Miracle-dz/UniFAR-FTUs huggingface dataset:
Effective Computational Efficiency Maximization in Cooperative NOMA Based MEC System

The paper mainly studies how to better apply non-orthogonal multiple access (NOMA) to mobile edge computing (MEC) system under the condition of considering offload interruption, and adopts user collaboration(UC) and wireless power transfer (WPT) technology. First, two users use WPT to collect energy from the base station equipped with the MEC server. Then, to better increase the system capacity and performance, the near user acts as a relay to help the far user offload some of the tasks to the MEC server, using the NOMA protocol for both users. To obtain an energy-efficient MEC design, we first derive the outage probability for both users, then the offloading time allocation and power allocation are jointly designed to maximize the effective computational efficiency, which is a non-convex problem. To deal with the problem, we first transform it into a relaxation problem and then solve it using the sequential convex approximation (SCA) method. Finally, numerical results demonstrate that the proposed scheme introduces NOMA and UC into MEC simultaneously, which has better advantages compared with other schemes.
Designing Reward Functions in Multi-Agent Reinforcement Learning for Intelligent Intersection Control

Road transportation is a significant contributor to the global greenhouse gas emissions. The number of traffic jams, thus the number of hours spent in cars increases year by year. In addition, traffic in an increasing number of cities grew to unbearable levels, significantly increasing emissions globally and locally. The main traffic hubs in cities are intersections with traffic lights, generally supplied with a fixed cycle control, thereby impeding optimal traffic flow and thus optimal emissions. With the spread of reinforcement learning, more and more research addresses this problem. One approach to the problem concentrates on Traffic Signal Control, from which various research emerged in recent times. However, most research uses classical literature rewarding, such as queue length or average speed. This study presents and compares a novel rewarding method using Multi-Agent Reinforcement Learning. The results show that this technique outperforms its rivals in diverse metrics.
A comparison of Series- and Parallel-Hybrid Compenstion Topologies for EV Wireless Charging Systems

Wireless Power Transfer (WPT) is capable of transferring power in a non-contact manner and has excellent application scenarios for electric vehicle (EV) charging. This paper presents a comparison between the series and parallel hybrid compensation topologies, and with the normal series and parallel compensation networks as reference. Those two hybrid topologies are dual-circuits where the negative feedback in the main power transfer channel is built using the complementarity characteristics between the normal series and parallel compensation network. As the misalignment of the WPT pads, the dual-circuit construction counteracts the effects of parameter changes and eliminates the need for sensors and feedback controllers. Meanwhile, those dual-circuits have different charging profiles that may suitable for different EV wireless charging scenarios. Therefore, 400 mm of air gap and 160 mm of lateral and longitudinal pad misalignment were taken into account in the 3.3 kW system and compared side by side. The system efficiency, ZVS operating region are all presented in order to clarify the key advantages, as well as, the limitations of the hybrid compensation topologies with considering the practical end-user experience.
On performance of the zero forcing receiver in presence of transmit correlation

Spatial multiplexing (SM) is an emerging spatial signaling technique that achieves high spectral efficiency in MIMO wireless communication links. We study the performance of the zero forcing receiver (used to recover the transmitted data stream) in flat fading channels with transmit correlation. The results for interference rejection in i.i. d. environments presented by Salz, Winters and Gitlin (1994) may be interpreted in a MIMO single user context to conclude that the zero forcing receiver achieves M /sub r/ -M/sub t/ + 1 order diversity on each stream. We provide an alternative derivation based on Wishart matrix analysis for the same result. In addition our formulation allows the inclusion of transmit correlation in the channel. We show that the SNR on each decoded strewn is a weighted Chi-squared variable with 2(M/sub r/ $M/sub t/ + 1) degrees of freedom. The weight characterizes the degradation in SNR and is accurately quantified by the corresponding diagonal element of the inverse of the transmit correlation matrix.
Erratum to: "The Stochastic Heat Equation with Fractional-Colored Noise: Existence of the Solu- tion"

