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Real-Time Signal Processing for Mitigating SiPM Dark Noise Effects in a Scintillating Neutron Detector

IEEE TRANSACTIONS ON NUCLEAR SCIENCE 2021年第7期68卷 1519-1527页

作者： Pritchard, K. Chabot, J. P. Robucci, R. Choa, F. S. Osovizky, A. Ziegler, J. Binkley, E. Tsai, P. Hadad, N. Jackson, M. Hurlbut, C. Baltic, G. M. Majkrzak, C. F. Maliszewskyj, N. C. NIST US Dept Commerce Gaithersburg MD 20899 USA Univ Maryland Baltimore Cty Dept Comp Sci & Elect Engn Baltimore MD 21250 USA Rotem Ind IL-85339 Lehavim Israel Nucl Res Ctr Negev IL-84190 Beer Sheva Israel Eljen Technol Sweetwater TX 79556 USA

A (LiF)-Li-6:ZnS(Ag)-based cold neutron detector with wavelength shifting (WLS) fibers and Silicon photomultiplier (SiPM) photodetector was developed at the NIST Center for Neutron Research. For neutron scattering applications at the NCNR, detector false positives severely diminish the quality of very faint neutron scatter patterns. Thermal noise generated by the SiPM significantly increases the likelihood of false positives by the detector/discriminator. This article describes and evaluates a digital real-time algorithm implemented on a field programmable gate array (FPGA) which quickly differentiates SiPM thermal noise and noise pulse pile-up from neutron signals. The algorithm reduces deadtime spent on examining noise pulses as well as reduces the number of false positives.

关键词： Neutrons Detectors Electronic mail Photonics Thermal noise Shape filtering algorithms Dark noise LiF ZnS neutron detector real-time scintillator silicon photomultiplier (SiPM) WLS fiber

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An Integrated EMI Filter Scheme for Paralleled Inverter With Zero-CM PWM Algorithm

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IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS 2021年第1期9卷 716-726页

作者： Zhang, Yechi Shen, Zewei Jiang, Dong Huazhong Univ Sci & Technol Sch Elect & Elect Engn Wuhan 430074 Peoples R China

Motor drive system generates harmful differential mode (DM) and common mode (CM) electromagnetic interference (EMI) when it is working. To solve this problem, the integrated EMI filter scheme for paralleled inverter using zero-CM pulsewidth modulation (PWM) algorithm is proposed in this article. The zero-CM PWM algorithm and the coupled inductor constitute a holistic solution for the ac side EMI suppression. By using the zero-CM PWM algorithm, the CM leakage current of the system can be suppressed without adding additional CM EMI filter and only DM EMI needs to be considered. In addition, the DM inductor is naturally integrated in the coupled inductor, leakage inductance of the coupled inductor instead of the discrete DM inductor is used as the DM inductor which has less restrictions on the inductor material. The DM EMI filter can be constructed by just adding DM capacitors. Thus, using the integrated EMI filter scheme has advantageous over traditional scheme since the CM EMI filter is omitted and only DM capacitor is added which can simplify and reduce the size of EMI filter in the ac side and has the potential to increase the power density of the system since other solutions require both the DM and CM EMI filter.

关键词： Electromagnetic interference Inductors filtering algorithms Inverters Inductance Pulse width modulation Capacitors Common mode (CM) leakage current coupled inductor differential mode (DM) capacitor integrated electromagnetic interference (EMI) filter leakage inductance zero-CM pulsewidth modulation (CM PWM) algorithm

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Design of FIR Half-Band Filter With Controllable Transition-Band Steepness

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IEEE ACCESS 2021年 9卷 52144-52154页

作者： Cho, Woon Chung, Daewon Kim, Yunsun Kim, Ingyun Jeon, Joonhyeon Dongguk Univ Dept Elect & Elect Engn Seoul 04620 South Korea

Maximally flat (MAXFLAT) half-band filters usually have wider transition-band than other filters although the frequency response is maximally flat (i.e., no ripples) in the passband and stopband. This is due to fact that the maximum possible number of zeros at z = +/- 1 is imposed in half-band close form solution, which leaves no degree of freedom, and thus no independent parameters for direct control of the frequency response. This paper describes a novel method for the design of FIR half-band filters with an explicit control of the transition bandwidth. The proposed method is based on a generalized Lagrange half-band polynomial (g-LHBP) with coefficients parameterizing a 0-th coefficient h(0) and allows the frequency response of this filter type to be controllable by adjusting h(0). Then, h(0) is modeled as a steepness parameter of the transition-band and this is accomplished through theoretically analyzing a polynomial recurrence relation of the g-LHBP. This method also provides explicit formulas for direct computation of design parameters related to choosing a desired filter characteristic (by a reasonable trade-off between the transition-band sharpness and passband & stopband flatness). The examples are shown to provide a complete and accurate solution for the design of such filters with relatively sharper transition-band steepness than other existing half-band filters.

