Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimum Feature Selection Using Hybrid Grey Wolf Differential Evolution for Motor Imagery Brain Computer Interface

One of the challenges in improving the performance of brain computer interface systems is to overcome the large number of extracted features from EEG signals. Feature selection can reduce noisy data, overtraining effects, necessary storage, computational complexity, and can improve the performance of the classifier. Different feature selection methods have been used to achieve these goals. In this study, a new hybrid feature selection method is proposed. It employs a filter bank common spatial pattern for feature extraction and a grey wolf optimization algorithm to search and generate optimal feature subset with performance evaluated by support vector machine classifier. Also, In order to increase the search performance of the proposed feature selection algorithm, a new parallel combined grey wolf and differential evolution optimization algorithm is proposed. Experimental results show that the proposed methods improve the performance of motor imagery brain computer interface system in comparison to the state-of-the-art methods, even with small training data.
A Q-Learning-Based Approach for Enhancing Energy Efficiency of Bluetooth Low Energy

Bluetooth low energy (BLE) is a promising candidate technology for use in the Internet of Things (IoT) because of its ultra-low-power communication. Although BLE devices are designed to run on a small battery for a few years, several attempts have been made to extend BLE lifetime through various techniques. In particular, emerging approaches such as artificial intelligence (AI) can be utilized to further improve the BLE energy efficiency. For this purpose, this article proposes a Q-learning-based scheduling algorithm for BLE. The proposed scheduling algorithm dynamically adjusts the key parameters that govern the operation of the BLE transmission scheme. These key parameters, namely, the length of connection interval and the number of packets to transmit during the interval, have a profound effect on energy efficiency and the quality of service (QoS) specified in terms of maximum latency. According to the framework of reinforcement learning, our Q-learning-based scheduling algorithm is appropriately constructed to simultaneously provide a longer network lifetime and satisfy the QoS requirement. The numerical results show that the proposed Q-learning-based approach significantly increases the network lifetime compared to alternative methods while meeting QoS requirements.
High Performance and Efficient Microstrip Square Patch Antenna Design for 5G Wireless Network Technology Useful for Smart TV Applications

5G is the upcoming major phase in a wireless communication system which provides high broadband speed and ultra-reliable high-speed web connectivity. The use of Smart TV has tremendously increased in the developing nations of the world because of its wide range of advanced features that are available similar to smartphones such as live-streaming of online TV serials, online educations, live sports and other wide range of applications, especially during the ongoing lockdown COVID-19 pandemic. However, these smart TVs require the high speed of the internet for efficient use of its applications. Therefore, looking at the ongoing situation authors have proposed a design of microstrip square patch antenna with Rogers substrate 5880 having a dielectric constant of 2.2 and dielectric loss tangent of 0.009 at a resonant frequency of 26.5 GHz for 5G wireless network useful for Smart TV applications. The design is implemented using FEKO software which is user-friendly software. After the simulation, we have found a return loss of −25.24 dB, a bandwidth of 2.396 GHz, VSWR in the required range 1≤ VSWR≤ 2, a gain of 10 dBi, and antenna radiation efficiency is nearing 100%.
Galvanic Skin Response signal based Cognitive Load classification using Machine Learning classifier

Cognitive load (CL) classification is an important research issue in the human-computer interaction paradigm. It is evident from recent research that Galvanic Skin Response (GSR) can be used to sense cognitive load. CL analysis is important for understanding the mental growth of the child and the psychology of patients going through the different traumatic situation. Inspired by such a novel application, this model is designed. In this work, a technique has been demonstrated to measure and evaluate the level of human cognitive load for different tasks by collecting GSR from 40 student participants. The students are asked to sit for a test to solve three different tasks — reading comprehension, solving mathematics, and cracking Sudoku. Time-domain features have been extracted from participant’s GSR signals while these tasks are being analyzed. Some parameters such as Correlation Dimension (CD), Lempel-Ziv Complexity (LZC), Hurst Exponent (HE) and Shannon Entropy (SE) are used to analyze CL and these are used as features while accomplishing classification. The Level of stress or cognitive load is strongly observed with the one-way ANOVA test and box-whisker plots. Next, several machine learning algorithms are used to classify the various level of cognitive load. Using the 10 fold cross-validation and Naïve Bayes algorithm 91.5% accuracy is obtained.
Coordinated Optimal Power and Heat Dispatch in a Multi-Energy Microgrid

