Miracle-dz/UniFAR-FTUs huggingface dataset:
The repair problem under connectivity constraints: Explicit MSR codes for the rack-aware model of distributed storage

Recent papers of Min Ye and the second-named author introduced constructions of MSR codes based on parity-check equations. In this note we modify the approach of these works to construct explicit MSR codes for the rack-based model of distributed storage. The codes that we construct can recover any single failed node by downloading information from a subset of the surviving nodes relying on the rack structure of the system. The underlying finite field is of size at most $n^{2}/u$, where n is the block length of the code and u is the size of the rack.
PAPR Reduction in DCO-OFDM Visible Light Communication Systems Using Optimized Odd and Even Sequences Combination

This paper proposes a novel peak-to-average power ratio (PAPR) reduction scheme, which has several advantages, such as low-complexity candidate construction and no side information (SI) transmission in direct current-biased optical orthogonal frequency division multiplexing (DCO-OFDM) visible light communication (VLC) systems. In the proposed scheme, the candidates can be generated by linearly combining time-domain even and odd sequences with cyclic shift and phase rotation. Even and odd sequences are obtained by adopting an inverse fast Fourier transform on the even and odd parts of a Hermitian symmetry encoded signal, respectively. Furthermore, a deterministic and efficient selection method of cyclic shift and phase rotation is derived to improve PAPR reduction performance and ensure that each candidate is unique, so that the receiver can recover the transmitted candidate signal without the use of SI. Simulation results show that the proposed scheme can achieve comparable PAPR reduction performance with the conventional selected mapping (CSLM) scheme with random and chaotic sequences, but at a considerably low computational complexity. Moreover, the proposed blind SI detection scheme provides almost the same bit error rate (BER) performance as that of the CSLM scheme with a perfect SI detection and symmetric selected mapping for a DCO-OFDM VLC system with 1024 subcarriers and 16-QAM. In contrast to other related time-domain sequences that combine methods used for DCO-OFDM systems, the proposed scheme improves BER performance with a slight increase in computational complexity and PAPR.
Parameter-Free Density Estimation for Hyperspectral Image Clustering

This paper investigates multivariate kernel density estimation for hyperspectral data. More specifically, it focuses on designing a data-driven, parameter-free and fast estimator that is able to deal with clusters of different size, shape and density. We first propose a new criterion to compare density estimates, based on how evenly they treat the different classes in the data. In addition, we propose a simple, parameter-free and non-iterative kernel bandwidth selection approach. We show that it is substantially more adaptive than existing estimators on six remote sensing hyperspectral datasets.
Cascaded Half-Bridge Multilevel Inverter with Reduced Number of Power Switches

This paper proposes a novel multilevel inverter. The proposed topology is synthesized with several basic modules and one complementary module. Each basic module is able to realize either a positive or negative voltage level but not a zero voltage level. Therefore, the complementary module, which is able to realize positive, negative, and zero voltage levels, makes up this disability of the basic modules. Using half-bridge cells instead of full-bridge cells, the proposed topology is able to reduce the number of power switches required to construct a multilevel inverter. This topology has a modular structure with symmetric dc sources. Simulation results along with experimental results are provided to validate the feasibility of the proposed topology.
On Differentially Private Gaussian Hypothesis Testing

Data analysis for emerging systems such as syndromic surveillance or intelligent transportation systems requires testing statistical models based on privacy-sensitive data collected from individuals, e.g., medical records or location traces. In this paper, we design a differentially private hypothesis test based on the generalized likelihood ratio method to decide if data modeled as a sequence of independent and identically distributed Gaussian random variables has a given mean value. Analytic formulas for decision thresholds and for the test’s receiver operating characteristic curve show explicitly the performance impact of the privacy constraint. We then apply the algorithm to the design of a differentially private anomaly (or fault) detector and study its performance for the analysis of a syndromic surveillance dataset from the Centers for Disease Control and Prevention in the United States.
The Making of 5G: Building an End-to-End 5G-Enabled System

