Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of Two-Stage Fully-Differential Driver in SAR ADC with Indirect Feedback Compensation Technique

This work presents a driver amplifier suitable for fully differential low-frequency Successive approximation register analog to digital converter (SAR ADC) with 14 bit resolution. The indirect feedback compensation technique is implemented for the compensation of a fully differential two-stage operational amplifier (op amp). The global common mode feedback (CMFB) circuit fix output common mode (CM). The PMOS input device reduces flicker noise at low frequency. The op amp design produces 73dB DC gain and 64 PM. The driver takes single-ended or fully differential input, consumes 430μW from 1.8V supply, has −93dB total harmonic distortion (THD) for 2VPP differential output.
Prognosticating Colorectal Cancer Recurrence using Machine Learning Techniques

Colorectal cancer is among the top three most commonly occurring cancers worldwide, and around 30-40% of patients treated by curative intent surgery will experience cancer recurrence. Proactive prognostication would enable clinicians to better plan treatment modality and intensity, and follow-up frequency to reduce recurrence. Here, we study the application of machine learning models to predict cancer recurrence in a cohort of 904 post-resection colorectal cancer patients. We employ heterogeneous structured and temporal clinical features including demographic and diagnostic information, tumour stage and location details, biochemistry and molecular typing results, as well as surgical details and treatment parameters. We characterize the performance of multiple machine learning classifiers including logistic regression, support vector machine, gradient boosting and multi-layer perceptron on structured data. Our best model achieved a sensitivity of 80.7% and a specificity of 88.2%. This is comparable to and even exceeding the performance of carcinoembryonic antigen (CEA), a tumour marker commonly used in the clinic for colorectal cancer monitoring. We also demonstrate feasibility for accurate forecasting of recurrence up to 4 months in advance, as well as the possibility of predicting recurrence as early as 6 months post-surgery. Our results have positive implications for better management of colorectal cancer patients in the post-resection setting.
Modified control of Virtual Synchronous Generator for Microgrid Stability Improvement

The power generation from renewable energy sources-based power systems is increasing rapidly, and virtual synchronous generator (VSG) has recently drawn considerable attention. It is anticipated that VSG improve the power and frequency regulation of islanded microgrid. However, the large disturbance and parallel integration of VSG with conventional synchronous generators cause significant fluctuation in power and frequency due to sudden change in the virtual rotor speed of VSG. In order to suppress the effects of these instability elements, a modification is proposed in VSG control. According to the proposed modification, an auxiliary inertia unit is incorporated into VSG control along with additional power loop. Moreover, MATLAB/Simulink simulation is developed to analyze and evaluate the effects of the proposed modification in the islanded microgrid, and the results show the effectiveness of the proposed modification.
Semantic and Trade-Off Aware Location Privacy Protection in Road Networks Via Improved Multi-Objective Particle Swarm Optimization

Location privacy protection is an essential but challenging topic in the field of network security. Although the existing research methods, such as ${k}$ -anonymity, mix zone, and differential privacy, show significant success, they usually neglect the location semantic and the proper trade-off between privacy and utility, which may allow attackers to obtain user privacy information by revealing the semantic correlation between the anonymous region and user’s real location, thus causing privacy leakage. To solve this problem, we propose a location privacy protection scheme based on the $k$ -anonymity technique, which provides practical location privacy-preserving through generating an anonymous set. This paper proposes a new location privacy attack strategy termed semantic relativity attack (SRA), which considers the location semantic problem. Correspondingly, a semantic and trade-off aware location privacy protection mechanism (STA-LPPM) is presented to achieve privacy protection with both high-level privacy and utility. To be specific, we model the location privacy protection as a multi-objective optimization problem and propose the Improved Multi-Objective Particle Swarm Optimization (IMOPSO) to generate the optimal anonymous set calculating the well-design fitness functions of the multi-objective optimization problem. In this way, the privacy scheme can provide mobile users with the right balance of privacy protection and service quality. Experiments reveal that our privacy scheme can effectively resist the semantic relativity attack while preventing significant utility degrading.
Distributed Event-Triggered Consensus-Based Control of DC Microgrids in Presence of DoS Cyber Attacks