We give a correction and an extension of Theorem 3.13 of Balan and Tudor (2008). 1. Correction of Theorem 3.13 of Balan and Tudor (2008) Let Ẇ be a Gaussian noise, which is fractional in time (with Hurst index H > 1/2), and colored in space (with spatial covariance kernel f). Theorem 3.13 of Balan and Tudor (2008) gives the necessary and sufficient condition for the existence of the solution of the stochastic heat equation: ut = 1 2 ∆u+ Ẇ , (t, x) ∈ (0, T )× R, (1.1) with u(0, ·) = 0. This condition is equivalent to saying that ‖gtx‖HP < ∞, where gtx(s, y) = [2π(t− s)] −d/2 exp{−|x− y|/[2(t− s)]} := pt−s(x− y) and ‖φ‖HP := αH ∫ T 0 ∫ T 0 ∫
WND-CHARM:Multi-purposeimageclassificationusingcompound imagetransforms

We describe a multi-purpose image classifier that can be applied to a wide variety of image classification tasks without modifications or fine-tuning, and yet provide classification accuracy comparable to state-of-the-art taskspecific image classifiers. The proposed image classifier first extracts a large set of 1025 image features including polynomial decompositions, high contrast features, pixel statistics, and textures. These features are computed on the raw image, transforms of the image, and transforms of transforms of the image. The feature values are then used to classify test images into a set of pre-defined image classes. This classifier was tested on several dierent problems including biological image classification and face recognition. Although we cannot make a claim of universality, our experimental results show that this classifier performs as well or better than classifiers developed specifically for these image classification tasks. Our classifier’s high performance on a variety of classification problems is attributed to (i) a large set of features extracted from images; and (ii) an eective feature selection and weighting algorithm sensitive to specific image classification problems. The algorithms are available for free download from openmicroscopy.org.
Cloud-Edge-End Cooperative Detection of Wind Turbine Blade Surface Damage Based on Lightweight Deep Learning Network

Blade health is directly related to the safety and efficiency of wind turbine (WT) operation. In this article, a cloud-edge-end collaborative detection method for WT blade surface damage is proposed based on lightweight deep learning network. The blade images are obtained by unmanned aerial vehicle. The YOLOv3 is optimized on the cloud server, including backbone network replacement, filter pruning, and knowledge distillation. After model training, the lightweight deep learning model YOLOv3-Mobilenet-PK is obtained and deployed on edge device to detect the surface damage of the WT blades, then the detection results can be viewed through the portable mobile device. The results show that the mean average precision (mAP) of the detection method proposed in this article is over 90%, the detection speed is about two times that of the YOLOv3-DarkNet53. This method has the advantages of fast detection speed, high accuracy, and less occupation of bandwidth.
Machine learning & deep learning algorithm reviews for diabetic retinopathy detection

An artificial neural network, expressed as (ANN) a very popular algorithm in machine learning, developed from artificial neurons, has less computation capability, and so is less popular amongst the researchers. Deep learning or may also be called as deep neural network (DNN) has evolved with great computation capabilities with graphic processing units and also large amount of data acquisition is possible with DNN. This paper is the pure analysis and review of the performances of the ML and DL algorithms and also compares the parameters of the fundus images as it aims the detection of Diabetic Retinopathy (DR) with better accuracies. It also speaks about the analysis of another type of images particularly Optical Coherence tomography angiography also called as OCTA used for detection of DR and shows how its accuracies of detection and grading of the disease differs from that of the fundus images. DR is an impairment of diabetes has an effect on eyes and is a consequence of damaged retinal blood vessels. If these complications are identified in the very early stage, then the patient's vision misfortune can be avoided.
Estimation of the Parameter of the Selected Binomial Population

