Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Design of Highly Efficient and Highly Compact PCB Winding Inductors

In industrial power electronic systems, power density and costs are key design criteria. Therefore, very high switching frequencies are commonly used in order to minimize the volume of the inductive components. However, especially in high-current applications, the well-known skin and proximity effects as well as the fringing field around the air gaps of inductors, impede the design of these components, as they are usually all reducing the effective cross-sectional area of solid inductor windings. Consequently, litz wire windings would have to be used, which is often impossible, mainly due to cost reasons. This paper introduces a new, simple and efficient approach on how to integrate inductor windings directly into the PCB, by mitigating the high-frequency losses due to a relocation of the air gap. In this way, its fringing field can be used to partly compensate the parasitic magnetic fields causing the skin and proximity effects. Hence, very low production costs and high power densities can be achieved. In a first step, simple design guidelines are derived and verified by means of simulations. Finally, a possible hardware implementation of the proposed concept is presented.
Bayesian Inference for Multi-Line Spectra in Linear Sensor Array

For a linear sensor array, using line spectra is a common technique for estimating directions of arrival (DOA) of single-tone sources. Yet, very few papers consider multitone sources. For the first time, we provide the optimal Bayesian inference for multi-line spectra, i.e. a superposition of line spectra, and estimate the DOAs of the multi-tone sources. For tractable computation via fast Fourier transform, we apply a grid-based method, in which source's tones and sensor's array measure are both uncorrelated. Exploiting this method, we interpret the superposition of sensor's data as a complex Gaussian mixture of multi-tone signals. We then estimate DOA via conjugate Von-Mises, also known as circular Gaussian distribution. Our simulation shows that the multitone method is superior to traditional single-tone method for detecting multi-tone source's frequencies, particularly for the sources with overlapping frequencies. The posterior DOA's resolution can be tuned via Von-Mises' parameter a priori, which enhances the sparsity of DOA's estimation.
A Keyword-based Scholar Recommendation Framework for Biomedical Literature

With the development of modern technology, more and more research papers have been published and shared in various digital databases. However, it is time-consuming for researchers to find suitable scholars who study the same research field with them. To address this issue, we focus on proposing a keyword-based scholar recommendation framework, which can help users to advance their research by recommending scholars that align with the users interests. We first utilize keywords that are extracted from abstract to construct a word-word co-occurrence graph in each query. Based on the graph, we propose an approach to find core nodes to solve cold start problem by treating these core nodes as the users interests. We then combine these core nodes and authors to build a bipartite graph, and adopted the PersonalRank algorithm to rank authors based on the bipartite graph. Finally, we design a recommendation evaluation criterion by comparing our recommendation lists with the results lists of Microsoft Academic search. Experimental results show that our recommendation framework can effectively and efficiently generate scholar recommendation in the situation that lack of the citations times, user behavior and other important indicators.
Optimization of the Power Consumption for the Biped Robots in a Single Support Phase

Robots have to be able to move on different surfaces while ensuring the motion feasibility. One of the issues that have always attracted the attention of researchers in this field is optimization of power consumption along with maximum stability during the motion. Lots of basic gait parameters can be expressed by a planned seven-linked bipedal robot. In this study, parameter optimization being used in order to optimize the problem of motion obtained in a way that maximizing stability of robot and minimizing energy and also preserving its stability. Zero moment point scale is being used to calculate the stability of a robot. Experimental tests were performed for motion analysis of walking correctly and the results were validated. Finally, due to the presented model and optimized issue and predicted path, the robot can move like a person in a way that has maximum stability along with minimum power consumption. Comparison of experimental results versus presented model was used in each step to validate the accuracy of the proposed method.
Design of a UAV-Based Videosurveillance System with Tactile Internet Constraints in a 5G Ecosystem

One of the main goals of the new fifth generation of cellular networks (5G) is to provide the programmability, flexibility, and modularity that are required to create multiple logical networks, called network slices, each tailored for a given vertical use case, on top of a common network infrastructure. A particular network slice that is expected to be able to create a plethora of new opportunities and applications, is the Tactile Internet, aiming at guaranteeing e2e delays not greater than 1 ms to its users. Matching this very severe requirement does not only depend on the length of the physical path that information should follow flowing from sensors to actuators, but also on the size of each information. This is the case of a video-surveillance system that uses Unmanned Aerial Vehicles (UAV) to capture high-resolution images with a frame rate of thousands frames per second. Considering this kind of scenarios, this paper proposes a Tactile Internet architecture for video-surveillance systems. An analytical model based on Markov chains is defined to evaluate delays and job loss probability in order to allow designers to dimension system elements to be compliant with the Tactile Internet requirements.
Inhibition in Random Neuronal Networks Enhances Response Variability and Disrupts Stimulus Discrimination

