques used for video documentary, not only saves valuable the time to know how things are changed but at the same time reduces the size of documentary. The differential time lapse video is a technique where frames which holds some movement compared to the previously stored frame are only stored. The differential time lapse generated video contains movements only, which is done during the time of recording. If time-stamping is not done, the action time is unfamiliar, hence time stamping is necessary. Time stamping is done using visual watermarking and hidden steganography for different purpose. The log file is also created along with the differential time-lapse video, in order to authenticate the differential time lapse video during the legal issue. This time lapse video is put in to the internet. In this system we also put an another high tech system suppose someone put an standby image program will turn second camera which is hide in another place this information of camera is secret. This system is also uploading in internet and mobile application so whenever the movement occurs the user is inform by message. In this paper, MATLAB based algorithm is used to generate the differential time-lapse video along with time-stamping and steganography and also log file using the image comparison techniques from real time video streaming is captured by image capturing equipment to the system. [ABSTRACT FROM AUTHOR]

Fourier ptychography imaging technology is a method developed in recent years to achieve high-resolution imaging. In the traditional macro Fourier ptychography technology, the scanning method when the camera captures low-resolution images mostly uses the rectangular linear grid format. These acquired images contain a small amount of complementary information, and a large number of low-resolution images are needed to achieve high-resolution imaging. Redundant measurements will extend the sampling and reconstruction time, and require more computing resources. In this paper, we propose to obtain the target image spectral energy distribution by pre-sampling. And according to the energy distribution, we use irregular and non-uniform sampling modes to restore the target image. With the same number of samples and same reconstruction time, higher resolution imaging can be achieved compared with traditional methods. Simulation and experimental studies are carried out in this paper, and the results confirm the effectiveness of the proposed methods. Compared with the traditional sampling mode, the two sampling modes proposed in this paper increase the resolution from 4.49 lp/mm to 5.66 lp/mm and 5.04 lp/mm respectively. [ABSTRACT FROM AUTHOR]

This paper is focused on the security control problem for the dynamic event-triggered networked T-S fuzzy control system with non-uniform sampling and stochastic network attacks. Different from existing research, the signals from sensor are sampled in a non-uniform period. To improve communication, a novel dynamic event-triggered method is proposed, which can adjust the trigger parameters dynamically according to the changes of external output. Then, a T-S fuzzy dynamic output feedback control model with event-triggered mechanism and network attacks is constructed. Furthermore, by introducing a set of slack matrix, the asynchronous premise variable problem caused by event-triggered method is solved, and some sufficient conditions that insure the asymptotic stability with an H ∞ performance requirement of the established closed-loop system can be obtained by utilizing Lyapunov function method and stochastic analysis technique. Moreover, the gains of the fuzzy dynamic output feedback controllers are found by solving the linear matrix inequalities (LMIs). Finally, two practical examples are given to support the merits and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The convergence of sensing, computing, communication and control elements drives the traditional point-to-point control systems towards networked control systems. Sampled-data control systems, which focus on the significant interplay between sampling and control, play a critical role in modern networked control systems, including intelligent transportation systems, smart grids, and advanced manufacturing systems. This paper presents a survey of methods and trends in non-uniform sampled-data control systems, where sampling and control actions are performed in an aperiodic manner. First, some fundamental issues of both continuous- and discrete-time sampled-data control systems are discussed. Next, main methods in both continuous-time and discrete-time domains are elaborated, respectively. Then, event-triggered sampling, under which sampling is executed only when the system needs attention, is examined. Typical triggering mechanisms in the existing literature are reviewed and classified into four types according to different threshold functions. Furthermore, two applications in terms of automated vehicle platoons and islanded microgrids are provided to demonstrate that sampled-data control methods are capable to support relevant practical application scenarios. Finally, several challenging issues are envisioned to direct future research. [ABSTRACT FROM AUTHOR]