Suppose we have p (p ⩾ 2) independent binomial Bin(1, θi) populations, where θi’s are unknown. Suppose the ith population is selected if where denotes the mean of a random sample of size ni from the population associated with θi. In this paper, we estimate the parameter of the selected population. It is shown that neither unbiased nor risk-unbiased estimators exist. When additional observations from the selected population are available, we obtain an unbiased, risk-unbiased, and the two-stage UMVCU estimators. The bias and the risk of the obtained estimators are compared through simulation.
Joint Resource Allocation and Location Optimization for UAV-Assisted IoT Wireless Networks in the 6G Era

In this paper, unmanned aerial vehicle (UAV) is deployed between base station (BS) and internet of things (IoT) devices to provide ultra-reliable and low-latency communication (URLLC) services via using short-packet communication. In order to ensure fairness, this paper investigates the problem of maximizing the minimum effective amount of information received by IoT devices in the system under the condition that the amount of effective information transmitted from BS to UAV is greater than the sum of the effective amount of information transmitted from UAV to IoT devices. The problem is a very complex mixed integer non-convex optimization problem. To solve it, we first deduce the closed-form solution of blocklength allocation with the given UAV's position and channel allocation, and then design an alternate iterative algorithm based on successive convex approximation technique and stochastic learning automata approach. The simulation results show that under different parameters, the proposed algorithm can converge to the optimal solution.
Dost-Mental Health Assistant Chatbot

Mental health is a topic that is still new and must be introduced to everyone with tremendous care and caution and slowly made mainstream, so people don't view it as taboo and get appropriate treatment from professionals. Until then, chatbots will play an integral part in mental health assistance, for citizens who can't afford expensive treatments. The main aim of this paper is to introduce a mental health assistant, Dost, a chatbot built on Rasa framework and deployed on Telegram. The aim is to understand the user's problems and proceed to suggest ways to improve the user's condition through daily, casual conversations. Chatbots will play an integral role in the future of healthcare, to make mental health facilities accessible to everyone, from students to senior citizens and offer 24/7 assistance in the absence of doctors.
Study of diffusion control in circular jet using concentric-type DBD plasma actuator

An investigation was carried out into diffusion control for a circular jet using a concentric dielectric barrier discharge plasma actuator, focusing on the effects of electrode size, driving frequency and applied voltage. The actuator was found to induce a flow that was first directed toward the center of the jet, and that was then ejected along the jet central axis. The plasma-induced flow caused an increase in the velocity of the jet, allowing control of velocity fluctuations and the three-dimensional collapse of the jet structure. Increasing the size of the electrode led to an increase in both the volume flow rate and the width of the main jet. In contrast, a smaller electrode caused enhanced contraction of the jet. The results indicated that the actuator provided an effective means of controlling diffusion in the jet.
Thermal Simulation and Characterization of GaN HEMT using Gate Resistance Thermometry and Thermoreflectance imaging

Two electrical methods and one optical method are used, in order to measure the temperature of the hot spot of a 150nm, 2×150µm GaN HEMT. One of the electrical methods is based on Gate Resistance Thermometry (GTR) [1] and the other one is based on the On State resistance Ron obtained from Id(Vgs, Vds) drain current characterization [2]. The optical one uses the reflectivity variation principle to measure the surface temperature and is based on CCD-thermoreflectance [3]. The measurement results are supported by nonlinear simulation, using Finite Element Method (FEM). The use of the GTR electrical method allows a characterization of the temperature along the gates close to the hot spot region and averages the temperature over the gate width. This method demonstrates a relative linearity in the variation of thermal resistance as function of the applied power dissipation and notably a higher thermal resistance value compared to the conventional way of RON extraction. We may notice that with the On State resistance, the average is realized over the Source-Drain length. The optical method based on the ThermoReflectance (TR) allows a measurement of the surface temperature of the component at the hot spot. The values obtained are consistent with those found by electrical methods and FEM simulation up to 10W/mm.
Application of Transformer with Auto-Encoder in Motor Imagery EEG Signals

