Sampling Rate Of Signal Research Articles

Deep learning networks for capacity estimation for monitoring SOH of Li‐ion batteries for electric vehicles

Data-driven modeling using measurable battery signals tends to provide robust battery capacity estimation without delving deep into electrochemical phenomenon inside the battery. Nowadays, with the advent of artificial intelligence, deep neural networks are playing crucial role in data modeling and analysis. In this article, models of three different families of network architectures such as feed-forward neural network (FNN), convolutional neural network (CNN), and long short-term memory neural network (LSTM) are proposed for battery capacity estimation. Measurements from a set of two rechargeable Li-ion batteries are considered for the model performance evaluation. The battery capacity estimation by different models has been evaluated by considering the effect of certain parameters such as model complexity, sampling rate of battery measurable signals and type of battery measurable signals. With its ability to process time-series data efficiently by memorizing long-term dependencies, LSTM outperforms other model architectures in estimating battery capacity more accurately and flexibly with 4.69% and 19.16% decline in average test root mean square error (RMSE) as compared with FNN and CNN, respectively. Simpler architectures of LSTM and FNN are able to perform well as compared with CNN, which needs architecture with certain hidden layers to interpret the battery aging process. Moreover, investigations reveal that sparsely sampled battery signals help all the proposed models to learn the battery dynamics in a better way as compared to densely sampled battery signals which also entails for less complex model learning process. Further, among all battery measurable signals, battery temperature has relatively less weightage in estimating battery capacity.

High sampling rate or high resolution in a sub-Nyquist sampling system

A digital signal acquisition system consists of two steps: sampling and quantization. Sampling maps a continuous signal to a digital signal, which then is quantized into a finite number of bits. Generally, a high sampling rate can ensure robustness to noise, while high resolution means less distortion. However, an analog-to-digital converter (ADC) cannot provide a high sampling rate and high resolution simultaneously. The bit rate is constrained, and there is a tradeoff between sampling rate and resolution. In this paper, we investigate the signal reconstruction in the framework of a compressed sensing based sub-Nyquist sampling system. We also study the noise introduced in the sampling stage and the quantization stage and evaluate the recovered signal-to-noise ratio (RSNR) with respect to the sampling rate and resolution. Considering potential application, we study the tradeoff involved in choosing the sampling rate and number of quantization bits according to the input SNR. Finally, we derive a relationship between RSNR and signal sparsity order, sampling rate, and number of quantization bits.

Clutter mitigation in sub‐Nyquist radars using difference set codes

In this study, the authors address the issue of clutter mitigation in monostatic pulse-Doppler radars, using sub-Nyquist sampling and compressed sensing (CS) recovery algorithms. Generally, clutter is modelled as a coloured random process and a whitening filter is employed to maximise the signal to clutter ratio. Here, the authors apply this approach to the case in which the received signal sampling rate is much smaller than the Nyquist rate. They also generalise the recently proposed sub-Nyquist method based on difference set (DS) codes, which uses very few samples to detect targets in the presence of only white Gaussian noise. By using the frequency-coded modulation waveform with the frequencies selected in accordance to the DS, the detection performance of the proposed method can be significantly improved compared with the previously proposed sub-Nyquist methods in the presence of clutter. Furthermore, the authors show that the proposed method can increase the target recovery resolution due to the features of the DS codes and the employed CS model.

A method for detecting image information leakage risk from electromagnetic emission of computer monitors

The unintentional electromagnetic (EM) emission of computer monitors may cause the leakage of image information displayed on the monitor. Detection of EM information leakage risk is significant for the information security of the monitor. The traditional detection method is to verify EM information leakage by reconstructing an image from EM emission. The detection method based on image reconstruction has limitations: adequate signal sampling rate, accurate synchronization signal, and dependence on operational experience. In this paper, we analyze the principle of image information leakage and propose an innovative detection method based on Convolutional Neural Network (CNN). This method can identify the image information in EM emission to verify the EM information leakage risk of the monitor. It overcomes the limitations of the traditional method with machine learning. This is a new attempt in the field of EM information leakage detection. Experimental results show that it is more adaptable and reliable in complex detection environment.

