Miracle-dz/UniFAR-FTUs huggingface dataset:
A Quad-Polarization Agile Microstrip Antenna with Diode Loaded Cross Slot and Microstrip Line

This paper describes a novel microstrip patch antenna with switchable four polarizations. The proposed antenna consists of a patch antenna embedded with four PIN diodes in cross-slot shape and four microstrip lines (MSL) are connected at the four corners of the patch. By switching the state of the PIN diodes, the sense of rotation of the electric fields in circular polarization (CP) can be reconfigured. Moreover, with the aid of the half-wavelength shorted MSL and four extra diodes, the proposed antenna can switch linear polarization (LP). The concept is validated by the simulation and found to be feasible. The simulated 3-dB axial ratio bandwidth is around 1% for both RHCP and LHCP.
Internet of Things Based Multi-Sensor Fusion For Assistive Mobility Devices

Internet of Things has had a tremendous effect in several fields, including the healthcare sector, to enhance network operation and users' quality of experience. In healthcare applications, people with cognitive, mental, and physical impairment rely on some form of assistive mobility device for their mobility needs and a better quality of life. The traditional wheelchairs are effective for physical impairments but poorly suited for individuals with a combination of physical and cognitive or perceptual impairments. To accommodate users with cognitive impairment, several researchers have used technologies originally developed for mobile robots to create smart mobility devices. With this significant interest, real-time autonomous navigation for the healthcare sector still requires more attention. This paper presents a review of the different existing literature on the internet of things frameworks and multi-sensor fusion techniques for assistive mobility devices (smart wheelchair). An Internet of things framework that takes into consideration scalability and security is proposed. A multi-sensor fusion pipeline for accurate localization and monitoring of assistive mobility devices for navigation purposes is also proposed.
Application of a Complementary Split Ring Resonator Based Biosensor for Detection of Micromolar Glucose Concentrations in Aqueous Solution

This paper presents the application of a complementary split ring resonator (CSRR) based biosensor to determine the concentration of glucose in aqueous solution. Two tightly coupled concentric CSRRs loaded on a patch were realized on a Rogers RT/Duroid 5870 substrate and then a glucose solution was applied on it. The resonant frequency of the bare (without any glucose solution applied) resonator was 5.0 GHz. Change in glucose concentration results in a corresponding change in dielectric properties of the solution. Due to changes in dielectric properties of the surrounding material, the resonant frequency of the resonator shifts. Such a shift can be used to measure the glucose level in the solution. The proposed sensor uses this principle and can sense the glucose concentration in the range of 3 – 7 mg/mL. The resonator has a sensitivity of 1.0 MHz/mgmL−1 and a simple structure. Thus the sensor has the potential to be used for noninvasive blood glucose level measurement.
Ultrasensitive Electrochemical Sensing of Biotin-Avidin Interaction on Gold Electrode Bio-Conjugated With Silver Nanoparticles

The influence of silver nanoparticles (AgNP) labeled with biomolecules on the electrochemical sensing of avidin-biotin interaction was investigated. Self-assembled monolayers of mercapto propionic acid on gold wire electrode were coupled with AgNP labeled biotin or avidin through bio-conjugation of complementary biomolecules. The electrochemical sensing of biotin-avidin interaction on the gold electrode modified with AgNP labeled biomolecules was compared with that of the unlabeled biomolecule layers. The sensing performances were studied using cyclic voltammetry and electrochemical impedance spectroscopy methods in ferric/ferrous redox system with varying concentration of bio-affinity molecules. The peak currents of AgNP labeled layers were gradually increased as concentration of bio-affinity molecules was increased in the electrolyte while opposite effect was observed for unlabeled layers. Here, the sensing mechanism was established to explain the role of AgNP as the electron transfer mediator. The AgNP modified electrode might be considered as ultrasensitive probe to detected biotin-avidin couple below 20 ng L−1 and to sense change of concentration above 10 or 20 ng L−1 at higher concentration region as well. This type of ultrasensitive detection of biotin-avidin interaction should be considered to explore robust and highly sensitive assays in combination with other primary-secondary antibodies for the bio-analyses, separations and clinical diagnostics.
Deep Neural Network for Drowsiness Detection from EEG

This study aimed to detect drowsiness and find optimal electrode set by collecting and classifying the EEG dataset labeled with three classes: awakeness, drowsiness, and sleep. Blindfolded subjects were presented short audio stimulus in random duration and instructed to push button according to audio stimulus. For classification of 3 classes, EEG signal was segmented and labeled according to the sequence of button response. Then, segmented data were directly fed into classifier without further transformation. Training was accomplished by the proposed deep learning including four LSTM layers. The proposed drowsiness detection deep learning network resulted 82.8% accuracy with 18 channels, and 79.8% accuracy with 3 channels located at premotor area of right hemisphere.
Lithium-Ion Battery Pack Robust State of Charge Estimation, Cell Inconsistency, and Balancing: Review