The motor imagery brain-computer interface (MI-Bel) based on motor imagery has always been the focus of scholars. However, traditional motor imagery electroencephalogram (EEG) recognition systems cannot accurately extract signals with significant or redundant features and have low classification and recognition accuracy. To overcome this difficulty, this study proposes a novel motor imagery EEG based on deep learning. This method applies the Transformer based on the attention mechanism to the field of motor imagery EEG signals, and uses auto-encoder (AE) for dimensionality reduction. The experimental results verify that the proposed method has high average classification accuracy, kappa coefficient and Fl-score by comparing with the combination of Linear Discriminant Analysis (LDA) and K-Nearest Neighbor (KNN) algorithms. It is more suitable for binary classification of motor imagery.
A Survey on Dental Disease Detection Based on Deep Learning Algorithm Performance using Various Radiographs

Dental disease is a significant problem in humans and deep learning is increasingly being used in the field of dentistry. The purpose of this literature review is to identify dental problems such as tooth identification, caries, treated teeth, dental implants, and endodontic treatment using deep learning approaches in dental image analysis which help dentists in their decision-making process. Dental radiographs are essential for the diagnosis and detection of dental issues. The study focuses on the development and use of several image segmentation/ classification algorithms in the extraction of regions of interest from dental radiographs. To predict different forms of impacted teeth, a convolutional neural network is trained, validated, and tested using dental images with labelled images datasets. Our research suggests that Hybrid models such as CNN-SVM, CNN-KNN or CNN-LSTM or K-mean can be trained over mixed data sets to produce excellent results whereas compared to other image segmentation algorithms, UNet architecture performs better at segmenting dental Xray images.
Auto-Tuning of Raw Filters for FPGAs

Many Big Data applications include the processing of data streams on semi-structured data formats such as JSON. A disadvantage of these formats, however, is that applications may require a significant portion of their processing time to unselectively parse all data. As a remedy, so-called raw filters have been introduced in the past, aiming to reduce the data load before the costly parsing stage. Since filtering unparsed data can also become very costly, raw filters can be designed to filter data approximately, in the sense that they allow false positives to occur, in order to be implemented efficiently. While previously proposed CPU-based solutions are restricted to just string filtering, FPGA approaches have recently been proposed with much more expressive raw filters, allowing also to capture numbers and structural relationships. Yet, as a consequence of the variety of filter possibilities as well as the limited amount of resources available on FPGAs, the selection of optimal filters before their deployment has been identified as a complex problem resulting in the potential need to select less expressive filters in order to consume fewer resources. Many Big Data applications (e.g., stream processing) operate on incoming real-time data over long, potentially unlimited time periods. As a consequence, the conditions for which such a filter is optimized can change over time after its deployment. In this realm, this paper presents a new methodology which automatically adapts the hardware accelerator for raw filtering by means of dynamic hardware reconfiguration. Data is sampled on-the-fly during operation and used by an optimizer-in-the-loop to select and generate a raw filter with optimized selectivity for these data samples. As the optimizer has to take into account the resource costs of the hardware accelerator, we introduce models to estimate the resource costs in order to avoid performing a full synthesis. The filter selection problem can thus be solved within a few minutes with results close to the accurate resource cost estimation. If the selectivity of a query changes over time, such as seasonal differences in the analysis of IoT data, the system can auto-tune its filter to adapt to the situation. Depending on the query and the variability of inherent data changes, significant improvements in the amount of filtered data are presented, resulting in a significant parsing speedup in comparison to a state-of-the-art non-adaptive approach.
An Overview of Factors Influencing the Mass Adoption of Self-Driving Vehicles

