Miracle-dz/UniFAR-FTUs huggingface dataset:
A 433MHz Transmitter Based on Injection-Locking and Frequency Multiplication

In recent years, the MICS band has become more and more versatile, and at the same time, the power consumption issue of the transmitter has been plaguing people. We designed a highly integrated 433 MHz MICS band transmitter which is based on cascaded multi-phase injection locking and frequency multiplication to enable low power operation. The transmitter is designed with the TSMC 180-nm CMOS process. The core area of the transmitter is only 0.0018 mm2. This structure consumes a total power of 0.58mW and outputs -28dBm power on 50 Ω load. It is fully integrated except for the crystal and the matching network.
Ultra-Low-Power Intelligent Acoustic Sensing using Cochlea-Inspired Feature Extraction and DNN Classification

We present our recent progress in ultra-low-power intelligent acoustic sensing that harnesses the high power and energy efficiency of cochlea-like analog feature extraction and binarized neural network classification. Compared with conventional methods including the fast Fourier transform-based feature extraction plus neural network classification, and the more aggressive approach based on end-to-end neural network models, the analog filter bank-based handcrafted feature extraction inspired by mammalian cochlea has the promise of achieving the minimum power consumption for many existing and emerging always-on audio applications. System considerations and circuit techniques that are used to achieve the high power efficiency will be presented and comparison with some state-of-the-art works, and future directions will be discussed.
A Synchronous Rectifier Energy Collection Circuit with Two Energy-Transfer Schemes for A 65% Power Efficiency

This study presents an efficient and self-startup energy harvesting circuit consisting of two energy-transfer schemes. One energy-transfer scheme is based on a transformer, and the other energy-transfer scheme is based on a boost converter. At the same time, a new and improved rectification architecture is adopted. The MOS transistor is used to replace the diode, which greatly reduces the rectification loss, so that the circuit can achieve higher efficiency. The final output is stabilized by a negative feedback circuit, and the output voltage is stabilized at 2V. The design is implemented by the use of 0.4 um BCD process. With the minimum input voltage is 50 mV, maximum efficiency of 65% can be achieved.
A Hierarchical Clustering Based Feature Word Extraction Method

Feature words extraction is an important task in protocol reverse engineering. How to simplify the feature word extraction process and how to increase the recall ratio remains a considerable issue. In this paper, we propose a hierarchical clustering based approach to extract feature words from unknown protocols. The approach does not need any prior knowledge of the unknown protocols. Feature words are extracted on the basis of hierarchical clustering and longest common sequences. We cluster the similar sequences, and then extract their longest common subsequences. Finally, we extract feature words from longest common subsequences through position information of every byte. Experimental results show that the proposed approach can reach a better recall result compared with traditional methods.
Design and Implementation Of a Separate Control Driven High Current Synchronous Rectified Forward Converter with Non-Dissipative Snubber Reset

This paper explains the techniques employed in forward converter to maximize the efficiency for a high current output. Usually with high current (>10A), forward converter will have significant diode conduction losses occurring at the secondary side, to minimize it Synchronous Rectification (SR) technique has been adapted with each diode replaced with two SR MOSFETS connected. Lossless snubber is connected across primary MOSFET for resetting of flux in Toff period there by achieving wide range of duty cycle operations. Also, it reduces the voltage stress imposed on the MOSFET and provides zero turnoff switching. Operations and design considerations of the employed techniques are studied. A forward converter has been designed to operate at switching frequency of 100 KHz for 5V/30A output with wide input voltage range of 16V to 45V. The hardware is developed for the proposed design and tested for performance parameters. The results obtained are well within the specified values.
Clustering and topic modeling over tweets: A comparison over a health dataset

Twitter became the most popular form of social interactions in the healthcare domain. Thus, various teams have evaluated Twitter as an additional source where patients share information about their healthcare with the potential goal to improve their outcomes. Several existing topic modeling and document clustering applications have been adapted to assess tweets showing that the performances of the applications are negatively affected due to the nature and characteristics of tweets. Moreover, Twitter health research has become difficult to measure because of the absence of comparisons between the existing applications. In this paper, we perform an evaluation based on internal indexes of different topic modeling and document clustering applications over two Twitter health-related datasets. Our results show that Online Twitter LDA and Gibbs LDA get a better performance for extracting topics and grouping tweets. We want to provide health practitioners this comparison to select the most suitable application for their tasks.
Time-SWAD: A Dataflow Engine for Time-Based Single Window Stream Aggregation

