Miracle-dz/UniFAR-FTUs huggingface dataset:
Sensitivity analysis of a label-free detection using Opto-plasmonic nano-structured antenna

This paper reports a design method of optoplasmonics sensor that consider a pair of elliptical gold nano antenna mounted on a quartz substrate. A 2D array of gold nano antenna can be used for a variety of biomedical applications due to its key electronic and optical properties which are shape and size dependent. An elliptical shaped coupled gold nano antenna with 100 nm major axis, a and 10 nm minor axis, b was optimized with separation distance, g and height, h to achieve a high sensitivity with a narrow full-width half-maximum (FWHM) and a good figure of merit (FOM). By exploiting the resonance shift of transmission spectra and high electric field confinements, the sensitivity of the proposed sensor was obtained nearly 526-530 nm/RIU and the FWHM and FOM were calculated around 110 nm and 8.1, respectively. Additionally, the proposed sensing structure have been explored to measure the 2-propanol (IPA) concentration in water.
Congestion, Stochastic, and Similarity Effects in Stochastic: User-Equilibrium Models

Different effects in stochastic user-equilibrium formulations are compared. The starting point is the logit assignment formulation for the stochastic user-equilibrium model. Recently, extended logit-based models were developed as solutions of equivalent stochastic user-equilibrium problems. These extended logit models are theoretically superior to the simple logit model, because they take into account the similarity among routes. The similarity indexes were defined based on physical parameters of the network, such as link lengths, and therefore did not depend on congestion. The assumption that similarity coefficients are independent of congestion means that the similarity effect and the congestion effect are treated separately. However, both similarity and congestion effects are taken into account in the equilibrium formulations. Simple network examples are presented to illustrate the sensitivity of the link flow pattern (and path flow pattern) to the three different effects in stochastic assignment models: the congestion effect, the stochastic effect, and the similarity effect. The relative influence of those effects on the flow patterns is discussed.
An Efficient Hybrid Parallel Compression Approximate Multiplier

Approximate computing has been widely used in many fault-tolerant applications. Multiplication as a key kernel in such applications, it is significant to improve the efficiency of approximate multiplier to achieve high computational performance. This paper proposes a novel approximate multiplier design based on using different compressors for different regions of partial products. We designed two Preprocessing Units (PUs) to explore the best efficiency via increasing the number of sparse partial products. Multiple 8-bit multipliers are designed using Verilog and synthesized under the 45-nm CMOS technology. Compared with the conventional Wallace Tree multiplier, experimental results indicate that one of our proposed multipliers reduce Power-Delay Product (PDP) by 58.5% at most with 0.42% normalized mean error distance. Moreover, a case study of image processing applications is also investigated. Our proposed multipliers can achieve a high peak signal-to-noise ratio of 51.87dB. Compared to the state-of-the-art, the proposed multiplier has a better comprehensive performance in accuracy, area and power consumption.
Membrane Antenna Arrays Fed by Substrate Integrated Waveguide for V‐Band Communication

A low‐cost membrane antenna arrays based on FR4 and thin flexible Pyralux substrate for high speed 60 GHz communication systems are proposed. The thin substrate used for making the antenna helps for the easy integration with millimeter wave components and circuits. The low‐cost thin Pyralux substrate with a thickness of 75μm, relative permittivity of εr = 2.4 and tan δ = 0.002 is used. In this article, we are proposing a slot coupled single patch with substrate integrated waveguide (SIW) technology, 1 × 4 antenna array with (a) a combination of SIW and microstrip feed network and (b) SIW complete feed network, and finally a 1 × 16 array of patch antennas fed by complete SIW feed technology. Measurements of the prototypes show good results in terms of impedance bandwidth and antenna gain around 60 GHz. The measured results are in agreement with the numerical simulations. © 2013 Wiley Periodicals, Inc. Microwave Opt Technol Lett 55:1746–1752, 2013
Fungus Detection and Identification using Computer Vision Techniques and Convolution Neural Networks

In this paper we have presented a fungus detection system. As we all know that Fungus is a very big risk for the human Life as it can come in contact to humans through food, water, Air etc. Exposure to high concentrations of fungal spores from air can cause many health problems, such as bronchial asthma and other deadly diseases. Fungus is the main risk for food logistics. We aim to build a model which can correctly detect and identity fungus. Datasets plays a critical role throughout the procedure. For detection purposes, an accurate dataset is required. The dataset we are collecting was made using an optical acquisition system. The main task of our project is the image analysis. Image analysis can be done using computer vision techniques or with the help of machine learning. We have explained both the methods of analysis of CVT using Histogram oriented gradients (HOG) and Support Vector Machine and Convolution neural networks (CNN) in detail with various processes and layers involved.
Non-destructive Image Processing Analysis for Defect Identification and Maturity Detection on Avocado Fruit