A self-driving vehicle used to be found in science fiction but would promisingly and gradually become a reality in our daily life in the foreseeable future. Self-driving or autonomous vehicles can bring about disruptive changes to the current transportation system, commuting ways, the concept of vehicle ownership, risk management, legal liability, regulations, urban planning, etc. A highly/fully self-driving vehicle is one of the most complex man-made systems and involves various aspects of modern society. Nevertheless, the public seems to lack a clear enough understanding of self-driving vehicles. To further the development of self-driving technologies, it is necessary to develop a comprehensive understanding of multifaceted factors that could hinder the mass adoption of self-driving vehicles. Therefore, this paper intends to present an overview of some technical factors that could influence the advancement of self-driving vehicles. First, this paper reviews state-of-the-art architectures of a self-driving vehicle. Various sensor technologies are followed with a discussion of their limitations. As two essential software subsystems for realizing a self-driving vehicle, the perception and decision-making systems are presented with an overview of their major tasks and challenges. This paper is concluded with a summary of the technical challenges, being a helpful resource for engineers, the public, policy-makers, and social researchers.
A Novel Feature-based PageRank Algorithm for Node Importance Ranking

Node importance ranking is one of key problems in the study of complex networks. The classical PageRank algorithm only focus on the network structure, which lead to inaccurate ranking result. By introducing the features of node attributes and user preference, a novel feature-based PageRank (FBPR) algorithm is proposed to identify the important nodes accurately and efficiently. The weight matrix and the fixed teleportation vector are redesigned by the feature similarities in the FBPR model. For different application scenarios, we can get different ranking results by adjusting the node attributes factor and the user preference factor. Finally, several simulation experiments are presented to verify the effectiveness of the FBPR algorithm.
Study of Li-ion Battery Exchange Stations in Future Power System

The Lithium-ion (Li-ion) battery is a promising rechargeable battery that converts chemical energy to electrical energy for power consumption. Predicting the runtime and current-voltage (I-V) performance of Lithium-ion batteries is frequently employed in modern applications. The aim of the paper is to investigate the working principles of existing Li-ion battery electrical models and to assess the fidelity of the models. Contributions for electrical models of Li-ion batteries will be simulated by MATLAB/Simulink software to see in terms of mimicking the actions of real-life batteries if they are accurate and valid. The simulation results show that the three RC branches electrical Li-ion battery model is valid when it comes to predicting the behaviors during operations. The proposed strategy is emerging as an attractive technology option to be conducted in the Li-ion battery exchange stations in future power systems.
Classification of Personalized Daily Data from Continuous Glucose Monitoring

During the last few decades, the number of people suffering from diabetes has been increasing. The artificial pancreas (AP) is a closed-loop glucose controller that successfully controls blood glucose (BG) levels, provide automatic insulin delivery, and reduce the risk of hypoglycemia. While recently proposed methods for the AP have shown good results, there many challenges remain in designing successful algorithm. Precise and continuous monitoring of BG levels is required for effective treatment of patients. In this paper, we present a comparison of daily continuous glucose monitoring (CGM) time series data and perform classification for automated control systems. A training dataset of six different patients with type 1 diabetes (T1D) was used to analyze their daily CGM profiles. We compared daily CGM profiles to calculate their minimum value using the root-mean-square error (RMSE) and Manhattan distance. A correlation was made between similar daily CGM profiles using the unweighted pair-group method with arithmetic mean (UPGMA) hierarchical clustering method. The results indicated that the proposed clustering method forms groups with similar values thus indicating various stages of patient's condition.
Admittance Control of Dual-arm Cooperative Platform

The developing requst for flexibility in industrial production leads to the new development of the robotic system in varieable environments. Collaborative Robot can improve the safety and lower manufacturing costs. In this context, base the feedback force information of the end of the manipulator, the active compliance control strategy was adopted to control the force. A position-based impedance control model was proposed. Through position compensation, the dynamic contact relationship between the end of the manipulator and the environment was dynamically adjusted to correct the interference effect of external force on the system. Our approach aims at improving the performance of the task by acting on two levels: estimating and compensating effects on each side.