Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Detection and Image Recognition of Precision Agriculture for Citrus Diseases

In recent years, the development of precision agriculture is a new technology. The main reason for the automation of agricultural processes is to save the time and energy required to perform repeated farming tasks and to increase production by treating each crop separately and applying smart agricultural concepts. In this paper, an automatic detection and image recognition of citrus diseases is presented that can help farmer find the disease and identify it from the captured images. This method use YOLO(You Only Look Once) algorithm which is an object detection model to detect and recognize the diseases from citrus leaf images. YOLO can realtime detect the disease and circle around it on the image and video. The dataset includes images of citrus leaf with two kinds of diseases: Citrus Canker, Citrus Greening.
Mobility-Aware Trajectory Design for Aerial Base Station Using Deep Reinforcement Learning

In this paper, an unmanned aerial vehicle (UAV) assisted wireless network is investigated, where an aerial base station (ABS) is deployed for serving both ground and aerial users. The objective is to maximize the uplink sum-rate of all users by designing the trajectory of the ABS, while considering the mobility of users. Specifically, it is assumed that all aerial UAVs move individually with the random waypoint mobility model (RWM). Meanwhile, some ground users move with the reference point group mobility model, while others follow the RWM as well. Due to the mobility of both ground and aerial users, conventional reinforcement learning (such as Q-learning) is very likely to fail for the curse of dimensionality. Therefore, it is non-trivial to solve the trajectory design problem in the large-scale highly dynamic environment. By approximating the value functions with neural networks, a deep reinforcement learning-based algorithm is proposed for the ABS to autonomously provide network services in the rapidly changing environment. Simulation results demonstrate that our designed double deep Q-network (DDQN) can learn the environment information very well with our elaborate rewards. Compared to the conventional DDQN with fully connected network, our proposed DDQN converges faster and has better stability.
Trajectory Tracking of the Manipulator using Adaptive Computed Torque Control

In the current manufacturing environment flexibility and enhancing the efficiency of articulated manipulators for safe robot manipulation is a fundamental requirement. The goal of targeting the intact planning and control of a manipulator because of physical contact with obstacles is essential in the absence of external sensing, or it can cause an unexpected collision. Furthermore, in the well-structured environment, a manipulator may face uncertainty concerning the parameters describing the dynamic properties of the grasp load, such as the unknown moment of inertia. For tracking the calculated trajectory accurately, the potential of a robotic manipulator gets limited because of difficulty in computing these uncertain parameters. This paper focuses on ensuring the safe reaction while trajectory tracking of the rigid robot manipulator due to physical contact, having some unknown parameters by applying Adaptive Control along with Computed Torque Control. To prevent the damage of the manipulator or even the object, the applied mathematical model of Adaptive Computed Torque Control is demonstrated on the Rethink Sawyer manipulator in the Simulink environment. The simulation results depict the efficient trajectory tracking of the Sawyer manipulator having parameter uncertainty in link masses, with a safe reaction after colliding with the planer object.
Development of a 1-THz 4th-Harmonic Gyrotron

A design of a 1-THz 4th Harmonic Gyrotron is proposed in this paper. Fourth harmonic interaction is adopted to lower the strength of the external magnet. The magnet can be as low as 10T. Such a low-intensity magnetic field can be easily proposed by a superconduct magnet, which make the generation of continuous waves at 1 THz possible. Seeing that the mode competition is extremely fierce, a large orbit gun is employed as the driven source. An 80-kV, 0.7-A electron beam is injected into the interaction circuit to excite the fourth-harmonic electromagnetic waves. According to the cold dispersive diagram, the TE4,8 mode of the cylindrical waveguide is selected as the operating mode to further suppress the mode competition induced by the fundamental modes, the 2nd harmonic modes and the 3rd harmonic modes. It could be inferred based on the multi-mode time-domain theory that an output power of 1.15 kW can be achieved. The scheme diagram for the hot test is proposed. The LOG is chosen as the electron gun. Particular structure is employed for improving the output efficiency in the interaction circuit.
Small-Signal Modelling of the Three Switch 1L2C Boost Converter

The paper derives the small-signal state-space model, the continuous and discrete transfer functions for the three switch $1\mathrm{L}2\mathrm{C}$ boost DC-DC converter. A capacitive loop occurs in the first topological state of the converter and thus it is difficult to calculate the small-signal model using usual approaches. The paper presents the analytical and simulation results together with the computations in $\mathrm{MATLAB}^{\mathrm{T}\mathrm{M}}$ and finally the validation provided by measurements on a practical prototype.
A Backhaul and Access Game based Adaptive Power Allocation for MIMO-Radar

