Miracle-dz/UniFAR-FTUs huggingface dataset:
Privacy Preserving Lightweight Searchable Encryption for Cloud Assisted e-Health System

Large number of health organizations have started making use of cloud assisted technologies to collect, share and process medical records. Wireless Body Area Sensor Network (BASN) is employed to record information such as blood pressure, heart rate, etc from the patient’s body. The collected data is stored on the cloud such that it could be shared among different users for providing better services. However, there are many security challenges in using cloud backed large scale commercial e-health applications. The basic challenge is to maintain confidentiality of data while it is transmitted over the communication channel, or stored and searched by cloud provider. Further, unauthenticated or malicious sensor could upload the fake patient reports to the cloud. These problems are amplified due to resource constrained devices like sensor nodes of BASN as resource exhaustive security operations are not affordable by them. Moreover, a malicious cloud could also violate the privacy of a user by tracking its cloud usage pattern. In this paper, we propose a lightweight and secure searching scheme over encrypted data for e-Health environment. We make use of hybrid encryption scheme including symmetric encryption and optimized identity-based encryption (IBE) scheme to ensure data confidentiality over communication channel and for enhancing cloud privacy. Further, due to its lightweight nature, it is affordable for constrained sensors with minimal computation and storage resources. Implementation results confirm the lightweight nature of our proposed scheme.
A Waveguide-Fed 35-GHz Rectifier With High Conversion Efficiency

A high-efficiency 35-GHz rectifier is proposed in this letter. By employing a shielded microstrip line instead of the commonly used open microstrip line, the rectifier reduces its radiation loss and is fed by a rectangular waveguide rather than a microstrip line to lower the insertion loss. It is designed by an HFSS-ADS cosimulation technology, and consists of a Schottky diode MA4E1317, a waveguide-to-microstrip probe transition, an impedance matching circuit, a dc-pass filter, and a dc load. The proposed rectifier exhibits higher conversion efficiency than the same structure that is designed with an open microstrip line. A prototype is fabricated and measured. High MW-to-dc conversion efficiency up to 67% is obtained at 35 GHz with 19-dBm input power.
Planar Array-Antenna with Improved Radiation Characteristic Using Spiral Shaped DGS

In this paper, the improvement of radiation characteristic for planar array-antenna by incorporating spiral shaped defected-ground-structure (DGS) is presented. The proposed planar array-antenna which is designed to work at the frequency of 2.4GHz consists of 4×4 elements in which the inset feed method is employed to excite each antenna element. The planar array-antenna is implemented using a 1.6 mm thick FR4 epoxy dielectric substrate and analyzed through a simulation. The simulated result of radiation characteristics for the planar array-antenna is then validated by experimental characterization. It shows that the spiral shaped DGS incorporation yields measured radiation characteristics of the planar array-antenna to have the bandwidth response of 140 MHz and the gain achievement of 10.47 dB at its resonant frequency. This is comparable to the simulated result which is showing the potentiality of spiral shaped DGS usage in the improvement of radiation characteristics.
Towards Enhancing Robustness of Prescribed Performance Controllers in the Presence of Control Input Delays*

In this paper, we propose a state-feedback controller, designed with the help of the prescribed performance control methodology, to achieve prescribed performance attributes (i.e., maximum overshoot, minimum convergence rate, maximum steady-state error) on the output tracking error, for a class of uncertain nonlinear systems of known high relative degree, and despite the presence of delays in the control input, which are constant and known. Besides being continuous, the proposed solution does not utilize either bounds of the system nonlinearities or high order derivatives of the desired output trajectory. In addition, no hard calculations, analytic or numerical, are required; making its implementation straightforward. Simulation studies clarify and verify the approach.
Optimal Contention Window Based on Performance Analysis for Full Duplex WLANs