High throughput and low latency streaming aggregation is essential for many applications that analyze massive volumes of data in real-time. Incoming data need to be stored in a single sliding window before processing, in cases where incremental aggregations are wasteful or not possible at all; this puts tremendous pressure to the memory bandwidth. In addition, particular problems call for time-based windows, defined by a time-interval, where the amount of data per window may vary and as a consequence are more challenging to handle. This paper describes Time-SWAD, the first accelerator for time-based single-window stream aggregation. Time-SWAD is a dataflow engine (DFE), implemented on a Maxeler machine, offering high processing throughput, up to 150 Mtuples/sec, similar to related GPU systems, which however do not support both time-based and single windows. It uses a direct feed of incoming data from the network and has direct access to off-chip DRAM, enabling ultra-low processing latency of 1-10 µsec, at least 4 orders of magnitude lower than software-based solutions.
Solar assisted Switched reluctance motor drive using Asymmetrical bridge converter

This paper describes the solar photovoltaic array fed to switch reluctance motor drive. The simulation of the Switch reluctancemotor with asymmetrical bridge converter considerably increases the efficiency of the proposed system. The controlling of the drive in this system is obtained by using a hysteresis current control system that controls the phase excitation by controlling the switching of the converter switches. The simulation of the system with switched reluctance motor is carried out using Matlab/Simulink.
Development of a Waste-Heat Boiler Model Based on a Fuzzy Logic Apparatus

The work is devoted to the problems of modeling systems with incomplete or complex mathematical description using the fuzzy logic apparatus. The object of modeling is the heat-recovery boiler of the gas turbine power station. The object is represented as a complex, thermodynamic system with non-stationary dynamics and a variety of forms of communication of input and output parameters, which influence the heat exchange processes. Due to the complex specifics of the system or the conditions of incomplete information about the object, the creation of an adequate mathematical description using the methods of the classical theory of automatic control becomes a difficult task. The use of the apparatus of fuzzy logic for modeling objects with complex dynamics has several advantages: they function well in conditions of incomplete information about the object of research, do not require a strictly mathematical description of the object, have the ability to adjust existing models without making many changes to them. Having information about the processes in the object of research, knowing the degree of influence of input parameters on the output characteristics of the object and knowing the limits of changing parameters, one can create a model that will describe the dynamic characteristics of the object with a sufficient degree of adequacy by means of the fuzzy logic apparatus. As a result of the research, a model of a heat recovery boiler was developed using a fuzzy logic apparatus. Linguistic variables, membership functions of linguistic terms, focused on the subsequent solution of the identification problem are defined. A rule base has been developed to describe the behavior of the model. The method of defuzzification of output linguistic variables is selected. The identification of a fuzzy model based on historical trend curve in equipment operation has been carried out.
Synchrotron and optical probing of hybrid organic-inorganic perovskite halides for photovoltaics

The Goldschmidt’s Tolerance Factor is a reliable figure of merit or empirical index to forecast the formation of preferred and stable structures and phases with ABX3 hybrid organic-inorganic perovskite tolerance factors in the range of 0.9 to 1. Here, we probe perovskites of compositional variations ABX3 with tolerance factors in the range of 0.9 to 1.0, and a large effective ionic radius greater than 200 pm. We report on the structural and optical properties of hybrid perovskite halides for photovoltaic (PV) applications, MAPbI3, FA0.8MA0. 1Cs0.1Pb(I0.86Br0.14)3, and FA0.8Cs0. 2Pb(I0.86Br0.14)3, with tolerance factors of 0.91, 0.964 and 0.967 respectively. PV devices were fabricated using these high tolerance factor hybrid perovskite halides. We report we have achieved power conversion efficiencies (PCEs) greater than 21% using the high tolerance factor perovskites investigated. Note, the high PCEs reflect unusually high Jsc’s which may be due to lack of equipment calibration and will be further investigated.
An FPGA Implementation of GCN with Sparse Adjacency Matrix

Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are typically represented in the Euclidean space. However, there is an increasing number of applications where data are generated from non-Euclidean domains and are represented as graphs with complex relationships and interdependency between objects. The complexity of graph data has approaches for graph data have emerged. Graph convolution networks (GCNs) in particular, try to replicate the success of CNN in graph data by defining graph convolutions via graph spectral theory or spatial locality. This paper presents a new GCN accelerator for the graph convolution layers of ST-GCN [1], which is successfully applied in action recognition. The accelerator breaks down the graph convolution into convolution and matrix multiplication with adjacency matrix. To optimize the power efficiency, the dataflow of the convolution is designed properly and a sparse matrix-vector multiplication is proposed to make use of the sparsity of the adjacency matrix. The accelerator is implemented on NSA.241 accelerator and can reach its peak performance of 46.0GOP/s under 188MHz with about 220 DSP, which achieves high DSP efficiency.
PVOPEL: A Scalable Opto-Electrical Performance Model of PV systems using Ray Tracing and PVMismatch