Multiple Input Multiple Output (MIMO) radar is enormously used in communication systems due to its target detection capacity, high resolution, and better target detection estimation. However, the restricted power of radar affects the throughput of the MIMO radar. Therefore, an optimal power allocation is required to obtain reliable data communication between the MIMO radars. In this article, the backhaul and access game based power allocation is proposed to assign the appropriate power between the clusters of MIMO radar. Here, the MIMO radar is classified into primary and secondary clusters by using the Naive Bayes classifier. The angle of arrival is considered to enable the respective radar of the cluster instead of enabling all radars in the cluster. The performances of the backhaul and access game based power allocation are analyzed in terms of power density factor and throughput (i.e., capacity) and it is evaluated by using the existing method called generalized Nash game theory. The power consumption of the proposed method for cluster 1 is 0.0456, it is less when compared to the generalized nash game theory.
A Novel Approach to Predict Chronic Kidney Disease using Machine Learning Algorithms

A staggering 63,538 cases have been registered according to India’s health statistics on Chronic Kidney Disease (CKD). The average age of nephropathy for humans lies between 48-70 years. CKD is more prevalent among males than females. Bitterly, India ranks among top 17 countries in CKD since 2015, which is characterized by a gradual loss of excretory organ performance over time. Earlier detection of the illness followed by treatment could keep this dreaded disease at the shore. Machine Learning, is making sensible applications in the areas such as analyzing medical science outcomes, sleuthing fraud etc. For the prediction of chronic diseases various machine learning algorithms are implemented.Our main aim is to differentiate the performance of various machine learning algorithms that are primarily based on its accuracy. This research work has idolized Rcode to compare their performance. The pivotal purpose of this study is to analyze the Chronic Kidney Disease dataset and conduct CKD and Non CKD classification cases.
A low jitter 50Gb/s PAM4 CDR of Receiver in 40nm CMOS Technology

This paper presents a low jitter 50 Gb/s clock and data recovery (CDR) circuit of receiver for 4-level pulse-amplitude modulation (PAM4) signal. This PAM4 CDR mainly includes a comparator which works as a 3-level slicer to convert the PAM4 signal into three path NRZ temperature codes signal, a decoder that converts the thermometer code signal into NRZ signal output, and a half-rate CDR which recovers the clock signal from the input data. Simulation results show that the peak-to-peak jitter of the recovered clock and data are 1.6ps and 3ps, respectively. In 40nm CMOS technology, the power consumption of the whole PAM4 CDR is about 450mW with supply voltage of 1.2V.
Application of NER and Association Rules to Traditional Chinese Medicine Patent Mining

Traditional Chinese Medicine (TCM) patents contain much valuable information, and this paper studies the application of named entity recognition (NER) and association rules for the TCM patent mining. A NER approach combining the method based on rules and bootstrapping is proposed to recognize TCM entities in a large number of TCM patents. Besides, to explore the law of medicine use, we use the Apriori algorithm to generate association rules between TCM entities. By analyzing these association rules, we verify the effectiveness of association rules mining for the TCM patent mining, which provides a reference for the TCM treatment and research.
Baseline procedure for conceptual designing of an eVTOL for Urban Air Mobility

Urban Air Mobility (UAM) is a long-desired service which will revolutionize the present transportation facilities, by introducing air mobility for on-demand aviation. Due to the continuous increase in population, road traffic is increasing at alarming rates and the problem of congestion is also increasing in metro cities. These new services will provide improved ways for individuals to go around urban communities and urban territories while decreasing blockage. eVTOLs are aircraft that are capable of Vertical Take-off and Landing (VTOL) with no requirement of run-up space and are tremendously getting popular when it comes to UAM due to the important factor of clean propulsion with zero-emission. The industry is rapidly proposing and adopting new vehicle concepts to meet consumer demands and hence conceptually designing these vehicles with basic guidelines will set a preliminary foundation for more innovative technologies to come. This research work sets a baseline procedure and an initial iteration example for the industries and upcoming companies in this sector hence focuses on the Conceptual designing of eVTOLs. The novelty of the presented work lies in enhanced eVTOL configuration using coaxial ducted tilt rotors with wings.
BiLSTM-Autoencoder Architecture for Stance Prediction

The recent surge in the abundance of fake news appearing on social media and news websites poses a potential threat to high-quality journalism. Misinformation hurts people, society, science, and democracy. This reason has led many researchers to develop techniques to identify fake news. In this paper, we discuss a stance prediction technique using the Deep Learning approach, which can be used as a factor to determine the authenticity of news articles. The Fake News Stance Prediction is the process of automatically classifying the stance of a news article towards a target into one of the following classes: Agree, Disagree, Discuss, Unrelated. The stance prediction task’s input is the news articles containing a pair: a headline as the target and a body as a claim. This paper proposes a deep learning architecture using Bi-directional Long Short Term Memory and Autoencoder for stance prediction. We illustrate, through empirical studies, that the method is reasonably accurate at predicting stance, achieving a classification accuracy as high as 94%. The proposed stance detection method would be useful for assessing the credibility of news articles.
High-Performance Implementation of Adaptive IQ Mismatch Compensator in Direct-Conversion Transceiver