Lithium-Ion battery packs are an essential component for electric vehicles (EVs). These packs are configured from hundreds of series and parallel connected cells to provide the necessary power and energy for the vehicle. An accurate, adaptable battery management system (BMS) is essential to monitor and control such a large number of cells. Series and parallel connected cells also experience different production and operational conditions, which makes it challenging for the BMS to ensure the safe operation of each individual cell. The main functions of the BMS include battery state estimation, cell balancing, thermal management, and fault diagnosis. Robust estimation of the state of charge (SOC) is crucial for providing the driver with an accurate indication of the remaining range. This paper presents the state of art of battery pack SOC estimation methods along with the impact of cell inconsistency on pack performance and SOC estimation. Cell balancing methods, which are necessary due to cell inconsistencies, are discussed as well. Four categories of pack SOC estimation methods are presented, including individual cell, lumped cell, reference cell, and mean cell and difference estimation methods. The SOC estimation methods are compared in terms of algorithm type, computational load, and engineering effort to help practitioners decide which method best fits their application.
Design and Performance Analysis of A Low Power, Low Noise 1.6GHz Charge Pump Integer-N PLL in Different PVT Corners

A fast locking low phase noise 1.6GHz PLL has been presented in this paper. The PLL was designed in 28nm technology with 1V nominal supply voltage. The phase noise of the PLL has been reduced by implementing current starved ring oscillator VCO. The lock time of the PLL is reduced by using a current steering charge pump (CP). The PLL has been designed and simulated in different PVT corners. The simulation results show that the PLL achieves lock state at only 951ns in standard typical PVT (Process, Voltage and Temperature) corner. A low phase noise of only -60.67dBc/Hz at 1MHz has been achieved. It consumes a very low area of only 50u x 45u. The layout of the circuit along with pre and post layout simulation results have been incorporated in this paper.
Broadband Folded Reflectarray Antenna Using Single-Layer Cross-Polarization Conversion Subwavelength Elements

A broadband folded reflectarray antenna (FRA) is presented employing a novel single-layer cross-polarization conversion (CPC) subwavelength element in this letter. The proposed element has a very simple structure and consists of only a square ring resonator loaded with two splits, thereby realizing easy fabrication with low cost. By varying the length of the ring and mirroring the element, a 360° phase change range is achieved with low losses at the center frequency. These characteristics make the proposed configuration unique, as most of the reported FRA elements rely on complex and multilayer structures to realize the broadband CPC. Furthermore, an FRA using the proposed elements is designed and measured at 22 GHz. A good agreement between the simulated and measured results is obtained. The measured results demonstrate that the proposed FRA realizes a 1 dB gain bandwidth of 20.9% and a maximum aperture efficiency of 47.8%.
Bengali Handwritten Isolated Compound Characters Recognition by Applying Transfer Learned Deep Convolutional Neural Network

Optical character recognition (OCR) has been an area of interest for researchers for decades. Many researchers have contributed largely to the development of OCR for script-specific recognition. Handwritten characters recognition has been a big part of this field. Although some literatures have received well-established outcomes, others still haven’t experienced remarkable outcomes yet. Despite being the fifth most spoken language of the world by 228 million people, Bengali has not yet received a remarkable contribution to handwritten characters recognition. Some researchers have offered some promising results for basic Bengali handwritten characters recognition but very few researches offered the recognition of Bengali compound handwritten characters. These few researches have applied support vector machine classifier and some deep neural network classifiers for classification but the outcomes were not much satisfactory. In this research, we considered 171 Bengali compound handwritten characters and apply a modified ResNet-50 model for recognition. We achieved an overall accuracy of 96.15% which outperformed the previous best result of 90.33% by a remarkable margin.
Design, Modeling and Fabrication of a Flexible Actuator

At present, most exoskeleton robots use rigid devices such as motors and rigid links as actuators, but their comfort is low. In contrast, flexible exoskeleton robots made of pneumatic silicone actuators and artificial muscles have the characteristics of high flexibility, strong environmental adaptability, strong compatibility and versatility, and have become the trend of exoskeleton development. The difference between exoskeleton robots is the difference between actuators. At present, it mainly includes flexible actuators (silicone, memory alloy, etc.) and rigid actuators (motors, etc.). Flexible actuators have many potential benefits over rigid actuators, including safer human-computer interaction, diversity of behaviors and adaptability to unstructured environments. This paper will use 3D printing technology to make mold, design and manufacture flexible actuator, and carry out modeling and simulation. Using the actuator can participate in the design and manufacture of flexible robot, and the prospect is bright.
Base Station Antenna Systems for mm-Waves