Inhibition is considered to shape neural activity, and broaden its pattern repertoire. In the sensory organs, where the anatomy of neural circuits is highly structured, lateral inhibition sharpens contrast among stimulus properties. The impact of inhibition on stimulus processing and the involvement of lateral inhibition is less clear when activity propagates to the less-structured relay stations. Here we take a synthetic approach to disentangle the impacts of inhibition from that of specialized anatomy on the repertoire of evoked activity patterns, and as a result, the network capacity to uniquely represent different stimuli. To this aim, we blocked inhibition in randomly rewired networks of cortical neurons in-vitro, and quantified response variability and stimulus discrimination among stimuli provided at different spatial loci, before and after the blockade. We show that blocking inhibition quenches variability of responses evoked by repeated stimuli through any spatial source; for all tested response features. Despite the sharpening role of inhibition in the highly structured sensory organs, in these random networks we find that blocking inhibition enhances stimulus discrimination between spatial sources of stimulation, when based on response features that emphasize the relation among spike times recorded through different electrodes. We further show that under intact inhibition, responses to a given stimulus are a noisy version of those revealed by blocking inhibition; such that intact inhibition disrupts an otherwise coherent, wave propagation of activity.
An FPGA-Accelerated Design for Deep Learning Pedestrian Detection in Self-Driving Vehicles

With the rise of self-driving vehicles comes the risk of accidents and the need for higher safety, and protection for pedestrian detection in the following scenarios: imminent crashes, thus the car should crash into an object and avoid the pedestrian, and in the case of road intersections, where it is important for the car to stop when pedestrians are crossing. Currently, a special topology of deep neural networks called Fused Deep Neural Network (F-DNN) is considered to be the state of the art in pedestrian detection, as it has the lowest miss rate, yet it is very slow. Therefore, acceleration is needed to speed up the performance. This project proposes two contributions to address this problem, by using a deep neural network used for object detection, called Single Shot Multi-Box Detector (SSD). The first contribution is training and tuning the hyperparameters of SSD to improve pedestrian detection. The second contribution is a new FPGA design for accelerating the model on the Altera Arria 10 platform. The final system will be used in self-driving vehicles in real-time. Preliminary results of the improved SSD shows 3% higher miss-rate than F-DNN on Caltech pedestrian detection benchmark, but 4x performance improvement. The acceleration design is expected to achieve an additional performance improvement significantly outweighing the minimal difference in accuracy.
Feature-Selection-Based Ransomware Detection with Machine Learning of Data Analysis

Ransomwares are continuously produced in underground markets such that increasingly high-level and sophisticated ransomwares are spreading all over the world, significantly affecting individuals, businesses, governments, and countries. To prevent large-scale attacks, most companies buy intrusion detection systems to alert regarding any abnormal network behavior. However, they cannot be detected using conventional signature-based detection even though ransomwares belong to the same family. In this study, a method is provided to develop a network intrusion detection model that is based on big data technology. The system uses Argus for packet preprocessing, merging, and labeling the known malicious data. A concept of Biflow was proposed to replace the packet data. Further, we observe that the data size is reduced to 1000: 1. Additionally, the characteristics of a complete traffic are obtained. Six feature selection algorithms were combined to achieve a better accuracy in terms of classification. Finally, the decision tree model of the supervised machine learning was used to enhance the performance of intrusion detection system.
Fast Projection onto the $\ell_{\infty,1}$ -Mixed Norm Ball Using Steffensen Root Search

Mixed norms that promote structured sparsity have broad application in signal processing and machine learning problems. In this work we present a new algorithm for computing the projection onto the $\ell_{\infty,1}$ ball, which has found application in cognitive neuroscience and classification tasks. This algorithm is based on a Steffensen type root search technique, with a number of improvements over prior root search methods for the same problem. First, we theoretically derive an initial guess for the root search algorithm that helps to reduce the number of iterations to be performed. Second, we change the root search method, and through an analysis of the root search function, we construct a pruning strategy that significantly reduces the number of operations. Numerical simulations show that, compared to the state-of-the-art, our algorithm is between 4 and 5 times faster on average, and of up to 14 times faster for very sparse solutions.
Exploring the Interplay of Context and Interaction in the Field

Today, an increasing amount of people take care of their everyday communication while being on the move. Here, mobile context potentially has a severe influence on the quality of interaction and the communication task at hand, for instance if the user has a high mental workload by monitoring the environment and is too distracted for the mobile task. For exploring the resulting interplay of context and interaction with the help of modern sensors on smartphones, this work presents CoCoNU $T$, a toolkit for measuring context and interaction metrics which has been developed to gain a deeper understanding of mobile interaction in the field. In addition, results of an initial study are presented and analyzed.
Segmentation of Laser Point Clouds in Urban Areas by a Modified Normalized Cut Method

