Miracle-dz/UniFAR-FTUs huggingface dataset:
Key Band Image Sequences and A Hybrid Deep Neural Network for Recognition of Motor Imagery EEG

Deep neural network is a promising method to recognize motor imagery electroencephalography (MI-EEG), which is often used as the source signal of a rehabilitation system; and the core issues are the data representation and the matched neural networks. MI-EEG images is one of the main expressions, however, all the measured data of a trial are usually integrated into one image, causing information loss, especially in the time dimension; and the neural network architecture might not fully extract the features over the <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> frequency bands, which are closely related to MI. In this paper, we propose a key band imaging method (KBIM). A short time Fourier transform is applied to each electrode of the MI-EEG signal to generate a time-frequency image, and the parts corresponding to the <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula> bands are intercepted, fused, and further arranged into the EEG electrode map by the nearest neighbor interpolation method, forming two key band image sequences. In addition, a hybrid deep neural network named the parallel multimodule convolutional neural network and long short-term memory network (PMMCL) is designed for the extraction and fusion of the spatial-spectral and temporal features of two key band image sequences to realize automatic classification of MI-EEG signals. Extensive experiments are conducted on two public datasets, and the accuracies after 10-fold cross-validation are 97.42% and 77.33%, respectively. Statistical analysis shows the superb discrimination ability for multiclass MI-EEG too. The results demonstrate that KBIM can preserve the integrity of the feature information, and they well match with PMMCL.
Alzheimer’s Disease Classification Based on One Dimensional Convolutional Neural Network

This paper aims to solve the difficulties of Alzheimer’s disease (AD) diagnosis by constructing a classification framework based on a one-dimensional convolutional neural network (CNN), which provides finer clinical cues and additional performance gain for AD diagnosis. For the selected MRI image data, it is divided into three types according to the degree of disease for further discussion, Mild Cognitive Impairment (MCI), and Normal Control (NC) Classification of the population. The framework first uses deep learning network to perform feature extraction on all pre-processed MRI slices, and then classifies them through 1-D CNN to achieve automatic classification of AD. A large amount of experimental data shows that the classification accuracy of the 1-D CNN framework for AD vs. MCI vs. NC, AD vs. MCI, AD vs. NC, MCI vs. NC has reached the average accuracy of 0.9550, 0.9933, 0.9945 and 0.9597, respectively, which show the proposed framework is fast and simple. It effectively improves the classification results over all the categories and achieves a superior classification performance for AD diagnosis.
Investigation of Flexible Sweat Sensor for Sodium-Ion Concentration with a Combination of Two Sensing Mechanisms

We present a flexible microelectrode array for analyzing the NaCI electrolyte of the sweat of human beings based on two sensing mechanisms. The sensor is fabricated on polyimide (PI) substrate which is low-cost, flexible, and biocompatible as the wearable application for healthcare monitoring. Based on the electrolyte impedance measurement transduction, the two adjacent microelectrodes show a linear response with a sensitivity of 8.78 mS/M to the electrolyte concentration of human sweat range from 10 to 120 mM. In comparison with the impedance method, the open-circuit potential (OCP) measurements are carried out with a modified ion-selective electrode versus a commercial reference electrode. The OCP results show the rational Nernst response (66.2 mV/decade) in a range of 10−5~10−1 M and with the limit of detection (LOD) of 8.2×10-6 M.
Automatic Generation of Informative Video Thumbnail

Thumbnail plays a vital role in boosting the discovery and viewership of online video. Although it can be easily obtained by simply selecting a certain image from the video sequence, most of popular videos in today’s video sharing platforms come with elaborately designed custom thumbnails to better showcase the highlight within the video. Unfortunately, both the selection of salient content from thousands of frames and the creation of an eye-catching thumbnail are very time-consuming and require highly specialized skills. In this paper, we present a fully automatic approach for generating informative and eye-catching video thumbnails. The proposed pipeline first splits up a given video into shot units and then selects a set of keyframes using keyframe representativeness and quality assessment metric. After performing salient region detection, a synthetic thumbnail can be obtained by embedding as many as salient regions of keyframes into the carrier image. Experimental results demonstrate the feasibility and effectiveness of our method in informative thumbnail creation.
Statistical Scattering Component-Based Subspace Alignment for Unsupervised Cross-Domain PolSAR Image Classification

