Miracle-dz/UniFAR-FTUs huggingface dataset:
Bipartite Graph Partition Based Coclustering With Joint Sparsity for Hyperspectral Images

Clustering for hyperspectral images (HSIs) is a very challenging task because HSIs usually have large spectral variability, high dimensionality, and complex structures. Recently, the sparse representation and dictionary-learning-based spectral clustering (SC) approaches have achieved great success in clustering HSIs. However, SC deals with hyperspectral clustering by one-way clustering, i.e., either dictionary atoms or sparse-representation-based clustering, and only clusters the columns information of adjacency matrix. To overcome these problems, we propose a novel coclustering based on bipartite graph partition (BGP) with joint sparsity for two disjoint sets: dictionary atoms and hyperspectral pixels. First, the structured dictionary constraint is incorporated into a unified joint sparsity constrained optimizing model to learn a structured sparse representation by incorporating spectral and contextual characteristics of HSIs where the joint sparsity coefficients provide related information between hyperspectral pixels and dictionary atoms. Then, a BGP-based classifier is proposed to simultaneously group hyperspectral pixels with the learned dictionary atoms by mapping the joint sparsity coefficients to a non-negative affinity matrix of graph, and simultaneously clusters rows and columns information of adjacency matrix. To solve the BGP problem, we use spectral graph partition algorithm, which uses the left and right singular vectors of an appropriately dictionary-signal adjacent matrix to yield good bipartitionings and the singular vectors solve a real relaxation of the NP-complete BGP problem. The proposed algorithm was tested on three widely used hyperspectral datasets, and experimental results demonstrate that the proposed algorithm outperforms other state-of-the-art methods.
Deep Learning with Convolutional Neural Network and Long Short-Term Memory for Phishing Detection

Phishers sometimes exploit users’ trust of a known website’s appearance by using a similar page that looks like the legitimate site. In recent times, researchers have tried to identify and classify the issues that can contribute to the detection of phishing websites. This study focuses on design and development of a deep learning based phishing detection solution that leverages the Universal Resource Locator and website content such as images and frame elements. A Convolutional Neural Network (CNN) and the Long Short-Term Memory (LSTM) algorithm were used to build a classification model. The experimental results showed that the proposed model achieved an accuracy rate of 93.28%.
Three-Phase Modular Multilevel Converter (MMC) for Low-voltage Applications: Improved Modulation Technique Toward Less Capacitance Requirement

Modular Multilevel Converter (MMC) has become one of the promising topologies in medium and high power applications. Modularity, scalability, and reliability are the main features that give the MMC some advantages over other multilevel topologies when considering high number of levels. This paper presents an alternative modulation technique for a modified modular multilevel converter (M-MMC) topology for low-voltage applications. By using level-shifted carrier (LSC) modulation and the proposed sorting algorithm, the capacitance requirement for this topology can be reduced. Finally, the proposed modulation scheme and the reduction of capacitance are verified by simulation results using PLECS.
Demand Side Management Using GUI

proposed work in this paper describe a software package tool by using MATLAB GUI for solving demand side management problem using load shifting technique. This helps user to improve the input data details in according to load used by the consumer and calculate the expenditure. This paper proposes software for load shifting consumption scheme to understate the peak demand and in turn save electricity billing. The load shifting technique of demand side management is used in this software for residential area to shift their load part from maximun to minimum hours with the help off time of the day tariff structure.
Mechanical Shock Propagation Reduction in Robot Legs

This letter shows how the mass distribution in a robot's leg affects the propagation of mechanical shocks from the foot to the torso. An example is given of a leg design that propagates no shock at all; and a formula is given for the propagation of shock in a general robot leg, modelled as a chain of rigid bodies, assuming that the foot makes a point or small-area contact when it strikes the ground.
A Leaf Recognition Method Based on Integration of Multi-migration Model

