Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient Low-Cost Distributed MPPT Method for Energy Harvesting in Grid-Tied Three-Phase PV Power Optimizers

The partial shading and module mismatches represent the most critical causes of low efficiency and degraded performance in energy harvesting systems of photovoltaic (PV) generation. The distributed structures of maximum power point tracking (distributed MPPT) using cascaded modules of power optimizers have become promising solutions for PV energy harvesting system. However, these cascaded structures have made the PV systems more complex and expensive. Therefore, this paper presents a new simple low-cost distributed MPPT technique for energy harvesting PV systems. Individual dc-dc power converters are attached to each PV module for extracting the maximum available power of the PV module. The new proposed system achieves the control of MPPT of each module through employing the modulation technique of the inverter system. The results and implantation of the new proposed systems verify the simpler and lower cost implementation of the PV energy harvesting system.
Throughput Optimization in Grant-Free NOMA with Deep Reinforcement Learning

Grant-free non-orthogonal multiple access (GF- NOMA) is a promising paradigm for reducing the access delay and improving the spectrum efficiency. As the signals of multiple users are superimposed in GF-NOMA systems, each user is required to select a user-specific pilot sequence to distinguish its own signal from the signals of other users. Packet collisions in the uplink occur when multiple users select the same pilot sequence. In this paper, we first formulate a pilot sequence selection problem for aggregate throughput maximization in GF-NOMA systems. We then design a deep reinforcement learning (DRL)- based distributed algorithm for each user to select its pilot sequence via learning from the past pilot sequence selections. The proposed algorithm does not rely on information exchange between the users and does not require centralized scheduling by the base station. Packet-level simulations show that, for the considered system parameters, the proposed DRL distributed algorithm can achieve an average aggregate throughput which is within $90\%$ of the optimal value, and has a better performance than both acknowledgement-based and random selection GF-NOMA schemes.
Hierarchical Neural Networks for Detecting Anomalous Traffic Flows

Intrusion Detection System (IDS) is designed to protect network assets by inspecting specified region or properties of network traffic. To represent hierarchical traffic flows comprehensively, this paper proposes a deep learning architecture for anomaly detection. We employ CNN model to automatically extract an adequate feature vector for each message composed of unidirectional sequence of packets. Based upon the CNN structure, we build BiLSTM with attention mechanism to summarize the sequential message vectors as a representative flow feature vector. Empirical evidence from reduced UNSW-NB15 dataset indicates the superior performance of the proposed method for detecting anomalous traffic flows.
A Survey of Approximate Quantile Computation on Large-Scale Data

As data volume grows extensively, data profiling helps to extract metadata of large-scale data. However, one kind of metadata, order statistics, is difficult to be computed because they are not mergeable or incremental. Thus, the limitation of time and memory space does not support their computation on large-scale data. In this paper, we focus on an order statistic, quantiles, and present a comprehensive analysis of studies on approximate quantile computation. Both deterministic algorithms and randomized algorithms that compute approximate quantiles over streaming models or distributed models are covered. Then, multiple techniques for improving the efficiency and performance of approximate quantile algorithms in various scenarios, such as skewed data and high-speed data streams, are presented. Finally, we conclude with coverage of existing packages in different languages and with a brief discussion of the future direction in this area.
NSSC: Novel Segment Based Safety Message Broadcasting in Cluster-Based Vehicular Sensor Network