关键词： Finite impulse response filters Filter banks Frequency response Passband filtering algorithms Frequency control Bandwidth Maximally flat FIR filters FIR digital filters interpolator closed-form polynomial transition-band steepness

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Hybrid Binary Grey Wolf With Harris Hawks Optimizer for Feature Selection

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IEEE ACCESS 2021年 9卷 31662-31677页

作者： Al-Wajih, Ranya Abdulkadir, Said Jadid Aziz, Norshakirah Al-Tashi, Qasem Talpur, Noureen Univ Teknol PETRONAS Dept Comp & Informat Sci Seri Iskandar 32160 Perak Malaysia Univ Teknol PETRONAS Ctr Res Data Sci Seri Iskandar 32160 Perak Malaysia Univ Albaydha Fac Adm & Comp Sci Albaydha CV46 6X Yemen

Despite Grey Wolf Optimizer's (GWO) superior performance in many areas, stagnation in local optima areas may still be a concern. Several significant GWO factors can be explored to enhance the performance of selection in classification, with two conflicting concepts to be considered in using or modeling a metaheuristic method, exploring a search field, and exploiting optimal solutions. Balancing exploration and exploitation in a good manner will improve the search algorithm's performance. To achieve a good balance, this paper proposes a binary hybrid GWO and Harris Hawks Optimization (HHO) to form a memetic approach called HBGWOHHO. The sigmoid transfer function is used to transfer the continuous search space into a binary one to meet the feature selection nature requirement. A wrapper-based k-Nearest neighbor is used to evaluate the goodness of the selected features. To validate the performance of the proposed method, 18 standard UCI benchmark datasets were used. The performance of the proposed hybrid method was compared with Binary Grey Wolf Optimizer (BGWO), Binary Particle Swarm Optimization (BPSO), Binary Harris Hawks Optimizer (BHHO), Binary Genetic Algorithm (BGA) and Binary Hybrid BWOPSO. The findings revealed that the proposed method was effective in improving the performance of the BGWO algorithm. The proposed hybrid method outperforms the BGWO algorithm in terms of accuracy, selected feature size, and computational time. Similarly, compared with BPSO and BGA feature selection algorithms, the proposed HBGWOHHO surpassed them yield better accuracy, the smaller size of selected features in much lower computational time.

关键词： Feature extraction Classification algorithms Optimization Genetic algorithms filtering algorithms Particle swarm optimization Sociology Classification feature selection Grey Wolf Optimizer hybrid feature optimization Harris Hawks Optimizer

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Partition-Based Cache Replacement to Manage Shared L2 Caches

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Chinese Journal of Electronics 2025年第3期23卷 464-467页

作者： Juan Fang Jing Wang Chengyan Li Zhicheng Yao Weimao Ke College of Computer Science Beijing University of Technology Beijing China College of Information Engineering Capital Normal University Beijing China College of Computing and Informatics Drexel University Chapel Hill NC USA

The Least recently used (LRU) replacement has been widely employed by on-chip shared caches. With the increase of cache associativity, the performance gap between LRU and theoretical optimal re-placement algorithm is significant, motivating us to design alternative replacement algorithms to improve cache performance. We propose a Partition-based cache replacement (PCR) algorithm to manage multi-core shared L2 caches. PCR takes into consideration recency and frequency information of cache access, and cache resource interference among multiple competing applications. PCR is effective to filter out the less frequently reused blocks through a dynamic insertion/promotion policy based on the target partitions, and ensures that some fraction of the working set is retained in the cache to contribute to more hits. Our evaluation shows that PCR improves performance of an 8-core system by an average of 11.3% over LRU-based cache.