In communication transceivers utilizing I/Q direct-conversion, the mismatches in gain and phase between analog I and Q branches are inevitable, which severely causes degradation of their image rejection. A compensator of I/Q mismatches is, therefore, essential to improve the performance of these communication systems. In this paper, we present a high-performance implementation of an adaptive I/Q mismatch compensator in direct-conversion transceivers using fixed-point representation to keep low complexity. Through experiments, the observed results demonstrate that our fixed-point compensation approach delivers good correction performance. Furthermore, it consumes reasonable hardware resources, and achieve a high operating frequency. Therefore, this compensator can be deployed in low-power and high-performance communication systems efficiently.
Extractive Multi-Document Arabic Text Summarization Using Evolutionary Multi-Objective Optimization With K-Medoid Clustering

The increasing usage of the Internet and social networks has produced a significant amount of online textual data. These online textual data led to information overload and redundancy. It is important to eliminate the information redundancy and preserve the time required for reading these online textual data. Thus, there is a persistent need for an automatic text summarization system, which extract the relevant and salient information from a collection of documents, that sharing the same or related topics. Then, presenting this extracted information in a condensed form to preserve the main topics. This paper proposes an automatic, generic, and extractive Arabic multi-document summarization system. The proposed system employs the clustering-based and evolutionary multi-objective optimization methods. The clustering-based method discovers the main topics in the text, while the evolutionary multi-objective optimization method optimizes three objectives based on coverage, diversity/redundancy, and relevancy. The performance of the proposed system is evaluated using TAC 2011 and DUC 2002 datasets. The experimental results are compared using ROUGE evaluation measure. The obtained results showed the effectiveness of the proposed system compared to other peer systems. The proposed system outperformed other peer systems for all ROUGE metrics using TAC 2011. We achieved an F-measure of 38.9%, 17.7%, 35.4%, and 15.8% for Rouge-1, Rouge-2, Rouge-L, and Rouge-SU4, respectively. In addition, the proposed system with DUC 2002 dataset achieved an F-measure of 47.1%, 23.7%, 47.1%, 20.4% for Rouge-1, Rouge-2, Rouge-L, and Rouge-SU4, respectively.
Iterative Deconvolution for Multidimensional Signals

Cadmium Zinc Telluride Imager (CZTI) is one of five payloads of AstroSat, a multi-wavelength satellite, which has observed multiple stellar phenomena using coded mask imaging. This mask convolves with the observation before being detected. Thus, some preprocessing of this detected data is required before it can be analysed. Deconvolution is often implemented in such situations where we try to recover the original source by removing the effect of convolution on the observed data. Deconvolution is often seen in filtering or for removal of interfering phenomena such as point spread functions. This paper proposes a new method for deconvolution of two multi-dimensional discrete signals, resulting in the recovery of the original source intended for capture. The discrete signals are represented as matrices and then deconvolved in the Z-domain. This has been verified using two and three dimensional matrices and has been thus been extended for m-dimensional signals.
A Novel Scheme for Access Control Policy Generating and Evaluating in IoT based on Machine Learning

The ever-increasing demand for data exchanges has boosted the development of the Internet of Things (IoT). IoT has brought a new revolution to a variety of industries by integrating smart devices as well as information and communication technologies into traditional systems. However, due to the dynamic and heterogeneous structure of IoT, unauthorized access and data leakage may be much easier. Attribute-based access control (ABAC) is suitable for complex and changeable access control environments due to its flexibility and universality in capturing authorizations in terms of the attributes of users and resources. However, the dynamic nature of IoT bring new challenges to access control. On the one hand, new services and applications continue to be deployed, administrators need to formulate new policies for those services and applications. Therefore, manual development of ABAC polices in IoT environment is time consuming and expensive. On the other hand, because the access environment is constantly changing, access control policies may become unsuitable for current environment. Manual identification of these low-quality rules is often after they cause severe consequences. To address the above two problems, we propose a scheme for generating and evaluating polices in IoT based on machine learning. This scheme referred to as PGEML, contains two module, policy generalization (PG) and policy evaluation (PE). In the PG module, we define a novel measure, resource similarity, and integrate it into policy mining so that policies could generalize among related resources. In the PE module, we introduce a quantitative method to assess rules and prune rules of low-quality. We conduct our experiments on a real-world enterprise access logs from Amazon. The experimental results has qualitatively and quantitatively showed the effectiveness of our proposed scheme.
Energy Usage of the Sugar Industry in West Maharashtra