Extensive research attention has been devoted to the Vehicular Sensor Network (VSN) owing to its great potential in environment monitoring. Still, it is difficult to diminish the broadcast storm and data collisions in vehicular senor environment. Due to improper broadcasting of safety message and transmission of packet at same time from multiple vehicles leads to collision. Our key intention is to overwhelm these shortcomings in VSN. Hence, we propose Novel Segment based Safety message broadcasting in Cluster (NSSC) based VSN. Our NSSC is mainly concentrated in three successive processes that are Cluster Formation, Collision Avoidance and Safety Message Broadcasting. Cluster formation is performed originally to sustain stable vehicular environment. Here, Variant based Clustering (VbC) Scheme is proposed to elect Cluster Head (CH) and to form clusters. CH is selected using Chaotic Crow Search (CCS) algorithm. In accord to mitigate the data collision during transmission between CH and Cluster Member, we propose Adaptive Carrier Sense Multiple Access/Collision Avoidance (Ada-CSMA/CA) protocol. Safety message broadcasting adopts Segment based Forwarder Selection (SFS) scheme which selects optimal forwarder using Fuzzy-Vikor method. In this, optimal forwarder is selected concerning to broadcast safety message which reduces the broadcast storm. In regard to validate the proposed NSSC, we have conducted simulations on Omnet++ and SUMO simulator based Veins framework. The acquired results are auspicious in terms of succeeding metrics reachability, average number of collision, duplicate data packets, latency, packet delivery ration and throughput.
Analysis of User Behavior Based on Tweets

Microblogging today has turned into an exceptionally well known specialized communication tool among Internet users. A great many messages are seeming day by day in well-known sites that give administrations to microblogging, for example, Twitter, Tumblr, and Facebook. Sentiment analysis can be an extremely valuable viewpoint for the extraction of helpful data from text documents. The main idea for sentiment analysis is how people think for a particular online review i.e. product review, movie review etc. Sentiment analysis is the process where these reviews are classified as positive or negative. In our paper we are deciding polarity in seven different categories like Strongly Negative, Weakley Negative, Negative, Strongly Positive, Weakley Positive, Positive and Neutral. The machine learning algorithm is used in determining the polarity of the tweets.
The Detection Algorithms for Similar Duplicate Data

This paper studied and analyzed three algorithms which can be used to detect similar duplicate data records. Among them, the two commonly used duplicate data detection algorithms are basic sorted-neighborhood method (SNM) and priority queue algorithm. Both of them are based on sorting-merger thought. The third one is Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The paper also discussed some key problems in the application and implementation of algorithms, including how to choose the attributes of data records and calculate the similarity between these attributes of two records, the choice of the sort algorithm, the setting of sliding window size and queue length. The paper realized three algorithms by programming based on Matlab platform, as well as some technologies related to algorithms. The paper verified related conclusions and evaluated three algorithms by testing a lot of loaded data sets. The paper finally provided the instruction to apply these algorithms.
VibeRo: Vibrotactile Stiffness Perception Interface for Virtual Reality

Haptic interfaces allow a more realistic experience with Virtual Reality (VR). They are used to manipulate virtual objects. These objects can be rigid and soft. In this letter, we have designed and evaluated a vibrotactile hand-held device (VibeRo) to achieve haptic cues of different soft objects. The proposed method is based on combining the vision-driven displacement produced by the pseudo-haptics effect with the haptic illusion of a limb displacement obtained from force-driven synthesis of vibration of a contact surface. VibeRo features a voice-coil actuator and force-sensitive resistors for generating squeeze forces at fingertips. We present an evaluation of VibeRo's pseudo-haptic and haptic illusion effects to render soft virtual objects. The efficacy of the approach was validated in experiments with human subjects.
2.5D Evidential Grids for Dynamic Object Detection

Perception is a crucial and challenging part of Intelligent Transportation Systems. One of the main issues is to keep up with the moving objects in complex and dynamic environments. This paper proposes a method for dynamic object detection using Evidential 2.5D Occupancy Grids. The approach is based on a map representation for occupation modeling and navigable area definition. At each time step, a local grid is derived from the sensor data. Belief Theory is then retained to perform a grid fusion over time in order to keep track of the moving objects in the grid. The description of the dynamic behavior of objects in a scene is related to the conflict issued after the temporal fusion. Finally, the construction of the objects themselves is realized with a segmentation based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN). In order to validate the efficiency of the proposed approach, experimental results are provided based on the KITTI dataset. Performances are evaluated through comparison with the ground truth.
Dual-Ascent Inspired Iterative Transmit Precoding Approaches for Multiple Access Spatial Modulation

