Miracle-dz/UniFAR-FTUs huggingface dataset:
IoT Enabled Efficient Detection and Classification of Plant Diseases for Agricultural Applications

This article is aimed to present a simple and efficient Internet of Things (IoT) enabled disease detection system to detect and classify the bunchy top of banana and sigatoka diseases in hill banana plant. The proposed system utilizes image processing and IoT to process the images of the plants and extract its texture features. From the GLCM features, classification is done using Random Forest Classification (RFC) technique at the monitoring site and analyzed by the agriculture experts to provide solutions. Apart from pathogens climatic changes also induce diseases in plants. Hence, it is mandatory to monitor the environmental parameters of the agriculture field including field temperature, soil moisture. These parameters are measured using temperature/ humidity sensor and soil moisture sensor that are interfaced with Raspberry Pi3. The proposed disease detection system has obtained an overall detection accuracy of around 99%. The performance results confirm that RFC- GLCM based classification performs better for hill banana dataset.
A Fresh Look at Combining Logs and Network Data to Detect Anomalous Activity

As data rates have increased, network administrators have increasingly turned to Software Defined Networking (SDN) to increase efficiency, as well as to react quicker to changing network states. However, as SDN flows become the norm to manage network traffic, Network Intrusion Detection Systems (NIDS) still rely on processing packet data directly using techniques such as Deep Packet Inspection (DPI). SDN flows provide only a high level representation of the packets traversing the network, reducing the amount of data available to NIDS. In particular Deep Learning based NIDS may be affected. Deep Learning has been proposed as a solution to 0-day attacks, but these models typically require large volumes of training data with many data points. This paper proposes a solution to this dilemma, by providing more data points for an IDS to monitor through the abstraction of log data generated by the flows. Past papers have shown that the quality of training data can have a marked effect on performance of Deep Learning models. This paper builds on these works by showing that high quality data points can be added in a computationally inexpensive manner, and through adding these data points, accuracy on a real world dataset can be increased by upwards of 10
A Compact Tapered Fed Dodecagon MIMO Antenna for UWB Applications

In this paper, the design of a compact two element MIMO antenna for ultrawideband application is presented has a compact size of 30mm x 22mm. The antenna consists of a dodecagon shape radiating patch tapered fed by a 50 microstrip line, have impedance bandwidth from 2.5 to 11 GHz. A small rectangular block is notched at the ground to achieve isolation better than -15dB (-20dB in most of the band). Radiation performance in terms of emission patterns, gain and efficiency are studied, Envelope Correlation Coefficient (ECC) and Diversity Gain (DG) is evaluated to analysis the MIMO performance. The appropriate simulated results of the proposed antenna, making it preferable for the application of the portable UWB devices.
Iterative Learning Control of FES with Embedded Simultaneous Volitional EMG

Seventeen million people are left with limb impairment following stroke, and there is an urgent need for new effective rehabilitation technology. Functional electrical stimulation (FES) has been shown to facilitate motor re-learning by artificially activating muscles during practice of motor tasks, however clinical outcomes depend on how closely FES matches the intended motion. Electromyography (EMG) signals recorded from muscle can capture voluntary intention, and have successfully been employed in openloop FES controllers. Likewise, model-based controllers using force and/or position data have yielded accurate movement control in clinical trials. Iterative learning control (ILC) in particular has been successful in clinical tests, since the process of rehabilitation is inherently iterative. This paper develops a new control strategy to combine both EMG and model-based control. It begins by developing a novel hybrid model of muscle dynamics incorporating both EMG and FES. ILC is then employed to enable precise control over both variables, thereby offering substantial improvements over existing control schemes in the domain of stroke rehabilitation. Experimental results confirm efficacy of the hybrid control scheme, as well as its suitability for clinical application.
Method to Predict Bursty Hot Events on Twitter Based on User Relationship Network

In the field of hot event prediction on online social networks, not considering user information leads to poor prediction effect. In this paper, a novel method that considers the behaviors and characteristics of users is proposed to identify and predict suspected bursty hot events. First, the keywords in each tweet are extracted and divided into different sets according to part of speech, and then similar topics are clustered according to semantic similarity. Second, the growth rates of topics are monitored in the sliding timestamp and the suspected bursty hot events are marked. Then, a user relationship network is constructed based on the information of the registered users on Twitter. Finally, according to the propagation trend of suspected bursty hot events in the network, the quasi-burst hot events are marked and sorted in descending order. Experimental results show that only using the historical re-tweeting behavior of users as the judgment basis to predict the current re-tweeting probability of users will lead to the phenomenon of error cascading, while taking the information of users into account can effectively improve the prediction performance. Compared with the existing methods, the proposed method improves the prediction precision rate by 27.38%, accuracy rate by 23.49%, and recall rate by 20.16%, demonstrating that it can predict bursty hot events effectively.
Classification of Wafer Maps Defect Based on Deep Learning Methods With Small Amount of Data