In this study, we introduce a novel Photovoltaic Energy Performance (PV) modeling framework "PVOPEL" to study performance of a PV array using a cell level resolution ray tracing technique and the resulting system level electrical response. Conventionally, PV performance modeling tools model the energy output of a given PV system for a unit, such as a single module or a row of modules, and make an approximation that the output of the system scales with the increasing system size. While some of the factors affecting the output of a PV system do scale linearly with the system size, other factors such as mismatch losses scale nonlinearly based on the shading scene and PV system. PVOPEL framework provides a first-of-its-kind, highly scalable and accurate solution for modeling annual energy output of PV systems. It is expected to shorten product development lifecycles, reduce uncertainty in design decisions, lower the burden on test campaigns, and reduce business risk for solar industry overall.The case study presented here showcases the capability of this framework for assessing the performance impact of Module Level Power Electronics (MLPE) such as DC optimizers and Microinverters as compared to a traditional string inverter based PV system. The case study demonstrates that although the monthly energy impact of introducing MLPEs into PV systems can vary from 1.5% loss to up to 6% gain, the annual energy assessment for the given configuration presents a more realistic figure of up to 1% gain relative to string inverter based PV systems. With the rapid growth of MLPE deployment, this case study demonstrates a tool that the solar industry can utilize to assess the technical value proposition of MLPEs in detail.
Secure Internal Communication of a Trustzone-Enabled Heterogeneous Soc Lightweight Encryption

Security in TrustZone-enabled heterogeneous system-on-chip (SoC) is gaining increasing attention for several years. Mainly because this type of SoC can be found in more and more applications in servers or in the cloud. The inside-SoC communication layer is one of the main element of heterogeneous SoC; indeed all the data goes through it. Monitoring and controlling inside-SoC communications enables to fend off attacks before system corruption. In this article, we study the feasibility of encrypted data exchange between the secure software executed in a trusted execution environment (TEE) and the secure logic part of an heterogeneous SoC. Experiment are done with a Xilinx Zynq-7010 SoC and two lightweight stream ciphers. We show that using lightweight stream ciphers is an efficient solution without excessive overheads.
Market Power Assessment Based on Placement of FACTS Devices

Deregulation has offered improved quality of power supply due to entry of a number of competitive private entities in Electrical Generation Sector. But there is also a possibility of collusion amongst certain members thereby driving the cost of electrical energy above the competitive level for a considerable time. This depends upon the amount of market power possessed by these members. This paper assesses the market power possessed by different entities and relative changes after placement of FACTS devices in the power network. TCSC and STATCOM (FACTS Devices) are placed on their optimum location and market power is being analyzed before and after their placement in the network. The analysis is being done on IEEE-14 bus network at MATLAB platform.
GNDD: A Graph Neural Network-Based Method for Drug-Disease Association Prediction

Potential drug-disease association prediction is important to facilitate drug discovery. However, most of existing drug-disease association prediction approaches rely on assembling multiple drug (disease)-related biological information, which is usually not comprehensively available, and they always fail to explore the latent information in drug-disease network. To tackle these challenges, we propose a graph neural network-based method for drug-disease association prediction, dubbed GNDD, with capturing the complex information between drugs and diseases dispense with any side information. Specifically, GNDD introduces the idea of collaborative filtering in recommendation system to avoid the dependency on multi-data. Furthermore, an embedding propagation strategy is exploited to model the high-order relationships in drug-disease network. We conduct experiments on the Comparative Toxicogenomics Database, demonstrating the effectiveness of our method in drug-disease association prediction.
Power Allocation for Downlink Hybrid Power Line and Visible Light Communication System

Light-emitting diode (LED)-based visible light communication (VLC) is a great promising high-speed wireless access solution due to its low power consumption, no electromagnetic interference, large available bandwidth, and no authentication required. However, visible light cannot be used directly as an information source, and must be combined with other communication technologies to avoid becoming an information island. In hybrid power line and visible light communication (HPV) system, power line communication (PLC) can be used to deliver data to VLC transmitters. Multiple users can share the same time and frequency resources to provide higher spectral efficiency using non-orthogonal multiple access (NOMA) method than traditional multiple access method. Therefore, we investigate the power allocation strategy for a downlink of HPV system based on NOMA. Our goal is to maximize achievable sum rate of the HPV system while considering user fairness. To solve this optimization problem, we present an optimal power allocation (OPA) algorithm that finds optimal solution by transforming the non-convexity of the original formulated model into the convex programming. Numerical results show that the proposed OPA algorithm significantly outperforms the gain ratio power allocation (GRPA) and the fixed power allocation (FPA) algorithm in terms of the sum rate, and improves the fairness among users.
A Review on Semantic Segmentation from a Modern Perspective