In this paper, we investigate the multiple access spatial modulation (MASM) in multiple-input multiple-output (MIMO) systems with the aid of dual-ascent inspired iterative transmit precoding (TPC) methods. To find a global optimum against the maximum minimum Euclidean distance (MMD) problem, as well as to reach a fast convergence rate with low-complexity, we first study the peculiarities of the convex optimization methods that take the dual- ascent method into account. This beneficially provides us an insight into developing a new dual- ascent method which is capable of bringing robustness to the existing dual-ascent method. Then, we introduce an evolving MASM-MIMO system by imposing non-stationary time-varying TPC parameters, and thereby resulting in a guaranteed Euclidean distance (GED) aided TPC method. Numerical results reveal that our proposals are capable of promoting a significant bit error rate (BER) performance improvement comparing with the existing methods for the MASM-MIMO systems, as well as facilitating a fast convergence with low- complexity.
The Digital Hand

In The Digital Hand, James W. Cortada combines detailed analysis with narrative history to provide a broad overview of computing's role in sixteen industries, accounting for nearly half of the U.S. economy. Beginning in 1950, when commercial applications of digital technology began to appear, Cortada examines the ways different industries adopted new technologies, as well as the ways their innovative applications influenced other industries and the U.S economy. In addition, to this account of computers' impact on industry, Cortada also demonstrates how industries themselves influenced the nature of digital technology. Managers, economists, and anyone interested in the history of modern business will appreciate this historical analysis of digital technology's many roles and its future possibilities in a wide array of industries. A detailed picture of what the infrastructure of the Information Age really looks like and how we got there, The Digital Hand is a sweeping survey of how computers transformed the American economy. Available in OSO: http://www.oxfordscholarship. com/oso/public/content/management/9780195165883/toc.html
Implementation of Enhanced Parallel port interface for Frequency analysis in a configurable Ring Oscillator PUF circuits on Xilinx Spartan 3E architecture

Hardware security has evolved from physical one-way functions to Physically Unclonable Functions (PUFs). A PUF produces a response for a given challenge by performing a functional operation. This paper demonstrates the Configurable Ring Oscillator (CRO) based PUF circuit with frequency meter. Frequencies are read through Enhanced Parallel Port (EPP) interface to enable efficient communication between the host and the Field Programmable Gate Array (FPGA) device. As a part of the work, 128 CROs are implemented on 128 Configurable Logic Blocks (CLBs) of the Spartan 3E FPGA device and frequencies are measured for every configuration of the CRO to generate the Challenge Response Pair (CRP) for each device. This experimental setup is carried out on 4 different FPGA devices and specific methodologies are used to generate responses which are consistent with time for every reading analyzed and different for different FPGA devices. The process involves selecting the optimum unit time pulse window to measure the frequencies of CROs and optimum number of CROs grouped as hard macro to enhance inter and intra Hamming Distance (HD) consequently improving uniqueness, reliability and uniformity metrics.
Revisit Nyquist-Bode Stability Criteria for Power Electronic System with Non-minimum Phase System

After a long period of development, stability criteria are gradually improved. Among them, Nyquist-Bode stability criterion plays an important role. However, traditional Nyquist stability criterion is only applicable to minimum phase system. With the development and wide application of power electronic technology, minimum phase system is just a small portion of all the power electronic systems. To analyze the stability of non-minimum phase system, a more general Nyquist-Bode stability criterion is essentially needed.
Investigation on latency issues and objective measurements of non-linear blind bandwidth extension