High energy consumption by thermal loads such as space cooling and space heating are major contributors in peak demand of electrical power. Integration of heat and power system can help in reducing this peak demand and also increases energy utilization efficiency. Development of integrated power and heat system is supported by combined heat and power (CHP)units. Accordingly in this work multi-energy system (MES) has been proposed for catering to thermal and electrical needs of microgrid and a framework has been developed for operation and scheduling of MES. Heat demand is fulfilled according to an economic dispatch framework and the heat flow in pipelines is analyzed through an iterative algorithm. Optimal power flow formulation has been developed for optimal dispatch of power generating sources. Proposed framework is demonstrated on the IEEE 33-bus power distribution network and 32-node heat distribution network. Numeric results depicts the effectiveness of developed model.
An 18-Pulse Uncontrolled Star Rectifier for Input Line Current Harmonic Reduction

This article introduces a delta/fork transformer based 18-pulse uncontrolled star rectifier for large current and low voltage application. The proposed rectifier consists of two 9-pulse parallel connected uncontrolled star rectifier, delta/fork transformer and interphase transformer (IPT). Delta/fork transformer is designed and modeled optimally to produces two 9-phase supplies for two 9-pulse star rectifiers. The proposed rectifier enables to element lower than 17th order harmonics from the input line voltage and input line current. Total harmonic distortion (THD) of the input line voltage is less than 5% under wide load variation condition. The proposed rectifier suitable where isolation is required such as aluminum potline, traction substations, zinc electrolysis, graphitizing furnaces, copper refining and low voltage ac drives etc. Proposed rectifier is analyzed, designed, modeled and simulated in MATLAB software environment.
A Trial Deployment of a Reliable Network-Multicast Application across Internet2

A continuing trend in many scientific disciplines is the growth in the volume of data collected by scientific instruments and the desire to rapidly and efficiently distribute this data to the scientific community. Transferring these large data sets to a geographically distributed research community consumes significant network bandwidth. As both the data volume and number of subscribers grows, reliable network multicast is a promising approach to reduce the rate of growth of the bandwidth needed to support efficient data distribution. In prior work, we identified a need for reliable network multicast: scientists engaged in atmospheric research subscribing to meteorological file-streams. Specifically, the University Cooperation Atmospheric Research (UCAR) uses the Local Data Manager (LDM) to disseminate data. This work describes a trial deployment of a multicast-enabled LDM, in which eight university campuses are connected via corresponding regional Research-and-Education Networks (RENs) and Internet2. Using this deployment, we evaluated the new version of LDM, LDM7, which uses network multicast with a reliable transport protocol, and leverages Layer-2 (L2) multipoint Virtual LAN (VLANIMPLS). A performance monitoring system was deployed to collect real-time performance of LDM7, which showed that our proof-of-concept prototype worked significantly better than the current production LDM, LDM6, in two ways: (i) LDM7 can distribute file streams faster than LDM6. With six subscribers, an almost 22-fold improvement was observed with LDM7 at 100 Mbps. And (ii) to achieve a similar performance, LDM7 significantly reduces the need for bandwidth, which reduced the bandwidth requirement by about 90% over LDM6 to achieve 20 Mbps average throughput across four subscribers.
SuperVO: A Monocular Visual Odometry based on Learned Feature Matching with GNN

In this paper, we propose a novel Visual Odometry (VO) system using a feature detector and feature matcher based on neural networks. The networks for feature detectors and descriptors learning consists of a conventional CNN for feature detection and description, and a graph neural network (GNN) final feature matching. The learned feature has several advantages over traditional handcrafted features such as being robust to light variation and scale. By applying state-of-the-art deep learning-based feature Matcher-SuperGlue, we developed a new monocular VO framework which can exploit the advantages of deep learning-based feature detector and matcher, which performs better than many other learning-based VO methods.
Modelling of magnetodipolar striction in soft magnetic elastomers

The origin of the so called magnetostriction effect, i.e., the deformation of a soft magnetic elastomer (SME) in response to a uniform magnetic field is discussed. We note that up to now there exists a number of cases, where the theory is unable to explain the facts well established for real SMEs. In our view, the essential issue, formerly not adequately accounted for, is the type of structural short-range order in the magnetic microparticle assembly. Two-dimensional model problems are formulated and solved, which demonstrate the validity of the hypothesis.
Securing Control and Data Planes From Reconnaissance Attacks Using Distributed Shadow Controllers, Reactive and Proactive Approaches

