Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Gesture Recognition Framework Based on Forearm EMG Activity

The electric activity produced by the forearm level muscles contains valuable information regarding the performed gesture. The analysis of electromyographic (EMG) signals is essential for developing intelligent prostheses as well as for creating intuitive control interfaces for a variety of devices. This paper presents in detail all the necessary steps for EMG signal classification, starting with developing the EMG recording module, continuing with the EMG dataset collection and ending with a deep learning based approach classification for the acquired signals. We studied 3 neural network based classifiers following 3 distinct paradigms. We present an end-to-end approach using Long-Short Term Memory (LSTM) cells; a spectral based classifier trained on a Convolutional Neural Network (CNN); and finally we develop a Fully Connected Network (FCN) that discriminates between time descriptors extracted from EMG data. Working with electromyographic signals recorded from 50 participants, each one performing 15 different gestures, we achieve a classification accuracy of 98.67%.
A Survey on Computational Approaches for Raga Identification

Music is the most fascinating and popular art forms in the society. In Indian context, ragas are the main pillars of different music types and genres. They have a strong academic foundation. Light music is most widely heard and appreciated form of music worldwide. Every light music composition has its roots somewhere in the core classical music and ragas. Hence the raga identification of an Indian classical raga is an academically attractive research problem in computational musicology which has attracted attention of various research scholars. A computational approach for automatic raga identification can enable raga-based Music Information Retrieval and popularization of Indian Classical Music. This paper presents a survey of computational approaches attempted till date. The approaches used are also discussed in detail.
Activity and Dropout Tracking in Moodle Using UBUMonitor Application

Teaching with online learning platforms should simplify the monitoring of students’ activity, particularly when evaluating student dropout. Popular learning environments such as Moodle should implement visual analytic tools that facilitate such tasks, nevertheless, institutions are usually reluctant to incorporate them. This paper presents UBUMonitor, a desktop application that allows the visualization of student’s activity data, extended as a proof of concept with a module for dropout tracking. Therefore, by using UBUMonitor, teachers will be able to easily visualize their students’ engagement with their subject, which can facilitate early action to prevent students from dropping out more effectively.
Smartwatch-based Eating Detection and Cutlery Classification using a Deep Residual Network with Squeeze-and-Excitation Module

Several machine learning and deep learning algorithms have been developed to tackle the human behavior detection issue, emphasizing everyday tasks. Nevertheless, an intriguing and challenging human activity recognition (HAR) subject involves more complicated human behaviors, including eating-related actions. This study provided a smartwatch-based eating identification system based on hand-movement data. In the framework, we introduced a deep residual network named the ResNet-SE model that enhanced detection capability by using the benefits of shortcut connections and squeeze-and-excitation units. In addition, the effectiveness of standard deep learning models (CNN and LSTM) is evaluated and compared to that of the proposed model. In addition, we investigate the use of wristwatch sensor data for categorizing six kinds of food cutlery. To validate the correctness of the model, the accuracy, F1-score, and confusion matrices of the HAR metrics are applied to the EatingDetectionIJS dataset for evaluating the proposed frame-work. Experimentation findings demonstrate that the ResNet-SE model surpasses existing deep residual models, achieving the greatest F1-score of 91.81% for eating identification and 91.43% for cutlery categorization.
Utilizing Cryogenic CO2 Spray process for Removal of Lunar Regolith Dust Adhered to Spacesuits

The contamination of spacesuits by lunar regolith dust during extravehicular activity (EVA) is one of the biggest problems facing NASA's planned return to the moon with the Artemis Program. To control this, we propose using cryogenic CO2 spray as a practical and efficient method of mitigating dust contamination. This form of cryogenic cleaning is a novel application of technology widely utilized for removing metallic dust particles, like those found in lunar environments, from microelectronics, photomasks, and other sensitive surfaces. The proposed solution includes a handheld CO2 sprayer with an adjustable nozzle for alternation between general and localized cleaning, as well as a potential large-scale design for full-suit cleaning.
SAR system for searching and detecting objects in the forest area, based on UAVs

The report considers the task of radar search and detection of objects hidden from direct optical observation by vegetation. A radar system for remote sensing of the earth remote sensing is proposed, placed on board an unmanned aerial vehicle and using the method of aperture synthesis (SAR) in the VHF frequency band. The report substantiates the choice of technical parameters of the system and its parts, and provides the results of designing a prototype remote sensing system based on an octocopter. Optimal signal processing algorithms using a variational Bayesian approach are proposed.
A CPW Fed Compact Multiband Antenna For Wireless Applications