Image processing techniques and computer intervention has been widely accepted in smart agriculture and smart farming applications such as classification, quality grading, sorting, detection, evaluation in the agricultural field. Defect identification and maturity detection on avocado fruit give a challenging task by integrating a deep learning system nearer to a human-level recognition. The proposed research will benefit local markets and traders of purple avocados as it will provide automatic sorting and grading evaluation based on local standards. This paper aims to develop a non-destructive image processing analysis for defect identification and maturity detection on avocado fruit capable of classifying the type of defect and its maturity based on a convolutional neural network (CNN) classifier. This study had an overall accuracy of 93% for defect identification and 98.97% for maturity detection.
Okullarda Tek Tip Ya da Serbest Kiyafet

Bu makalede okullarda kiyafet uygulamalarina iliskin gorus, uygulama ve arastirma sonuclarinin bir sentezini sunmaktadir. Eldeki verilere gore tek tip kiyafet zorunlulugunun hem olumsuz hem de olumlu etkilerinden soz etmek mumkundur. Herhangi bir kiyafet uygulamasina gecis icin pilot uygulamalara, toplumsal ve kulturel etkilerin analiz edilmesine ihtiyac vardir
Internet of Things based User Identification and Hand Sanitization System with Non-Contact Temperature Measurement

With the spread of SARS-CoV-2, the demand for students’ and employees’ health monitoring and non-contact methods of sanitization has escalated. This paper details the design and implementation of an economic, non-contact sanitization and user data recording system using the Internet of Things (IoT). The design includes a non-invasive temperature measurement technique, user identification using Radio Frequency Identification (RFID), data recording over Wi-Fi using ESP8266, and a contactless hand sanitizer dispenser. The prototype is targeted at educational institutes and small-scale commercial entities.
Factors Influencing the Accessibility and Reliability of Health Information in the Face of the COVID-19 Outbreak—A Study in Rural China

Introduction: Rural residents have been shown to have limited access to reliable health information and therefore may be at higher risks for the adverse health effects of the COVID-19. The aim of this research is 2-fold: (1) to explore the impacts of demographic factors on the accessibility of health information; and (2) to assess the impacts of information channels on the reliability of health information accessed by rural residents in China during the COVID-19 outbreak. Methods: Mixed methods research was performed to provide a relatively complete picture about the accessibility and reliability of health information in rural China in the face of the COVID-19. A quantitative research was conducted through surveying 435 Chinese rural residents and a qualitative study was performed through collecting materials from one of the most popular social media application (WeChat) in China. The logistic regression techniques were used to examine the impacts of demographic factors on the accessibility of health information. The Content analysis was performed to describe and summarize qualitative materials to inform the impacts of information channels on the reliability of health information. Results: Age was found to positively associate with the accessibility of health information, while an opposite association was found between education and the accessibility of health information. Rural residents with monthly income between 3,001 CNY and 4,000 CNY were the least likely to access health information. Rural residents who worked/studied from home were more likely to access health information. Meanwhile, health information tended to be derived from non-official social media channels where rumors and unverified health information spread fast, and the elderly and less-educated rural residents were more likely to access health misinformation. Conclusions: Policy makers are suggested to adopt efficient measures to contain the spread of rumors and unverified health information on non-official social media platforms during the outbreak of a pandemic. More efforts should be devoted to assist the elderly and less-educated rural residents to access reliable health information in the face of a pandemic outbreak.
An Autoencoder-based Method for Targeted Attack on Deep Neural Network Models

This paper presents an autoencoder-based method for a targeted attack on deep neural network models, named AE4DNN. The proposed method aims to improve the existing targeted attacks in terms of their generalization, transferability, and the trade-off between the quality of adversarial examples and the computational cost. The idea of AE4DNN is that an autoencoder model is trained from a balanced subset of the training set. The trained autoencoder model is then used to generate adversarial examples from the remaining subset of the training set, produce adversarial examples from new samples, and attack other DNN models. To demonstrate the effectiveness of AE4DNN, the compared methods are box-constrained L-BFGS, Carlini-Wagner ‖L‖2 attack, and AAE. The comprehensive experiment on MNIST has shown that AE4DNN can gain a better transferability, improve generalization, and generate high quality of adversarial examples while requiring a low cost of computation. This initial result demonstrates the potential ability of AE4DNN in practice, which would help to reduce the effort of testing deep neural network models.
Diagnostic Value of Radiomics Analysis in Contrast-Enhanced Spectral Mammography for Identifying Triple-Negative Breast Cancer