Speech coding plays a significant role in voice communication and improving network bandwidth efficiency for applications that require long-distance communication or storage space utilization. Non-uniform sampling (NUS) is a technique for the same, which performs data reduction by sampling at irregular intervals. In the literature, researchers use the structural property of the speech waveform for studying various NUS methods, such as LCSS, MMD, IPD, and zero-crossing point. However, in this paper, we consider the speech signal’s statistical properties to propose an optimal NUS approach. The proposed technique statistically analyzes the speech signal to sample the abrupt changes over a time frame and approximates the signal with minimal reconstruction error using cost and linear penalty functions for avoiding the over-fitting problem. The proposed technique further performs the optimization using the branch-and-bound. To evaluate the proposed NUS, we design a speech waveform encoder called Block Adaptive Amplitude Sampling (BAAS). A BAAS encoder can directly perform statistical analysis on the speech waveform to select data samples corresponding to the most significant changes in the signal. The decoder approximates the eliminated values using linear interpolation. We experimentally study the proposed technique using various matrices and measures such as POLQA and MUSHRA test. The evaluation shows that the proposed NUS technique retains only 25% of data samples to get an acceptable quality signal regeneration. In addition, comparative studies with MMD and IPD show that the proposed algorithm performs 1.6% better with 30% lower MSE scores.

Hyperspectral imaging is a popular style of image acquisition which captures a multitude of two-dimensional images at different frequencies to yield more information from a scene. For imaging in certain frequency ranges, point-scanning images pixel-by-pixel is the only option for imaging and consequently can require prohibitively long measurement times. This paper introduces a non-uniform compressed sensing strategy for recovering whole hyperspectral images with much fewer samples than normally required. Our methods use the known variability of measurements across training subjects to weight random sampling toward unreliable hyperspectral points. This strategy is used to heavily reduce remote hyperspectral image acquisition time while maintaining or improving recovery performance. To test our methods, we focus on the problem of recovering hyperspectral images for non-destructively detecting malicious dormant hardware Trojans hidden in integrated circuits. These non-destructive detection techniques require remote hyperspectral measurements of the backscattering electromagnetic side channel; which can only be performed by point-scanning with existing technologies. We detect covert hardware Trojan circuit modifications with state-of-the-art performance while requiring up to ten times fewer measurements than prior methods. We compare performance of a uniform-random and our weighted-random compressed sensing strategy for the 2-dimensional discrete cosine transform bases as well as with learned dictionary bases.

The problem of detecting defective turned-off elements in antenna arrays from near-field measurements is addressed. In particular, the focus here is to reduce the number of measurements in order to positively affect the acquisition time. Such an issue is achieved by adopting the recently developed warping sampling method. Two commonly antenna diagnostics methods, i.e, the Back Transformation Method (BTM) and the Matrix Method (MM), are considered in view of this new sampling strategy and compared to the usual half-wavelength sampling. In particular, in order to identify the fault locations, outcomes returned by BTM and MM undergo a detection step based on a cell-averaging CFAR (CA)-CFAR technique borrowed from the radar literature. It is shown that the warping sampling method provides performance close to the uniform half-wavelength one with a reduced number of data. Numerical simulations are carried out in order to verify the results with different fault layouts and tapered currents.

There has been increasing interest in deploying non-line-of-sight (NLOS) imaging systems for recovering objects hidden behind corners. Existing solutions need to calibrate the imaging system using auxiliary apparatus and additional detectors. We present an online calibration technique that directly decouples the transients, which are acquired by onsite scanning on a relay surface, into line-of-sight (LOS) and hidden components. We use the former to directly (re-)calibrate the system upon changes of scene $-$ surface configurations, scannable regions, and sampling patterns, and the latter for hidden object recovery via spatial-, frequency-, or learning-based techniques. We also calculate a Gamma map from the LOS component to preview calibration effects for accurate transient measurements. The entire process of our calibration for 64 scanning points takes no more than 14 seconds on an Intel i7-6600H CPU. In particular, our technique avoids using auxiliary calibration tools such as mirrors or checkerboards and supports both uniform and non-uniform sampling in an onsite NLOS imaging system. Comprehensive experiments via calibration evaluation and NLOS reconstruction demonstrate the efficiency and effectiveness of our solution. Besides, we have made our data and code open-source on GitHub to the research community.