This paper investigates the algorithmic latency of non-learning blind bandwidth extension (BWE) approaches, and evaluates their quality with objective measurements. BWE methods are regarded as methods for restoring high-frequency losses caused by band limits. To satisfy the restriction and provide high-quality sound, low-latency BWE techniques are required. This paper focuses on the non-learning blind BWE methods, such as spectral shifting-based approaches and non-linear function based BWE (N-BWE). The results proved that the N-BWE method can be performed with low-latency.
A Controller for Hybrid Microgridwith Battery Storage in Utility Connected and Isolated Mode of Operation

This paper deals with the analysis of a control strategy for hybrid microgrid with utility connected and isolated mode of operation. The controller achieves continuous and reliable supply to the loads during both utility connected and isolated mode of operation. Energy storage system helps to mitigate the variation in power of renewable energy sources. A DC-DC boost converter with maximum power point tracking is used to extract peak power from the PV source. A bidirectional buck boost converter with current control is used for charging and discharging the battery. A three phase voltage source converter transfers power from DC subgrid to AC subgrid. Control techniques used are Vf control for isolated mode of operation and PQ control for utility connected mode of operation. During isolated mode Vf control maintains the voltage and frequency constant. During utility connected mode PQ control controls power transferred by the microgrid. Smooth transition from utility connected to isolated mode of operation and vice versa is obtained using self-synchronisation control.
Hand Written Indian Numeral Character Recognition using Deep Learning approaches

Hand written Character recognition is evolving topic as the size and shape of the hand written characters could not be uniquely characterized. The proposed work considers the numeral characters in the image form in varieties of orientation and shape for the digits. The features are extracted and subjected to NaïveBayes classifier, BayesNet classifier and the results associated with the metrics are worked out. Recently, the deployment of machine learning techniques is widespread and has proven improved performance. In the proposed work we have deployed the deep learning network with one stage of convolution layer with 20 numbers of 5x5 filters, Rectifier Linear Unit, Max pooling layer, fully connected softmax layer. The MNIST dataset with 60000 number of training images and 10000 numbers of testing images are used to experiment the proposed network. The results observed using the deep learning techniques are superior to the existing classifier such as NaïveBayes and BayesNet classifier and also the recent work proposed using deep learning techniques with 3x3 filers in the convolution layer.
An Lipreading Modle with DenseNet and E3D-LSTM

For Chinese lip reading, the paper proposes an improved DenseNet network structure to strengthen the ability of short-term dependence of the model. At the backend,E3D-LSTM is adopted to conduct time modeling of features extracted from CNN. The loss function of CTC is used to solve different speech habits of speakers, which will lead to different time dependence of the same word. LRW-1000 datasets are used for training, and experiments show that the Chinese recognition rate is better than the traditional method. In the diffferent length of sequences, our work arrives 38.96%in easy,38.49% in medium and 37.92% in hard, respectively.
High Gain Module Integrated Converter Combating Partial Shading on PV Panel

Modular, environment-friendly, low maintenance and cost effective converter fed off-grid power solution utilizing PV and battery for Telecom power supply is a requisite. This paper proposes a module integrated converter system which does the MPPT and also boosts the voltage upto usable value under various conditions of insolation variation and partial shading conditions. The current control scheme evacuates the maximum power during PV generation amidst intermittent conditions presents a slave source to a battery fed nanogrid. The performance of the system is evaluated under Matlab/Simulink environment. Presented simulation results show close conformity with design and validates the effectiveness of the system proposed.
Estimation of Electron Trap in Hydrocarbon-Based Thermosetting Resin / Fullerene Composite by Computational Science

