Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of Temperature on the Illumination Time Dependent Photocurrent of Amorphous InZnO Thin-Film Transistors

The influence of temperature on the time dependent photocurrent $(I_{ph})$ of amorphous InZnO (a-IZO) thin-film transistors (TFTs) under illumination with different wavelengths is investigated. Strong dependence of photocurrent on temperature is observed with the photocurrent follows the Arrhenius relationship. As the Iph increases with the increasing illumination time, the activation energy $(E_{a})$ of Iph extracted from the Arrhenius plots shows two trends, with the Ea decreases firstly, and increases subsequently after a minimal value is reached. The decreasing and increasing trends suggest that the barrier lowering effect at the source side near the back channel and the screening effect of the accumulated photo-induced ionized oxygen vacancies within the channel region near the front channel are the dominating physical mechanisms of Iph, respectively.
Reducing Latency: Improving Handover Procedure Using Machine Learning

We present a Machine Learning (ML) use-case for reducing latency in wireless networks, by replacing some of the traditional and time-consuming procedures. The proposed method reduces the time needed for collecting a large number of measurements for the target cell selection during the inter-frequency handover procedure. Our paper contributes to the current literature, firstly, by showcasing how ML can replace traditional procedures in order to reduce the latency. Secondly, by presenting results based on the real measurements from a live network. Finally, by training a huge number of models over a large geographical area, where multiple base stations are deployed, reporting the performance over all those models. This contribution extends previous work in the area, that was based on simulations and improves confidence in the applicability of the proposed solution in real networks. The proposed method not only reduces the total handover delay and service disruption but can also be considered as an enabler for new verticals and use-cases, as we discuss in this paper.
A High Speed Montgomery Multiplier used in Security Applications

Security plays a major role in data transmission and reception. Providing high security is indispensable in communication systems. The RSA (Rivest–Shamir–Adleman) cryptosystem is used widely in cryptographic applications as it offers highly secured transmission. RSA cryptosystem uses Montgomery multipliers and it involves modular exponentiation process which is attained by performing repeated modular-multiplications. This leads to high latency and owing to improve the speed of multiplier, highly efficient modular multiplication methodology needs to be applied. In the conventional methodology, Carry Save Adder (CSA) is used in the multiplication and it consumes more area and it has larger delay, but in the suggested methodology, the Reverse Carry Propagate (RCP) adder is used in the place of CSA adder and the obtained output shows promising results in terms of area and latency. The simulation is done with Xilinx ISE design suite. The proposed multiplier can be used effectively in signal processing, image processing and security based applications.
Joint Trajectory and Power Optimization for UAV Covert Transmission

In this work, we consider behavioral security in unmanned aerial vehicle (UAV) communication networks, which means the UAVs implement the communication process without causing third-party perception. Specifically, we consider a scenario where the UAV aims to transmit a message to Bob during its flight as undetectably as possible in the presence of a warden Willie. We jointly optimize the UAV’s transmit power and flight trajectory in order to maximize Willie’s detection error probability. To solve the non-convex optimization problem, we decompose the problem into two sub-problems and propose an iterative approach that optimizes the trajectory and power allocation in an alternating manner. Simulation results are provided to verify the effectiveness of the proposed scheme.
Beam Management Based Multi-cell Interference Suppression for Millimeter Wave Communications

Millimeter wave (mmWave) communications is a promising solution for next-generation systems with a higher data rate. However, due to the narrow beamwidth characteristic of mmWave, inter-cell interference (ICI) is mainly caused by beams used by neighboring cells. In this paper, we aim to maximize the achievable sum rate of users at a mmWave cell in the downlink communication scenario of multi-cell systems. Since the formulated problem is a mixed-integer nonlinear programming problem, we decompose the problem into two subproblems, where one is solved by a multi-cell least beam interference (MLBI) algorithm to mitigate such ICI. And another is to guarantee edge users’ (UEes’) Quality of Service (QoS) and improve the achievable rate through a multi-user multi-beam prioritized power allocation (MPPA) algorithm. Finally, simulations and analyses are conducted to verify the efficiency of the proposed algorithms.
Cylindrical Massive MIMO System for HAPS: Capacity Enhancement and Coverage Extension

Ultra-wide coverage is one of the important concepts of the 6th generation (6G) mobile communications. High-altitude platform stations (HAPS) have attracted a great deal of attention as a leading candidate for the coverage extension. The service link capacity can be increased significantly by means of multiuser multiple-input multiple-output (MIMO) technologies combined with a conventional large-scale planar array antenna. However, the coverage achieved by the planar array is limited to the circle whose radius is only tens of kilometers. This paper proposes a cylindrical massive MIMO (mMIMO) system for achieving ultra-wide coverage such as 100 km as well as high system capacity in HAPS. Assuming the area radius of 100 km, simulation results show that the proposed system improves signal-to-interference-plus-noise ratio by about 10 dB at the boundary of the area, and moreover it achieves up to about 2.1 times as high capacity as the conventional mMIMO with the planar array.
Wireless Power Transmission in Electric Vehicle Using Solar Energy