Plants are important to humans, and the plant classification based on leaf images has also attracted more and more attention. As a concrete implementation of deep learning in the field of image recognition, convolutional neural network has achieved many satisfactory results. In this paper, in order to solve the problem that the model recognition accuracy is not high due to the rare sample size, we integrate multiple migration models to recognize and classify the leaf images. A total of three models were selected, Squeezenet, Googlenet and Resnet for transfer learning and integration. We first pre-trained three networks on the Imagenet dataset. Then, the three models were fine-tuned on the LZU blade dataset. Finally, the three models are integrated by an integration strategy based on the voting results and prediction probabilities of each model. Through experiments, our leaf recognition method based on migration model has achieved 99.49% recognition accuracy on LZU blade dataset, which has certain advantages compared with other methods or networks.
Power Factor Improvement of BLDC Motor Drive using Boost PFC Converter

This paper presents brushless dc (BLDC) motor drive with boost power factor correction to overcome power quality problem at AC mains. Boost PFC converter is applied to achieve unity power factor at AC mains. A reduction in harmonics is also observed. Here we are analyzing a conventional BLDC motor drive and comparing with Boost PFC based BLDC motor drive and expected improvement in overall power factor is seen. In this paper we simulate two cases of BLDC motor drive with and without PFC converter using Matlab/Simulink.
A Novel Tubular Switched Reluctance Linear Machine Shielding From Longitudinal End Magnetic Effect

Traditional linear machines are under great influence of longitudinal end effect because of their finite stacking lengths in transverse and longitudinal directions. Longitudinal end effect causes imbalance of phase performance so that linear machine's force fluctuation is aggravated. The negative impact of longitudinal end effect on a flat type switched reluctance linear machine (SRLM) is verified in this paper. In order to avoid this problem, a novel tubular switched reluctance linear machine (TSRLM) is proposed, which can shield from unexpected impact of end effect. In structure, there is no transverse cut in tubular linear machines, so tubular linear machines can evade the influence of the transverse end effect. The equivalent magnetic circuit of this TSRLM is analyzed in this paper, which proves that the TSRLM is also not influenced by longitudinal end effect. A prototype is manufactured, and its corresponding hardware platform is established. A series of simulations and experiments verify that there is no deviation performance among phases in the proposed TSRLM. Accordingly, the traditional longitudinal end effect in linear machines caused by longitudinal cuts can be thoroughly evaded in proposed TSRLM.
Comparison of TPT and Thermally Conductive Backsheets in One-Cell Modules: Weather and Climate Dependence

Various improved backsheet materials have been developed and used by the industry to reduce the module operating temperatures. The focus of this paper is to evaluate the thermal characteristics of modules having thermally conductive backsheets (TCB) exposed in three different climatic conditions over two years. In this work, 30 one-cell PV modules with glass/EVA/cell/EVA/backsheet were fabricated using 2 different thermally conductive backsheets (TCB_A and TCB_B) and their backsheet temperatures were compared with conventional tedlar-polyester-tedlar (TPT) under different climatic conditions. The results clearly show that TCB_A operates at lower temperatures compared to TPT under hot climatic conditions with higher ambient temperatures and wind blowing from west direction at speeds 1-2 m/s. On the other hand, TCB_B operates at lower temperature than TPT under temperate climatic conditions with ambient temperatures between 25 and 35 °C and wind blowing from east direction at speeds 2-3 m/s.
A gastrointestinal tumor recognition model based on logistic regression algorithm