A partition method, with an efficient divide and conquer partition strategy, for the non-uniform sampling signal reconstruction based on Fourier reconstruction sparse inversion (FRSI) is developed. The novel partition FRSI(P-FRSI) is motivated by the observation that the partition processing of multi-dimensional signals can reduce the reconstruction difficulty and save the reconstruction time. Moreover, it is helpful to choose suitable reconstruction parameters. The P-FRSI employs divide and conquer strategy, and the signal is firstly partitioned into some blocks. Following that, traditional FRSI is applied to reconstruct signals in each block. We adopt linear or nonlinear superposition to determine the weight coefficients during integrating these blocks. Finally, P-FRSI is applied to two-dimensional seismic signal reconstruction. The superiority of the new method over conventional FRSI is demonstrated by numerical reconstruction experiments.

Imperfect processes experience fault productions over time due to specific causes. Integrating the statistical process control, maintenance policy, and economic production quantity has led to more favorable results for the imperfect processes in literature. When monitoring a process, multiple assignable causes (ACs) may shift it to an out-of-control state. As indicated recently, if the interdependency of ACs is neglected, the total cost will be underestimated. Moreover, the mean and variance can simultaneously be affected by the occurrence of ACs. A non-central chi-square (NCS) chart was suggested for its decent performance against X-R chart in detecting the process disturbances and lowering quality loss cost. Besides, the increased occurrence rate of ACs over time leads to higher quality and maintenance costs. Employing a non-uniform sampling (NUS) scheme can significantly reduce costs. In the literature of modeling for imperfect processes under multiple ACs, all input parameters have always been fixed. The effectiveness of the models depends somewhat on the accurate estimates of these parameters. In reality, the estimation of parameters may be associated with uncertainty. For the first time, a robust design approach is proposed for designing NCS chart by considering the interval estimation of uncertain parameters. A particle swarm optimization (PSO) algorithm is used to present solutions. The proposed model is investigated through a real numerical example. [ABSTRACT FROM AUTHOR]

There has been increasing interest in deploying non-line-of-sight (NLOS) imaging systems for recovering objects hidden behind corners. Existing solutions need to calibrate the imaging system using auxiliary apparatus and additional detectors. We present an online calibration technique that directly decouples the transients, which are acquired by onsite scanning on a relay surface, into line-of-sight (LOS) and hidden components. We use the former to directly (re-)calibrate the system upon changes of scene $-$ surface configurations, scannable regions, and sampling patterns, and the latter for hidden object recovery via spatial-, frequency-, or learning-based techniques. We also calculate a Gamma map from the LOS component to preview calibration effects for accurate transient measurements. The entire process of our calibration for 64 scanning points takes no more than 14 seconds on an Intel i7-6600H CPU. In particular, our technique avoids using auxiliary calibration tools such as mirrors or checkerboards and supports both uniform and non-uniform sampling in an onsite NLOS imaging system. Comprehensive experiments via calibration evaluation and NLOS reconstruction demonstrate the efficiency and effectiveness of our solution. Besides, we have made our data and code open-source on GitHub to the research community. [ABSTRACT FROM AUTHOR]

Fourier transform spectrometers typically use a presumed monochromatic reference source to track and correct errors in optical path difference changes. This paper will conduct a theoretical analysis to show that non-monochromatic light sources with symmetric spectral profiles can also be used as reference sources without adding errors. An experiment was carried out using a symmetric broadband superluminescent diode (SLED) as reference light to measure the spectrum of some other SLED light sources to experimentally demonstrate this finding. [ABSTRACT FROM AUTHOR]

Solving a kernel regression problem usually suffers from expensive computation and storage costs due to the large kernel size. To tackle this problem, the Nyström method is proposed and widely applied to large-scale kernel methods as an approximate solution. The key idea of this method is to select a subset of columns of the kernel matrix and rebuilds a low-rank approximation to the dense kernel matrix. To reduce computational costs of sparse kernel regression, we take the merits of the Nyström approximation and present two non-uniform Nyström methods with theoretical guarantees for sparse kernel regression in this paper. In detail, we first provide an upper bound to the solution of sparse kernel regression via Nyström approximation. Based on this bound, we prove the upper bounds of the optimal solutions when adopting two notable non-uniform landmark selection strategies, including Determinantal Point Processes (DPPs) and Ridge Leverage Scores (RLS). Compared with the uniform Nyström method, we empirically demonstrate the superior performance of non-uniform Nyström in sparse kernel regression on a synthetic dataset and several real-world datasets. [ABSTRACT FROM AUTHOR]