An Event-Driven Quasi-Level-Crossing Delta Modulator Based on Residue Quantization

This article introduces a digitally intensive event-driven quasi-level-crossing (quasi-LC) delta-modulator analog-to-digital converter (ADC) with adaptive resolution (AR) for Internet of Things (IoT) wireless networks, in which minimizing the average sampling rate for sparse input signals can significantly reduce the power consumed in data transmission, processing, and storage. The proposed AR quasi-LC delta modulator quantizes the residue voltage signal with a 4-bit asynchronous successive-approximation-register (SAR) sub-ADC, which enables a straightforward implementation of LC and AR algorithms in the digital domain. The proposed modulator achieves data compression by means of a globally signal-dependent average sampling rate and achieves AR through a digital multi-level comparison window that overcomes the tradeoff between the dynamic range and the input bandwidth in the conventional LC ADCs. Engaging the AR algorithm reduces the average sampling rate by a factor of 3 at the edge of the modulator’s signal bandwidth. The proposed modulator is fabricated in 28-nm CMOS and achieves a peak SNDR of 53 dB over a signal bandwidth of 1.42 MHz while consuming 205 $\mu \text{W}$ and an active area of 0.0126 mm2.

Heartbeat Sound Signal Classification Using Deep Learning.

Presently, most deaths are caused by heart disease. To overcome this situation, heartbeat sound analysis is a convenient way to diagnose heart disease. Heartbeat sound classification is still a challenging problem in heart sound segmentation and feature extraction. Dataset-B applied in this study that contains three categories Normal, Murmur and Extra-systole heartbeat sound. In the purposed framework, we remove the noise from the heartbeat sound signal by applying the band filter, After that we fixed the size of the sampling rate of each sound signal. Then we applied down-sampling techniques to get more discriminant features and reduce the dimension of the frame rate. However, it does not affect the results and also decreases the computational power and time. Then we applied a purposed model Recurrent Neural Network (RNN) that is based on Long Short-Term Memory (LSTM), Dropout, Dense and Softmax layer. As a result, the purposed method is more competitive compared to other methods.

WiSH: WiFi-based real-time human detection

Sensorless sensing using wireless signals has been rapidly conceptualized and developed recently. Among numerous applications of WiFi-based sensing, human presence detection acts as a primary and fundamental function to boost applications in practice. Many complicated approaches have been proposed to achieve high detection accuracy, but they frequently omit various practical constraints such as real-time capability, computation efficiency, sampling rates, deployment efforts, etc. A practical detection system that works in realworld applications is lacking. In this paper, we design and implement WiSH, a real-time system for contactless human detection that is applicable for whole-day usage. WiSH employs lightweight yet effective methods and thus enables detection under practical conditions even on resource-limited devices with low signal sampling rates. We deploy WiSH on commodity desktops and customized tiny nodes in different everyday scenarios. The experimental results demonstrate the superior performance of WiSH, which has a detection accuracy of >98% using a sampling rate of 20 Hz with an average detection delay of merely 1.5 s. Thus, we believe WiSH is a promising system for real-world deployment.

Numerical Implementation of High-Order Vold–Kalman Filter Using Python Arbitrary-Precision Arithmetic Library

The Vold–Kalman (VK) order tracking filter plays a vital role in the order analysis of noise in various fields. However, owing to the limited accuracy of double-precision floating-point data type, the order of the filter cannot be too high. This problem of accuracy makes it impossible for the filter to use a smaller bandwidth, meaning that the extracted order signal has greater noise. In this paper, the Python mpmath arbitrary-precision floating-point arithmetic library is used to implement a high-order VK filter. Based on this library, a filter with arbitrary bandwidth and arbitrary difference order can be implemented whenever necessary. Using the proposed algorithm, a narrower transition band and a flatter passband can be obtained, a good filtering effect can still be obtained when the sampling rate of the speed signal is far lower than that of the measured signal, and it is possible to extract narrowband signals from signals with large bandwidth. Test cases adopted in this paper show that the proposed algorithm has better filtering effect, better frequency selectivity, and stronger anti-interference ability compared with double-precision data type algorithm.