This paper attempts to solve the problem of defect classification for the purpose of automation of the processing of flaw detection results. Article proposes an algorithm based on deep convolutional neural networks (DCNN) for recognizing patterns of defects in semiconductor wafers. In order to train the model, a composite training data set was created and applied. Its basis consists of synthetic data and an extra small amount of experimental data including about 20 examples. Verification of the work was carried out on an open data set WM-811K. The resulting classification accuracy is about 87.8%. This is a satisfactory result from a practical point of view. The developed algorithms can be used both in software systems for data analysis in the production of semiconductor wafers, as well as part of separate software modules for electronic flaw detectors.
DFB Laser Chip Defect Detection Based on Successive Subspace Learning

After the acceptance criteria been defined between distributed feedback (DFB) laser chip manufacturer and laser module assembly house, there is a need of reliable tool for quality engineers to carry out the acceptance sampling plan. There are variations of DFB lasers with different wavelength, power, threshold, operating temperature requirement etc. To make acceptance sampling reliable for semiconductor laser chip producer who need to control the specificity and for laser module assembly factories who need to control the sensitivity, a classifier with on-device training capability is needed. In this paper, an embedded automated distributed feedback (DFB) lasers chip defect classifier based on successive subspace learning (SSL) is developed for such application to classify active, surface, edge and coating defects generated by manufacturing processes. The designed classifier was verified with accuracy=97.43%, precision=96.80%, sensitivity=91.32%, specificity=97.66% and F1 score=0.968.
Detection of Congestive Heart Failure by Autocorrelation Analysis of Heart Rate Variability

This paper introduces the autocorrelation analysis of heart rate variability (HRV) signals to diagnose cardiac diseases and presents a methodology for the automated detection of congestive heart failure (CHF). The autocorrelation is defined as the correlation (a mutual relationship between two or more processes) of a signal with a delayed version of that signal. The proposed methodology has been tested on signals acquired from physionet open access database for clinical relevance. The HRV signals of normal and CHF subjects have been divided into segments of 1000 samples each. Next, the Pearson’s correlation, Rank Correlation (Kendall’s rank correlation and Spearman’s rank correlation), and Partial correlation coefficients have been computed with a delay of τ = [1, 2, … , 10] samples. The clinically significant features obtained by performing one way ANOVA (Analysis of Variance) are fed to decision tree (DT), and neural network (NN) classifiers. The classification results show an accuracy of 87.6% with sensitivity and specificity of 84.9% and 90.3%, respectively. The experiment results reveal that the autocorrelation in HRV signals of normal and CHF subjects is significantly different and suggests a possibility of the diagnosis of CHF from these features.
Aggregate capacity for TCLs providing virtual energy storage with cycling constraints

The coordination of thermostatically controlled loads (TCLs) is challenging due to the need to meet individual loads quality of service (QoS), such as indoor temperature constraints. Since these loads are usually on/off type, cycling rate is one of their QoS metrics; frequent cycling between on and off states is detrimental to them. While significant prior work has been done on the coordination of air conditioning TCLs, the question of cycling QoS has not been investigated in a principled manner. In this work we propose a method to characterize aggregate capacity of a collection of air conditioning TCLs that respects the loads cycling rate constraints (maximum number of cycles in a given time period). The development is done within the framework of randomized local control in which a load makes on/off decisions probabilistically. This characterization allows us to propose a reference planning problem to generate feasible reference trajectories for the ensemble that respect cycling constraints. The reference planning problem manifests itself in the form a Nonlinear Programming problem (NLP), that can be efficiently solved. Our proposed method is compared to previous methods in the literature that do not enforce aggregate cycling. Enforcing individual cycling constraint without taking that into account in reference generation leads to poor reference tracking.
Limiting Global Warming by Improving Data-Centre Software

