Miracle-dz/UniFAR-FTUs huggingface dataset:
ReRAM Crossbar-Based Analog Computing Architecture for Naive Bayesian Engine

Recent advances in Resistive RAM (ReRAM) have explored the in-situ Matrix-Vector Multiplication (MVM) ability of crossbar arrays to achieve high energy-efficiency Process-In-Memory (PIM) architectures for Convolutional Neural Network (CNN), image processing, and so on. However, the existing ReRAM-based PIM architectures suffer from considerable additional auxiliary logic and device variations. In this work, we propose a novel analog computing architecture NB Engine for classification by implementing Naive Bayesian (NB) algorithm on ReRAM crossbar arrays. The two key steps of the NB algorithm, that is, probability calculation and electing the class that has the highest probability, are elaborately accomplished in our architecture. The ReRAM arrays are both used as storage and computation components. We store the pre-calculated prior probabilities and conditional probabilities of every class in crossbar arrays. Then the probability calculation step is completed in parallel through the MVM operation of the array. In general, the election step is a multiple-comparison procedure and is normally implemented by a comparison tree. Here, we reuse the max pooling module in a conventional CNN PIM architecture to realize a compatible comparison logic. However, neither of the two designs can avoid the overhead of costly high bit-precision Analog-to-Digital Converters (ADCs). So we introduce a novel analog parallel comparison design which does not need any ADCs or other computing logic with better energy-saving and area-efficiency. Our proposed NB Engine is tested by 11 various datasets. The influence of several non-ideal device properties is discussed and the NB Engine exhibits great tolerance to these variations. The experiment results show that our design offers a runtime speedup up to 2289.6x compared with the software-implemented NB classifier with negligible accuracy loss. In addition, the NB Engine saves 96.2% energy consumption and 45.2% array area compared with the CNN PIM compatible design.
A Solar Energy Absorber Design Using Metamaterial Particles for Renewable Energy Solutions

In this paper, a highly efficient solar energy absorber is proposed within the visible light regime, for future integration with renewable energy solutions, for instance solar cells and other smart renewable energy resources. The absorber is based on metamaterials concept and comprises the design of an infinite array of multi-resonant particles that are at the nanoscale. Due to the dimensions of the proposed highly resonating structure being very much smaller than the incoming solar energy operating wavelegnth, the absorber allows for much absorption of sun’s radiation energy, more absorbed photons, and hence higher absorption rate. Numerical studies are carried out in this work using COMSOL Multi-Physics solver. After optimizing the metamaterials-based absorber, a unity absorptance value at 540THz was achieved, while at the same time achieved absorptance rates over 80% for other sub-frequency bands within the solar radiation spectrum. The achieved results in this paper are promising to invest on metamaterials for generation and construction of low-cost smart solar cells solutions with highly efficient absorbers.
Internet of Things based Smart Energy Audit using Evolutionary Fuzzy Association Rule Mining

Energy audits are investigations, studies and examination of power vitality utilization, with the point of proficiency without lessening the exhibition of the system. The paper presented Internet of Things (IoT) based Smart Energy Audit by implementing electrical data collection using Wireless Sensor Network (WSN) and Decision Support System (DSS) using Evolutionary Fuzzy Association Rule Mining (EFARM). The developed system aims to collects data using IoT nodes and summarise data pattern using EFARM in interpretation of Fuzzy Rules and Tree Based Evolutionary Computation (EC). The evaluation performed in five rooms within a week and shown EFARM capable to: Interpreted patterns of electricity consumption in load samples by achieving a high average, confident and score; Compare consumption patterns between measurement areas and the type of measured load; Summarise the rules similarity between areas as general rules and dissimilarities that only exist in some areas as a specific rule, using singletree interpretation; Interpretation of comparison between power consumption patterns with measurable performance, namely temperature and light intensity.
Performance Comparison of Multiple Speech Features for Speaker Recognition using Artifical Neural Network