In this paper, the problem of distributed event-based control of large scale power systems in presence of denial-of-service (DoS) cyber attacks is addressed. Towards this end, a direct current (DC) microgrid composed of multiple interconnected distributed generation units (DGUs) is considered. Voltage stability is guaranteed by utilizing decentralized local controllers for each DGU. A distributed discrete-time event-triggered (ET) consensus-based control strategy is then designed for current sharing in the DGUs. Through this mechanism, transmissions occur while a specified event is triggered to prevent unessential utilization of communication resources. The asymptotic stability of the ET-based controller is shown formally by using Lyapunov stability via linear matrix inequality (LMI) conditions. The behavior of the DGUs subject to DoS cyber attacks are also investigated and sufficient conditions for secure current sharing are obtained. Towards this end, a switching framework is considered between the communication and attack intervals in order to derive sufficient conditions on frequency and duration of DoS cyber attacks to reach the secure current sharing. The validity and capabilities of the presented approach is confirmed through a simulation case study.
THE ANTECEDENT OF CLIENTS SOCIAL AND CLIENTS ORGANISATIONAL IDENTIFICATION TOWARDS THE OBJECTIVITY OF AUDITORS’ ASSESSMENT

This study investigates the impact of antecedent of clients’ Social Identification and clients organisational Identification on the objectivity of the auditors' assessment in Central Java. Clients’ Social identification is the process experienced by the auditors in socializing with clients while on duty. The identification of the clients’ organization is gaining the knowledge of the clients’ ongoing business. Theory of Individual Behavior Framework in Gibson et.al (2003) said that an individual is influenced by his or her work and non-work environment. Thus, as the antecedent, the researcher sets experience, age and gender as the variables. Someone who has experience will have the knowledge that is created from his/her past memory influenced by the environment. Age becomes a proxy of maturity, and gender will affect the COI (clients’ organisation Identification) and CSI (Clients’ social identification). The result of the research using the External Auditors in Central Java with 59 respondents shows that Gender, age and experience have a direct and significant impact on CSI. And the effect COI shows a direct and significant effect on Objectivity.
Spatially-Distributed RISs vs Relay-Assisted Systems: A Fair Comparison

This paper aims to make a comparison between decode-and-forward (DF) relays and reconfigurable intelligent surfaces (RISs) in the case where only one relay or RIS is selected based on the maximization of the signal-to-noise-ratio (SNR). Our study accounts for the spatial distribution of RISs and relays, which is assumed to follow a Poisson point process (PPP). It considers two different path loss models corresponding to RIS/relays randomly located in the near-field and the far-field of the transmitter. Based on the Gamma distribution moment matching method and tools from stochastic geometry, we derive approximations for the outage probability (OP) as well as the energy efficiency (EE) of the RISs-assisted system in the near-field and the far-field scenarios separately. Under the same conditions as RIS, the expressions for OP and EE of the half-duplex and the full-duplex DF relays-assisted systems are also derived. Simulation results are presented to corroborate the proposed analysis and compare between the three technologies. Our results show that RIS is the best choice in the near-field case, regardless of the OP or EE criterion. Compared to half-duplex relays and full-duplex relays, the RIS based system is the most energy-efficient solution to assist communication. Moreover, RIS allows for an improvement in both OP and EE when equipped with more reflecting elements or more densely deployed.
Intelligent Agricultural Machinery Using Deep Learning