Carbon emissions, greenhouse gases and pollution in general are usually related to traditional factories, so the most modern computing factories have gone unnoticed for the general-public opinion. We empirically show through extensive and realistic simulation that: 1) energy consumption, and consequently CO2 emissions, could be reduced from ~15% to ~60% if the correct energy-efficiency policies are applied; and 2) such energy-consumption reduction can be achieved without negatively impacting the correct operation of these infrastructures. To this end, this work is focused on the proposal and analysis of a set of energy-efficiency policies which are applied to traditional and hyper-scale data centres, as well as numerous operation environments, including: 1) the top resource managers used in industry; 2) eight energy-efficiency policies, including aggressive, fine-tuned and adaptive models; and 3) three types of workload-arrival patterns. Finally, we present a realistic analysis of the environmental impact of the application of such energy-efficiency policies on USA data centres. The presented results estimate that 11.5 million of tons of CO2 could be saved, which is equivalent to the removal of 4.79 million of combustion cars, that is, the total car fleet of countries such as Portugal, Austria and Sweden.
EEG-Based Emotion Classification Using Spiking Neural Networks

A novel method of using the spiking neural networks (SNNs) and the electroencephalograph (EEG) processing techniques to recognize emotion states is proposed in this paper. Three algorithms including discrete wavelet transform (DWT), variance and fast Fourier transform (FFT) are employed to extract the EEG signals, which are further taken by the SNN for the emotion classification. Two datasets, i.e., DEAP and SEED, are used to validate the proposed method. For the former dataset, the emotional states include arousal, valence, dominance and liking where each state is denoted as either high or low status. For the latter dataset, the emotional states are divided into three categories (negative, positive and neutral). Experimental results show that by using the variance data processing technique and SNN, the emotion states of arousal, valence, dominance and liking can be classified with accuracies of 74%, 78%, 80% and 86.27% for the DEAP dataset, and an overall accuracy is 96.67% for the SEED dataset, which outperform the FFT and DWT processing methods. In the meantime, this work achieves a better emotion classification performance than the benchmarking approaches, and also demonstrates the advantages of using SNN for the emotion state classifications.
A Study on Recent Trends in Face Spoofing Detection Techniques

Face recognition systems gaining significant importance in today's world as it has numerous applications such as surveillance, forensic investigations, access control etc. Face recognition system verify the identity of the user using the features of the face. Security issues on face recognition systems have become one of the primary concerns. Face recognition systems are vulnerable to spoofing attacks like photographs, masks, and video attack. Although several methods have been proposed for face spoofing detection, the spoofing attack is still a challenging problem. Robust solutions are needed to secure the face recognition system against spoofing attacks. This paper provides a review on recent face spoofing detection techniques for defending various types of attacks.
Algorithm for Automatic Crack Analysis and Severity Identification

Concrete structures are increasing every day, to facilitate people's lives. With this expansion, the traditional manual maintenance method becomes unpractical, costly and time-wasting. The fast detection and maintenance of concrete surfaces defects is necessary to save people's lives, reducing maintenance cost, and increase the lifetime of concrete structures. Thus, the researches came up over the last twenty years to find an automatic way in order to maintain, apply regularly check-ups over concrete structures and assist engineers to take fast decisions. The most researches came up with high precision algorithms to allocate cracks and defects over the concrete surfaces with no human intervention. Nowadays, the computer programs can be dependable to capture large data sets of concrete structure, and then give precise locations of cracks. However, there exists a lack of researches that work on crack interpretation and automatic decision-making, which is considered as a critical part of those systems. Therefore, there exists a need for methods that describe the crack characteristics in terms of width, length, and other morphological attributes. In this paper, a crack interpretation algorithm is proposed to extract crack geometrical attributes and support the decision maker.
ICCIU: A New Real-Time Lane-Level Positioning Algorithm

Previous real-time lane-level positioning algorithms for in-vehicle navigation systems have problems of inaccurate positioning and insufficient robustness. Therefore, this paper proposes a new lane-level positioning algorithm integrating Improved filter method, Curve Circle method and Improved Unet (ICCIU) method. Specifically, in ICCIU algorithm, we design improved filter method to improve the accuracy of original position by combining three filters to enhance image features, propose curve circle method to achieve real-time curve positioning by introducing two movement indicators to get precise curve position and design a light improved Unet method that integrates residual thought and cascading thought to detect the lane, and integrates two new parameters to get more accurate position by reducing the horizontal position errors The experiment results show that evaluation indicators of the improved Unet method are over 20% higher than those of the existing algorithms, the running time of single point positioning is about 28ms and lane-level accuracy is over 96% used by ICCIU, which demonstrates the pretty performance both in feasibility and efficiency of the new algorithm.
Performance Comparison of Fuzzy Gain Scheduling based LFC Controller in Deregulated AGC of a Multi Area System Incorporating Wind Energy