This article documents one of the world's first standards-compliant pre-commercial end-to-end fifth generation (5G) systems. Focus is on a standardized 5G architecture that includes the underlying 3GPP components but also the ETSI network functions virtualization management and orchestration capabilities. The truly innovative character of 5G enabling fundamental changes to architecture and implementation is discussed, as well as details of monitoring and orchestration approaches that are deemed instrumental in unlocking the full potential of 5G. Finally, it is important to us to share the lessons learned, which we hope are of use to industry and academia alike when building, deploying, and testing emerging 5G systems.
Feature Extraction from Dermoscopy Images for an Effective Diagnosis of Melanoma Skin Cancer

The aim of this paper is to extract some distinct geometric features from dermoscopy images to classify benign and malignant melanomas. To avoid skin biopsy which is an invasive technique, diagnosis of melanoma skin cancer from dermoscopy images was developed. It is a very challenging task due to some reasons. Firstly, high degree of intraclass variation exists among melanoma images while low interclass variation is found between melanoma and non-melanoma images. Secondly, benign and malignant melanoma images are visually similar to a great extent. And finally noises like hair are always present in skin images which make difficult to analyse the images. In this work, we used fundamental ABCD rule to detect malignant melanoma and benign lesion based on quantitative measures. In our proposed technique, we extracted a new feature which is the difference between maximum and minimum Feret diameters of the best fit ellipse to skin lesion. This discriminative feature alone classified the melanomas with 86.5% accuracy. In our approach, we applied the feature extraction block containing all parameters to 200 images and the overall accuracy of 98% was achieved to detect malignant and benign melanoma from the images. A Back-propagation Neural Network (BNN) model was developed and eventually used as a classifier in this proposed method.
Deep Reinforcement One-Shot Learning for Change Point Detection

We consider the problem of detecting a change in a time series quickly and reliably, where only a few training instances are available. Examples include identifying changes in network traffic due to zero-day attacks, and computer vision applications where changes in series of images that represent significant events needed to be detected. These are known as cases of one-shot learning. We develop a novel Deep Reinforcement One-shot Learning (DeROL) framework to address this challenge. The basic idea of the DeROL algorithm is to train a deep-Q network to obtain a policy which is oblivious to the unseen classes in the testing data. Then, in real-time, DeROL maps the current state of the one-shot learning process to operational actions based on the trained deep-Q network, to maximize the objective function. We tested the algorithm using the OMNIGLOT dataset to demonstrate the efficiency of the DeROL framework.
Implementation for EEA3 algorithm Based on 3GPP Standard

The 3rd Generation Partnership Project (3GPP) uses three standard algorithms to achieve confidentiality in the 4th Generation (4G) cellular network. The latest one is Evolved Packet System Encryption Algorithm (EEA3), depend’s on ZUC algorithm. EEA3 is a stream cipher used in the Long Term Evolution (LTE) network. This paper discusses a brief description for the EEA3 algorithm. C implementation of the algorithm based on 3GPP standard is introduced, solving the last Ciphertext word in case of padding. Also, an FPGA implementation of the EEA3 algorithm is presented using the VHDL language on a XILINX Spartan6 board. An analysis and evaluation of the implemented algorithm and the used resources are discussed.
Predictive Control for an Induction Motor Drive Based on a Quasi-Linear Model

This paper is concerned with presenting a predictive control (PC) approach for an induction motor (IM) drive based on a quasi-linear model. The advantage of proposed control approach is obvious through the reduction in the system nonlinearity; this is achieved via introducing a mathematical model of IM which consists of two entirely decoupled electromagnetic and mechanical subsystems. Another advantage of proposed controller is the absence of PWM that is utilized regularly by classic vector control approaches for managing the voltages; this has been achieved through selecting directly the optimal voltages by means of finite control set (FCS) strategy. Upon to these merits, the simplicity of the controller is increased saving the computational time, meanwhile a fast dynamic response is achieved by the help of a hysteresis based predictive controller. For validating the effectiveness of the control procedure, Matlab/Simulink software is utilized for this purpose. The obtained results confirm the validity of the proposed control procedure in achieving the control targets.
Deep Spectral-spatial Features of Snapshot Hyperspectral Images for Red-meat Classification