Flow regime identification for air valves failure evaluation in water pipelines using pressure data

Air valve failure can cause air accumulation and result in a loss of carrying capacity, pipe vibration and even in some situations a catastrophic failure of water transmission pipelines. Air is most likely to accumulate in downward sloping pipes, leading to flow regime transition in these pipes. The flow regime identification can be used for fault diagnosis of air valves, but has received little attention in previous research. This paper develops a flow regime identification method that is based on support vector machines (SVMs) to evaluate the operational state of air valves in freshwater/potable pipelines using pressure signals. The laboratory experiments are set up to collect pressure data with respect to the four common flow regimes: bubbly flow, plug flow, blow-back flow and stratified flow. Two SVMs are constructed to identify bubbly and plug flows and validated based on the collected pressure data. The results demonstrate that pressure signals can be used for identifying flow regimes that represent the operational state (functioning or malfunctioning) of air valves. Among several signal features, Power Spectral Density and Short-Zero Crossing Rate are found to be the best indictors to classify flow regimes by SVMs. The sampling rate and time of pressure signals have significant influence on the performance of SVM classification. With optimal SVM features and pressure sampling parameters the identification accuracies exceeded 93% in the test cases. The findings of this study show that the SVM flow regime identification is a promising methodology for fault diagnosis of air valve failure in water pipelines.

Event-Driven Encoding Algorithms for Synchronous Front-End Sensors in Robotic Platforms

Asynchronous, event-driven sampling techniques adapt the sampling rate of sensory signals to their dynamics by effectively compressing the data with respect to synchronous, clock-driven sampling. In robotics, such techniques offer data and bandwidth reduction, together with high temporal resolution and low latency. Despite the fact that vision and auditory event-driven sensors are currently available, robots are still equipped with a plethora of other sensors that might benefit from the event-driven encoding. In this paper, we study five estimation algorithms that implement the event-driven encoding for off-the-shelf clock-driven sensors. Digital accelerometer datasets were used to validate the system in robotic applications; other datasets have been used to assess the general performance of the proposed approach. The two best algorithms in terms of six performance parameters have been implemented on a Xilinx Artix-7 FPGA platform, using 2892 LUTs and 3620 flip-flops and reducing the output bandwidth from −44% to −75%, over the considered datasets.

Improved Rise Approximation Method for Pulse Arrival Timing

This paper describes the deduction of pulse arrival times from digital waveforms recorded with a multichannel data-acquisition (DAQ) system. A linear rise approximation (LRA) arrival timing method provides restricted timing resolution for pulses with nonlinear rise. It reaches 1/20th of the sampling period, if the relation between signal shaping and sampling rate is optimized. We introduce a nonlinear rise approximation (nLRA), which reduces the sampling phase error (SPE) down to less than 1/100th of the sampling period. The proposed timing algorithm uses a single free parameter that can easily be adjusted for various radiation detectors. The technique permits using a rather slow pulse shaping and low sampling rates, thus strongly reducing power consumption and the costs of the system. A high-density DAQ system integrating over 2000 channels inside an OpenVPX crate is presented. A prototype has been tested in the proton beam at cooler synchrotron (COSY) at Julich Research Center (Germany).

An Automated Scanning System for the Acquisition of Nonuniform Time-Varying Magnetic Fields

This paper presents an automatic scanning solution for measuring nonuniform magnetic fields captured by a tri-axial sensor connected to a time-domain acquisition system over a robotic arm. The sensor has been realized and calibrated through a canonical configuration consisting of Helmholtz coils. The acquisition system is instead executed by a Red Pitaya, which is an FPGA-based board with high performance and reliability regarding the sampling rate of the input signal. A LabVIEW software has then been developed for the acquisition of the sampled signal over time, the calculation and post-processing of the magnetic field, and the data saving. Finally, an Arduino-based robotic arm has been employed to make the acquisition system fully automated. This is particularly useful for the compliance assessment against electromagnetic field exposures at workplaces.

Vibration displacement immunization model for measuring the free spectral range by means of a laser self-mixing velocimeter.

In this paper, a vibration-displacement immunization model is proposed to measure the free spectral range of a resonant cavity by using a laser self-mixing velocimeter. The validity of this method is demonstrated by the experimental results, which can effectively get rid of low measurement accuracy related to the self-mixing vibration system due to the vibratory displacement. According to the periodic waveform separation characteristic of the self-mixing velocity signal, the free spectral range of a multilongitudinal mode diode laser is calculated to be 88.24 GHz. Moreover, the influences of different target velocities and signal sampling frequencies on the free spectral range have been analyzed in detail from the theoretical analysis. In the case of high signal sampling rate and low velocity, from which the undistorted velocity signal waveform at the integral order external cavity mode keeps stable, it is possible to obtain relatively accurate measured results.