Wireless full-duplex (FD) communication enables one to double the throughput of any link in theory. To achieve such benefits for FD wireless networks, it is very beneficial to develop medium access control (MAC) protocols for wireless FD networks. A backoff mechanism for FD MAC is designed based on distributed coordination function (DCF). In a fundamental backoff operation for FD MAC, nodes transmit data frames as a secondary transmitter even when they have not counted up their backoff timer to zero, which is the difference between the conventional DCF and FD MAC. By considering such operation, several theoretical models to analyze the FD networks have been developed. Whereas, there are few works for deriving the throughput maximization for FD MAC because the FD MAC operation makes the analysis complex and difficult. This paper presents an optimal CW for FD MAC based on theoretical performance analysis regarding FD wireless local area networks (FD WLANs), As an essential contribution in this paper, a theoretical expression for the optimal value of the initial contention window ($CW_{\min}$) is derived, which gives the maximum throughput in networks, from the analytical model for FD MAC. By applying the derived optimal $CW_{\min}$ in FD WLANs, the network throughput is improved significantly for any number of contention nodes.
A geometric phasor extremum seeking control approach with measured constraints

In this paper, we propose an extension for the phasor extremum seeking control approach to solve constrained optimization problems. The proposed technique uses phasor estimates of the objective function and the constraints to compute a geometric constraint satisfaction approach that avoids the violation of the constraints. The proposed method is illustrated to solve several nonlinear optimization problems subject to equality and inequality constraints. Finally, the effectiveness of the proposed approach is illustrated for the optimal operation of a parallel isothermal stirred-tank reactor system.
Classification of Faults in a Distributed Generator Connected Power System Using Artificial Neural Network

Transmission lines are high voltage lines which carry electricity from the power plant to the substation and it is further transmitted to different areas. Distribution lines of low voltage lines for residential and commercial use that bring power from substations to end users. Various types of switching and protecting devices and instruments are used during the transmission and distribution of electrical power. Day by day the demand for electric power is increasing, hence different types of Distributed Generator (DG) such as wind, tidal, solar, diesel generator, etc. are used to increase electrical power generation. The diesel generator is linked to the grid through long transmission networks in this work. Faults must be resolved as soon as possible with respect to customer satisfaction & service quality. The detection method ought to be correct and sharp in order to clarify the fault very soon. The methodology of artificial neural network is a quick-witted method used for classification of fault that can classify the fault. MATLAB and SIMULINK are used for system modeling in this work. The extracted voltage signal fed to the ANN as input which is accurately trained and tested.
Machine Learning Model for Predicting Insomnia Levels in Indian College Students

Insomnia is a widely prevalent sleep disorder that has an adverse effect on the lifestyle of millions across the world. This paper proposes a novel probabilistic approach to identify insomnia. The proposed technique makes use of the positive predictive values of the Insomnia Severity Index and Athens Insomnia Scale to provide a stochastic measure of the existence of insomnia in a college student. A machine learning model for prediction of these levels on the basis of the Depression, Anxiety and Stress levels and their Social Adjustment level has been developed. The dataset was created with 158 samples collected from students by conducting online surveys based on standardizes tests. A neural network model was developed to predict the probability of insomnia. The results predicted by the neural network were compared with their actual values, giving a RMSE value of 0.13, which is deemed viable. The important contributions of this paper are creation of a new dataset, analysis and a probabilistic modelling of insomnia scores which should provide a significant improvement to the current class-based approach of classification.
Transformer Timer Coding Technology

This work is devoted to transformer technology techniques of quantitative information compression by accelerated time-regimented enumeration in unary- binary number system (NS). Symbols computer communication language (CL) alphabets used in information messaging (IM) are concentrated in primary coding tables of ASCII type. They are located in the base memory (BM) of the digital computer (DC) with the entropy in the order of their occurrence in IM. Quantitative compression takes place when the base information units (BIU) are enumerated by digital codes. Multiple repeated compression code (CC) enumeration coding decreases its quantitative loading. Big Data (BD) compression is ensured by its subsequent differentiation of BM multiples.
SDR-Based Reliable and Resilient Wireless Network for Disaster Rescue Operations