Moving Target Defense (MTD) is an emerging proactive Cyber Security approach. MTD constantly changes the attack surface for making cyber-attacks difficult for the invaders. Software Defined Networking(SDN) provides dynamic network design capabilities with its centralized control plane. In this paper, SMCDS (SDN based Moving Target Defense for control and data planes Security) has been proposed. The SMCDS framework safeguards against the reconnaissance attacks targeted at both data and control planes. The concept of distributed shadow controllers is introduced for securing the control plane. The MTD effect is created through the use of shadow controllers that respond to the malicious probing traffic in place of the actual controller. The availability of the distributed control plane is enhanced through the used of these shadow controllers as well. The proposed framework adopts the reactive and proactive approaches for securing the servers connected at the data plane. The reactive approach capitalizes the technique of shadow servers for providing defense against reconnaissance attacks. The proactive approach provides security enhancement through the technique of IP and port shuffling. The novelty of SMCDS framework is its capability to provide protection of both data and control planes by exploiting SDN based MTD approach. The SMCDS framework was evaluated in terms of the attacker effort, defender cost. From the results, it can be observed that the proposed framework provides security against reconnaissance attacks at a low computational cost. The prototype of the proposed SMCDS was implemented using Mininet emulator and ONOS controller.
Research on Optimal Setting of Cement Decomposing Furnace Temperature based on Case Reasoning

Precalciner is the main equipment to undertake the task of pre-decomposition of cement raw materials. Its outlet temperature can indirectly reflect the decomposition rate of cement raw materials, and its operation status plays a decisive role in the final quality of cement products. However, in cement enterprises, the temperature setting value of calciner is mostly given by the operator with experience, and the subjectivity of each operator often leads to the fluctuation of clinker qualification rate. In view of the above situation, this paper proposes a case-based reasoning method to solve the optimization setting problem of calciner temperature setting value. First of all, build the original case library, and then through case retrieval and matching, case reuse and update storage, the optimal setting value of calciner temperature of the current target case is predicted in real time, so as to improve the optimal control level of cement production. The simulation results show that the case-based reasoning method can solve the problem of optimizing the setting value of calciner temperature, and the absolute value of prediction accuracy error is less than 2°C. The research results provide a reliable method for the decision-making of calciner temperature setting value.
Bi-LSTM and Ensemble based Bilingual Sentiment Analysis for a Code-mixed Hindi-English Social Media Text

India is a multilingual and multi-script country and a large part of its population speaks more than one language. It has been noted that such multilingual speakers switch between languages while communicating informally. The code-mixed language is very common in informal communication and social media, and extracting sentiments from these code-mixed sentences is a challenging task. In this work, we have worked on sentiment classification for one of the most common code-mixed language pairs in India i.e. Hindi-English. The conventional sentiment analysis techniques designed for a single language don’t provide satisfactory results for such texts. We have proposed two approaches for better sentiment classification. We have proposed an Ensembling based approach which is based on hybridization of Naive Bayes, SVM, Linear Regression, and SGD classifiers. We have also developed a bidirectional LSTM based novel approach. The approaches provide quite satisfactory results for the code-mixed Hindi-English text.
Visual Tracking Via Sparse Representation With Compressed PCA Basis Vectors

In this paper, we present a novel visual tracking algorithm based on sparse representation. In contrast to the methods using target templates or PCA basis vectors and trivial templates to sparsely represent the object appearance model, which require to solve the l1 Regularized Least Square(l1 RLS) problem with rather expensive computational cost to obtain the sparse coefficients, we propose an effective representation with a linear combination of compressed PCA basis vectors and trivial templates. First, a sparse measurement matrix is applied to project the candidate image patch into a low dimensional compressed subspace. Then, the object tracking is converted to a sparse approximation problem via compressed features and achieved by solving the l1 RLS problem in a Bayesian inference framework. It decreases the computational complexity without loss of tracking accuracy. Furthermore, an inversely indicative strategy is proposed to search the image patch with the largest observing likelihood in original image space according to corresponding result in compressed subspace. Then, different processes are adopted to update the observation model according to the occlusion-ratio of the selected image patch for handling appearance variation. Both qualitative and quantitative evaluations on challenging image sequences demonstrate that the proposed algorithm performs favorably against several state-of-the-art methods in a wide range of tracking scenarios.
MoS2, rGO, and CuO Nanocomposite-Based High Performance UV-Visible Dual-Band Photodetectors