This paper presents a coplanar waveguide fed microstrip antenna developed with the aim of providing multiband resonance with compact & simple structure. The CPW fed antenna is of size $18.2\times 20\text{mm}^{2}$ which is printed on the substrate (FR4- Epoxy) which has relative permittivity 4.4 and thickness of 1.6mm. The proposed antenna has a meander line structure and a rectangular slot along with L-shaped slots on the ground plane. The meander line structure is responsible for resonance at 2.62 GHz, the rectangular slot gives resonance at 5.37 GHz and the L-shaped slots are responsible for resonance at 4.43 GHz & 7.3 GHz. The proposed antenna has return loss greater than −10dB and shows good bandwidth characteristics at all four resonant frequencies. The proposed antenna has peak gain of 0.133dB/2.7dB/3.4dB/4.33dB at 2.622/4.43/5.37/7.3GHz frequencies respectively. The proposed antenna can be used for WLAN, WIFI, Narrowband & X-band applications.
Restoration of Power Distribution Systems With Multiple Data Centers as Critical Loads

In this paper, a post-event restoration method is proposed for power distribution systems with Internet data centers (IDCs) as critical loads. The proposed method is formulated as a mixed integer linear programming-based optimization model which considers emergency operation decisions in IDCs and restoration decisions in power distribution systems, and can minimize the utility loss of IDCs and other loads during the restoration process. Since the original form of the proposed method may face some problems in practical application, such as confidential information sharing and long computing time, a decoupling method is proposed to convert the integrated model into several sequential parts which are operated by power distribution systems or IDCs independently, so that the confidential information sharing issue is solved, and the computing time is significantly reduced. The proposed restoration method is tested on the IEEE 123-Bus Feeder with multiple IDCs to illustrate its feasibility and efficiency.
Reconfigurability For Industry 4.0 Middleware Software Architectures

Industry 4.0 (I4.0) is about creating an intelligent network of machines to handle readjustment in the production system and increase productivity effectiveness. Understanding reconfigurability of the Cyber-Physical System (CPS) becomes important towards successful architecting advanced production systems. This paper proposes a definition of reconfigurability for I4.0 middleware software architecture. The contribution of the paper is a definition of reconfigurability that enables reasoning about reconfigurable I4.0 middleware software architectures. We demonstrate how the reconfigurability definition supports architectural reasoning about reconfigurable middleware through a case study on actual running middleware in the university I4.0 laboratory.
Implementation of rotor field orientation by voltage control on dSPACE system

The aim of this paper is the implementation of the field oriented control for a drive system with induction motor and voltage inverter. The control algorithm implementation was done based on the dSPACE DS1104 prototyping board, on a comprehensive experimental stand. The use of the dSPACE board allowed the control system implementation in the Matlab Simulink environment, therefore convenient transition from the simulation studies to the experimental studies. The control model can be later compiled and converted to high level program code, for example, C, to be integrated in the control program of a microcontroller based industrial equipment.
[WIP] Unlocking Static Images for Training Event-driven Neural Networks

Event driven cameras have the potential to revolutionise the real-time visual sensory processing paradigm. These asynchronous sensors detect change in the environment with low latency and high dynamic range, allowing for orders of magnitude faster systems than the state of the art using intensity cameras. On the other hand, deep artificial neural networks have refashioned machine vision in the last decade, greatly expanding the reach of viable tasks, supported by the creation of many large scale image datasets. In this work, we present a modality to leverage these large scale datasets for the purpose of training off-the-shelf deep learning architectures and re-appropriating them for event-based tasks. To this end, we describe an event representation, EROS, and a method to convert images to an EROS-like representation such that image datasets can train neural networks for event driven applications.
Automated CCA-MWF Algorithm for Unsupervised Identification and Removal of EOG Artifacts From EEG

Affective brain computer interface (ABCI) enables machines to perceive, understand, express and respond to people’s emotions. Therefore, it is expected to play an important role in emotional care and mental disorder detection. EEG signals are most frequently adopted as the physiology measurement in ABCI applications. Eye blinking and movements introduce lots of artifacts into raw EEG data, which seriously affect the quality of EEG signal and the subsequent emotional EEG feature engineering and recognition. In this paper, we propose a fully automatic and unsupervised ocular artifact identification and removal algorithm named automated canonical correlation analysis (CCA)-multi-channel wiener filter (MWF) (ACCAMWF). Firstly, spatial distribution entropy (SDE) and spectral entropy (SE) are computed to automatically annotate artifact segments. Then, CCA algorithm is used to extract neural signal from artifact contaminated data to further supplement the clean EEG data. Finally, MWF is trained to remove ocular artifacts from multiple channel EEG data adaptively. Extensive experiments have been carried out on semi-simulated EEG/EOG dataset and real eye blinking-contaminated EEG dataset to verify the effectiveness of our method when compared to two state-of-the-art algorithms. The results clearly demonstrate that ACCAMWF is a promising solution for removing EOG artifacts from emotional EEG data.
Measuring Consumer Perception for P2P Platform: NLP Approach