Human speech is the natural way of communication between humans. Speech signal contains among others, information that conveys the message being spoken by a person, speaker characteristics and language. It also carries information regarding the speaker’s identity. The objective of Speaker Recognition Systems (SRS) is to automatically identify the speaker using the features extracted from their speech signals. Currently, SRS is one of the most popular biometric technique. It is used for surveillance, forensic speaker recognition, authentication, and such similar activities. In this work, Artifical Neural Network (ANN) based text-independent speaker identification is done. We compare the performance of Relative Spectral Amplitude-Perceptual Linear Prediction (RASTA-PLP), Mel-Frequency Cepstral Coefficient (MFCC) and Power Normalized Cepstral Coefficient (PNCC) features. To improve the performance accuracy of the system we extract features from the voiced frames of the signal. It was observed that MFCC outperforms the other two features namely PNCC and RASTA-PLP. TIMIT database was used for the experiments.
Deep Learning Local Descriptor for Image Splicing Detection and Localization

In this paper, a novel image splicing detection and localization scheme is proposed based on the local feature descriptor which is learned by deep convolutional neural network (CNN). A two-branch CNN, which serves as an expressive local descriptor is presented and applied to automatically learn hierarchical representations from the input RGB color or grayscale test images. The first layer of the proposed CNN model is used to suppress the effects of image contents and extract the diverse and expressive residual features, which is deliberately designed for image splicing detection applications. In specific, the kernels of the first convolutional layer are initialized with an optimized combination of the 30 linear high-pass filters used in calculation of residual maps in spatial rich model (SRM), and is fine-tuned through a constrained learning strategy to retain the high-pass filtering properties for the learned kernels. Both the contrastive loss and cross entropy loss are utilized to jointly improve the generalization ability of the proposed CNN model. With the block-wise dense features for a test image extracted by the pre-trained CNN-based local descriptor, an effective feature fusion strategy, known as block pooling, is adopted to obtain the final discriminative features for image splicing detection with SVM. Based on the pre-trained CNN model, an image splicing localization scheme is further developed by incorporating the fully connected conditional random field (CRF). Extensive experimental results on several public datasets show that the proposed CNN based scheme outperforms some state-of-the-art methods not only in image splicing detection and localization performance, but also in robustness against JPEG compression.
Preference Oriented Multi-Objective Optimization for Tuning of Controllers: A Reference Point Based Approach

This paper proposes a reference point based controller tuning approach to include the designer's preference in multi-objective optimization (MOO) procedure. As the MOO procedure gives a Pareto-optimal set of solutions, the designer preferred reference points are considered to include the required specifications. The reference point based nondominated sorting genetic algorithm (R-NSGA-II) is applied to find the set of solutions near those preferred points only. Extensive studies have been conducted on various types of linear and nonlinear systems with and without delay for fractional order PID (FOPID) controllers. The results obtained demonstrate the superiority of proposed approach to achieve robust controller design. Eventually, better set point following, disturbance rejection, and variable input following are observed.
Simulation on Deposition Characteristics of Contamination Particles in Fog-Haze Environment

Fog-haze weather can aggravate the particles deposition on the insulator surface, which will cause pollution flashover. In this paper, the multiphysics software COMSOL was used to numerically simulate the particle deposition characteristics on the surface of XWP2-160 porcelain insulator under wind tunnel conditions, and the simulation results were compared with wind tunnel test results to verify the rationality of the simulation model and the method. Based on this, the contamination deposition characteristics of this insulator on 110kV transmission lines under fog-haze environment were simulated. And the effects of wind speed, contamination concentration, and voltage type on particle deposition were analyzed. The results show that under the same conditions, the NSDD under DC voltage is the largest and it is the smallest under unelectrified state. The particle deposit amount increases approximately linearly with increasing wind speed and contamination concentration under DC voltage. In moderate fog-haze, the influence of wind speed on AC transmission line is larger than that on DC transmission line. The proportion of NSDDDC to NSDDAC displays a trend of gradual decrease with increasing wind speed and tends to be gentle gradually, and the ratio under all conditions is greater than 4.
Maximizing the Profit for Industrial Customers of Providing Operation Services in Electric Power Systems via a Parallel Particle Swarm Optimization Algorithm