Wireless Power Transfer [WPT] using the magnetic induction technology which could avoid humans from the hazardous accident caused due to the using of cables. We improve power transfer efficiency between the coils by using MOSFET (metal oxide semiconductor field effect transistor) switches in the inverter. Due to this higher frequency, the charging of the battery will be fast and high efficient. This will enhance the EV driving experience. By introducing WPT in an electrically powered vehicle, the barriers of charging time, range, and price can be easily controlled. The WPT era is developing rapidly in recent years.
MHA-Net: Multipath Hybrid Attention Network for Building Footprint Extraction From High-Resolution Remote Sensing Imagery

Deep learning approaches have been widely applied to building footprint extraction using high-resolution imagery. However, the traditional fully convolution network still has problems in recovering spatial details and discriminating buildings with varying sizes and styles. We propose a novel multipath hybrid attention network (MHA-Net) to address these challenges. We design a separable convolution block attention module and an attention downsampling module as the basic modules with separable convolutions and channel attention. The MHA-Net architecture consists of three components: the encoding network, multipath hybrid dilated convolution (HDC), and dense upsampling convolution (DUC). The encoding network is used to encode the high-level semantic contexts of images. The multipath HDC aggregates multiscale features by combining rich semantic representations extracted by HDCs, which can achieve promising results in extracting tiny buildings. The DUC is capable of recovering precise spatial information of buildings. We evaluate our network on two public datasets: the WHU aerial building dataset and the Massachusetts building dataset. According to the experimental results, MHA-Net outperforms other classical semantic segmentation models and several recent building extraction models. In particular, MHA-Net can improve the extraction accuracy of small buildings and is robust to complicated building roofs.
Comprehensive Investigation on Electrical Properties of 4H-SiC VDMOS Under Uniaxial and Biaxial Mechanical Strains

In this paper, we have comprehensively stud-ied the performance boosts of 4H-SiC VDMOS under the mechanical strain. The electrical properties of 4H-SiC VDMOS under the biaxial and uniaxial tensile and compressive strains along the channel direction are examined thoroughly. We find that the biaxial and uniaxial compressive strain benefit the VDMOS, and the biaxial and uniaxial tensile strain loss the VDMOS. Because the mechanical strain affects carriers’ transportation in 4H-SiC’s inverted channel distinctly, the strain effects are strongly correlated with the gate voltage. It shows that the mechanical strain can enhance the drain current to a greater degree at lower gate voltage, and it also shows that both biaxial and uniaxial strain can cause threshold voltage’s shift.
Accurate Fall Localization for Patient based on GPS and Accelerometer Sensor in Outside the House

Falling of the elderly patients was increased every year and can cause head injury or broken bones. The elderly cannot move or making calls to the medical emergency center by himself after fall, which leads to increase the danger of fall injuries. This paper aimed to design and implemented a wearable fall detection system (FDs) which is used for detecting the falling for the elderly and helps them after a fall by making automatic communication between them and caregivers. A proposed system which includes the Bio-sensor, localization module based on GPS, controller unit, and GSM unit will alert the caregivers by sending messages contained his/her health status and geolocation (latitude and longitude coordinates) immediately when he or she passes a selected threshold of falling. The proposed system was validated by using statistical analysis based on mean absolute error. The fall detection system appeared the ability to distinguish between fall and normal daily activities of the elderly with an accuracy of 99.2% based on the proposed algorithm which compared acceleration magnitude and fall threshold values, called the FDB-FTAM algorithm. The result showed, the proposed system was satisfactory performance and more accurate when get the patient location with 1.938×10-5 °, and 1.327×10-5 ° of mean absolute error for latitude and longitude information respectively.
Cooperative Relative Positioning using Signals of Opportunity and Inertial and Visual Modalities

The global navigation satellite system (GNSS) is primarily employed for positioning by most modern navigation systems. However, the application requirements of fully autonomous vehicles cannot be satisfied solely by GNSS and thus a combination of positioning and navigation approaches need to be explored. This work investigates how reliable relative positioning can be achieved in GNSS-challenged application scenarios using a combination of signals of opportunity (SOPs), as well as inertial and vision data. The proposed cooperative relative positioning system (CRPS) exploits this data for real-time positioning, and employs a vehicle tracking algorithm to accurately estimate the vehicle’s trajectory in space and time without the use of any GNSS information. Experiments conducted in an outdoor setting demonstrate the applicability of the proposed CRPS and its performance against standalone positioning approaches including GNSS.
Joint Edge Content Cache Placement and Recommendation: Bayesian Approach