Normalized Cut is a well-established divisive image segmentation method, which we adapt in this paper for the segmentation of laser point clouds in urban areas. Our focus is on polyhedral objects with planar surfaces. Due to its target function, Normalized Cut favours cuts with “short cut lines” or “small cut surfaces”, which is a drawback for our application. We therefore modify the target function, weighting the similarity measures with distance-dependent weights. We call the induced minimization problem “Distance-weighted Cut” (DWCut). The new target function leads to a generalized eigenvalue problem, which is slightly more complicated than the corresponding problem for the Normalized Cut; on the other hand, the new target function is easier to interpret and avoids some drawbacks of the Normalized Cut. We point out an efficient method for the numerical solution of the eigenvalue problem which is based on a Krylov subspace method. DWCut can be beneficially combined with an aggregation in order to reduce the computational effort and to avoid shortcomings due to insufficient plane parameters. We present examples for the successful application of the Distance-weighted Cut principle and evaluate its results by comparison with the results of corresponding manual segmentations.
Virtual Reality Glove for Falconry

This project is a virtual reality based game. Basically it's about hunting pray-using different types of falcons. And the main idea behind this project is to give more knowledge to the player about a cultural game in more fun way, also we want this game to be for all ages above 18 years old less then 18 years old and for girls, and this can be done with collaboration with Qatari Society of Algannas. The game can help those who want to try hunting with falcons and practicing the real professional techniques by using advanced technologies, with the appropriate professional tools used by the falconers. The two main hardware platforms are the glove and feeding bag, where the movement of the glove should be detected virtually. Also the glove should be modified to exert pressure and weight on the player's hand (depending on the falcon's type). The player should illusory feel the real sensation of a falcon standing on his/her hand. And the role of the feeding bag is to cover the falcon's prey to make it think that it ate the whole pray so it can continue hunting. The objective of this project is to make the student familiar with the research culture principles and skills, to investigate the traditional hunting sport, and to keep our heritage using new technology.
Saliency Detection via Multi-Center Convex Hull Prior

Saliency detection has been a hot topic in computer vision. Among existing approaches, a representative one is to use the convex hull prior to find the salient object in the image; and there are many variants that are based on the convex hull prior. Most of these works used a single center to construct the convex hull center prior map, while few attention has been made on the use of multiple centers. In this paper, we propose a multi-center convex hull prior based solution for saliency detection. Particularly, our solution also integrates two non-trivial optimizations: one is for obtaining an enhanced global color distinction prior map, and another is for refining the preliminary saliency map. We experimentally evaluate our solution through comparing against state-of-the-art algorithms. The results demonstrate the effectiveness and superiorities of the proposed solution.
Hypercomplex Tensor Completion with Cayley-Dickson Singular Value Decomposition

Expressing multidimensional information as a value in hypercomplex number systems (e.g., quaternion, octonion, etc.) has great potential, in signal processing applications, to enjoy their nontrivial algebraic benefits which are not available in standard real or complex vector systems. Strategic utilizations of such benefits would include, e.g., hypercomplex singular value decomposition (SVD) and low rank approximation of matrices. In this paper, as powerful mathematical tools for wider signal processing applications, we first propose novel definitions of SVD and best low rank approximation of matrices based on algebraic translations of Cayley-Dickson (C-D) number systems. We then derive an algorithmic solution to hypercomplex tensor completion problem based on a convex optimization technique. Numerical experiments in a scenario of color tensor completion problem show that the proposed algorithm recovers much more faithfully the original color information, masked randomly by noise, than a part-wise real tensor completion algorithm.
Study protocol for a cohort study of patients with advanced heart failure in Singapore

Introductio Understanding the symptom and health expenditure burden among patients with advanced congestive heart failure (CHF) and their family caregivers is essential to reform policy and practice needed to provide quality care to these patients at affordable prices. The proposed cohort study titled Singapore Cohort of Patients with Advanced Heart Failure aims to describe trajectories of quality of life among patients and their primary informal caregivers, quantify healthcare utilisation and expenditures, assess changes in patient and caregiver awareness of and preferences for knowing diagnostic and prognostic information, awareness and utilisation of palliative care services, preferences for treatments and decision making, perceived quality of care, self-care, caregiver psychological distress and caregiver burden. Methods This cohort study will recruit 250 patients with New York Heart Association Classification class III and IV CHF from inpatient wards at two public tertiary healthcare institutions in Singapore. Patients and their primary informal caregiver are being surveyed every 4 months until patients’ death; caregivers are followed until 8 weeks postpatient death. Medical and billing records of patients are obtained and merged with patients’ survey data. Ethics and dissemination The study has been approved by an ethics board. Results from the study will be disseminated through publications and presentations targeting researchers, policy makers and clinicians interested in understanding and improving care for patients with advanced CHF. Trial registration number NCT03089034.
Simple Graph Comparison Inspired on Metabolic Pathway Correlation

