Miracle-dz/UniFAR-FTUs huggingface dataset:
A Review of Artificial Neural Network Applications in Variants of Optimization Algorithms

The artificial neural network (ANN) is typically one of the most famous artificial intelligent (AI) field which inspired based on biological human brain model. The model of real human brain known as neurons have been transformed into a mathematical formulation, works as an artificial neuron that connected one to another in very systematical manners. Connected neurons imply an optimization notion as a major practice for training the neurons. Two main optimization problems; constraint and unconstraint have both viewed as a decision problem techniques use to find the best vector of decision variables over all possible vectors in certain optimization problems. The maximization of objectives function can be considered as main factor to determine convergence of trained neurons. Most popular of optimization algorithm such as Gradient, Newton’s, Conjugate or Quasi-Newton have shown different results depend on efficiency, accuracy, convergence time and overall performance based on the problem to be solved. The feedforward or backpropagation neural network models mostly apply the optimization algorithm as mentioned. Therefore, the goal of this paper is to review and study the difference types of optimization techniques used in neural network applications. Besides, the purpose of this review is also to give an overview of how optimization algorithms and its modified models have been applied and implemented in neural network training rule. Besides, this paper is intended to study in what manner optimization can change the execution of performance result and training analysis.
Sensitivity analysis for stereo SAR based on the range-Doppler equations

Stereo synthetic aperture radar (SAR) technique can retrieve the target's high-precision three-dimensional coordinates, which is a promising solution for creating the ground control points. In order to better study the stereo SAR performance influencing factors, a sensitivity analysis method is proposed in this paper. Considering multiple SAR observations are often used in the stereo SAR to improve the localization accuracy, a least square based method is adopted to solve the overdetermined sensitivity equations. Then the stereo SAR localization accuracy sensitivity in terms of the measured error of the range between the radar and the target, the satellite's location and velocity are derived. Furthermore, the impacts of the baseline length and the observation times on the localization accuracy are analyzed based on the proposed sensitivity equations. Finally, some numerical experiments are provided to demonstrate the effectiveness of the proposed method.
Noise suppression of ISAR micro cluster targets based on Generated Adversarial Network

When the inverse synthetic aperture radar (ISAR) is used to detect micro cluster target in the air, the noise in the detection process will seriously affect the range profile resolution of the cluster target. Especially when each target in the cluster target is close to each other, the noise will cause the signal of the cluster target to be difficult to identify, and the imaging quality will be greatly reduced. Therefore, it is very necessary to design an algorithm to improve the signal-to-noise ratio of the cluster target signal. This paper combines the generative adversarial network to suppress the noise of the micro cluster target detected by ISAR, and the measured data after pulse compression are added to the optimization process of neural network. The simulation method will be used to generate training data. This method improves the targets' range profile resolution to a certain extent by processing the measured data and effectively suppresses the background noise.
Joint Route Optimization and Multi-Dimensional Resource Management for Multiple Airborne Radars System in Target Tracking

In this article, a joint route optimization and multi-dimensional resource management (JRO-MDRM) scheme is developed for multiple airborne radars system in target tracking application. The basis of the proposed scheme is to minimize the target tracking error and probability of intercept of the underlying system in the meantime by jointly optimizing the route planning, transmit power, dwell time, waveform bandwidth, and pulse length of each airborne radar node subject to certain system constraints. Since the corresponding variables are coupled in both objective function and constraints, and the resulting optimization model is non-linear and non-convex, an efficient solution algorithm, incorporating particle swarm optimization and cyclic minimization techniques, is put forth to deal with the above optimization problem. Simulation results illustrate the effectiveness of the developed JRO-MDRM scheme.
Bi - level Coordinated Optimal Control Strategy Based on Port Energy Routing System

In order to reduce the effect of large distributed generation and impact load connected to distribution network on loss and power quality, and improve the voltage stability and safety of port distribution network, a new Coordinated optimal control strategy based on energy routing structure is proposed. In the upper layer, a multi-objective optimization model with the maximum wind power consumption and netload fluctuation is established. In the lower layer, a multi-objective optimization model with the lowest network loss and the minimum grid voltage quality deviation is established. By taking the energy routing structure as the center of the large power grid and the port distribution network, the power quality control of the grid-connected point is realized by using the current and voltage control strategy of the routing structure, and the optimal allocation of power and energy is realized. Finally, the actual distribution network of a port is simulated as an example, and the simulation results show that the proposed coordinated optimal control strategy can effectively reduce network loss and improve the power quality of the system.
Detection of COVID-19 by Cough Sound: A Method Based on DSC + BiLSTM