We investigate the potential and accuracy of snapshot hyperspectral imaging for authentication and classification of red-meat species. Snapshot hyperspectral images are acquired of lamb, beef, and pork samples. We consider 13 muscles types of standard loin and leg chops. We propose a deep 3D convolution neural network (CNN) architecture for extracting and classifying spectral-spatial learned features of red-meat. We present a comparison with state-of-the-art models including partial least-square discriminant analysis and support vector machines. Our results show that the proposed 3D-CNN model outperforms the state-of-the-art models with 95.81% and 96.01% for overall accuracy and average F1score, respectively. Visualization results show that the proposed 3D-CNN model is able to convert snapshot hyperspectral image data into an intelligent representation with accurate separation between red-meat types. This study opens the door for more research towards real-time and completely portable hyperspectral imaging systems due to the ability of snapshot hyperspectral cameras to work at video rate.
An Efficient Broadband Slotted Rectenna for Wireless Power Transfer at LTE Band

A compact and broadband rectifying antenna (rectenna) which consists of a novel slotted antenna and an efficient rectifying circuit is presented for wireless power transfer at LTE-2300/2500 band. Three different broadband antennas have been investigated and compared. It is found that the newly designed antenna which combines the annular slot radiation patch and a slotted ground plane (GP) has the widest bandwidth and the best matching characteristic. A new broadband dual-stub matching network is proposed to maximize the transmission of wireless power and enhance the reliability of the rectifier when the input power and load varies. In addition, the ground of the rectifier is directly connected to the GP of the antenna for compactness and low energy loss. The proposed designs have been simulated, fabricated, and tested. The results indicate that the proposed receiving antenna performs well at LTE band with a wide bandwidth from 2 to 3.1 GHz. The maximum efficiency of the proposed rectenna reaches 70% at 2.5 GHz under the input power of 5 dBm. Furthermore, the rectenna remains at a relatively efficient power conversion when the load varies in a wide range from 2 to 20 $\text{k}\Omega $ under different low input power, which is of great significance when the ambient radio frequency(F) signal is weak. Therefore, the rectenna is potentially applicable to a wide range of low-power electronic devices by harvesting RF energy at LTE band.
Smart Charging and Discharging of Plug-in Electric Vehicles for Peak Shaving and Valley Filling of the Grid Power

From the power grid perspective, the widespread of plug-in electric vehicles (PEVs) integration into the electric networks rises concerns regarding power system components overloading and power quality issues. However, PEVs would bring beneficial opportunities to the power system in the future. In order to alleviate the negative effects of PEVs, smart charge/discharge scheduling algorithms are developed. Moreover, with PEVs smart scheduling, the vehicles fleet can be used to support the power grid through vehicle-to-grid (V2G) technology by injecting power into the grid during peak periods. This paper presents a centralized smart charge/discharge scheduling algorithm to optimize the charging/discharging of PEVs with the aim to achieve peak shaving and valley filling of the grid load profile subjected to various power grid and PEV s constraints. The solution of the optimization problem is realized using a particle swarm optimization (PSO) technique. The algorithm of charge/discharge scheduling is developed and tested within MATLAB environment. The simulation results verify the potential of the algorithm to achieve the objective function and satisfy the various constraints which in turn lessen the adverse impacts of PEVs charging.
Fabrication and Characterization of Single-Step Optical Microfiber Mach–Zehnder Interferometer Refractive Index Sensors