Artificial intelligence, deep learning, big data, self-driving cars…, these are words that have become familiar to most people and have captured the imagination of the public and have brought hopes as well as fears. We have been told that artificial intelligence will be a major part of our lives, and almost all of us witness this when decisions made by algorithms show us commercial advertisements that specifically target our interests while using the web. In this paper, the conversation around artificial intelligence focuses on a particular application, agricultural machinery, but offers enough content so that the reader can have a very good idea on how to consider this technology for not only other agricultural applications such as sorting and grading produce, but also other areas in which this technology can be a part of a system that includes sensors, hardware and software that can make accurate decisions. Narrowing the application and also focusing on one specific artificial intelligence approach, that of deep learning, allow us to illustrate from start to end the steps that are usually considered and elaborate on recent developments on artificial intelligence.
A Polyhouse: Plant Monitoring and Diseases Detection using CNN

Poly-house farming is broadly used way for farming in now days. Poly-house will provide the required amount of water and fertilizers that result in high yields. The lack of air circulation causes breeding ground for insects. To overcome this, continuous monitoring of crops is needed. Hence, monitoring parameters like temperature and intensity is essential for efficient farming by indicating the required amount of water, nutrients, and pesticides at right time. This would also indirectly suppress the onset and spread of diseases. Thus, motivated by this arduous exercise, our objective is to suggest a solution for monitoring the plant and detecting the plant disease at early stage. Automated plant disease detection techniques are useful to detect the symptoms of diseases at early stage in big farms. The dataset used for this work contain images of various plants consisting of both diseased and healthy leaves. Convolution Neural Network (CNN) is used train the model for detecting the plant diseases. The plants considered include Corn, Strawberry, Grape, Tomato and Potato plants. The model predicts the health of most of the plants with optimal accuracy of prediction being 85% and negligible loss of 0.25 was observed in the course of training the data.
Research on the wake of the ducted propeller with POD and DMD

The paper attempts to apply the Proper Orthogonal Decomposition (POD) and Dynamic Mode Decomposition (DMD) to the ducted propeller wake analysis. The ducted propeller produces a complex wake flow field with multifrequency vortex structure coupling under the influence between blades and nozzle. Traditional analysis methods such as time-averaged flow, phase-averaged flow are only displaying the coupling results. In contrast, data mining technologies such as POD and DMD can extract independent flow patterns from the massive data obtained from physical experiments and numerical simulations. The paper focuses on the influence of computational subdomain (based on the OpenFOAM), snapshot sampling interval and data coverage revolutions on the modal convergence in the data preprocessing before employing POD and DMD. Taking a single ducted propeller wake data with the advance coefficient of $\mathrm{J}=0.4$ as an example, it is found that the sampling interval of snapshots are independent of modal convergence after reaching a certain level and only affect the frequency range of the captured modes. The scope of the computational subdomain and the revolutions of data covered by snapshot matrix are vital factors to affect the modal convergence. At last, under the premise of modal convergence, the paper reconstructs the ducted propeller wake flow field by POD and DMD respectively, where errors between the original data can be ignored.
A Comprehensive Investigation on High-Frequency Oscillation in DC Microgrid

This paper presents the small-signal stability performance of a dc microgrid (MG) and investigates the interactions between the converter controllers by studying the critical modes. A comprehensive small-signal analysis using both the impedance-based method and eigenvalue-based analysis method has been presented thoroughly to investigate the high-frequency oscillation in the dc MG. The results show that there are inherent weak modes within the dc MG which could destabilize the MG in the presence of disturbances. Moreover, the controller gains and dc-link capacitance are found to be the most sensitive to substantial disturbance and have a significant influence on the weak modes leading to oscillation and resonance. The source side converter is found to be more influential than the load side converter in regard to various disturbances.
Remote Sensing Estimation of Chlorophyll-A in Case-II Waters of Coastal Areas: Three-Band Model Versus Genetic Algorithm–Artificial Neural Networks Model