In recent years, the outbreak of COVID-19 has brought a new round of challenges to global health care, and daily large-scale testing has also increased the consumption of medical resources. However, studies have shown that the cough sounds of patients with COVID-19 are significantly different from other Characteristics of respiratory infectious diseases. Therefore, this paper considers the use of the patient’s cough as a detection sample to give the preliminary screening results. The research was conducted on the COUGHVID dataset. The experiment is divided into two stages: (1) Preprocessing stage: use Pitch Shift and Time Stretch to perform data enhancement on audio data, and use spec Augment to perform data enhancement on mel spectrogram. (2) Model construction stage: use two layers of DSC and one layer of BILSTM to splicing to obtain a classification model. Finally, the method is compared with the baseline method using only two layers of LSTM. The results show that accuracy has increased by 1.9%, F1 has increased by 1.9%, and AUC has increased by 1.6%.
A Scattered Wave Deceptive Jamming Method Based on Genetic Algorithm against Three Channel SAR GMTI

To reduce difficulty of obtaining jamming parameters, combining scattered wave deception jamming method structure with genetic algorithm, a scattered wave deceptive jamming method based on genetic algorithm is proposed against three Channel SAR GMTI. Four-dimensional jamming parameter searching problem is separated as a one-dimensional optimization problem and a two-dimensional optimization problem, which will reduce computational burden and jamming parameter acquirement time and raise fidelity of deceptive jamming. In addition, the problem, that tolerance of Doppler rate mismatch is difficult to describe, is solved by transforming an objective function of the two-dimensional optimization problem. By utilizing jamming quality parameters, simulation experiments are provided to verify the effectiveness of SGA method against the three-channel SAR GMTI.
Continuous Control for Autonomous Underwater Vehicle Path Following Using Deep Interactive Reinforcement Learning

With the increasing demand for ocean exploration, higher requirements on both autonomy and intelligence have been put forward on the development of Autonomous Underwater Vehicle (AUV). To this end, deep reinforcement learning methods have started being used to improve AUV's autonomy and intelligence in recent years. However, low learning efficiency and high learning cost of traditional deep reinforcement learning prevent from applying them to physical AUV systems in real underwater environments. Therefore, this paper proposed a deep interactive reinforcement learning method based on the Deep Deterministic Policy Gradient (DDPG) algorithm for continuous motion control of AUV path following. The highlight of our proposed method is the design of a new reward allocator. Specifically, different from current deep interactive reinforcement learning methods, we allow the human trainer to provide a preferred action based on the evaluation on AUV's current situation. Then, the reward allocator is used to assign rewards indirectly based on the preferred action to deal with the high frequency of continuous action changes of AUV. The proposed method was tested in a sinusoids curve following tasks in the Gazebo simulation platform with an AUV simulator of our lab. The experimental results and analysis show that AUV path following with our proposed method can learn a more stable policy about 100 episodes faster than learning from only environmental rewards or only human rewards.
Robust adaptive beamformer based on minimum dispersion with a broad null by linear programming

The standard minimum dispersion distortionless response (MDDR) beamformer can provide a performance improvement for non-Gaussian signals compared to the minimum variance distortionless response (MVDR) beamformer. However, the variety of the direction of the interference may have a great influence on the performance of the MDDR beamformer. To solve the problem, a robust beamformer with a broad null by linear programming is proposed for sub-Gaussian signals. Unlike the beamformers based on the minimum variance criterion, the proposed beamformer minimizes the $\ell_{p}$ -norm of the output and meanwhile constrains not only the target response to be unity, but also the average power of the interference's dynamic angular sector to zero. The resulting optimization problem is cast as a linear programming and hence can be solved efficiently. Simulation results indicate that the proposed method offers a significant performance improvement if the direction of the interference varies with time.
Cache Pricing Mechanism for ICN on ISP Peering

With the feature of intra-network caching, the information-centric networks (ICN) can reduce the transmission of duplicate traffic, reduce the burden on servers, and solve the problem of today's surging network traffic. With the development of ICN, content-peering has attracted interest, which allows peering internet service providers (ISPs) to access each other's caches for mutual benefit. Solving the cache pricing problem of peering ISPs in ICN can economically motivate the large-scale promotion and deployment of ICN, thus better solving the existing network congestion problem, improving the network service quality, and bringing users a better experience. In this paper, we study and analyze the non-cooperative game relationships between ISPs and content providers (CPs) in ICN, and establish a peering link between access ISPs so that they can collaborate on caching. We analyze and prove the existence of a Nash equilibrium to maximize the benefits of each entity by using a game-theoretic approach. The results of the simulation experiments show that we can reasonably allocate cache based on the demand from the end-users, and each entity can determine the optimal pricing strategy based on its equilibrium point. The model provides incentives for operators to deploy ICN network caching and guidance for optimal resource allocation.
Visual Analytics for Market Research: A Case Study of F&B Industry