In this paper, exploiting a new single-step process, in-line Mach–Zehnder interferometers (M-MZIs) on microfibers of different diameters are fabricated and their refractive-index (RI) sensitivities and temperature stabilities are characterized. The M-MZI comprises a microfiber with two functionalizing miniature down-tapers inscribed along its longitudinal direction. It is fabricated exploiting a selectively modulated arc-discharge assisted fiber heating and pulling one-step process. In this new technique, inserted short time off-switches in the heating arc signal, while the fiber ends are pulled, induce additional micro-tapers on the thinned fiber is being fabricated. The resultant one-step M-MZIs show extremely high interference visibilities up to ~34 dB and high RI sensitivities up to 2271.3 nm/RI-unit around RI = 1.368. The results show that, with reducing the diameter of the structures, in addition to the higher RI sensitivity, very low temperature coefficient (~5.2 pm/°C for $8~\mu \text{m}$ sample) will be achieved. These M-MZIs have high stability, flexibility, reproducibility, and low fabrication cost.
Fast Fault Diagnosis Method for Hall Sensors in Brushless DC Motor Drives

Brushless direct current motors with Hall sensors have been widely used in myriad industrial and commercial applications due to their simplicity and low cost. Meanwhile, the fault tolerant control (FTC) of Hall sensor drives has attracted renewed research attention. During FTC, the motor drives need to diagnose the Hall-sensor fault(s) and reconstruct the faulty signal(s). In this context, we propose an innovative fast fault diagnosis (FFD) method for analyzing different fault cases and diagnosing faulty signal(s) in the shortest possible time. Compared with established fault diagnosis methods, the FFD method does not require complicated vector-tracking observers, and it is capable of handling up to two simultaneous faults while greatly improving the diagnostic speed. Our simulation and experimental results verify the effectiveness of the proposed method and demonstrate its advantages over established methods particularly during transient operations. Due to the simplicity of diagnostic algorithm, the proposed FFD method can be readily implemented in conventional drive systems, either through extra coding within existing controllers or as an auxiliary circuit.
Data Generation Using Sequence-to-Sequence

Sequence to Sequence models have shown a lot of promise for dealing with problems such as Neural Machine Translation (NMT), Text Summarization, Paraphrase Generation etc. Deep Neural Networks (DNNs) work well with large and labeled training sets but in sequence-to-sequence problems, mapping becomes a much harder task due to the differences in syntax, semantics and length. Moreover usage of DNNs is constrained by the fixed dimensionality of the input and output, which is not the case with most of the Natural Language Processing (NLP) problems. Our primary focus was to build transliteration systems for Indian languages. In the case of Indian languages, monolingual corpora are abundantly available but a parallel one which can be directly applied to transliteration problem is scarce. With the available parallel corpus, we could only build weak models. We propose a system to leverage the mono-lingual corpus to generate a clean and quality parallel corpus for transliteration, which is then iteratively used to tune the existing weak transliteration models. The results that we got prove our hypothesis that the process of generation of clean data can be validated objectively by evaluating the models alongside the efficiency of the system to generate data in each iteration.
Heterogeneous Integration of GaN and BCD Technologies and Its Applications to High Conversion-Ratio DC–DC Boost Converter IC

This letter presents a novel technology for the integration of gallium nitride (GaN) power devices with silicon control circuits. It comprises stacked GaN power transistors and bipolar-CMOS-double-diffused metal-oxide-semiconductor (DMOS) (BCD) circuits. It leverages on both advantages of the high-voltage low-loss GaN devices and the high-integration BCD circuits. Using conventional manufacturing, packaging, and assembly techniques and equipment, the proposed technology is technology transferrable and applicable for commercial power electronic applications. To validate the concept, a 3.3–70 V dc–dc boost converter is designed, implemented, and verified experimentally. It features a conversion efficiency of 70.3%, output power of 1.68 W, and compact size of <inline-formula><tex-math notation="LaTeX">${\text{0.32}}\times {\text{0.18}}\,{\text{cm}}^{2}$</tex-math></inline-formula>.
Fixing and Aligning Methods for Dielectric Resonator Antennas in K Band and Beyond