Study of energy use in the sugar industry, a novel energy usage of the sugar industry in west Maharashtra is presented. In which, co-generation and transportation energy is included compared to the conventional study of energy usages in different industry. Here, we pay special attention to the western region of Maharashtra, India. In the sugar industry, Electrical and diesel fuel energy are the primary sources, but the energy consumption and electrical energy as well as diesel fuel rates are growing rapidly. This energy use in sugar plant can not only take into account the energy of MSEDCL electricity, bagasse and diesel generators, but also in irrigation and distillery, transport energy. Furthermore, energy generated, energy supplied to MSEDCL and energy consumption are compared by collecting three years of sugar industry data.
Cloud Platform Performance Evaluation Using Multi-level Execution Tracing

Performance evaluation is always an important issue in cloud environment. Problems can arise at different layers of the system with several different reasons and that is why detecting and then identifying the origin of a problem can be a difficult task. This research proposes a multi-level execution trace-based approach to detect performance issues in cloud services. The method works by collecting run-time data from several layers including user-space, kernel space and virtualization layer, correlating, modeling and analyzing in order to detect abnormal performance behavior. We have tested our method in virtual machine migration scenarios where it is difficult to collect trace data. Our results demonstrate that the proposed method can detect service inefficiency issues caused from various service and system level reasons.
High Efficiency Early-Complete Brute Force Elimination Method for Security Analysis of Camouflage IC

We propose a high efficiency Early-Complete Brute Force Elimination method that speeds up the analysis flow of the Camouflage Integrated Circuit (IC). The proposed method is targeted for security qualification of the Camouflaged IC netlists in Intellectual Property (IP) protection. There are two main features in the proposed method. First, the proposed method features immediate elimination of the incorrect Camouflage gates combination for the rest of computation, concentrating the resources into other potential correct Camouflage gates combination. Second, the proposed method features early complete, i.e. revealing the correct Camouflage gates once all incorrect gates combination are eliminated, increasing the computation speed for the overall security analysis. Based on the Python programming platform, we implement the algorithm of the proposed method and test it for three circuits including ISCAS’89 benchmarks. From the simulation results, our proposed method, on average, features 71% lesser number of trials and 79% shorter run time as compared to the conventional method in revealing the correct Camouflage gates from the Camouflaged IC netlist.
Electricity Demand Forecasting for Smart Grid Based on Deep Learning Approach

Electric power load demand forecasting plays a very indispensable role in energy management, such as suitable planning and investing in developing more infrastructure, and moderate operating the electricity system. Moreover, the accuracy of the electricity demand forecasting must be paid special attention. Although research on this problem has been conducted in recent years, the authors noticed that elevated accuracy outcomes have not been gained yet and have provoked controversy about forecasting results. In this paper, an electricity demand forecasting method based on a Deep Learning model, namely Long-Short-Term-Memory (LSTM), which is an improvement of Recurrent Neural Networks (RNNs) is proposed. The proposed network architecture includes four layers: sequence input, LSTM, fully connected, and regression output. The proposed method was implemented on the power consumption data in six years, from 2012 to 2017, of Tien Giang province, Vietnam. The forecasting result, evaluated using Root Mean Square Error (RMSE), was 9.63, suggesting that this would be a great contribution to studies in the energy sector.
NOMA in Cell-Free mMIMO Systems with AP Selection

In this paper we proposed access point (AP) tiered in cell-free (CF) massive multiple input multiple output (mMIMO) non-orthogonal multiple access (NOMA) system with AP selection. Based on AP tiered, we proposed the new method of user grouping and power allocation in the system. In current works on CF mMIMO systems, AP selection is getting more and more attention. AP selection means the system is user centric and the central processing unit (CPU) needs to determine whether one AP can serve a user by considering the channel condition between them. So users in the system can choose the corresponding AP subsets to provide service. As a result, different users may be served by different AP subsets and the traditional user grouping and power allocation method in NOMA can’t adapt to these users with inconsistent serving APs. As each user chooses an AP subset to provide service, the APs are clustered and these APs must be in different intersections of the AP clusters, we can note these relations as AP tier and group. Based on AP tiered, the user grouping and power allocation problem in the CF mMIMO NOMA system with AP selection can be clearly solved. Through simulation, we can prove that, by performing superposition coding (SC) as the corresponding AP group, NOMA can better adapt to the proposed system. And the optimization of power allocation in the proposed system can be simplified.
A Single Electrode Blink for Text Interface (BCI)

Brain-Computer Interface (BCI) provides a pathway between the human brain and a computer. Based on typing done using the On-Screen Keyboard available on Windows Operating System single blinks operation is implemented. It is designed to help the physically disabled and those suffering from motor neuron diseases. ElectroEncephaloGram (EEG) signal of eye blinks is acquired using the Neurosky Mindwave headset. This signal is processed on OpenVibe software to send commands to the keyboard. In this paper, the methods used for preprocessing, feature extraction, classification and conversion of signals into commands are described.