Lower efficiencies of power amplifiers and increased path loss at higher frequencies are two reasons why utilizing millimeter-wave frequencies for future wireless communications systems is challenging. In this paper, two distinct antenna systems are presented which address this challenge by using high antenna gain. This allows lower transmit power levels, compared to conventional phased array antennas, while still providing high effective isotropic radiated power.
A Transformer Based Approach To Detect Suicidal Ideation Using Pre-Trained Language Models

Detection of Suicidal Ideation in social media has gained special attention in recent years. Different mental health issues like depression, frustration, hopelessness etc directly or indirectly influence suicidal thoughts. Early detection of suicidal thoughts can help people to diagnose and get proper treatment in time. Machine Learning and Deep Learning are playing a vital role in this area to automatize Suicidal Ideation detection. In our study, we wanted to take this trend to the next level and proposed a new detection approach with the help of the Transformer models in the language domain. Basically with Transformers we wanted to analyze raw social media posts and classify the indication of suicidal ideation in them. First, we applied BiLSTM(Bidirectional Long Short- Term Memory) and from there took a jump to Transformer models like BERT (Bidirectional Encoder Representations from Transformers), ALBERT, ROBERTa and XLNET - a complete robust Transformer model based study. The main advantage of Transformer models is having pre-trained language models. Because of this, these models usually perform better. We found they work far better than conventional Deep Learning architecture like Bi-LSTM in our work.
Roulette: A Pruning Framework to Train a Sparse Neural Network From Scratch

Due to space and inference time restrictions, finding an efficient and sparse sub-network from a dense and over-parameterized network is critical for deploying neural networks on edge devices. Recent efforts explore obtaining a sparse sub-network by performing network pruning during training procedures to reduce training costs, such as memory and floating-point operations (FLOPs). However, these works take more than <inline-formula> <tex-math notation="LaTeX">$1.4\times $ </tex-math></inline-formula> the total number of iterations and try all possible pruning parameters manually to obtain sparse sub-networks. In this paper, we present a pruning framework Roulette to train a sparse network from scratch. First, we propose a novel method to train a sparse network by Pruning through the lens of Loss Landscape iteratively and automatically (PLL). We do a theoretical analysis that the curvature of the loss function is higher in the initial phase and can conduct us to start network pruning. According to our results on CIFAR-10/100 and ImageNet dataset, PLL saves up to <inline-formula> <tex-math notation="LaTeX">$4\times $ </tex-math></inline-formula> training FLOPs than prior works while maintaining comparable or even better accuracy. Then we design <bold>push</bold> and <bold>pull</bold> operations to synchronize the pruned weights on different GPUs during training, scaling PLL to multiple GPUs <bold>linearly</bold>. To our knowledge, Roulette is the first network pruning framework supporting multiple GPUs linearly.
EBG Inspired Pattern and Polarization Reconfigurable Antenna

This paper presents the use of electromagnetic bandgap (EBG) structure in the pattern and polarization reconfigurable antenna for beam-steering configurations. The proposed antenna consists a group of EBG structure to control the pattern reconfigurability. While two triangular slits are introduces on the square patches to give polarization reconfigurability. The PIN diode is used as the switching method. A maximum +15° and -15° of beam steering is achieved at both linear and circular polarization.
Detection of Residual Yarn in Bobbin Based on Hough Transform and BP Neural Network

In order to promote the intellectualization of textile industry, in this paper, a step-by-step residual yarn detection algorithm based on Hough transform and BP neural network is proposed. According to the geometric characteristic’s transformation of bobbins with a large amount of residual yarn, the images are converted to the grayscale, then the edges of image are detected by Canny-Otsu algorithm and the outlines of bobbin are detected by Hough transform, the bobbins with a large amount of residual yarn are eliminated by outline detection results. The images which cannot eliminated by outline detection are converted to HSV color space and divide to several sub regions, the HSV values are extracted as the input layer of BP neural network, the residual yarn condition of bobbins as the output layer. The experimental results show that the detection accuracy of algorithm is up to 96%, which meets the requirements of practical application.
Softwarized IoT Network Immunity Against Eavesdropping With Programmable Data Planes