The rapid globalization as well as the technological advancement has led to the world economies to be increasingly integrated. The integration of world economies and the lowered trade barriers have also prompted many businesses to venture beyond their country of origin in order to obtain greater market share and sales revenue. Numerous such failed global expansion projects can be attributed to the lack of understanding of the international market. Therefore, it is important for companies to perform thorough analysis in order to identify a target region and direct the analytical resources on the target region before the expansion and such an analysis becomes highly possible with the abundance of data. This paper focuses on applying visual analytics for the purpose of market research in the food industry from various points of view. Visual data analytics has been deployed to ascertain the region that holds the most potential. Analysis also includes the performance of the major competitors within the identified region to draw insights and conclusion on the market.
Lego: Dynamic Tensor-Splitting Multi-Tenant DNN Models on Multi-Chip-Module Architecture

Modern deep neural network (DNN) accelerators target the acceleration of a single DNN model and limit the throughput for multi-tenant DNN data center applications. The multi-chip-module (MCM) architecture breaks a monolithic accelerator into multiple small chiplets. The MCM is a promising approach that dispatches DNN models across chiplets with equal PEs. However, it is challenging to distribute data of DNN model layers with different parameters across chiplets while maximizing the chiplet utilization. This work proposes Lego MCM architecture that dynamically adapts to the size of DNN model layers and improves the throughput of multi-tenant DNN applications by increasing the chiplet utilization. Lego's dynamic scheduler achieves the geometric average 1.51× speedup over a monolithic DNN accelerator.
Adaptive Granularity On-die ECC

DRAM vendors have introduced on-die ECC (Error Correction Codes) to protect memory from errors. The current on-die ECC provides weak Single Error Correction (SEC) protection, yet growing errors demand stronger protection. This paper proposes a novel memory protection scheme called Adaptive Granularity On-die ECC (AGO-ECC). AGO-ECC adjusts access granularity and ECC protection strength based on memory locality and reliability requirements. For critical or high-locality data, AGO-ECC increases the granularity and applies Triple Error Correction (TEC). AGO-ECC can improve not only reliability but also performance.
Condition Monitoring of IGBT Module Based on Peak Rise Rate Appearance Time of Case Temperature during the Start-up Process of Converter

In this paper, we proposed a new method to monitor the IGBT thermal path degradation of IGBT module using the peak rise rate appearance time of case temperature right below the center of the chip during the start-up process of converter. The correlation between the peak rise rate appearance time and 3rd-order Cauer thermal parameters is analyzed. Results show that the degradation of IGBT module can be monitored by detecting the peak rise rate appearance time. Simulation and experimental tests are performed to verify the effectiveness of the proposed method.
COLFISPOOF: A new Database for Contactless Fingerprint Presentation Attack Detection Research

Contactless fingerprint recognition is known for its high user comfort and low hygienic concerns. However, contact-less fingerprint recognition, especially in mobile and un-supervised scenarios, is vulnerable to presentation attacks. Presentation Attack Detection (PAD) in biometric systems like contactless fingerprint recognition is more challenging compared with contact-based modalities because many de-tection mechanisms rely on direct contact between the finger and the surface of the capture device. Hence, in contactless scenarios it is generally possible to present more Presentation Attack Instruments (PAIs) like printout or replay arte-facts. In this work, we introduce COLFISPOOF, a new database for contactless fingerprint PAD. The database is acquired using a contactless fingerprint recognition system utilizing a smartphone as capturing device. It comprises 7,200 samples of 72 different PAI species and was captured with two different smartphone models. The database is publicly available for research purposes such that interested researchers can download and use it to develop new PAD algorithms. Moreover, we define evaluation protocols for training and testing of machine learning algorithms such that future PAD algorithms can be benchmarked on this database in a comparable and reproducible way.
An Enhanced Expanding and Shift Scheme for Underdetermined DOA Estimation

An enhanced expanding and shift (EEAS) scheme, which is based on the original expanding and shift (EAS) scheme with coprime array (CPA), is proposed. By suitably exploiting the relationship among the sub-arrays in the fourth-order difference co-array (FODC) of the original EAS scheme and further enlarging the sensor spacing in the expanded sparse array, the proposed EEAS scheme can achieve increased consecutive sensors in its FODC. Compared with some representative FODC based sparse arrays, the proposed one can achieve much longer consecutive virtual array. Especially, when the sensor number approaches large, the number of consecutive virtual sensors will be significantly improved. Simulations experiments are carried out to verify the performance. Though the preliminary study is confined to some conditions, the proposed sparse array is effective and expected to be extended to a generalized condition.
Fast ISAR Image Prediction for Targets with Coating Defects Through Deep Learning