关键词： Power demand Multicore processing Heuristic algorithms Education Interference filtering algorithms Information filters Partitioning algorithms System-on-chip

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A Novel Adaptive Fault Diagnosis Method for Wind Power Gearbox

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IEEE ACCESS 2021年 9卷 11226-11240页

作者： Duan, Nengquan Wang, Jingtai Zhao, Tiansheng Du, Wenhua Guo, Xiaoming Wang, Junyuan North Univ China Shanxi Prov Key Lab Adv Mfg Technol Taiyuan 030051 Peoples R China Taiyuan Heat Grp Co Ltd Taiyuan 030001 Peoples R China Zhengzhou Mech & Elect Engn Inst Zhengzhou 450015 Peoples R China North Univ China Sch Mech Engn Taiyuan 030051 Peoples R China

In the noisy environment, fault characteristics of the composite faults of the wind power gearbox are coupled with each other, which makes the extraction features more difficult. In order to extract the characteristics of composite faults, a new fault diagnosis method for wind power gearbox is proposed in this paper, namely the modified Savitzky Golay Laplacian of Gaussian filter (MSGloG). The method can not only solve the defects that the scale parameters of the Modified Laplacian of Gaussian filter (MloG) filter are not adaptive, but also overcome the problems that the smoothing effect is too much affected by noise. Firstly, determining the Laplace model of Gaussian filter, and using the least square convolution smoothing process to improve the signal-to-noise ratio of the vibration signal. Secondly, a new marginal envelope spectrum entropy index is proposed to measure the complex fault characteristics. Finally, a new chaotic grey wolf optimization algorithm is proposed, which uses the marginal envelope spectral entropy as the fitness function, and the purpose is to make the MSGloG noise reduction adaptive. The method extracted the faults of the bearing outer ring and rolling elements successfully.

关键词： Feature extraction Fault diagnosis Finite impulse response filters Information filters filtering algorithms Wind power generation Wind turbines Wind power gearbox fault diagnosis modified Savitzky Golay Laplacian of Gaussian filter marginal envelope spectral entropy

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Late-Reverberation Synthesis Using Interleaved Velvet-Noise Sequences

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2021年 29卷 1149-1160页

作者： Valimaki, Vesa Prawda, Karolina Aalto Univ Dept Signal Proc & Acoust Acoust Lab FI-02150 Espoo Finland

This paper proposes a novel algorithm for simulating the late part of room reverberation. A well-known fact is that a room impulse response sounds similar to exponentially decaying filtered noise some time after the beginning. The algorithm proposed here employs several velvet-noise sequences in parallel and combines them so that their non-zero samples never occur at the same time. Each velvet-noise sequence is driven by the same input signal but is filtered with its own feedback filter which has the same delay-line length as the velvet-noise sequence. The resulting response is sparse and consists of filtered noise that decays approximately exponentially with a given frequency-dependent reverberation time profile. We show via a formal listening test that four interleaved branches are sufficient to produce a smooth high-quality response. The outputs of the branches connected in different combinations produce decorrelated output signals for multichannel reproduction. The proposed method is compared with a state-of-the-art delay-based reverberation method and its advantages are pointed out. The computational load of the method is 60% smaller than that of a comparable existing method, the feedback delay network. The proposed method is well suited to the synthesis of diffuse late reverberation in audio and music production.

关键词： Reverberation Signal processing algorithms Speech processing White noise Delay lines Finite impulse response filters Delays Acoustics audio systems digital signal processing filtering algorithms

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Early Fault Diagnosis of Shaft Crack Based on Double Optimization Maximum Correlated Kurtosis Deconvolution and Variational Mode Decomposition

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IEEE ACCESS 2021年 9卷 14971-14982页

作者： Ma, Tongwei Zhang, Xiangfeng Jiang, Hong Wang, Kedian Xia, Lei Guan, Xiangheng Xinjiang Univ Coll Mech Engn Urumqi 830047 Peoples R China Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Peoples R China