In this paper a fuzzy gain scheduling based controller for multi area multi source power system is presented. Area-1 comprises of thermal and hydro plant and area-2 consists of thermal and hydro power plant with wind turbine generator and both areas are connected via tie line. Frequency deviation caused by load fluctuation is controlled by PI controller. For optimal response the PI controller is tuned via Ziegler Nichols (ZN) technique and Fuzzy Gain Scheduling (FGS) method. From MATLAB simulations it is observed that FGS based PI controller outperforms ZN tuned PI and conventional PI. The proposed model is further investigated with an integrated wind turbine generator and here also superior performance of fuzzy gain scheduling based PI compared to other control techniques is observed.
Pilot-Decontamination in Massive MIMO Systems Using Interference Alignment

Pilot contamination (PC) is a practical drawback of massive multiple-input multiple-output (mMIMO) systems. It prevents the exploitation of the inherent spectral-efficiency (SE) of mMIMO systems, especially in dense networks. In this letter, we consider multi-antenna users in a multi-cell network and propose a novel approach based on interference alignment (IA) to remove PC. We show that the extra degrees of freedom (DoFs), provided by multi-antennas at the users’ side, can be used efficiently when employed for pilot decontamination, especially in heavy-loaded networks. Thus, while the intra-cell interference is removed due to the properties of mMIMO the out-of-cell interference is eliminated by the proposed IA-based pilot decontamination. The proposed algorithm uses linear zero-forcing (ZF) and needs no continuous cooperation between the base stations (BSs). Moreover, the users need no channel state information (CSI). Our simulations show that the proposed method outperforms the conventional PC precoding algorithms significantly, particularly in heavy-loaded networks.
IOT Based Water Quality Monitoring with Android Application

Monitoring the quality of water with conservative methods is testing the water samples which we collected manually in the laboratories or testing centers is a time consuming process. It will result in the wastage of cost, man power and time. Inorder to make the process economical and effective, we introduced a water quality monitoring system with the help of various sensors which checks the water quality in real time. We used pH, conductivity, temperature and turbidity sensors to measure the pH value, conductivity, temperature and turbidity of water. The presence of impurities in the water could be detected by the values obtained in the sensors. The Arduino transferred the information collected from the various sensors to the microcontroller and then passed to the android application with a Wi-Fi module. As it is a user friendly application, the results can be easily viewed and understandable by the user. The water quality monitoring system keep on testing the impurity content of water resources to provide for a well surrounding with pollution free water.
Multi-Objective Optimization for Asynchronous Positioning Systems Based on a Complete Characterization of Ranging Errors in 3D Complex Environments

High-accuracy positioning is fundamental for modern applications of autonomous agent navigation. The accuracy and stability of predicted locations are key factors for evaluating the suitability of positioning architectures that have to be deployed to real-world cases. Asynchronous TDOA (A-TDOA) methodologies in Local Positioning Systems (LPS) are effective solutions that satisfy the given requirements and reduce temporal uncertainties induced during the synchronization process. In this paper, we propose a technique for the combined characterization of ranging errors–noise, and Non-Line-of-Sight (NLOS) propagation– through the Cramér-Rao Bound (CRB). NLOS propagation effects on signal quality are predicted with a new ray-tracing LOS/NLOS algorithm that provides LOS and NLOS travel distances for communication links in 3D irregular environments. In addition, we propose an algorithm for detecting multipath effects of destructive interference and disability of LOS paths. The proposed techniques are applied to sensor placement optimization in 3D real scenarios. A multi-objective optimization (MOP) process is used based on a Genetic Algorithm (GA) that provides the Pareto Fronts (PFs) for the joined minimization of location uncertainties (CRB) and multipath effects for a variable number of A-TDOA architecture sensors. Results show that the designed procedure can determine, before real implementation, the maximum capacities of the positioning system in terms of accuracy. This allows us to evaluate a trade-off between accuracy and cost of the architecture or support the design of the positioning system under accuracy demands.
Combating Depression in Students using an Intelligent ChatBot: A Cognitive Behavioral Therapy

Chatbots are special agents that respond with the user in natural language just as a human would reply. Specifically, social chatbots are the ones which establish a strong emotional relationship with the user. The main concept behind this chatbot was to provide mental relief to students who undergo different levels of stress and which can be the onset of an inimical depression. In this paper, we proposed an intelligent social therapeutic chatbot which distributes the text into emotion labels namely, Happy, Joy, Shame, Anger, Disgust, Sadness, Guilt, and Fear. Further, based on the emotion label, it identify the users' mental state such as stressed or depressed using users' chat data. For emotion detection, we deployed three popular deep learning classifiers namely, Convolutional Neural Network (CNN), Recurrent Neural Network (CNN), and Hierarchical Attention Network (HAN). In particular, the proposed methodology of the chatbot is domain specific where through the users' interaction, the chatbot will try to prevent the pessimistic actions and rebuild more constructive thoughts.