The pandemic has forced lenders and borrowers to switch to alternative borrowing. , investment solutions. This research explores the Google reviews of users of four P2P lending platforms in India. To understand user sentiments and emotions about P2P lending platforms. The researchers has analysed user sentiments using Vader and Liu Hu methods and defined the polarity as positive or negative sentiment. Further. , Plutchik”s wheel of emotions was used to relate with the emotions expressed by the users. A purposeful random sampling method was used to select only 4 out of 21 registered P2P lending platforms based on their date of incorporation. The research also defined a framework for carrying out the sentiment analysis process for this study. The overall results showed that 75.51 % of users had positive sentiments. , whereas. , only 19.35% of users had negative sentiments about the P2P lending platforms. As most of the reviews posted were from the borrower”s. , emotion of joy was seen in all 4 platforms. , followed by emotions of sadness. , surprise. , anger. , disgust. , and fear.
The Control Method of Hypersonic Vehicle Based on Machine Learning

Hypersonic aircraft has nowadays become the research hot button in various countries. However, since the control system of hypersonic aircraft is complicated, many effects such as nonlinear factors, state coupling, aerodynamic parameter changes, etc., and also the flight environment and aerodynamic characteristics change drastically, traditional flight control design methods are not capable anymore. For avoiding the application limitations of existing hypersonic aircraft attitude control methods, this paper proposes a hypersonic aircraft attitude control method based on deep reinforcement learning methods, which provides stably control the aircraft in flight scenarios with uncertain environment and drastic changes in aerodynamic characteristics attitude.
Evaluation of Precision of Three PPG Sensor Prototypes for Measurement in Weak Magnetic Field

The paper describes comparative and verification measurements of three developed prototypes of sensors based on the photoplethysmography (PPG) principle for non-invasive acquisition of vital information about the cardiovascular system of an examined person lying inside the scanning area of the running magnetic resonance scanner. All three prototypes are assembled from non-ferromagnetic components and all their parts are shielded. The performed experiments verify practical functionality of all developed sensors for real-time continual pickup of PPG signal in the weak magnetic field up to 0.2 $T$ with the inherent radiofrequency and electromagnetic disturbance. Comparative measurements by commercial pulse oximeter and blood pressure monitor (BPM) devices show good stability and proper accuracy of subsequently determined heart rate values. The supplementary mapping of pressure effect of the inflated BPM cuff certifies necessity of placement of the pressure cuff of the BPM device on the opposite arm than the tested PPG sensor.
Sensing Object-Human Interaction Through 4-Bit Spiral Resonator Based Passive Chipless WiFi Tags

This article demonstrates the chip-less Wi-Fi system design, and its working mechanism is discussed. Many human activities involve interaction with passive objects, and one of the best solutions is introducing Wi-Fi systems. As data can be transferred through the wire and wirelessly, Wi-Fi would be the better choice to transfer data wirelessly. Here, a Wi-Fi touch detection system consists of an antenna, Wi-Fi resonator and combined tag to sense object-human interaction through chipless Wi-Fi tags.
Wildfire risk assessment based on Light Gradient Boosting Machine model

With the expansion of the power grid and the limitation of the geographical environment, some areas have to adopt the way of crossing the forest area to arrange the transmission lines. Some forest areas are sparsely populated and the vegetation is lush. Once a mountain fire occurs, it is easy to spread to the vicinity of the transmission corridor, resulting in the failure of transmission line tripping and reclosing. In order to effectively predict wildfires, this paper proposes a wildfire risk assessment model based on LightGBM. Combining vegetation factors, meteorological factors, terrain factors, and human factors, the moderately correlated fire point characteristics were screened out based on correlation analysis, and a wildfire risk assessment model was constructed. After that, the fire point products of NPP and MODIS are used as the validation data of the model, and the acracy of the model is predicted by the accuracy, precision, recall, F1-Score and AUC values. A comprehensive evaluation showed that the accuracy of the model was 0.86 and the AUC value was 0.83. The results showed that the model could effectively predict wildfire risk.
Identification of multi-center spatial structure in Beijing core area based on multi-source data

The city center plays an important role in the functioning of the city and the multi-center spatial structure has become an important urban space model. In this paper, the urban POI (Points of Interest) data and road network data are used as the basic data sources, and the results of their kernel density analysis are used to construct an urban multi-center spatial structure identification method based on the localized contour tree. Taking the core area of Beijing as an example to achieve a more refined and efficient extraction of the multi-center spatial structure of the study area. Identifying the multi-center spatial structure of the core area of Beijing has reference significance for urban development planning.
Improving Routing System in Vehicular Ad Hoc Network using Real Time Vehicular Traffic Information on City Roads