Increasing amounts of polarimetric synthetic aperture radar (PolSAR) images from different sensors covering different scenes are available, but limited labeled samples and trained models can hardly work well in these cross-domain data interpretations. Fortunately, domain adaptation (DA) can transfer knowledge in existing images to new yet related images. DA shows attractive potential for PolSAR classification, and it is still challenging due to more complex domain shifts caused by different sensors, imaging conditions, and distributions. Inspired by the widely applicable polarimetric scattering mechanisms and DA ability of subspace alignment (SA), this article is devoted to constructing a robust unsupervised cross-domain PolSAR classification framework, by exploring scattering and statistical characteristics mapping between the source and target domains. First, classical scattering components of both source and target data were extracted, and Wishart clustering was adopted to derive the statistical information of scattering components at patch level. Second, the intrinsic polarimetric scattering components were estimated and extracted, which were called statistical scattering components (SSCs). Third, by applying SA, the source SSC was aligned with target SSC, and domain shift was further reduced. Finally, the target PolSAR image was classified based on labeled samples from source domain, and unsupervised cross-domain classification was achieved by SSC-based SA (SSC-SA). The unsupervised cross-domain experiments are conducted on 49 units among 11 data sets, including Radarsat-2, Gaofen-3, AIRSAR, and Pi-SAR images. With randomly selected labeled samples (about 2%–10%) from source domain, the accuracies of the proposed cross-domain classifications range between 80.20% and 95.64%. Also, the proposed SSC feature pattern is proved extensible for other polarimetric basis and decompositions.
MoniLog: An Automated Log-Based Anomaly Detection System for Cloud Computing Infrastructures

Within today’s large-scale systems, one anomaly can impact millions of users. Detecting such events in real-time is essential to maintain the quality of services. It allows the monitoring team to prevent or diminish the impact of a failure. Logs are a core part of software development and maintenance, by recording detailed information at runtime. Such log data are universally available in nearly all computer systems. They enable developers as well as system maintainers to monitor and dissect anomalous events. For Cloud computing companies and large online platforms in general, growth is linked to the scaling potential. Automatizing the anomaly detection process is a promising way to ensure the scalability of monitoring capacities regarding the increasing volume of logs generated by modern systems. In this paper, we will introduce MoniLog, a distributed approach to detect real-time anomalies within large-scale environments. It aims to detect sequential and quantitative anomalies within a multi-source log stream. MoniLog is designed to structure a log stream and perform the monitoring of anomalous sequences. Its output classifier learns from the administrator’s actions to label and evaluate the criticality level of anomalies.
A DRL-Based Approach for System Frequency Response Model Calibration

The system frequency response model (SFR) has been widely adopted in power grid dynamics analysis for describing the average frequency response of all elements in a power network. It is a challenging problem to accurately calibrate the parameters of the SFR model for a real bulk power grid. This paper proposes a deep reinforcement learning (DRL) based approach for adaptive SFR model parameter calibration. The frequency responses of bulk power systems under multiple contingencies are considered. Detailed parameter selection, architecture design, key component definition, and environment setup associated with the DRL implementation are analyzed and illustrated. The proposed DRL method for SFR parameter calibration is verified using a real bulk power system with thousands of buses in China considering the faults of ultra-high voltage direct current (UHVDC) lines. It is shown that the proposed method can optimize the SFR model parameters efficiently and accurately.
KNN Based Sleep Apnea Detection Using ECG Signals

Sleep Apnea (SA) is a serious sleep disorder. This is shown while individual breathing is blocked during sleep because of insufficient air supply to the brain and body. Detection of SA in the beginning step will provide better results from critical health issues. The accurate procedure to detect SA will improve the quality of individual life. Computer-Aided Detection (CAD) of SA was introduced by utilizing single-medium, single-signal can efficiently reduce the cost, and it also is utilized even at home. By using DWT, the ECG signal is decomposed into Approximate and Detailed coefficients. Mean, Median, Standard Deviation, Entropy, and Energy are computed to the approximated and detailed coefficients. A 5-fold cross-validation is used to decompose the dataset. The epoch of normal and Apnea are detected with the help of KNN (k-Nearest Neighbor) classifier. This method attained an Accuracy (AC), Sensitivity (SE), Specificity (SP), Precision, and F-score of 98.02%, 97.81%, 98.34% 98.85%, and 98.32%. This method performed well compared to the existing methods available by utilizing the same database and was found to be much effective, powerful, and very easy to utilize.
Indoor Localization in BLE using Mean and Median Filtered RSSI Values

Many forms of wireless connectivity such as Wi-Fi, cellular data, Zig bee, etc. exist in the fastest growing domain of wireless technology. However, one of the most important and popularly used wireless protocols is Bluetooth, which was originally designed for the solution of the wired mode of data transfer (RS232, UART). Indoor localization technology based on the latest avatar of Bluetooth protocols viz Bluetooth Low Energy (BLE) is used to address the inadequacies of global positioning systems in indoors such as buildings, offices, houses, etc. since people use to spend the majority of their time there. The use of BLE becomes significant as it is a power-efficient and low-cost technique. It provides 40 channels of 2MHz Bandwidth each with 3 advertising channels and 37 channels for data transfer. In this paper, a parameter based on the received signal power viz. The RSSI value is proposed to be used for efficient indoor localization applications using BLE technology. The problem of non-static values of RSSI due to its dependence on the surrounding environment is proposed to be overcome in this paper by stabilizing the received power using mean and median filters. The localization accuracy is enhanced through static values of RSSI obtained using both these filters.
Low Power IoT Based Implementation ECG & BP Monitoring System