Monosaccharides and disaccharides are important dietary components, but if insufficiently metabolized by some population subgroups, they are also linked to disease patterns. Thus, the correct analytical identification, quantification, and labeling of these food components are crucial to inform and potentially protect consumers. Enzymatic assays and high‐performance anion‐exchange chromatography with pulsed amperometric detection are established methods for the quantification of monosaccharides and disaccharides that, however, require long measuring times (60–180 min). Accelerated methods for the identification and quantification of the nutritionally relevant monosaccharides and disaccharides d‐glucose, d‐galactose, d‐fructose, sucrose, lactose, and maltose were therefore developed. To realize this goal, the NMR experiments HSQC (heteronuclear single quantum coherence) and acceleration by sharing adjacent polarization (ASAP)‐HSQC were applied. Measurement times were reduced to 27 and 6 min, respectively, by optimizing the interscan delay and applying non‐uniform sampling. The optimized methods were used to quantify d‐glucose, d‐galactose, d‐fructose, sucrose, and lactose in various dairy products. Results of the HSQC and ASAP‐HSQC methods are equivalent to the results of the reference methods in terms of both precision and accuracy, demonstrating that these methods can be used to correctly analyze nutritionally relevant monosaccharides and disaccharides in short times. [ABSTRACT FROM AUTHOR]

The sampling of the near-field radiated by a planar source observed over a finite planar aperture is addressed. To this end, we employ the warping method that amounts to properly change the observation variables and finding the sampling points as those that allow to approximate the singular values of the radiation operator up to the so-called number of degrees of freedom. In particular, the warping transformations allow to approximate the kernel function of the relevant operator as a band-limited function and hence the sampling theorem is adopted to devise the discretization scheme. Here, we generalize the warping method to the full vector case and introduce a spatially varying oversampling strategy that allows to deal with measurement apertures which are larger than the source. It is shown that the sampling points need to be non-uniformly arranged across the measurement aperture but their number is generally much lower than classical half-wavelength sampling. A numerical analysis is included to support the theoretical arguments. Finally, numerical experiment-based results concerning the radiation pattern estimation of a planar array antenna are presented. To this end, experimental data collected under a uniform half-wavelength sampling scheme are first interpolated over the required non-uniform grid and then processed to obtain the radiation pattern.

Classical sampling is based on acquiring signal amplitudes at specific points in time, with the minimal sampling rate dictated by the degrees of freedom in the signal. The samplers in this framework are controlled by a global clock that operates at a rate greater than or equal to the minimal sampling rate. At high sampling rates, clocks are power-consuming and prone to electromagnetic interference. An integrate-and-fire time encoding machine (IF-TEM) is an alternative power-efficient sampling mechanism which does not require a global clock. Here, the samples are irregularly spaced threshold-based samples. In this paper, we investigate the problem of sampling FRI signals using an IF-TEM. We provide theoretical guarantees for a recently proposed recovery method to perfectly recover an FRI input. In addition, we propose a modified sampling approach in the presence of noise that is more robust than existing techniques. This method is also proven to ensure recovery in the noise-free setting. The modified approach requires twice the number of measurements compared to the existing method, however, it results in lower error in the presence of noise for the same number of measurements. Our results enable designing low-cost and energy-efficient analog-to-digital converters for FRI signals. [ABSTRACT FROM AUTHOR]

Isomaltulose is a commonly used sweetener in sports nutrition and in products intended for consumption by diabetics. Because previously established chromatographic methods for quantification of isomaltulose suffer from long analysis times (60–210 min), faster quantitative approaches are required. Here, an HSQC (heteronuclear single quantum coherence) experiment with reduced interscan delay was established in order to quantify isomaltulose next to potential additional sugars such as d-glucose, d-fructose, d-galactose, sucrose, lactose, and maltose in 53 min. By using HSQC coupled to non-uniform sampling (NUS) as well as ASAP-HSQC (acceleration by sharing adjacent polarization), analysis times were reduced to a few minutes. Application of NUS-HSQC with reduced interscan delay takes 27 min, resulting in accurate and precise data. In principle, application of ASAP-HSQC approaches (with analysis times as low as 6 min) can be used; however, precision data may not suffice all applications.