Integration of renewable energy sources require an increase in the flexibility of power systems. Demand response is a valuable flexible resource that is not currently being fully exploited. Small and medium industrial consumers can deliver a wide range of underused flexibility resources associated with the electricity consumption in their production processes. Flexible resources should compete in liberalized operation markets to ensure the reliability of the system at a minimum cost. This paper presents a new tool to assist industrial demand response to participate in operation markets and optimize its value. The tool uses a combined physical-mathematical modelling of the industrial demand response and a Parallel Particle Swarm Optimization algorithm specifically tuned for the proposed problem to maximize the profit. The main advantages of the proposed tool are demonstrated in the paper through its application to the participation of a meat factory in the Spanish tertiary reserve market during a whole year using a quarter-hourly time resolution. The enhanced performance of the proposed tool with respect to previous methodologies is shown with these four flexible processes examples, where the maximum available profit obtained in the simultaneous consideration of all different flexible processes is computed. The flexible processes are technical and economically characterized in a way that makes the tool valid for most of the processes in the industry.
BER Performance of OFDM-Based Visible Light Communication Systems

In this paper, OFDM-based techniques designed for Intensity Modulation/Direct Detection (IMDD) modulation applied to Visible Light Communications (VLC) systems are evaluated. The evaluated techniques are Asymmetrically Clipped Optical OFDM (ACO-OFDM), Direct Current (DC) Biased Optical OFDM (DCO-OFDM), and Asymmetrically Clipped DC Biased Optical OFDM (ADO-OFDM). These techniques are evaluated and compared for different types of M-QAM modulation in terms of Bit Error Rate (BER). It is shown that ACO-OFDM, in general terms, presents a better performance compared to the conventional DCO-OFDM and ADO-OFDM schemes.
Real-World Application of Machine Learning and Deep Learning

The world today is running on the latest computer technologies and one of those is machine learning. The real life example that most of us know is speech recognition. Google Assistant is the common example for this Speech recognition. This google assistant is not only limited till ‘Ok Google’, but it responds to all your questions in a smart way. It can manage all your calls or can book appointments. Imagine you fell down while de-boarding a bus. So, Next time you take care so that you don't fall that is something that your brain has interpreted from your past experience. This is what exactly deep learning is, it imitates human brain works. Deep learning is sub-branch of machine learning. It is able to build all new things based on its previous experiences. Many of us have heard about driverless cars and medical diagnosis. Recently google has developed a new technology where all your cardiovascular events can be predicted by eye scan so, that doctors can get a clear view of what is inside the body of a patient. These all are developed using machine learning. It has a capability to change the human world into a complete robotic world. Anyways, it also has its own disadvantages. This article discusses about those, Scope of machine learning, its Market potential, financial growth and Current applications of machine learning.
Compact Reconfigurable Stacked Patch Antenna Using Liquid Crystal for 5G Networks

In this paper, an improved design is presented to achieve a compact reconfigurable wideband antenna based on GT3-23001 liquid crystal (LC) for millimeter wave (mmw) 5G networks. The proposed design consists of two stacked patch antenna using aperture coupled feeding. The GT3-23001 LC is an anisotropic dielectric material used as substrate and it has a variable permittivity that can be controlled by a biasing voltage. The dimensions of the designed stacked patch antenna are (5.34 × 5.34 × 2.06) in mm. The proposed antenna is suitable for radar, satellite communications and for 5G applications, such as theatres, shopping malls, convention centers and stadiums. It operates at millimeter wave from 25.4 to 33.5 GHz with a bandwidth of 8.16 GHz and a maximum antenna gain of 6.75 dB is achieved.
Smart Carnatic Music Note Identification (CMNI) System using Probabilistic Neural Network