Many researchers are implementing patient health monitoring system using different types of microcontroller, Arduino, Raspberry pi. Researchers are facing challenges to develop low cost, low power, portable and battery operated device which increase the device capabilities. In this paper, present low power IoT based ECG & BP monitoring device using MSP432P401r. Blood pressure sensor output is given to signal conditioning circuit and AD8232 ECG IC is helping to acquire ECG signal from patient using electrodes. Signals from sensors are interface to microcontroller as it has high performance processing capabilities; integrated with a 14 bit 1MSPS ADC and low power operation gives better resolution and Accuracy. The program is developed using code composer or embedded C coding language. The test results are displayed on computer screen using visual studio. The objective of implementing this system is to develop low power device to measure physiological parameter & uploading the data to cloud using IoT. It will help to provide better diagnosis and treatment to patient in remote/rural location.
Performance Analysis of Kotlin Coroutines on Android in a Model-View-Intent Architecture pattern

Kotlin Coroutines are generalised subroutines that can suspend and resume execution freely. They exhibit lower overhead than Java Virtual Machine (JVM) Threads, making them suitable for asynchronous applications in resource-constrained environments. This paper analyses the performance of Kotlin Coroutines framework on Android and measures its benefits over JVM Threads-based concurrency framework RxJava using a novel benchmarking methodology for reliable and reproducible results. It also highlights the performance improvements enabled by Kotlin Coroutines in real-world projects by comparing two Model-View-Intent (MVI) architecture libraries MvRx (by Airbnb), and Vector (by author Chauhan, K) based on RxJava and Coroutines, respectively.
3D printing of magnetically actuated miniature soft robots

Magnetically actuated soft robots possess great potential for applications in medical surgery, bioengineering, and micromanipulation due to its fast response, easiness to manipulate, and untethered control. However, it remains a challenge to fabricate flexible magnetically actuated soft robots by magnetic materials with a high viscosity (≥104 Pa.s). Therefore, we proposed a microscale Weissenberg effect-based 3D printing technique for fabrication of magnetically actuated soft robots via the programmable magnetization of high-viscosity magnetic polymer precursor prepared by mixing magnetic microparticles with elastic polymer. The magnetic microparticles in precursor solution can be reoriented by applying a magnetic field around nozzle during direct writing, which imparts the programmable sequences of magnetic polarities to printed structures. Meanwhile, we investigated the effect of main process parameters on the morphologies of printed structures. 3D shape transformations of a 2D patterned structure and the grasping experiment of a magnetically actuated cross shaped soft gripper verified the effectiveness of the programmable magnetization of high-viscosity flexible magnetic materials for the proposed 3D printing technique.
Dynamic Instrumentation Framework Based on Entity Equipment

Dynamic binary analysis is an important way of program analysis. Most of the existing dynamic analysis tools are based on dynamic binary instrumentation platforms. The existing gdb tool, which is often used for debugging of embedded devices, has low performance on dynamic instrumentation. This paper implements basic block-level instrumentation based on dynamic binary rewriting technology and provides a simple and flexible application programming interface. Through the evaluation, the local instrumentation efficiency of this system is mostly better than the Pin platform, and the remote instrumentation efficiency is about 28.7 times that of the gdb+gdbserver platform. The instrumentation performance has been greatly improved.
Effects of Picosecond Pulsed Electric Fields on U937 and C6 Cells

Experiments have been conducted to investigate the cell response to the picosecond pulsed electric fields (psPEF) with pulse repetition frequency (PRF) of 1kHz. Exposure of U937 cells and C6 cells to psPEF with PRF of 1kHz resulted in the decreased cell viability in both cell lines but the C6 cells were more sensitive than U937 cells. Moreover, the treatment of psPEF also significantly increased the intracellular fluorescence intensity in U937 cells which pre-incubated with Fluo-4, a common Ca2+ indicator. It demonstrated that the psPEF exposure led to the enhancement of intracellular Ca2+ level. While the application of Manidipine, a calcium channel inhibitor didn't block the Ca2+ induced activation of calmodulin kinase (CaMK), which indicated that the calcium channel inhibited by Manidipine did not play a role in the psPEF exposure induced elevation of intracellular Ca2+ level.
Physics-Based Stochastic Three-Dimensional Modeling for Metal–Oxide Resistive Random Access Memory