With the gradual application of precision medicine, data mining technology is widely used in the medical field. Along with the improvement of people's living standards, the incidence of digestive tract tumors is getting higher. Therefore, early detection and diagnosis of digestive tract tumors is of great significance to the prognosis of patients. In this paper, a data mining model is proposed based on the existing data of digestive tract tumors. The model is based on logistic regression algorithm and has been improved. It is used to establish a digestive tract tumor recognition model to try to identify early gastrointestinal cancer diseases. In the specific modeling calculation, the initial recognition model was first established using the improved logistic regression model. The initial model had a misrecognition rate of 6%, a missing ratio of 3%, a KS value of 0.606, and an AUC value of 0.863. After determining the initial model, ten sets of test data were applied to train the initial model, we using logistic regression, decision tree, and neural network to compare the results of each of the three methods. The result leads to the next training. After that, we get the final recognition model, which has a misrecognition rate of 5%, a missing ratio of 0%, a KS value of 0.653, and an AUC value of 0.886. From the results, the final model we established can better identify early digestive tract cancer diseases.
A Decade on Script Identification from Natural Images/Videos: A Review

Text present in an image provides high level straight forward information about the image/ video in which it is present. Nowadays, analysis of script identification either in the natural image or document image facilitates benefits to the number of important applications. Automatic script identification is a highly challenging task due to various complexities of the text as well as the image. This paper presents a survey on script identification of different scripts across the world. From the survey, it is noted that research in this area is limited to the document images only. Natural images are yet to be explored for automatic script identification.
A Survey on Intent-Driven Networks

Software defined network and network function visualization enhance the network flexibility and management agility, which increase network fragility and complexity. However, the vast majority of network parameters are manually configured, which makes the configuration failures still inevitable. Future networks should be self-configuring, self-managing, and self-optimizing. Intent-driven network (IDN) is a self-driving network that uses decoupling network control logic and closed-loop orchestration techniques to automate application intents. At present, a unified definition of IDN has not yet been presented, and the research background and current status of IDN are not clear. Considering the emerging applications and research of IDN, in this article, we survey existing technologies, clarify definitions, and summarize features for IDN. Specifically, we discuss the basic architecture and key technologies of IDN. In addition, diversity gains and challenges are analyzed briefly. Finally, some future work is highlighted and wider applications of IDN are provided for further research.
The Impact of Endurance Degradation in Analog RRAM for In-Situ Training

The analog resistive random access memory (RRAM) is attractive for neuromorphic computing systems as synaptic weights. However, cycling endurance is one of the most important limitations to reliability. In this work, two endurance degradation models for analog RRAM are proposed based on the measurement results. Then, the models are utilized to investigate the impact on in-situ training accuracy. We use the MNIST dataset trained in a 3-layer multilayer perceptron (MLP), with the cycling endurance of RRAM degrading. The results indicate that endurance degradation will decrease the in-situ training accuracy.
A Lifted Semi-Direct Monocular Visual Odometry

In this paper, we proposed a practical and efficient algorithm based on conventional semi-direct monocular visual odometry (SVO) algorithm, which mainly aims at the future application of the Simultaneous Localization and Mapping (SLAM) for embedded or mobile platforms such as robots and wearable devices. By applying the velocity momentum during the initial pose estimation, we present a novel algorithm for obtaining the initial pose, which is closer to the true value and more effective to solving the limitation of non-convergence in most existing approaches. A sparse image alignment module is also proposed to rectify the pose offset occurred at the corner, by elaborately resetting the relative pose at the location with large photometric error. The proposed lifted semi-direct monocular visual odometry has been extensively evaluated on benchmark dataset. The experimental result demonstrates that our method can explicitly generate the accurate initial poses without reducing the speed.
Laser Crystallization and Dopant Activation for a-Si:H Film in Carrier-Selective Contacts for Silicon Solar cells

Herein we present a pulsed laser processing method for crystallization and dopant activation of a highly n-doped amorphous silicon (a-Si:H) carrier selective layer for high conductivity, high carrier lifetime, low emitter saturation current and limited surface layer heating as opposed to high temperature furnace annealing induced heating of the bulk Si wafer. We demonstrate increased crystallinity and reduced layer resistivity, without impacting surface passivation quality. Additionally, we examine the role of forming gas annealing (FGA) on further improving the passivation of laser-processed polysilicon layers to achieve a <2 mΩ•cm film resistivity, 2.1 ms effective carrier lifetime, 685 mV implied open-circuit voltage (iVOC), and 27 fA/cm2 recombination current density (J0) with the potential for further passivation improvement via laser process optimization.
Ensemble supervised learning for genomic selection