Music is defined as an art which arranges the sounds to provide the inner feeling of happiness. Carnatic music is based on a Raga (Tune), Bhava (Emotion), Thala (Rhythm) and also characterized by Saptha swara (musical note), Sthayi and reference note (Shruthi). In this proposed work, an attempt is made to identify the swaras in Madhya Sthayi and constant Shruthi. The recorded music samples are analyzed in the frequency domain by using digital signal processing. The features are extracted and input to the probabilistic neural-network for the note identification. The performance of the system is verified for 25 samples for each note. The system success rate is above 90%.
Breast Cancer Detection Using Decision Tree, Naïve Bayes, KNN and SVM Classifiers: A Comparative Study

The breast cancer remains as the major cause for the fatality in the women. To predict and classify breast cancer at early stage, researchers have used various machine learning procedures. Various classification algorithms like decision tree, SVM, KNN have been used on breast cancer dataset to categorize a cancer stage as either nonthreatening or threatening. In this paper, the four basic models of classification i.e. decision tree, Naïve Bayes, KNN, SVM have been used to classify the cancer stage. Then, a comparative study is done on these classifier using different accuracy measures like precision, recall and f1-score. This study shows that decision tree classifiers are best among all classifiers in terms of accuracy.
Study of gate source-drain overlap/gate-channel underlap in Heteojunction (50nm Ge channel) n-Double Gate TFET for different $\kappa$-spacer

In this work a comparative study is performed between different gate source-drain overlap and gate channel underlap structures with conventional double gate tunnel field effect transistors (DGTFET) to overcome the challenges of scaling down of tunnel FET. To attain better ON current (ION), band-to-band tunneling (BTBT) and subthreshold swing (SS), low energy band gap material (Ge) is used in heterojunction n-type DGTFET. All device combinations are incorporated with various $\kappa$-values of dielectric spacer in order to investigate the best switching ratio keeping the leakage current (IOFF) into consideration for low power applications. It is observed that for n-type gate underlap tunnel FET, a high-$\kappa$ spacer with minimum length of 5 nm underlap provides best device performance. However, for a given spacer a large gate channel underlap or a gate source-drain overlap relatively lowers the device performance. Subthreshold swing provided by the 5 nm gate channel underlap n-DGTFET is recorded best as 13.93 mV/decade of 50 nm gate length. It also results maximum drain current (ION) as $1.13\times 10^{-4}\ \ \mathrm{A}/\mu\mathrm{m}$ for different $\kappa$-values of dielectric spacer at supply voltage (VDS) of 0.5 V.
Implementation Of PV Powered Wireless Healthcare Monitoring Using IOT

Now days in the busy schedule it has become very difficult for the members in a family to be made available for the near ones when they are suffering from any disorders. Therefore, it is necessity to monitor parameters such as temperature, pulse rate, sugar level, BP continuously. Since the world has entered into a new era in the medical field. The health care needs of the community outside the clinic can be satisfied by devices called as health care monitoring device. There are many individual devices available for measuring different parameters of the body. All the devices are integrated into a single unit. Usually physical presence of the doctor is required to measure the physiological parameters. It can be avoided using the monitoring device, thus reduces the burden of the doctor This paper proposes PV powered backup and Internet of Things (IOT) for wearable sensor. The methodology used for monitoring patient data in remote is by using GSM and GPS networks. Patient monitoring systems measure physiological characteristics continuously in a regular interval of time.
Adaptive Control of Cyber-Physical Distillation Column using Data Driven Control Approach

In this paper, the authors implemented a technique in controlling a batch distillation column system by utilizing data driven control. The authors used the pre-existing mini batch distillation column which has been integrated into a Cyber-Physical System by the previous research. Then, some minor modification in the network structure were added to enable the implementation of data driven control. The data driven control itself consists of a machine learning with Recurrent Neural Network model to mimic the behavior of the physical system and a Mataheuristics-based PID tuner such as Genetic Algorithm and Particle Swarm Optimization to adjust the plant's controller over time. The performance of the system was then compared with the conventional PID control scheme and also human in the loop control.
A Circularly Polarized Horn Antenna Based on an FSS Polarization Converter