This letter reports a Pd/MoS<sub>2</sub>/rGO/CuO/ITO structure based Metal-Semiconductor-Metal (MSM) photodetector. The proposed MSM device is obtained by vertical integration of a Pd/MoS<sub>2</sub> Schottky junction and a CuO /ITO (i.e., (Indium-doped Tin Oxide) Schottky junction separated by thin reduced graphene oxide (rGO) layer. The rGO layer is used to improve the quality of the MoS<sub>2</sub> layer by mitigating the effects of both the lattice mismatching and van der Waals gap between the MoS<sub>2</sub> (Molybdenum disulphide) and CuO (Copper Oxide) layer. The proposed photodetector shows a UV-Visible dual-band photoresponse when the device is illuminated by a monochromatic light intensity of <inline-formula> <tex-math notation="LaTeX">$13.6~\mu $ </tex-math></inline-formula> W/cm<sup>2</sup> at different wavelengths over 300-800 nm. Excellent responsivities and detectivities of 646.8 A/W and <inline-formula> <tex-math notation="LaTeX">$7.28\times 10 ^{14}$ </tex-math></inline-formula> Jones at ~300 nm (in the UV region) and; of 84.32 A/W and <inline-formula> <tex-math notation="LaTeX">$9.6\times 10 ^{13}$ </tex-math></inline-formula> Jones at ~ 498 nm (in the visible region) are measured at 1 V bias voltage, respectively. Also, the device also shows exceptionally high external quantum efficiency (EQE) of <inline-formula> <tex-math notation="LaTeX">$2.6\times 10 ^{5}$ </tex-math></inline-formula> % and <inline-formula> <tex-math notation="LaTeX">$3.7\times 10 ^{4}$ </tex-math></inline-formula> % at 300 nm and 498 nm, respectively.
Sound Event Separation and Classification in Domestic Environment using Mean Teacher

Sound intelligence is gaining major interest of research and development community over the last couple of years. Sound event detection (SED) and classification result with other modalities such as vision, sensors etc. is helping in development of many new use cases. Overlapping sound events classification (SEC) in domestic environment is a challenging research task. Domestic environment includes sound signals from human, animals, devices and utilities. Overlapping of these signals reduces performance of SEC systems. This paper proposes sound source separation as preprocessing block for SEC system. Time-dilated convolutional network (TDCN) mask network with Shorttime Fourier Transform (STFT) is used for sound source separation. Log mel features are extracted, then mean teacher model with context gating Convolutional Recurrent Neural Network (CRNN), is trained for SEC system. The paper concludes that, the source separation as preprocessing and fusion of classification results on each separated signal using SEC, helps in achieving enhanced accuracy.
A High-Resolution Study of Chemical Aging Prior to Electrical Tree Growth

A study of chemical aging prior to electrical tree initiation has been undertaken using a high-resolution chemical analysis technique, AFM-IR (atomic force microscopy-infrared spectroscopy). This technique has provided chemical characterisation with a spatial resolution of around 50 nm. It is seen that the use of tests on this scale provide information and insight which was not previously observable using lower resolution techniques. Previously unobserved degradation products are observed in epoxy resin prior to tree initiation with ethers (C-O) and methyl groups (CH3) appearing to form alongside carbonyls (C=O), each occurring in different locations around the needle tip. Such spatial resolution of aging chemistry will allow improved models of tree initiation and growth to be developed.
Application of FBG Technology in Additive Manufacturing: Monitoring Real-Time Internal Temperature of Products

This study presents an investigation of the influence of infill density on inner temperature change of Polylactic Acid (PLA) and carbon fiber filaments. Five groups of different infill densities (20%, 40%, 60%, 80%, and 100%) were considered for testing and analysis in additive manufacturing. Fiber Bragg grating (FBG) sensors were used to monitor the internal temperature change of all prototypes during additive manufacturing process. Monitoring tests indicate that relationships of temperature against printing time are characterized by three typical parameters including the first peak temperature (FPT), minimum temperature (MT), and completion temperature (CT). All FPT, MT, and CT values increase substantially as the increase of infill density values. MT values generally present at relatively lows infill density values but coincide with CT values at high infill density values. At low infill density levels (infill density is no higher than 60%), fabrication of the upper surface of prototypes leads to substantial temperature rise, and this rise appears to be limited at infill density values of 80% and 100%. This study can be used to reveal printing quality of 3D printed products arise from non-uniform distribution of temperature change.
Energy Saving Management with Suggestion Method in Home Automation based on User Habits

In designing an automation system to help make it easier for humans to control and monitor household appliances or smart houses often experience problems with the problem of estimating the increasing burden of electric current because it uses additional tools to control the household appliances. To reduce this view, it can be solved by regulating the energy to be consumed from the household appliance by using a smart meter as a monitoring device specifically designed with excess internet to send data taken from the energy meter and then the incoming data can be processed analyzed from the peak current demand of electric current using the User Habits method which will be combined with the energy scheduling method and combined with suggestion turning on or off the household appliances, so that it is expected to provide comfort and effectiveness in using Home Automation. Effectiveness means that all home appliances or intruments must be running properly first, such as KWH meters, the lamp, switches, dispensers, mini server (local saving), and data center (global saving). And then, after the effectiveness has been running good or smooth the author continues his research into efficiency, namely an energy saving mode with an energy management process based on user habits.