To meet the world's growing food and nutrition demands, agricultural breeders need to grow crops with improved phenotypes and create varieties that allow increased production. Genomic selection enables the breeders to select individuals with improved phenotypes even before growing them. Existing genomic selection is made mostly through statistical methods that do not accurately predict complex non-linear traits. Deep learning and other machine learning methods have been applied, but most of the deep learning methods are not specifically designed for genomic selection. Also, there has been relatively little comparison between different machine learning methods. We propose three ensemble learning methods: i) ensemble support vector regression; ii) ensemble deep convolutional neural networks and iii) random forests; to predict phenotype with high accuracy. We also propose a feature selection strategy that identifies important markers and contributes to improved phenotype prediction. The proposed marker selection strategy is independent of machine learning methods; thus, the markers that are selected remain the same when the machine learning model is changed. We employed our methods to Iranian wheat landraces. The result shows that ensemble learning methods are better than the single machine learning methods with the lowest PCC 0.339 for plant height and the highest PCC 0.747 for grain length. Our models are also robust as they rank both top twenty and bottom twenty individuals well with nDCG@20 ranges from 0.188 to 0.712.
Polymer Optical Fiber Sensor System for Multi Plane Bending Angle Assessment

In this paper, a polymer optical fiber (POF) sensor system for angle estimation in multi-planar movements is proposed. The sensor system comprises of three intensity variation-based POF sensor multiplexed via a time multiplexing technique with the side-coupling of the light sources. A theoretical analysis is performed for the sensor system as well as a numerical analysis using the finite element method (FEM) in order to investigate the cross-sensitivity for multi planar movement and the effect of the sensors positioning on the sensor sensitivity due to the stress distribution on the fiber. Such analyses indicate the possibility of not only increasing the sensor accuracy on single plane angle estimation by combining the responses of two sensors, but also the possibility of obtaining an angle estimation combining the sensor responses for multi plane movements. Then, an experimental analysis is performed on the sensor system, which corroborates the numerical and analytical responses and enable a threefold error reduction when two sensors are used on the single plane estimation instead of one. In addition, errors as low as 1.15% in the best case and 4.30% in the worst case were obtained on the tests with multi plane movement. The results obtained in this paper indicate the possibility of using this sensor system on the shape reconstruction applications in structural health monitoring, robotics and movement analysis.
High Performance GaAs Solar Cells Grown at High Growth Rates by Atmospheric-Pressure Dynamic Hydride Vapor Phase Epitaxy

We report GaAs solar cells grown at rates exceeding 300 µm/h by atmospheric-pressure dynamic hydride vapor phase epitaxy (D-HVPE). The surface morphology of the GaAs layers grown at those rates is smooth, with root mean square surface roughness of < 1.4 nm. We grew GaAs solar cells at rates from 35 – 309 µm/h with negligible degradation in open circuit voltages (VOC), which ranged from 1.04 – 1.07 V. We showed the slight decrease in Voc is due to lack of doping optimization at higher growth rates. Deep level transient spectroscopy measurements showed low point defect densities that increases slightly with growth rate from 35 – 309 µm/h. The low point defect concentrations and the high VOCs indicate that high material quality can be maintained at these extremely high growth rates, enabling the deposition of high efficiency cells in mere seconds instead of hours.
Demonstration of a hybrid optical fiber–wireless 5G fronthaul coexisting with end-to-end 4G networks