Several different pathologies, including many neurodegenerative disorders, affect the energy metabolism of the brain. Glutamate, a neurotransmitter in the brain, can be used as a biomarker to monitor these metabolic processes. One method that is capable of quantifying glutamate concentration reliably in several regions of the brain is TE-averaged (1) H spectroscopic imaging. However, this type of method requires the acquisition of multiple TE lines, resulting in long scan durations. The goal of this experiment was to use non-uniform sampling, compressed sensing reconstruction and an echo planar readout gradient to reduce the scan time by a factor of eight to acquire TE-averaged spectra in three spatial dimensions. Simulation of glutamate and glutamine showed that the 2.2-2.4 ppm spectral region contained 95% glutamate signal using the TE-averaged method. Peak integration of this spectral range and home-developed, prior-knowledge-based fitting were used for quantitation. Gray matter brain phantom measurements were acquired on a Siemens 3 T Trio scanner. Non-uniform sampling was applied retrospectively to these phantom measurements and quantitative results of glutamate with respect to creatine 3.0 (Glu/Cr) ratios showed a coefficient of variance of 16% for peak integration and 9% for peak fitting using eight-fold acceleration. In vivo scans of the human brain were acquired as well and five different brain regions were quantified using the prior-knowledge-based algorithm. Glu/Cr ratios from these regions agreed with previously reported results in the literature. The method described here, called accelerated TE-averaged echo planar spectroscopic imaging (TEA-EPSI), is a significant methodological advancement and may be a useful tool for categorizing glutamate changes in pathologies where affected brain regions are not known a priori. Copyright © 2016 John Wiley & Sons, Ltd.

Matrix completion has been well studied under the uniform sampling model and the trace-norm regularized methods perform well both theoretically and numerically in such a setting. However, the uniform sampling model is unrealistic for a range of applications and the standard trace-norm relaxation can behave very poorly when the underlying sampling scheme is non-uniform. In this paper we propose and analyze a max-norm constrained empirical risk minimization method for noisy matrix completion under a general sampling model. The optimal rate of convergence is established under the Frobenius norm loss in the context of approximately low-rank matrix reconstruction. It is shown that the max-norm constrained method is minimax rate-optimal and yields a unified and robust approximate recovery guarantee, with respect to the sampling distributions. The computational effectiveness of this method is also discussed, based on first-order algorithms for solving convex optimizations involving max-norm regularization.

Matrix completion has been well studied under the uniform sampling model and the trace-norm regularized methods perform well both theoretically and numerically in such a setting. However, the uniform sampling model is unrealistic for a range of applications and the standard trace-norm relaxation can behave very poorly when the underlying sampling scheme is non-uniform. In this paper we propose and analyze a max-norm constrained empirical risk minimization method for noisy matrix completion under a general sampling model. The optimal rate of convergence is established under the Frobenius norm loss in the context of approximately low-rank matrix reconstruction. It is shown that the max-norm constrained method is minimax rate-optimal and yields a unified and robust approximate recovery guarantee, with respect to the sampling distributions. The computational effectiveness of this method is also discussed, based on first-order algorithms for solving convex optimizations involving max-norm regularization.

Fiber-optic dynamic interrogators, which use periodic frequency scanning, actually sample a time-varying measurand on a non-uniform time grid. Commonly, however, the sampled values are reported on a uniform time grid, synchronized with the periodic scanning. It is the novel and noteworthy message of this paper that this artificial assignment may give rise to significant distortions in the recovered signal. These distortions increase with both the signal frequency and measurand dynamic range for a given sampling rate and frequency scanning span of the interrogator. They may reach disturbing values in dynamic interrogators, which trade-off scanning speed with scanning span. The paper also calls for manufacturers of such interrogators to report the sampled values along with their instants of acquisition, allowing interpolation algorithms to substantially reduce the distortion. Experimental verification of a simulative analysis includes: (i) a commercial dynamic interrogator of 'continuous' FBG fibers that attributes the measurand values to a uniform time grid; as well as (ii) a dynamic Brillouin Optical time Domain (BOTDA) laboratory setup, which provides the sampled measurand values together with the sampling instants. Here, using the available measurand-dependent sampling instants, we demonstrate a significantly cleaner signal recovery using spline interpolation. [ABSTRACT FROM AUTHOR]