This letter designed a circularly polarized horn antenna by loading a frequency selective surface (FSS) polarization converter at the aperture of a standard horn. The proposed polarization converter is based on multilayer nonresonant FSS. It is composed of a two-layer dielectric slab and three printed metal layers, which are etched on the top, middle, and bottom layers of the slab. The three metal layers consist of a two-layer patch with a square truncated corner and a one-layer grid line with a slot. By introducing perturbation to both the patch and the grid line, the polarization converter shows a low profile and subwavelength unit size, whose dimension is 0. 18λ0 × 0. 18λ0 while the thickness is only 0. 1λ0. The simulated results show that the axial ratio (AR) is less than 3 dB in the frequency range of 9.6–11.7 GHz, i.e., a fractional bandwidth of 19.7% within the targeted frequency band. The measurement results are in a good agreement with the simulation results, which proves the feasibility of the design.
A Brief Tutorial on Quadratic Stability of Linear Parameter-Varying Model for Biomathematical Systems

Many biological phenomena are investigated using models formulated in terms on nonlinear differential equations and, in general, the global stability is assured point-to-point by Lyapunov’s theory. However, many times the stability analysis becomes a hard task due to the complex interactions between variables and parameters. Furthermore, when some parameters vary over time, it is not possible to employ the conventional methods of stability analysis regarding linear time-invariant (LTI) theory. However, several of those nonlinear models can be rewritten as linear systems with time-varying parameters lying inside bounded and known intervals for which there exist in the control theory literature convex optimization procedures based on linear matrix inequality (LMI) conditions to test the asymptotic stability. Knowing that the Robust LMI Parser (ROLMIP) and YALMIP are openly distributed toolboxes for MATLAB® that allow easy programming of LMI conditions for systems with uncertain (possibly time-varying) parameters, this paper presents a implementation tutorial of a local stability analysis methodology that can be applied in linear parameter varying (LPV) models of biomathematical systems. A numerical example based on infectious disease is investigated.
Chloroquine Downregulation of Intestinal Autophagy to Alleviate Biological Stress in Early-Weaned Piglets

Simple Summary Weaning is one of the biggest challenges in a pig’s life. Autophagy is a catabolic process aimed at recycling cellular components and damaged organelles in response to diverse stress conditions. There are two autophagy-modifying agents, rapamycin (RAPA) and chloroquine (CQ), that are often used in vitro and in vivo to regulate this process. We speculated that the regulation of autophagy may have some effect on weaning pressure. In this study, we try to understand the role of autophagy in intestinal barrier function and inflammation during the first week after weaning. We examined the effects of modulation of autophagy via RAPA and CQ on growth performance, immunity, inflammation profile, and the intestinal barrier to find potential value for CQ as a feed additive agent for ameliorating weaning stress. Abstract Early weaning stress impairs the development of gastrointestinal barrier function, causing immune system dysfunctions, reduction in feed intake, and growth retardation. Autophagy was hypothesized to be a key underlying cellular process in these dysfunctions. We conjectured that rapamycin (RAPA) and chloroquine (CQ), as two autophagy-modifying agents, regulate the autophagy process and may produce deleterious or beneficial effects on intestinal health and growth. To explore the effect of autophagy on early weaning stress in piglets, 18 early-weaned piglets were assigned to three treatments (each treatment of six piglets) and treated with an equal volume of RAPA, CQ, or saline. The degree of autophagy and serum concentrations of immunoglobulins and cytokines, as well as intestinal morphology and tight junction protein expression, were evaluated. Compared with the control treatment, RAPA-treated piglets exhibited activated autophagy and had decreased final body weight (BW) and average daily gain (ADG) (p < 0.05), impaired intestinal morphology and tight junction function, and higher inflammatory responses. The CQ-treated piglets showed higher final BW, ADG, jejuna and ileal villus height, and lower autophagy and inflammation, compared with control piglets (p < 0.05). Throughout the experiment, CQ treatment was beneficial to alleviate early weaning stress and intestinal and immune system dysfunction.