Content caching is a key technology driving beyond 5G mobile edge computing and hence, an efficient mechanism is needed to satisfy ultra-reliable low-latency communication. One such mechanism to reduce latency is to use recommendation influenced caching algorithm. To enable a more efficient wireless caching, in this paper, joint optimization of both caching and recommendation is formulated and the influence of the recommendation on the popularity is modelled through a probability transition matrix. To maximize the cache hits, an algorithm is presented to find the optimal joint caching and recommendation actions. Two estimation algorithms namely Point estimation and Bayesian estimation methods are presented. Further, theoretical guarantees are provided on the performance of the algorithm. Finally, simulation results are provided to demonstrate that the proposed algorithm significantly outperforms the existing algorithms in terms of average cache hit rate.
Detecting Crowdsourcing Impasses

The adoption of crowdsourcing systems to support successful and effective collaboration among actors to perform a variety of tasks continues to grow. The success of such systems relies on actor willingness to contribute to task achievement in pursuit of a collective goal. This willingness may be negatively affected under certain circumstances. One area where this is the case is work progressing insufficiently or even grinding down to a halt. Such situations can be referred to as impasses and this has been captured more formally in the form of the Stasis pattern in previous work. In this paper, we first investigate the various forms stasis can take, next we characterize these forms in the form of a classification problem, and finally, detect their presence and predict the likelihood of their future occurrence in a practical setting. The latter is achieved through an exploratory case study involving computational analyses of development activities within an open-source crowd-sourced software platform (GitHub). Our findings contribute towards a rich understanding of stasis in crowdsourcing systems and how its various forms can be detected early (and thus mitigated) or even prevented altogether. As such, this work helps improve the chances of successful collaboration through the use of these systems.
ESPC_NASUnet: An End-to-End Super-Resolution Semantic Segmentation Network for Mapping Buildings From Remote Sensing Images

Higher resolution building mapping from lower resolution remote sensing images is in great demand due to the lack of higher resolution data access, especially in the context of disaster assessment. High resolution building layout map is crucial for emergency rescue after the disaster. The emergency response time would be reduced if detailed building footprints were delineated from more easily available low-resolution data. To achieve this goal, we propose a super-resolution semantic segmentation network called ESPC_NASUnet, which consists of a feature super-resolution module and a semantic segmentation module. To the best of authors’ knowledge, this is the first work to systematically explore a deep learning-based approach to generate semantic maps with higher spatial resolution from lower spatial resolution remote sensing images in an end-to-end fashion. The experimental results for two datasets suggest that the proposed network is the best among four different end-to-end architectures in terms of both pixel-level metrics and object-level metrics. In terms of pixel-level $F$1-score, the improvements are greater than 0.068 and 0.055. Regarding the object-level $F$1-score, the disparities between ESPC_NASUnet and other end-to-end methods are more than 0.083 and 0.161 in the two datasets, respectively. Compared with stage-wise methods, our end-to-end network is less impacted by low-resolution input images. Finally, the proposed network produces building semantic maps comparable to those generated by semantic segmentation networks trained with high-resolution images and the ground truth utilizing the two datasets.
Optimization of Cell Individual Offset for Handover of Flying Base Station

Flying base stations (FlyBSs) mounted on unmanned aerial vehicles (UAVs) are widely used in mobile networks to improve a coverage and/or quality of service for users. To ensure a seamless mobility of the FlyBSs among the static base stations (SBSs), an efficient handover mechanism is required. In this paper, we develop a novel handover mechanism determining the serving SBS for the FlyBS in order to increase the sum capacity of the users served by the FlyBS. We propose to dynamically optimize the handover by adjusting the cell individual offset of the SBS via Q-learning. The results show that the Q-learning converges promptly and the proposed approach increases the users capacity (by up to 18%) and their satisfaction with required minimum capacity (by up to 20%) comparing to state-of-the-art algorithms.
Energy-efficient Optimization for IRS-assisted Wireless-powered Communication Networks

Wireless-powered communication is a promising technique to provide convenient and perpetual energy for energy-constrained wireless devices. However, the uplink information transmission of wireless devices in wireless-powered communication networks relies on the harvested energy from the downlink-energy-transfer power station (PS). To tackle this issue, we propose a new intelligent reflecting surface (IRS)-assisted wireless-powered network architecture, where an IRS is deployed between a PS and multiple wireless-powered users to enhance the efficiency of energy harvesting. The total energy efficiency (EE) of all wireless-powered users is maximized by jointly optimizing the energy transfer matrix of the PS, the transmission time and power of users, and the phase shifts of the IRS. The formulated problem is non-convex and challenging to solve. Accordingly, the alternating optimization approach, Dinkelbach’s method, and the variable-substitution approach are used to solve it. Simulation results verify the effectiveness of the proposed algorithm.
Experimental Evaluation of a Wireless LAN Relay System Using Unmanned Aerial Vehicles