State-of-the-art mechanisms against eavesdropping first encrypt all packet payloads in the application layer and then split the packets into multiple network paths. However, versatile eavesdroppers could simultaneously intercept several paths to intercept all the packets, classify the packets into streams using transport fields, and analyze the streams by brute-force. In this article, we propose a programming protocol-independent packet processors (P4)-based network immune scheme (P4NIS) against the intractable eavesdropping. Specifically, P4NIS is equipped with three lines of defenses to provide a softwarized network immunity. Packets are successively processed by the third, second, and first line of defenses. The third line basically encrypts all packet payloads in the application layer using cryptographic mechanisms. Additionally, the second line re-encrypts all packet headers in the transport layer to distribute the packets from one stream into different streams, and disturbs eavesdroppers to classify the packets correctly. Besides, the second line adopts a programmable design for dynamically changing encryption algorithms. Complementally, the first line uses programmable forwarding policies which could split all the double-encrypted packets into different network paths disorderly. Using a paradigm of programmable data planes—P4, we implement P4NIS and evaluate its performances. Experimental results show that P4NIS can increase difficulties of eavesdropping and transmission throughput effectively compared with state-of-the-art mechanisms. Moreover, if P4NIS and state-of-the-art mechanisms have the same level of defending eavesdropping, P4NIS can decrease the encryption cost by 69.85%–81.24%.
Radio Channel Modeling in a Ship Hull: Path Loss at 868 MHz and 2.4, 5.25, and 60 GHz

This letter presents the analysis of a channel sounding campaign in the hull of a bulk carrier vessel at sub-6 GHz frequencies 868 MHz and 2.4 and 5.25 GHz as well as millimeter-wave (mmWave) frequency 60.48 GHz. For the sub-6 GHz frequencies, path loss (PL) is measured using omnidirectional antennas. The measured PL is fitted to a one-slope PL model that takes into account attenuation due to engine room floor crossings, obstructions, and walls. The fitted model has a reference PL lower than free space and a PL exponent around 1.2. For mmWave wireless communication, high gain directive antennas are typically used. Therefore, the directive radio channel for line-of-sight (LOS) communication is measured using the Terragraph channel sounder and an antenna beamwidth dependent PL model is created. At mmWave frequencies, LOS PL in the vessel is close to PL in a free-space environment, but angular spread values are lower compared to other indoor scenarios.
Cyber Attack Detection Method Based on NLP and Ensemble Learning Approach

In recent years the use of web applications has gone through fast growth. Billions of transactions, sharing information over the Internet have become a common phenomenon because of web applications. Users send necessary and confidential information to web-based applications and store them into databases. Web applications and connected databases are accessible through the Internet, which makes them prone to cyber-attacks. SQL Injection is a cyber-attack that is most pervasive. Attackers steal intended information by injecting SQL codes. Users of web applications could face damaging effects for SQL Injection. Through this research work, a SQL Injection detection method is proposed, which uses Natural Language Processing (NLP) and Ensemble Learning Algorithm. NLP generates feature patterns, extracts features, transforms each text into numerical representation in the form of a vector, and generates a bag-of-words model (BoW model). BoW model trains Random Forest Classifier. The Random Forest Classifier is an ensemble learning algorithm. This algorithm combines several machine learning algorithms and builds one predictive model to get a better detection ability. The trained classifier classifies SQL Injection payloads with higher accuracy from the dataset.
Monitoring land deformation due to groundwater extraction using Sentinel-1 satellite images: a case study from Chapai Nawabgonj, Bangladesh

Remote sensing provides a much more cost-effective and less time consuming alternative way to monitor land deformation than the traditional methods. In this study, land deformation changes in the Chapai Nawabgonj area of Bangladesh have been mapped using InSAR data acquired by the Sentinel-1 satellite between 2018 and 2019. The deformation rate was compared with two boreholes monitored by the Barind Multipurpose Development Authority (BMDA). Significant ground deformation has been found in agreement with the field-level hydraulic head changes in the boreholes in the different time frames in the study area. K-means algorithm was applied to visualize clusters in a map to understand the subsidence rate in the area better. Subsidence was found in the summer, which indicates the decrease in water level, and uplift was found in the winter, which indicates the increase in water level.
Solar PV Integrated Distributed Generation System Protection with Bridge Current Limiter Controller

Distributed generation (DG) systems are endangered to fault because of the rise of fault current level in power system. Further incorporation of DG by power electronic converters are increasing this danger even more day by day. The inverter based distributed generation (IBDG) system is used to work with a solar photovoltaic (PV). This paper proposes a bridge type fault current limiter (BFCL) based control scheme so that the IBDG system can be kept secure. The BFCL is connected after the VSC of the PV system and the system controller controls the impedance of BFCL. As an input to the BFCL activation controller, direct axis and quadrature axis voltage and current at the common coupling point (PCC) are used. To locate the fault in the device and enable the BFCL process, the deviation of current and voltage at the PCC is used. To demonstrate the effectiveness of the suggeseted controller, 3 line to ground (3LG) and 2 line to ground (2LG) faults are applied in grid with which the PV system is connected. With the suggested BFCL based controller, a major mitigation in the fault current magnitude is examined. The fault current mitigation performances have also been contrasted with the conventional series dynamic braking resistor (SDBR) control strategy and it has been demonstrated that the proposed BFCL controller is superior than SDBR.