Comparing two graphs is a computationally difficult task [9], [8]. After a work by E. Arias-Mendez and F. Torres-Rojas [7] about the correlation of metabolic pathways with two new proposed approaches to simplify the comparison of its associated graph representation, we extended this work to general graph structures as a simple way to compare them. The approach presented here is an extension of those algorithms to general graphs. The first algorithm proposed looks to transform the comparing graphs into linear sequences, to be analyzed using sequence-alignment tools from bioinformatics and get a numeric score as its value of similitude. The second proposed algorithm consists of the search of equal connected nodes between 2 graphs to eliminate then on both structures, only leaving the differences, as heuristic for comparison. These algorithms were developed as a low-cost process to correlate metabolic pathways showing good results; the suggestion is to use this information as a previous analysis to a deeper, more expensive, comparing tools use. Here we review the extension of this work as an application to a more general graph data structure. These methods have shown to be an effective way to treat the problem as listed in the results section.
An Analysis of Adaptive Digital Predistortion Algorithms in RF Power Amplifier

This paper develops an adaptive Indirect Learning Architecture (ILA) using either Least Mean Square (LMS) or Recursive Least Square (RLS) in digital predistortion for radio frequency power amplifiers. The adaptive ILA enables to update the coefficient fluctuation of the predistorter as the nonlinear behavior of the RF PA characteristics changes due to the Temperature, Voltage, Aging (TVA) drift and other factors. The performance efficiency of the offline Linear Interpolated Look-Up-Table (LILUT) based D LA is analyzed as the PA characteristics change due to the temperature. The experimental results show that the adaptive ILA is able to be more effectively characterizing the nonlinear behavior change of the PA characteristics induced by the TVA drift. Moreover, in order to get more linear performance, the offline LILUT-based DLA must be re-designed with respect to each PA model change.
Detecting Abnormal Events in University Areas

this paper presents a distinct video surveillance system which took place in the Lebanese International University Saida- Campus. , which is considered as a very crowded environment. , and reveals if there is an unusual event. Our main target is to apply simple procedures that will be present as a future”s benchmark. The work is split into three major parts. , starting by dividing the video frame into zones. , then to compute the magnitude of optical flow in each. , and finally to analyze these data and classify it. , based on a logical threshold. , as normal or abnormal events. We implement our results based on Histogram of Magnitudes for each zone (HOM) and the outcome met our expectations.
An Assessment of Future Clinical Pharmacy Service Delivery in the Patient-Centered Medical Home

Two health care reform initiatives—patient-centered medical home (PCMH) and payment reform—in combination have the potential to increase clinical pharmacy involvement in patient care. However, the effects of these reforms on clinical pharmacy are highly uncertain. In particular, which clinical pharmacy services will be provided, how the services will be requested and delivered, and in what practice settings the services will be provided are not known. To gain insight into future clinical pharmacy service delivery in the PCMH, the authors examined current clinical pharmacy service delivery models at 4 sites in Massachusetts and assessed how the service delivery would change in PCMH settings with a payment approach of comprehensive payments to the PCMH. The findings suggest that (1) clinical pharmacy participation in the PCMH will increase at ambulatory care sites if supported by payment reform and (2) changes in addition to payment reform will be necessary to increase participation of community pharmacists. Needed changes are described.
Tarm: A Turbo-Type Algorithm for Low-Rank Matrix Recovery

This paper is concerned with the affine rank minimization (ARM) problem for low-rank matrix recovery purposes. Inspired by the recently proposed Turbo-CS algorithm in the field of compressed sensing, we propose a turbo-type algorithm for ARM, termed Turbo-ARM (TARM). For matrix recovery problems with a large class of random measurement matrices, the performance of TARM can be analyzed via the state evolution framework. Our numerical results show that TARM achieves state-of-the-art reconstruction performance, and our results are further confirmed by state evolution analysis.
Design of Virtual Inductor Emulation for Soft Transition from Islanded Mode to Grid-Connected Operation

In order to reconnect an islanded ac microgrid to the utility grid, a synchronization process is required. Although errors in the synchronization are not usually considered, realistic errors in the voltage angles can actually lead to serious overcurrent in case of stiff grids. This paper proposes a virtual inductor design for a 1-MW inverter with LCL output filter. The design makes it possible to emulate a high inductance value ensuring at the same time stability for all operating conditions. Simulation results show that, thanks to the proposed inductor emulation, the transient overcurrent is significantly reduced.