In this work, a fully physics-based stochastic 3-D modeling of metal–oxide-based resistive random access memory (RRAM) device has been conducted. In contrast to general 1-D or 2-D modeling of RRAM devices, 3-D modeling is able to provide a picture of the evolution of the 3-D random distributions of oxygen vacancies and temperature inside the device during the forming and SET/RESET processes. The modeling confirms that a tunneling gap between the top electrode (TE) which serves as an oxygen ion reservoir and the tip of the conductive filament may form at a high resistive state, which is assumed in the frequently used compact models of RRAM devices. The modeling shows that the sites with high temperature are concentrated in a cylindrical region at the center of the RRAM device, running from the inert bottom electrode to the TE. The modeling can reproduce both typical current-voltage characteristics of metal–oxide RRAM devices in forming and RESET and SET operations under a dc voltage sweep and the modulation of RRAM conductance by SET/RESET pulses.
End-to-end 100-TOPS/W Inference With Analog In-Memory Computing: Are We There Yet?

In-Memory Acceleration (IMA) promises major efficiency improvements in deep neural network (DNN) inference, but challenges remain in the integration of IMA within a digital system. We propose a heterogeneous architecture coupling 8 RISC-V cores with an IMA in a shared-memory cluster, analyzing the benefits and trade-offs of in-memory computing on the realistic use case of a MobileNetV2 bottleneck layer. We explore several IMA integration strategies, analyzing performance, area, and energy efficiency. We show that while pointwise layers achieve significant speed-ups over software implementation, on depthwise layer the inability to efficiently map parameters on the accelerator leads to a significant trade-off between throughput and area. We propose a hybrid solution where pointwise convolutions are executed on IMA while depthwise on the cluster cores, achieving a speed-up of 3x over SW execution while saving 50% of area when compared to an all-in IMA solution with similar performance.
REVIEW OF STOCK PREDICTION USING MACHINE LEARNING TECHNIQUES

Stock prices change everyday by market forces (supply and demand). In recent years stock price prediction has been one of the most significant concern. Investors are investing on stock market on the basis of certain prediction. For prediction, stock market prices investors are applying some techniques and methods through which they get more profits and minimize their risks. Machine Learning methods are often used for the prediction of stock prices. This survey paper discusses various machine learning approaches (Supervised or Unsupervised) and methods through which the investors get to know the stock prices increase or decrease. It was done in five phases, such as data acquired, pre-processing of dataset, extraction of features, prediction of stock price using different techniques and display the result. In first phase, the data is collected from different social sites, historical data of companies. In second phase, the removal of incorrect, duplicate and dirt is done in pre-processing phase. In third phase, the reduction of data sets and the selection of useful data is done. In fourth phase, prediction is done using different machine learning techniques and approaches which is categorized as supervised and unsupervised learning techniques. Now, in last phase the accuracy is determined using different approaches.
Covid-19 Face Mask Detection Using Convolutional Neural Network and Image Processing

Object detection and image classifications are the popular research work in the rapid growth of advanced technology to detect and identify real-time problems in the governmental core-areas such as business shops, airlines, universities, and army place with the help of webcams surveillance cameras, and opensource platforms. The objective of this research work is to propose Convolutional Neural Network (CNN) and Open Source Computer Vision (OpenCV) to detect COVID-19 face mask detection from real-time (live streaming video) and image datasets. We have employed data argumentation pre-processing techniques to maximize our dataset and improved the performance of the proposed models, and in addition to this, we have used MobileNet CNN architecture to reducing the size and model complexity. This research work’s experimental result has been shown 96.50%, 97.07%, 99.27% accuracies obtained with the help of CNN, CNNdata augmentation, MobileNet2V, respectively.
Intelligent Intrusion Detection System Using Deep Learning Models

Cyber attacks are a very common issue in the modern world, and since there is a growing array of challenges in accurately detecting intrusion, this results in damage to security services, i.e. confidentiality, integrity, and availability of data. The attackers found new types of attacks day by day, first of all the type of attack should be analyzed properly with the help of IDS for the prevention of these types of attacks to offer the correct answers. IDS that play an important role in network security have three major features: first collection of data, selection of feature last is decision of engine. As data are increasing day by day due to which the attacks on the data also increases because of these increasing numbers of attacks on the data, the existing security applications are insufficient. SIDS and AIDS intrusion detection systems are separate proposed methods of intrusion detection to manage security threats. This paper has reviewed numerous deep learning algorithms that have been proposed to detect intrusion, i.e., Convolutional Neural Network, Recurrent Neural Network, Restricted Boltzmann Machine, Deep Brief Network and Auto encoder. It is designed to use IDS approach depending on a deep learning (DL) algorithm by using literature work comparisons and by providing the expertise either in intrusion detection or deep learning algorithms.