A reliable and resilient communication between survivors and the Search and Rescue (SAR) team, after a disaster, is critically important for efficient rescue operations. Unfortunately, in many cases, large-scale disasters like hurricanes, earthquakes and floods can damage the infrastructure of the local terrestrial wired and wireless networks. Several solutions have been proposed to provide reliable and resilient survivors connectivity using GSM base stations deployed by Flying Mesh networks (FMNs). The problem of this solution is that full coverage of the disaster area is not guaranteed and traffic having high bandwidth requirement cannot be supported. In this paper, we propose to offer survivors and rescuers connectivity through multiple radio access technologies (RANs). These RANs are carried by a Wireless Multi-Hop Network (WMHN) combining the existing FMNs with additional opportunistic Mobile Ad-Hoc Networks (MANETs) created by rescuers. We add to each peer node (in WMHN) the capability to be a multi-technology access point. GSM, WiFi and LoRa technologies are selected due to their native support by smartphones, and implemented by a single embedded Software Defined Radio (SDR) platform. In order to enhance the end-to-end Quality of Service (QoS) for survivors/rescuers, we prioritize the serving radio technologies based on the traffic type (SMS, voice and video). We show through simulation results that our solution reduces latency, packet loss and increases the connectivity of survivors.
Techno-Economic and Energy Efficiency Analysis of Optimal Power Supply Solutions for Green Cellular Base Stations

The widespread proliferation of internet access, affordable wireless gadgets, the user data demand and the corresponding extended cellular networks entailing significant energy consumption and carbon footprints. With the added benefits of renewable energy harvesting (REH) technology, telecom base stations (BSs) are predominantly supplied by green power sources to reduce operational expenditure (OPEX) and atmospheric pollution with guaranteed quality of service (QoS). Accordingly, this paper examines the plausibility of optimal power supply solutions such as standalone solar photovoltaic (PV), hybrid PV/wind turbine (PV/WT), hybrid PV/diesel generator (DG) and hybrid PV/electric grid (PV/EG) to feed the Long Term Evolution (LTE) BSs pertaining to technical, economic and environmental aspects in Bangladesh. An extensive Monte-Carlo based simulations are performed to evaluate wireless network performance in terms of throughput, energy efficiency (EE), energy efficiency gain (<inline-formula> <tex-math notation="LaTeX">$EE_{gain}$ </tex-math></inline-formula>), average energy savings, radio efficiency varying system bandwidth (<inline-formula> <tex-math notation="LaTeX">$\mathcal {B}$ </tex-math></inline-formula>) and BS transmission power (<inline-formula> <tex-math notation="LaTeX">$P_{TX}$ </tex-math></inline-formula>) considering the dynamic behavior of traffic intensity and renewable energy (RE) generation profile. By leveraging the cell zooming technique and a green traffic steering framework endeavors to minimize the net present cost and maximize the average energy savings as well. The simulation results reveal that the cell zooming technique attained energy savings yielding up to 36% and improvement of <inline-formula> <tex-math notation="LaTeX">$EE_{gain}$ </tex-math></inline-formula> achieved about 23% with effective modeling of REH. Subsequently, a comprehensive comparison of the aforementioned schemes is pledged for further validation.
Event-triggered Broadcasting for Distributed Smooth Optimization

This work addresses a class of distributed optimization problems where the global objective function is the sum of multiple local convex smooth functions privately held by a group of working agents. Upon modeling the unconstrained distributed optimization problem as a linearly constrained centralized one, a communication-efficient event-triggered first-order primal-dual algorithm that only requires light local computation at each generic time instant and peer-to-peer communication at sporadic triggering time instants is developed to solve the global problem. An $O\left( {\frac{1}{k}} \right)$ convergence rate is ensured, provided that the stepsize satisfies a condition that relates to the Lipschitz constant of the gradient and the Laplacian of the communication graph, and the time-varying triggering threshold is monotonically decreasing and summable. The proposed method is applied to a decentralized logistic regression problem to illustrate its effectiveness, especially in saving communication resources.
Design of a Dual Band Independently Tunable Microwave Absorber