Cardinal MK-spline signal processing: Spatial interpolation and frequency domain filtering

Altering the sampling rate of a digital signal by interpolation is a very important task in many digital signal processing applications. MK-spline is a kind of promising cardinal spline or explicit interpolation spline, which can achieve the purpose of interpolation without solving systems of coefficient equations, and which can inherit almost all good properties of B-splines, such as local support, high order smoothness, and central symmetry. For using MK-spline as a tool in signal and image processing, we first provide elementary theory which is composed of MK-spline convolution representation, related Fourier transform in continuous domain, and z transform of expanded MK-spline in discrete domain. Then, a cubic MK-spline interpolating filter (MKIF) is designed as a physically realizable FIR digital filter based on the discrete convolution operation which outputs directly a discrete smoothed signal from an input discrete signal. Next, frequency domain MK-spline lowpass and highpass filters are also devised in this study for image smoothing, sharpening, and denoising. Finally, simulated experiments are presented in different dimensional data formats in order to verify that the convolution-based MKIF is faster than those based on traditional continuous method, and that the proposed MK-spline filters have significantly good characteristics in the processes of signal reconstruction, image enlargement, and image denoising, when compared with traditional linear filters and classical B-spline filters.

NI P2P powered multi FPGA real-time data processing on tokamak diagnosis systems

On tokamaks, there are many diagnostics, which need real-time data acquisition and processing to provide useful information for plasma control. Some of the diagnostics required fast processing of multiple very high sampling rate signals. It is often difficult to achieve even with modern multi-core CPUs or GPUs. Moving large amount of data from the digitizers to the system ram will hurt the deterministic performance significantly. FPGA data pre-processing is a better choice when the data large and deterministic performance is required. When the task is too heavy for a single FPGA to implement, transfer large data between FPGAs is also required. On the polarimeter-interferometer diagnostics on J-TEXT, in order to calculate the density profile of the plasma in real-time, multiple chords of line-integrated density must be calculated first. The data acquisition system for the polarimeter-interferometer is equipped with 5 FlexRIO FPGAs, only one is acquiring the reference signal. Density calculation for every probing chord needs the reference signal. So this signal needs to be transferred to other FPGAs with strict real-time requirement. Mismatching a single sample, the result would be completely incorrect. NI P2P streaming allow the FPGA to transfer data without burdening the process. In the paper, the determinism characteristic and other performance of the P2P streaming is tested, and a mechanism to align local digitized samples with P2P streamed samples is presented. These results can be applied to other real-time data processing system that requires multiple FPGAs.

Novel Circuit Implementation Method for Pulse Signal Finite Rate of Innovation Sparse Sampling Based on an Improved Exponential Reproducing Kernel

Finite rate of innovation sampling is a new signal sparse sampling method based on signal information freedom, which can considerably reduce the amount of sampling data. However, the researches on hardware circuit direct implementation of the method are at an initial stage. Therefore, a novel hardware circuit implementation method based on an exponential reproducing kernel (ERK) was developed with an improved ERK sparse sampling theoretical framework, which comprised an analog rational kernel (ARK) and digital rational kernel (DRK). The parameter constraint conditions and selection method for the improved ERK were analyzed. The performance of the ARK hardware circuit and the DRK algorithm were also examined. The designed sparse sampling system was applied to a pipeline flaw ultrasonic inspection by using the sparse sampling data of the ultrasonic signal obtained directly. The experimental results revealed that the proposed method could directly obtain the signal sparse sampling data and accurately reconstruct the initial signal parameters by using a spectrum estimation algorithm. Moreover, the signal sampling rate and the amount of sampling data were reduced considerably.

Development of an 8 channel sEMG wireless device based on ADS1299 with Virtual Instrumentation

In this paper, a different approach on the use of the ADS1299 (an analog front-end with features for electroencephalogram and electrocardiography signal acquisition) is considered, proposing the development of a surface electromyography (sEMG) device. The main features of the device include simultaneous recordings of eight muscular channels, wireless transmission and virtual instrumentation with the use of LabVIEWTM software. The proposed sEMG device contains a specifically designed protocol to accommodate data transmission by reducing the data size while still delivering adequate resolution (34.33 μV), amplitude range (±17.57 mV) and sampling rate (1000 Hz) for sEMG signals. For the validation methods, a generated sine wave and a known sEMG data were evaluated. Moreover, the muscular recordings for all the eight channels of a human arm were successful and the results expose the isolated contractions of the triceps and the biceps with their amplitude range and frequency spectrum.