Chlorophyll-a (Chl-a), an important indicator of phytoplankton biomass and eutrophication, is sensitive to water constitutes and optical characteristics. An integrated machine learning method of genetic algorithm and artificial neural networks (GA–ANN) was developed to retrieve the concentration of Chl-a. <italic>In situ</italic> spectra and simultaneous water quality parameters of 107 samples from two reservoirs (Res) and coastal waters (CW) were used to calibrate GA–ANN and three-band models (TBM) for comparison of Chl-a estimation. Both GA–ANN and TBM methods perform well for the joint dataset (WGD) of Res and CW with the <italic>R</italic><sup>2</sup> exceeding 0.90, and the root mean square error (RMSE) of corresponding validation (<italic>N</italic> = 35) are 4.40 and 5.23 <italic>μ</italic>g/L, respectively. Similarly, for independent dataset of Res (<italic>N</italic> = 45), GA–ANN and TBM methods show robust performance: the <italic>R</italic><sup>2</sup> values are 0.87 and 0.80, respectively; and the corresponding RMSE values are 7.79 and 7.73 <italic>μ</italic>g/L, respectively. For CW dataset (<italic>N</italic> = 62), the <italic>R</italic><sup>2</sup> values of two methods are 0.81 and 0.62, respectively; and the corresponding RMSE values are 0.79 and 1.32 <italic>μ</italic>g/L, respectively. When the GA–ANN and TBM models were applied to retrieve Chl-a concentration from the calibrated Sentinel 2 MSI reflectance data in two Res on October 20, 2019, however, the validated results of MSI-derived Chl-a concentrations using quasi-synchronous <italic>in situ</italic> data (<italic>N</italic> = 36) indicated that the GA–ANN model outperforms TBM with higher <italic>R</italic><sup>2</sup> value (0.91 vs. 0.26) and smaller RMSE (4.41 vs. 13.85 <italic>μ</italic>g/L) and mean absolute errors (3.40 vs. 11.87 <italic>μ</italic>g/L) values. Although TBM has obvious overestimation of Chl-a concentration when applied to remote sensing image, we still thought that both GA–ANN and TBM are useful methods for Chl-a estimation in case-II waters, and GA–ANN performs marginally better with less deviation to measured Chl-a for multispectral remote sensing data. The ratio of TSS to Chl-a, experimental measurements, abundance of sampling points, and Chl-a concentration range are several important factors affecting the accuracy and robustness of GA–ANN and TBM methods.
An Investigation on Power Factor Enchancement using Bi-Directional Power Converter

In the contemporary research period, the significance of power factor improvement is remaining as a bottleneck. Nowadays, many industries are using different drives such as, traditional DC drives, advanced AC drives and featured special electric drives for their production and operating purposes. But commonly, the primary and vital challenge in the industrial drive is their poor power factor regulation. The major reason for the low power factor is industrial heating furnaces, harmonic currents, and varying load in power system. This paper proposes the novel bi-directional converter, which provides the enhanced power factor for industrial loads, which lead to mitigate the power quality oriented problems. The proposed bi-directional converter has been designed by using MATLAB/Simulink platform and the robustness can be validated by comparing it with other traditional converters such as buck converter, boost converter and buck boost converter.
Characterization of a Flexible based PI/PVDF Sensing for Pressure Sensor

A pressure sensor has been applied in many application of pressure measurement for instant level water sensor, load sensor and flowmeter sensor. The high sensitivity of pressure sensors have been developed however the fabrication and technique are complicated and costly. A flexible based PI/PVDF pressure sensor has been developed and characterized. Four designs (Design 1–4) for different total length, gap and width of electrode fingers have been developed. All these four designs have been tested by load experimental method. The results show the highest pressure sensitivity is achieved by Design 4 of 0.753 mV/Pa. The wide range of pressure application is achieved by Design 1. It can conclude that a long total length with wide gap flexible pressure sensor has high-pressure sensitivity, instead of the short total length with a narrow gap between electrode fingers flexible pressure sensor has wide range pressure application.
Assessment of Two Immunoassays Detecting Hepatitis B Virus Surface Antigen in Blood Donor Population