A dual band microwave absorber (MA) with independently tunability is proposed for X-Band applications. The unit cell of the MA is composed of cross-slot with unequal length on the top metallic layer. The dual band characteristics and its tunability is obtained with changing of the cross-slot lengths. The parametric studies on the slot lengths are performed to study the performance of the absorber at different frequencies in the X-Band. The surface current distribution at the different metallic layers are analysed to get more insight behind the absorption mechanism.
A Study on the Flexible Chip-on-Fabric Assemblies Using Anisotropic Conductive Films and Metal-Laminated Fabric Substrates

In this article, chip-on-fabric (COFa) assemblies using anisotropic conductive films (ACFs) and metal-laminated fabric substrates were investigated. To fabricate the metal-laminated fabric substrates, electroless nickel–immersion gold (ENIG) metal-finished Cu circuits were fabricated on the B-stage nonconductive films (NCFs), followed by laminating onto the fabrics. Then, the 50- $\mu \text{m}$ -thick Si chip was bonded onto the fabric substrates using ACFs with a thermocompression (T/C) bonding method. First, the ACFs’ joint properties, such as electrical resistances and adhesion strengths, were evaluated and compared with conventional chip-on-flex (COF) assembly. To enhance static bending flexibility without chip fracture, chip-in-fabric (CIFa) assemblies were developed after optimizing the thickness of a cover layer on the Si chip. Finally, mechanical and moisture-related reliability of the CIFa packages was evaluated through dynamic bending, 85 °C/85% relative humidity (RH), and washing reliability.
Novel Engineering of Smart Electronic Wheelchair with Physiotherapy Treatment Compatibility

Every single individual in this world wants to carry on with a typical human life however mishaps, maladies, senior ship make their longing into incapacity. In addition, there are heaps of impediments also; older folks just as the quantity of incapacitated individuals are expanding step by step. They generally need someone else in moving and need to go under some non-intrusive treatments under the direction of an advisor to recover their strain back. In this task the proposed framework encourages them to move uninhibitedly and securely and furthermore takes the exercises of an advisor in a financially savvy way. This framework is a mix of various controlling highlights, can distinguish deterrent and gives not many sorts of treatments. This system is a combination of different controlling features, has the ability to detect obstacle and provides few kinds of therapies. The person hand movement sensing the sensor to detect the particular position the hand motor will forward and reverse condition. The leg position to detect the position sensor particular limit above the motor will be automatically control the position. This project also send the message to authorized person if any emergency conditions.
Plucking Motions for Tea Harvesting Robots Using Probabilistic Movement Primitives

This letter proposes a harvesting robot capable of plucking tea leaves. In order to harvest high-quality tea, the robot is required to pluck the petiole of the leaf without cutting it using blades. To pluck the leaves, it is necessary to reproduce a complicated human hand motion of pulling while rotating. Furthermore, the rotation and pulling of the hand, and the time taken, vary greatly depending on conditions that include the maturity of the leaves, thickness of the petioles, and thickness and length of the branches. Therefore, it is necessary to determine the amount of translational and rotational movements, and the length of time of the motion, according to each situation. In this study, the complicated motion is reproduced by learning from demonstration. The condition is judged in terms of the stiffness of the branches, which is defined as the force received from the branches per unit length when the gripped leaf is slightly pulled up. Combining the learned motions probabilistically at a ratio determined by the branch stiffness, the appropriate motion is generated, even for situations where no motion is taught. We compared the motions generated by the proposed method with the motions taught by humans, and verified the effectiveness of the proposed method. It was confirmed by experiment that the proposed method can harvest high-quality tea.
Study of the linearly polarized modes and power analysis of double-clad refractive index profile of a dispersion flattened step index fiber