Among various vibrant areas of research in computer vision, scene understanding is the one, which has garnered exceptional attention from the research community. This increased popularity should be credited to the accelerated research in deep learning techniques, mainly during the last decade. In scene understanding, one important step is semantic segmentation where the goal is to give each pixel of an image a proper object category label so that each object gets properly delineated. This technique is also known as scene parsing. One major challenge in this task is that it combines the problem of object detection, image segmentation and multi-class recognition. It encompasses a wide variety of practical applications such as autonomous driving, indoor navigation, virtual/augmented reality etc. This paper gives a review on semantic segmentation from a modern perspective by giving a special attention to deep learning based scene parsing methods. In this review, we take up two central issues of semantic segmentation-accuracy (labeling quality) and efficiency (inference speed) to comparatively study the performance of existing methods. We also present the performance results of various methods both in terms of accuracy and efficiency and compare them based on the key strategies employed in the frameworks.
Use of Social Media/Microblogging Platform along with GIS for Mapping Disaster

Twitter is a vast store of textual data that can be helpful in determining disaster related crisis. In this research the aim is to implement a system that can acquire tweets from Twitter using the keyword ‘earthquake’ and determine the disaster location soon after the disaster. For prefiltering the tweets, a statistical approach has been developed. For determining out of context tweets a fuzzy matching approach has been used. To further categorize those tweets as relevant and irrelevant, Multinomial Naïve Bayes classifier has been used. Relevant tweets were further analyzed to determine probable location of the disaster using a clustering-based approach. After obtaining the probable location of the disaster; it is then visualized on map.
TID Controller in Two Area Multi Unit Power Systems with SMES and HVDC Link

Change in frequency with load is a great concern. In this paper a load frequency problem is address by taking two area power systems with hydro and wind power plant in each unit in the first instant. Then SMES and HVDC link is introduce in the system to improve the system performance. In all the cases TID controller is used and the parameters of controller, SMES and HVDC link are tuned using Differential Evolution (DE) algorithm. The whole system is design in MATLAB/SIMULINK model; different load pattern is applied in place of step load and results show that with SMES and HVDC link the improvement of system occurs in terms of deviation of frequency and tie line power.
A New Bandwidth Management Model using Software-Defined Networking Security Threats

Available bandwidth in network is limited, it is very critical to manage the network bandwidth efficiently. In this research, bandwidth management model based on Software-Defined Networking (SDN) architecture is examined. Through the recommended model, real-time traffic over network was given importance to approach the restricted bandwidth over non real-time traffic. SDN promises the simplification of network management compared to the traditional network. SDN architecture breaks the vertically integrated current network design and it introduces centralized network control through the SDN controller, which is considered as the greatest contribution of SDN in networking area. SDN architecture is adopted in this paper since it promotes centralized control features which enable grid programmability and supports improve network resources administration. Simulation was conducted using the EstiNet network simulator. Two groups of experiments with different situations have been conducted. Each experiment consisted of five simulations with different number of sender and receiver nodes. The first experiment simulated the traffics in a traditional networking; and the second experiment simulated the traffics in a proposed network model. In addition, bulk of the existing bandwidth was reserved to real-time traffic and the remaining quantity was reserved for non-real-time traffic. The real-time throughput results of the two different experiments have been compared to estimate the performance of the proposed SDN research model. The experiment results presented that the recommended model has successfully given urgency to real-time network traffic to approach the limited network bandwidth. As the number of nodes increases, the conventional real-time throughput of setup one and setup two decreases linearly. When nodes reach 10, the real-time ordinary throughput of setup one and setup two are 63SKBps and 473KBps respectively.
Handwritten Digit Recognition Using Machine Learning: A Review

The task for handwritten digit recognition has been troublesome due to various variations in writing styles. Therefore, we have tried to create a base for future researches in the area so that the researchers can overcome the existing problems. The existing methods and techniques for handwritten digit recognition were reviewed and understood to analyze the most suitable and best method for digit recognition. A number of 60,000 images were used as training sets of images with pixel size of 28×28. The images/training sets were matched with original image. It was found out after complete analysis and review that classifier ensemble system has the least error rate of just 0.32%. In this paper, review of different methods handwritten digit recognition were observed and analyzed.