The radar absorbing material (RAM) coating defects existing on the surface of stealth aircraft have important effects on their electromagnetic (EM) scattering characteristics. Considering the complexity and randomness of defects and the large electrical dimension of the platform, it is necessary to analyze their scattering characteristics through a series of complex and time-consuming processes including geometric modeling, meshing, EM simulation, and ISAR imaging, thus cannot meet the requirement of real-time analysis. To address this issue, this paper proposed a novel end-to-end deep neural network (DNN) based on residual U-net, which can perform time-efficient ISAR image prediction of a target with random coating defects from the input 2D geometric map of it. Compared to the method of shooting and bouncing ray (SBR) simulation and range-Doppler (R-D) ISAR imaging, the proposed DNN can accelerate the speed by three orders while ensuring a relative error lower than 1%. Numerical results are exhibited to verify the accuracy and efficiency of the proposed method.
Clutter Spectrum Estimation for Distributed Small Satellite Space-based Early Warning Radar

Distributed small satellite space-based early warning radar has the problem of grating lobes and obvious clutter, which has a negative effect on clutter suppression. To solve this problem, we establish the clutter signal model of the system and propose the combination of virtual and real apertures. In this method, the echo signal of the subsequent pulse is equivalent to the echo signal of the virtual array elements by using time for space. The simulation results of clutter spectrum and minimum detectable velocity show the effectiveness of the proposed method and we can get the same performance as a large ULA.
Estimating the Fraction of 'Questionable' Artwork by Henry Moore for Sale on Ebay: Application of Latent Class Screening Test Methodology

While the internet has provided consumers with new ways to purchase goods, it also has allowed less scrupulous businesses and individuals to offer poor quality or mislabeled items. In particular, the operators of internet auction sites may not guarantee the quality or genuineness of the items listed. A latent class approach originally developed to estimate the accuracy of medical tests and the prevalence of an infection is adapted to provide estimates of the prevalence of “questionable” art work offered on eBay. The method requires the existence of two subpopulations with a different prevalence of the trait and each subject is classified by two different tests. As Henry Moore produced less expensive lithographs and etchings, in addition to small sculptures and original drawings, two subpopulations of artwork were available. After discussing the issue of “fakes” with experts on Henry Moore, a Bayesian approach was adopted. The results showed that about 90% of the drawings and small sculptures listed on eBay are “questionable” while most, about 90 to 95% of signed prints are genuine. Because the methodology does not require a “gold standard” it can be adopted by consumer protection agencies or producers of expensive items to monitor the authenticity of items offered to the public on internet auction sites.
Feature Extraction on Multi-Channel ECG Signals using Daubechies Wavelet Algorithm

An electrocardiogram (ECG) is a recording of the heart’s rhythm and electrical activity over a period of time that can be obtained through an electrocardiography process. Through this process, the produced ECG can represent the condition of the heart whether it is healthy or not. Irregularity in heartbeat’s ECG signal causes several problems such as stroke and heart failure. Atrial fibrillation (AF) is a type of irregular heartbeat that is frequently found in a stroke patient. There have been studies exploring features of atrial fibrillation based on the ECG signal. However, most of them produce low accuracy due to the use of inappropriate features in identifying AF. To overcome the problem, this research proposes a study of feature extraction using Discrete Wavelet Transform (DWT) to increase accuracy in detecting AF. This research focused on analyzing both Daubechies Wavelet basis function and decomposition level of ECG signal as AF features. To classify the features either within AF or normal class, K-Nearest Neighbor (KNN) has been set up. Rigorous experiments have been conducted and show that Daubechies 2 (Db2) with decomposition level 6 produces the best performance of AF detection, i. e. accuracy 93.83%, sensitivity 96.77%, specificity 88.64%, 95.24% f1-score, and precision 93.75%.
Low-Complexity High-Performance Method for Calculating Arbitrary Logarithm Function

Logarithms are widely used in signal processing and other fields. In this paper, a hardware-friendly logarithm implementation method based on piecewise linear and the constant multiplier is proposed. Compared with other methods, it has a wider calculation range and stronger scalability. Our method maximizes functionality while minimizing resources. Also, it has the characteristics of low complexity and high precision.