The authors have reported that dielectric breakdown strength improves by 34 % by uniformly dispersing 0.6 wt% fullerene in epoxy resin. However, it is not easy to uniformly disperse fullerene in a polar epoxy resin. On the other hand, hydrocarbon-based thermosetting resin (HTR: Hydrocarbon¬based thermosetting resin) has more excellent mechanical and electrical characteristics than epoxy resin, and is almost nonpolar. Accordingly, HTR is expected to have high affinity with the nonpolar fullerene. In this paper, quantum chemistry calculations are performed on HTR / fullerene (C60) composites to estimate the electron trap depth from the electronic state density. As a result, it is found that the electron trap depth of the epoxy/fullerene composite and HTR/C60 are calculated as 3.0 and 3.7 eV, respectively. The increase in the electron trap depth suggests that the addition of fullerene to HTR can suppress acceleration of high energy electron leading to increase in the dielectric breakdown strength.
RCMDD: A Denoising Architecture for Improved Recovery of Reflectance Confocal Microscopy Images of Skin from Compressive Samples

The Compressive Sensing (CS) framework has demonstrated improved acquisition efficiency on a variety of clinical applications. Of interest to this work is Reflectance Confocal Microscopy (RCM), where CS can influence a drastic reduction in instrumentation complexity and image acquisition times. However, CS introduces the disadvantage of requiring a time consuming and computationally intensive process for image recovery. To mitigate this, the current document details our preliminary work on expanding a Deep-Learning architecture for the acquisition and fast recovery of RCM images using CS. We show preliminary recoveries of RCM images of both a synthetic target and heterogeneous skin tissue using a state-of-the-art network architecture from compressive measurements at various undersampling rates. In addition, we propose an application-specific addition to an established network architecture, and evaluate its ability to further increase the accuracy of recovered CS RCM images and remove visual artifacts. Our initial results show that it is possible to recover compressively sampled images at near-real time rates with comparable quality to established computationally intensive and time-consuming optimization-based methods common in CS applications.
Indoor Position Recognition and Interference Classification with a Nested LSTM Network

Indoor position information plays an important role in a wide variety of Internet of Things (IoT) applications. In recent studies, Wi-Fi channel state information (CSI) is becoming a popular tool for indoor positioning and environmental sensing due to the richer information it contains in multiple subcarriers. Deep neural networks (DNNs) and convolutional neural networks (CNNs) have been applied in training CSI data for indoor positioning and are shown to have fine performances. However, the potential sequential correlations among the subcarriers and consecutive CSI packets are yet to be exploited in those networks. In this study, we propose a two-level nested long short-term memory (LSTM) network named IPRIC to recognize the indoor positions and classify the type of interferences of Wi-Fi CSI data by analyzing the sequential correlations among the subcarriers and consecutive packets. We apply the discrete wavelet transform (DWT) for data preprocessing to minimize noise and improve classification. As evaluated in an office environment, IPRIC achieves higher classification accuracy than the DNN and CNN-based methods.
Design & Comparison of Lossless Polyphase Decomposition using Filter Bank for Onboard Digital Signal Processing Payload

The M-channel transmultiplexer's for geo communication satellite are design and simulation in this paper. The mathematical description has been discussed in detail. Modified Discrete Fourier Transform(MDFT) and andcosined Discrete Transform(DCT) have chosen for advanced communication satellite. MDFT and DCT provide perfect reconstruction. The theory of filter bank satisfying the perfect reconstruction using proto FIR filter of length N= 2*M*m+1; where M = No. of channel. A new set of objective functions has been derived to satisfy the transmultiplexer's requirements based on these approaches. This work is done under the technology development program of ISRO. In this paper a comparison of the MDFT and DCT approaches are also described.
Improving Temperature Control in Smart Buildings Based in IoT Network Slicing Technique

In smart buildings there are many different types of IoT devices that collect measurements of the environment. These sensors can vary in their characteristics and can also influence the topology of the smart building. For this reason, IoT devices collect heterogeneous measurements. Using complex network and clustering techniques we have designed a new technique that allows to transform heterogeneous data into homogeneous data, this technique is called IoT slicing. This technique consists of creating a graph with the measurements of the IoT network, and virtualizing layers based on the clustering of the graph. To validate the efficiency of this new technique we present the results of a case study using a smart building temperature control algorithm.