Design of double-clad or W refractive index profile of a dispersion flattened step index fiber which enables the understanding of various LP modes and their 2D as well as radial, intensity and amplitude plots. This index profile results in achieving high power in core for different values of core radius and waist radius of incident Gaussian beam. A comparative study is presented in this paper at three operating wavelengths. Optimal power in core (99.4 %) is observed at 1550nm for dispersion flattened fibers considering Fresnel reflection at the input end of the fiber.
Instant Bi-Lingual Captions

With rapid development of the internet, the tremendous growth has been observed in video sharing platform. Millions of users have been using this platform as source of knowledge, entertainment, self-development, but language barrier has confined the reach of this platform to certain amount of population. Also the hearing-impaired people could not make satisfactory use of this platform. Subtitles play important role in such scenarios, with the help of which people can understand the content of the video. Various applications have been developed for subtitle generation to resolve issue of language barrier and hearing impairment. In this paper we have surveyed different methods for speech recognition and bilingual subtitle generation and proposed a system for instant bi-lingual subtitle generation based on MFCC and HMM.
Input-Feedforward-Passivity-Based Distributed Optimization Over Directed and Switching Topologies

In this paper, a distributed optimization problem is investigated via input feedforward passivity. First, an input-feedforward-passivity-based continuous-time distributed algorithm is proposed. It is shown that the error system of the proposed algorithm can be interpreted as output feedback interconnections of a group of Input Feedforward Passive (IFP) systems. Second, based on this IFP framework, the distributed algorithm is studied over weight-balanced directed and uniformly jointly strongly connected switching topologies. Specifically, the continuous-time distributed algorithm for uniformly jointly strongly connected digraphs has never been considered before. Sufficient convergence conditions are derived for the design of a suitable coupling gain.
Miniaturized Band Stop Frequency Selective Surface for Stable Resonance Characteristics

In this paper, miniaturized 7.45 GHz resonant frequency band stop frequency selective surface (FSS) is designed. The unit cell dimensions of designed FSS is only about 0. 1λ0 at the 7.45 GHz. Proposed design involves a crossed dipole metallic element together with meander shape on the substrate. Simulation results provide about 800 MHz bandwidth (7.1 GHz - 7.9 GHz) with -20 dB insertion loss. The FSS properties are studied on a unit cell using electromagnetic (EM) solver to observe the characteristics. Proposed FSS demonstrates a stable resonance frequency behavior for the arbitrary angle of incidences in both the polarizations such as TM and TE modes. Thus, the design holds a polarization independent characteristic for all incident angles and polarizations. Finally, the FSS properties are validated by a fabricated array of 31×31 mm2.
Detection of Intent-Matched Questions Using Machine Learning and Deep Learning Techniques

Questions which are syntactically different, yet having the same intent, give a poor encounter to both the writer of an answer as well as the individual who searches for the answer in social Q&A online platforms such as Quora, Yahoo Answers, and StackOverflow. In order to maintain a rich and diverse database of answers, ensuring the uniqueness of every question on such Q&A platforms is of utmost necessity. The objective of this proposed work is to eliminate redundancy/duplicacy of userentered questions so as to increase the relevance of the answer(s) provided to semantically similar questions. This problem is a closed challenge taken from Kaggle, an online platform to learn and compete in data science challenges. The dataset worked upon in this paper has been obtained from Kaggle and LSTM with Euclidean Distance outperform other algorithms with log loss of 0.14.
Vehicle Logo Recognition using RCNN for Intelligent Transportation Systems

Vehicle logo recognition systems has a significant role in applications such as traffic monitoring, security system and surveillance system. To categorize variant vehicle logos with higher accuracy, deep learning based RCNN (Recurrent Convolutional Neural Network) is proposed. The quantitative analysis of the proposed RCNN are related with the state of art techniques viz., SVM, k-NN, Naive Bayes and Decision tree with HOG features in terms of recognition parameters like accuracy, specificity, sensitivity and precision values.