Multi-fidelity validation algorithm for next generation hybrid-electric vehicle system design

With the evolution of increasingly complex hybrid-electric vehicle powertrains, the process of creating validated system models has become progressively more difficult to achieve. Increasing levels of confidence in how instantaneous vehicle energy states are captured is needed to take full advantage of the fuel consumption and emissions reduction potential of the vehicle to move towards more sustainable transportation systems. While many strategies for model validation exist, the majority rely on ascertaining comparisons with global system characteristics, for instance, total fuel consumption over a fixed driving event. However, these methods do not necessarily account for the rapidly fluctuating energy states which need to be understood to optimise the vehicle’s energy management strategy. The current work proposes a new validation approach which captures these instantaneous characteristics taking advantage of the high signal sampling rates available from modern data acquisition equipment rather than relying on drive cycle average or cumulative global behaviours. The method proposed provides a holistic view of the behaviours demonstrated by the vehicle model and identifies regions of poor system validation targeting areas for further model refinement. The algorithm is demonstrated on a new post-transmission, parallel mild-hybrid-electric bus. The model was developed in the MATLAB Simulink modelling environment. The validation algorithm is tested against vehicle dynamometer and test track data. With an increasing volume of mild and full hybrid vehicle configurations emerging, validation strategies such as the one proposed here are increasingly important for the design of energy management strategies to deliver the full potential benefits of the vehicle. The algorithm is proposed in a step by step method which can be automated to limit required user input.

Quantitative Inversion of Stress and Crack in Ferromagnetic Materials Based on Metal Magnetic Memory Method

Ferromagnetic materials are widely used in engineering structures. Damages caused during manufacture and the use of ferromagnetic materials may seriously undermine the safety of the engineering structure, and even lead to serious industrial accidents. For the early damage testing problem of ferromagnetic materials, it is imperative to propose and adopt new nondestructive testing methods. The metal magnetic memory (MMM) method, which is also known as the micromagnetic testing method, can achieve the early detection of damages in ferromagnetic materials such as steels. However, due to the lack of the quantitative research, the MMM method is only applicable for damage localization. In this paper, the MMM method is utilized to quantitatively evaluate the stress and crack in ferromagnetic materials. The inverse model including the objective function and optimization parameters is proposed. The reconstruction approach is established based on the conjugate gradient inversion method and the exact line search algorithm. In the theoretical analysis, the effectiveness of the MMM method in quantitative determination regarding the position and size of defects is verified with the experimental signal of a hole defect. Following this, the theoretical analyses of localization and sizing of stress and crack are conducted by using the simulation results as the measured signals. It can be found from the results that the MMM method can be applicable for quantitative evaluation of surface rectangular cracks, surface stress concentration zone, convex-type defect, and so on. In addition, the influences of the signal selection, sampling rate, lift-off value, stress level, and noise amplitude on the quantitative analysis of early stress concentration are discussed in details.

SUMMeR: Sub-Nyquist MIMO Radar

Multiple-input multiple-output (MIMO) radar exhibits several advantages with respect to the traditional radar array systems in terms of flexibility and performance. However, MIMO radar poses new challenges for both hardware design and digital processing. In particular, achieving high azimuth resolution requires a large number of transmit and receive antennas. In addition, digital processing is performed on samples of the received signal, from each transmitter to each receiver, at its Nyquist rate, which can be prohibitively large when high resolution is needed. Overcoming the rate bottleneck, sub-Nyquist sampling methods have been proposed that break the link between radar signal bandwidth and sampling rate. In this paper, we extend these methods to MIMO configurations and propose a sub-Nyquist MIMO radar (SUMMeR) system that performs both time and spatial compression. We present a range-azimuth-Doppler recovery algorithm from sub-Nyquist samples obtained from a reduced number of transmitters and receivers, that exploits the sparsity of the recovered targets’ parameters. This allows us to achieve reduction in the number of deployed antennas and the number of samples per receiver, without degrading the time and spatial resolutions. Simulations illustrate the detection performance of SUMMeR for different compression levels and shows that both time and spatial resolution are preserved, with respect to classic Nyquist MIMO configurations. We also examine the impact of design parameters, such as antennas’ locations and carrier frequencies, on the detection performance, and provide guidelines for their choice.