As hepatitis is the most alarming disease all over the world, it can cause acute as well as chronic infections. Different serological test and molecular assays used for the detection of HBV infection. CLIA and ELISA are the most common techniques and widely used for the quantification of the anti-HBs antibody. To compare the detection efficacy of HBsAg in blood donors via two immunoassays i.e ELISA and CLIA and to determine the environmental risk factors associated with HBV in blood donor population. The qualitative and quantitative study was done to compare the detection efficacy of HBsAg performed by the help of by two immunoassays CLIA (ROCHE COBAS E411) and ELISA technique (ELISA Reader Plates) for a period of one year. Moreover, this study was carried out to investigate the environmental risk factors involved in the development of HBV infection conducted in the department of Biotechnology, SBK Women's University and RBC; Quetta, Balochistan. Overall, 89 blood samples were randomly collected from the blood donors. Cut of valve which is 0.00-0.99 was considered as standard for interpretation of data by CLIA and cut of value of ELISA was 0.15. The values which were below from the cut of value were interpreted as non-reactive and above values were interpreted as reactive. Data was statistically analyzed by using SPSS (IBM SPSS statistics version 20) and graphs were designed using Microsoft Excel. The sensitivity of CLIA (1) and ELISA (0.92) was also observed in the present study. Overall 89 samples were randomly collected from the blood donors. 37.70% and 40.44% positive results were obtained via ELISA and CLIA respectively. Some of the risk factors of HBV were blood transfusion (13%), sexual transmission (11%), causality cases (19%), unawareness about HBV (13%), saloon practices or tattooing (16%) and use of contaminated needles/surgical and dental equipment's (25%). Using SPSS software and applying one sample statistics T test the mean values and the standard deviation for both CLIA and ELISA assays were calculated as 2048.56+ 2592.80 and 3.12+1.17, standard error mean 432.13 and 0.20, the coefficient of variance were 126.56% and 37.65% respectively (table 4.6 and 4.7). In this study, highly significant difference was observed between both immunoassays (0.000***) with respect to reactivity of samples. Both CLIA and ELISA were correspondingly experienced in categorizing reactive and non-reactive antibody titer against HB antigen. However, there are some differences in quantitative measurement. The two testsdisagreed on three samples; these three samples are reported as non-reactive by ELISA were observed as reactive by CLIA. This might be due the variation in the standard calibrators used in each assay. Both assays can be used as substitute to each other.
Intrusion Detection System - Deep Learning Perspective

Nowadays the large amount of sensitive data set is a traverse through different devices and communication channels and continuously new invention in computer network technology, security problem comes out increasingly, and it has become difficult to ignore because the day is day intruders discover new attacks so it is necessary to protect from those attacks and Intrusion Detection System play important role in network security. That’s why to develop the advanced Intrusion Detection System (IDSs). Traditional Intrusion Detection Technologies faces a high false-positive rate, low accuracy, and many more problems. However, to detect the new attacks and analyze a large amount of data is one of the challenges in IDS. To improve the security of people now, adopt advanced technologies for protection. Deep learning is the rising area which deals with the large scale of data in a promising way. Also, various complex applications have been accomplishing by deep learning. Deep learning is a vast field of machine learning based on a hierarchical concept that teaches a computer to do what humans can do naturally: learn by observation. So, it will be composed of layers which might be multiple layers are called hidden layers which are neurons which is help to get the output using those hidden layers. Deep learning can govern broad-scale data and has shown efficient output in that field. This approach is helped with its mechanism to large data with efficient training time and provides good accuracy. However, there are some limitations to using deep learning in IDS. The proposed research work will discuss how deep-learning is used or which algorithm is used based on deep learning in the Intrusion Detection System to enhance the performance, its features, and also different approaches, limitations, and future implementations are discussed.