A wireless LAN relay system is designed to extend the transmission range while maintaining stable communication. In this paper, we propose a wireless LAN relay system using multicopter unmanned aerial vehicles (UAVs) and evaluate the communication performance. For the demonstration experiment, we employ air-to-air and ground-to-air communications, where the former is a link between UAVs, and the latter is a link between a UAV and ground node. We measure the throughput and the received signal strength indicator, and clarify the communication performance of the wireless LAN relay system.
Inter – intra observer variability using deep learning and traditional image processing for breast cancer

Breast cancer is one of the most common life-threatening diseases that affects women globally. Saudi Arabia is also one of the countries that suffer from a serious number of this disease among women. In terms of diagnosis modalities, a mammogram is the first line for detecting breast cancer. In addition, breast cancer can be screened by real-time ultrasound images, which are of relatively less quality and have more impact (noninvasive) images. Therefore, the purpose of this study is to develop image enhancement techniques using deep learning and image processing techniques. The main goal is to improve the ultrasound images in order to help radiologists screen the disease more accurately. For this study, ninety female patients of ages between 15 – 77 years are considered. These patients were already diagnosed using ultrasound with breast lesions. The images are visually graded and evaluated by two trained radiologists, both pre- and post-enhancement. In particular, two parameters were considered; 1) BI-RAD categories, 2) Breast cancer classification. The agreement between radiologists and post-enhancement was assessed using simple kappa and weighted kappa statistics. Moreover, sensitivity and specificity are also calculated.
Lung – Pleura Carcinoma Detection Using Machine Learning

Identification of pleural carcinoma using classification equipment with 98.30% accuracy is presented in this work. To evaluate the effectiveness of the Machine Learning Algorithms, which is divided into clinical, and health data from patients who were part of the collection of lung cancer diagnostic data. These algorithms used to predict and analyze the effectiveness of various machine-learning algorithms associated with lung disease based on medical and patient health data and to guide patients and physicians in early detection or early treatment options. Separation processes are performed with different machine learning algorithms and success levels are indicated. Various algorithms were tested to achieve success rates of approximately 98.30% obtained. Among the tried algorithms, Linear Discriminant Analysis provides the most effective isolation process.
Learning Target Template for Acoustic Event Detection From Low-SNR Training Data

Acoustic events of interest sometimes are inherently intermixed with strong background noise, like underwater sounds or mechanical fault signals. The noise usually creates a low-SNR scenario, making it more difficult to detect the events of interest. Suffering from lack of prior knowledge, it is a difficult job to extract “correct” features from noisy acoustic mixtures for the target events. This paper addresses the detection problem for acoustic events based on low-SNR training data. A novel learning algorithm is proposed to extract the target’s template from noisy training data. Based on a target-guided band-pass filter using wavelet packets, which has been proposed in our previous work for low-SNR event detection, the template of a specific event is represented in the context of the wavelet packet energy spectrum. Template learning is then formulated as an optimization problem maximizing the detection performance which is quantified as the output SNR of the filtered signal. It is further proved that the original complex optimization problem is equivalent to a linear program which is easy to solve. A relatively sufficient database was built to simulate the noise-polluted training signals and the low-SNR detection task. Results show that the learning method can work at −15 dB and above, and the trained templates exhibit excellent detection performance comparable to the templates extracted from clean samples.
Success-Probability-Based Power Allocation for Downlink PNC in Multi-way Relay Channels

In this paper, we propose a novel power allocation scheme for physical-layer network coding (PNC) in downlink multi-way relay channels (MWRC). The power allocation is formulated as a constrained optimization problem, where the aim is to maximize the success probability under a total power constraint when using Babai estimation for signal detection. Optimizing over this metric allows us to maximize the probability of successfully decoding a chain of network codes, which is of crucial importance in downlink multi-way PNC. Specifically, to meet the different requirements for transmission quality in applications, we consider different aggregate measures of success probability over the participating user terminals, i.e., the arithmetic mean, the geometric mean, and the maximin. For each measure, we formulate a constrained optimization and demonstrate the concavity of the objective, allowing us to obtain solution efficiently via iterative means. The performance of the proposed power allocation schemes for downlink PNC in MWRC is evaluated by means of computer simulations over Raylegih fading channels. The results demonstrate the effectiveness of the proposed schemes in improving the success probability in the reception of a chain of network codes.