The plethora of services envisaged to support 5G use-cases requires the development of a radio access network (RAN) infrastructure with the capability to integrate a variety of user-demanded mobile traffic characteristics. It is therefore interesting to study a 5G deployment scenario showcasing heterogeneous transmission. In order to address this challenge, in this paper, we propose and experimentally assess a coarse-wavelength-division-multiplexing-compatible optical access network for joint provisioning of 4G/5G RAN services, supporting the coexistence of both digital and analog radio-over-fiber (ARoF) fronthauling. Resorting to an integrated testbed, we demonstrate the transport of 100 MHz 5G downlink using low-latency and bandwidth-efficient ARoF over 8 km fiber, seamlessly coexisting on the same fronthaul infrastructure with a legacy full-fledged 4G long-term evolution (LTE) network supported by Ethernet-based digital RoF with different functional splits. In addition, we experimentally demonstrate the successful operation of the 4G/5G system in a hybrid fiber–wireless scenario including a free-space optics fronthaul extension with 55 m. The proposed heterogeneous RAN architecture is demonstrated to provide a flexible and robust integration path for novel 5G technologies, enabling their coexistence with currently deployed 4G LTE systems over a shared fronthaul infrastructure.
eParticipation Provision and Demand Analysis of a Regional Government: Insights from Metro City

The purpose of this study is to investigate the provision and demand of eParticipation of a local government, conducted in Metro City, Indonesia. Using the Citizens Web Empowerment Index (CWEI), an assessment of 60 Metro City government websites was conducted to measure eParticipation's provision. Furthermore, a survey involving 397 respondents from the Metro City residents was conducted to evaluate the demand of eParticipation. The results showed that eParticipation level provided by Metro city is low, having CWEI score of 13.42 percent. This research also found that citizens' needs for eParticipation were quite high, with specific participative purposes such as information disclosure, media for aspirations and complaints, online surveys / polls, online communication and consultation with the mayor, and digital voting. Unified Theory of Acceptance and Use of Technology (UTAUT) is utilized to determine citizen's intention to use eParticipation services, which reveal that all hypotheses were accepted. Finally, recommendations were made to improve eParticipation services with the aim of increased citizen empowerment in government.
A 96kb, 0.36V, Energy-Efficient 8T-SRAM with Column-Selection and Shared Buffer-Foot Techniques for EEG Processor

In previous standard single-ended (SE) 8T-SRAM design, rare research effort has been made to address a major issue related to column-selection. This work proposes a shared data-aware write technique which facilitates bit-interleaving architecture for the SE-8T SRAM. In addition, a shared buffer-foot scheme is also presented to achieve bitline leakage reduction. A 96kb SE-8T SRAM with the proposed techniques, aimed to be applied to an electroencephalogram (EEG) processor, is implemented using a 65nm CMOS. Measured results show that it achieves a VDDmin of 0.36V, and a minimal power of 5. 1μ W, as well as an optimal total energy consumption of 25.6pJ@0.5V.
Analysis of Oscillation Suppression Methods in the AC-AC Stage of High Frequency Link Converters

This paper addresses various oscillation suppression methods in the AC-AC cycloconverter stage of a high frequency link inverter. Such oscillations occur after the natural commutation of the switches due to parasitic parameters of the converter circuit. A mathematic analysis of the resonance process in the cycloconverter stage is presented, followed by a comparative analysis of different snubber circuits that allow to minimize the effect of oscillations. The theoretical analysis is verified with software models and experimental prototype of 1 kW DC-AC converter.
IoT System Implementation for ATmega328 Microcontroller Based Home Door Control

The house gate or home door control system uses a lot of infrared remote control, and it takes time to look for an infrared remote control if there is a loss of the remote. On the basis of this thought a design tool was made that was able to be used automatically. This home door control system works automatically when the electricity supply from PLN provides voltage to the power supply to turn on the ATMega328 microcontroller, Wi-Fi NodeMCU ESP8266 module, LCD and L298 shield motor driver. The Wi-Fi module will receive orders to open the house gate through a smartphone, and will provide notifications to LCD, LED and smartphone. This control system can accept orders to open and close the gate of house which depends on the speed of internet connection.