Vehicle ad-hoc network is an intelligent transport system. We investigate trifles of the ad-hoc networking system and discovered there are a number of inconclusive issues that could lead to the network collapse. In this paper we tried to ameliorate the road management system and solve the traffic jam issue in real time. VANET applications enable vehicles to connect to the internet to obtain real time news, traffic and weather reports. In real-time VANET is a scheme system, where assorted volatile vehicles and connecting devices come in concretion with a wireless medium. Vehicle to vehicle (V2V) communication is a wireless network, which exchange information about the speed and location of contiguous vehicles, shows great assurance to help fudge accidents, diminish traffic congestion and flourish the ambience. Recent studies have showed that routing protocols may redact on vehicle networks developed using dynamic road traffic information for select the most suitable forwarding path or node.
Attribute-based Hybrid Encryption & Privacy Preservation Framework for Securing Broadcast Communication Channels

Broadcast communication channels allow users to transact data packets with large groups of people. Which indicates that a greater number of nodes participate in communication, thereby increasing probability of spoofing, spying, and other network attacks. Due to which, encryption strength for this data must be higher than node-to-node unicast communications. A wide variety of models are available to perform this task, but these models compromise on communication quality of service (QoS) in order to enhance encryption performance, due to which they have limited scalability. In order to overcome this drawback, a novel attribute-based hybrid encryption & privacy preservation framework (ABHEPB) is proposed in this text. The proposed model uses attribute-based encryption with packet-lifetime awareness in order to avoid any intermediate attacks. This is achieved via combination of Fernet model with initialization vector (IV) based encryption. The ABHEPB model uses node-level attributes from all broadcasting nodes in order to create a broadcast key. This broadcast key is recreated during decryption via node-level attributes, which can mitigate key-leakage issues from the communication channel. The IV encryption model uses stochastic values of the same key-vector in order to further improve encryption strength, which imbibes automatic key-validation in the network. The encrypted data is further secured using an exponential mechanism to achieve differential privacy. Due to which, the broadcasting parties cannot accidentally gather identifiable information about other nodes. The proposed model was evaluated under a wide variety of attack scenarios, and its attack resilience along with QoS metrics were evaluated. It was observed that the proposed model had 23% lower delay, 19% better attack resilience, and 20% lower complexity when compared with state-of-the-art methods, thereby facilitating its use for real-time deployment scenarios.
Investigation of the influence of an aluminum cantilever on the polarization of a light field

In this paper, using finite-difference time-domain method, the influence of a pyramidal aperture cantilever on a measurement of light field polarization. It was shown that that the aluminum cantilever has a high effect on the TE polarized wave, while for the CVB, the changes in polarization are not so significant.
Comparative Analysis of Battery Charging Circuits using Solar PV System

This paper gives idea about the comprehensive study followed by comparative analysis of various maximum power point tracking (MPPT) techniques using different charging circuits for battery. The circuitry modeling has been done and implemented in MATLAB/Simulink environment. The peak power of photovoltaic (PV) array is tracked using three different algorithms, namely perturb and observe (P&O), fuzzy logic controller (FLC) and incremental conductance (IC) algorithms. Buck, Boost and Zeta topologies have been employed as a DC-DC converter for battery charging and battery charge controller implementation. A total of nine models have been developed and compared. Comparison of the model has been done with following performance parameters; battery rating, controller efficiency, steady state time, charging current, charging current ripple and charging voltage ripple. The performance and comparison results show which parameters are better in relevant model.
A Neural Network Model for Effective Dielectric Constant Prediction of a Two Layered Microstrip Transmission Line

In this paper, a neural network model is developed to calculate the effective dielectric constant of a two-layered microstrip transmission line. The neural network code is developed in Keras. The model is trained using effective dielectric constant of a two-layered dielectric substrate and dielectric values of substrates. The data required for the training of the network is obtained from a commercial EM solver TaraNG. The suitable network structure is 2-10-10-1. The predicted results are well matched with commercial EM TaraNG solver results.
Intra-User Analysis Based on Brain-Computer Interface Controlled Game

Brain computer interface (BCI) technology represents a growing field of research with extensive applications. This paper focuses on the use of brain signals as a direct communication pathway to an external device. Our goal was to implement a 2D game and control it using a commercially available electroencephalography (EEG) device, by developing an appropriate BCI. The proposed method consists of recording and processing EEG data using the Muse 2 headband, in order to control a 2D game built on Unity game engine. Five subjects were included in the study and each of them played the game when it was trained using EEG data from all other participants (including themselves). Results indicate that participants tend to achieve better scores when the BCI has been trained with their own EEG data, however this is not applicable for all subjects. Furthermore, longer periods of playing the game led to increased control.