Various conventional and novel fixing strategies for dielectric resonator antennas (DRAs) in K band and beyond are presented and investigated. Their effects on DRAs are studied in depth and explained theoretically through the perturbation theory and basic principles of DRAs. Multifunctional fixtures which achieve different antenna performance are then proposed and discussed. Their mechanisms for offering flexible bandwidth, miniaturization, frequency diversity, low cross polarization, and circular polarization are explained. In practical applications, glue is used besides fixtures to increase reliability, and its influence on antenna performance is difficult to predict. By considering various glue distributions in simulation beforehand, potential performance degradation is successfully mitigated. For validation, a four-element DRA array for 24-GHz automotive radars is fabricated. The results demonstrate that the proposed fixing strategy provides superior characteristics such as shock-resistance, fabrication and assembly simplicity, and robust antenna performance.
Direction Selective Contour Detection for Salient Objects

The active contour model is a widely used technique for automatic object contour extraction. Existing methods based on this model can perform with high accuracy, even in the case of complex contours, but challenging issues remain, like the need for precise contour initialization for high curvature boundary segments or the handling of cluttered backgrounds. To deal with such issues, this paper presents a salient object extraction method, the first step of which is the introduction of an improved edge map that incorporates edge direction as a feature. The direction information in the small neighborhoods of image feature points is extracted, and the images’ prominent orientations are defined for direction-selective edge extraction. Using such improved edge information, we provide a highly accurate shape contour representation, which we also combine with texture features. The principle of the paper is to interpret an object as the fusion of its components: its extracted contour and its inner texture. Our goal in fusing textural and structural information is twofold: it is applied for automatic contour initialization, and it is also used to establish an improved external force field. This fusion then produces highly accurate salient object extractions. We performed extensive evaluations, which confirm that the presented object extraction method outperforms parametric active contour models and achieves higher efficiency than the majority of the evaluated automatic saliency methods.
Power Management Scheme for a Wind-Photovoltaic Hybrid Autonomous System with Battery Storage

This paper presents power management scheme for a hybrid autonomous generation system (HAGS). This HAGS is the combination of wind, photovoltaic (PV) generation system and battery for storage purpose. The wind-PV system along with the battery are connected parallel to the dc-bus. The dc-bus voltage is controlled through a bi-directional converter. The controller has inner battery current control loop and outer voltage controller to maintain the dc bus voltage constant. This average current control method is used for power management between all sources and the dc load. The complete hybrid system is simulated in MATLAB/SIMULINK and the results represent the effective power balance for all modes of operation.
Face Extraction and Recognition from Public Images Using HIPI

Social networking services with public data are widely used nowadays. Billions of images uploaded to the internet each day over the world. This paper proposes the idea of a system which is currently being developed. The system collects images from public sources by some specific criteria, applies face detection and recognition algorithms on collected images, and provides the results in readable form. The Apache Hadoop library is used to increase performance of the system, and images are downloaded by the help of HIPI library. For testing purposes Labeled Faces in the Wild benchmark is used as a database for images containing over 13233 images of 5749 identities. Each image is jpg format in 250*250 resolutions. Results were more than good after testing the system with this database for face detection and recognition.
Operation of Brushless DC Motor to Drive the Electric Vehicle

This paper is to select the Brushless DC Motor size to be suitable to drive the electric vehicle. This selection is corresponding to the specifications of the electric vehicle. A comprehensive investigation of the brushless DC motor as well as the electric vehicle has been accomplished. The vehicle dynamics are also illustrated. And then A detailed method for the sizing the major components of the electric vehicle is presented. Eight switches bridges converter is used to drive the brushless DC motor. The motor characteristics are computed taking into account the effect of the chosen control circuit on the motor currents.