Aiming at the problem that the vibration energy of shaft crack early weak fault is small and the feature extraction was difficult, the fault diagnosis method of cuckoo search, maximum correlation kurtosis deconvolution and variational mode decomposition (CS-MCKD-VMD) was proposed to diagnose the early weak fault of shaft cracks. Firstly, considering the periodic characteristics of fault signal, MCKD was used to highlight the fault signal, and the kurtosis correlation of deconvolution signal processed by MCKD were enhanced. Then, VMD could be used to decompose the harmonic signal, and VMD was used to process the deconvolution signal to generate some intrinsic mode functions (IMF), and the correlation coefficient method was used to calculate the components with the maximum correlation. At the same time, in order to improve the correlation of parameters in MCKD and VMD, CS was used to optimize the parameters to reduce the error of human selection. Thirdly, the optimal component was calculated by the fast spectral kurtosis (FSK), and the envelope spectrum was obtained by filtering the optimal component with the parameters obtained. Finally, the simulation and experimental results show that the transfer frequency and its 2 and 3 times frequency were prominent in the envelope spectrum, which was consistent with the analysis of shaft crack fault mechanism, Therefore, it was proved that the CS-MCKD-VMD method can effectively extract the early weak fault features of shaft cracks.

关键词： Frequency shift keying Shafts Deconvolution Correlation Fault diagnosis filtering algorithms Feature extraction Maximum correlated kurtosis deconvolution variational mode decomposition cuckoo search algorithm shaft crack

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Compressing the Index on Distributed Data of Sensors

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IEEE SENSORS JOURNAL 2021年第10期21卷 12313-12321页

作者： Bhasin, Vandana Saxena, P. C. Kumar, Sushil Lal Bahadur Shastri Inst Management Dept Informat Technol New Delhi 110075 India Jawaharlal Nehru Univ Dept Comp & Syst Sci New Delhi 110067 India Jawaharlal Nehru Univ Sch Comp & Syst Sci New Delhi 110067 India

The most daunting and conflicting challenges accompanying the wireless sensor networks are energy and security. And data aggregation and compression techniques are two of the effective ways to reduce energy consumption. As it is known that the radio transceiver consumes energy which is proportional to the number of bits transmitted on the network;hence sending fewer bits on the communication channel implies lesser energy consumption. This paper works on compressing the index of secure index on distributed data (SIDD) technique;to reduce the number of bits of an index that is transmitted on the communication channel. The objective being to reduce energy consumption of SIDD. In this paper, we have worked to reduce the number of bits of the index sent on the communication channel, deploying difference encoding. The compression mechanism has established an upper bound on the energy consumption whilst all data items were unique. The scheme is scalable and can be deployed for saving energy consumption.

关键词： Indexes Sensors Energy consumption Wireless sensor networks Distributed databases Mathematical model filtering algorithms Data partitioning difference encoding indexing sensor networks

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Individual Attribute Selection Using Information Gain Based Distance for Group Classification of Elderly People With Hypertension

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IEEE ACCESS 2021年 9卷 82713-82725页

作者： Chaising, Supansa Temdee, Punnarumol Prasad, Ramjee Mae Fah Luang Univ Sch Informat Technol Comp & Commun Engn Capac Bldg Res Ctr Chiang Rai 57100 Thailand Aarhus Univ Dept Business Dev & Technol CTIF Global Capsule DK-7400 Herning Denmark

Attribute selection is the process of selecting relevant attributes being used in model construction to enhance model accuracy. For general medical oriented classification applications, classical attribute selection methods principally select common attributes in the dataset for all individuals. The idea of using individual attributes is proposed in this study to represent the difference among individuals for self-diagnosis. Consequently, this study proposes a new attribute selection method, called information gain based distance (IGD), for individual attribute selection, which represents an individual's health condition differently and can be used for effective classification. The proposed method combines the concept of information gain and objective distance to select individual attributes affecting classification. The IGD method is expected to provide higher classification performance than classical attribute selection methods. To assess the performance of the IGD method, classification accuracy between data with classical attribute selections and with the IGD method is compared. The case study is conducted with 971 secondary data used for group classification of elderly people with hypertension. The classification result of different classifiers was compared, including K-nearest neighbors, neural network, and naive Bayes. The comparison revealed that the classification of data with the IGD attribute selection method provided an average classification accuracy of 98.73%. In comparison, those classifications of data with classical attribute selection methods provided 62.99%, 62.99%, 62.62%, and 62.85% for information gain, Gini index, chi-squared, and decision tree, respectively. The results showed that data classification with the IGD method provided higher performance than those with the classical attribute selection methods.

关键词： Hypertension Senior citizens Medical services Indexes Feature extraction filtering algorithms Entropy Attribute selection individual attributes information gain hypertension elderly people

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