Purpose To evaluate the value of radiomics analysis in contrast-enhanced spectral mammography (CESM) for the identification of triple-negative breast cancer (TNBC). Method CESM images of 367 pathologically confirmed breast cancer patients (training set: 218, testing set: 149) were retrospectively analyzed. Cranial caudal (CC), mediolateral oblique (MLO), and combined models were built on the basis of the features extracted from subtracted images on CC, MLO, and the combination of CC and MLO, respectively, in the tumour region. The performance of the models was evaluated through receiver operating characteristic (ROC) curve analysis, the Hosmer-Lemeshow test, and decision curve analysis (DCA). The areas under ROC curves (AUCs) were compared through the DeLong test. Results The combined CC and MLO model had the best AUC and sensitivity of 0.90 (95% confidence interval: 0.85–0.96) and 0.97, respectively. The Hosmer–Lemeshow test yielded a non-significant statistic with p-value of 0.59. The clinical usefulness of the combined CC and MLO model was confirmed if the threshold was between 0.02 and 0.81 in the DCA. Conclusions Machine learning models based on subtracted images in CESM images were valuable for distinguishing TNBC and NTNBC. The model with the combined CC and MLO features had the best performance compared with models that used CC or MLO features alone.
Magnesium Limitation Leads to Transcriptional Down-Tuning of Auxin Synthesis, Transport, and Signaling in the Tomato Root

Magnesium (Mg) deficiency is becoming a widespread limiting factor for crop production. How crops adapt to Mg limitation remains largely unclear at the molecular level. Using hydroponic-cultured tomato seedlings, we found that total Mg2+ content significantly decreased by ∼80% under Mg limitation while K+ and Ca2+ concentrations increased. Phylogenetic analysis suggested that Mg transporters (MRS2/MGTs) constitute a previously uncharacterized 3-clade tree in planta with two rounds of asymmetric duplications, providing evolutionary evidence for further molecular investigation. In adaptation to internal Mg deficiency, the expression of six representative MGTs (two in the shoot and four in the root) was up-regulated in Mg-deficient plants. Contradictory to the transcriptional elevation of most of MGTs, Mg limitation resulted in the ∼50% smaller root system. Auxin concentrations particularly decreased by ∼23% in the Mg-deficient root, despite the enhanced accumulation of gibberellin, cytokinin, and ABA. In accordance with such auxin reduction was overall transcriptional down-regulation of thirteen genes controlling auxin biosynthesis (TAR/YUCs), transport (LAXs, PINs), and signaling (IAAs, ARFs). Together, systemic down-tuning of gene expression in the auxin signaling pathway under Mg limitation preconditions a smaller tomato root system, expectedly stimulating MGT transcription for Mg uptake or translocation.
Network Intrusion Detection using Machine Learning Approaches

At present network intrusion is regarded as a great threat in network usage and communication. Network intrusion detection system detects and prevents anomalous activities or attacks in networks. Many classifiers are used to detect network attacks. In this paper, we have evaluated the performance of four popular classifiers, namely, Decision Tree, Support Vector Machine, Random Forest and Naïve Bayes on UNSW-NB15 dataset using Python language along with its Pandas and SKlearn libraries. We have used the complete UNSW-NB15 dataset with 43 features. Experimental results have shown improvement of accuracy for Random Forest, Decision Tree and Naïve Bayes over previously reported results produced by Apache Spark and its MLlib.
SOS Algorithm based TID controller of AGC of interconnected Power system

This paper introduces the application of TID controller to solve the AGC issues. The gain parameters are optimized by SOS algorithm. A two-area power system with multi-source (Hydro, Thermal and Gas) generating units consisting GDB and GRC are taken in the system. The performance of proposed model is compared in between SOS optimized and the PFA optimized controller gain parameters. ITAE has taken as an objective function for solving the issues. Step load disturbances of 1 % (0.01 pu) is given in area-1. The roboustness analysis are done for investigation of effectiveness of SOS approached TID controllers against PFA optimized controller.
Source Code Plagiarism Detection in an Educational Context: A Literature Mapping

Detection of plagiarism in students' source codes in college-level programming courses is an important topic for instructors and institutions that seek to pursue project-based learning while enforcing honor codes and maintaining traditional grade-based skill assessment methods. There are different approaches for plagiarism detection currently being researched. This paper aims to answer the question: What does the literature report on source code plagiarism detection in university settings? To answer that, we used a systematic mapping process of recent literature. We selected 109 papers published between 2015 and 2020 that deal with this subject specifically in an educational context. We found that this research area is currently expanding and being studied worldwide. There were papers from 37 different countries, and the number of publications per year has been increasing since 2017. The most targeted programming languages are Java, C++, C, and Python. The most studied plagiarism detection tools are MOSS, JPlag, SIM, Plaggie, and Sherlock. Our study also identified new methodologies created to tackle this problem, such as the analysis of students' typing patterns or their coding style. We noticed that the proposed solutions are mainly based on static source code analysis instead of following the development process. This paper describes our findings.
TEMPERATURE DISTRIBUTIONS IN AUTOMOTIVE DRY CLUTCHES

SYNOPSIS A numerical method that involves setting up equations to express the heat balance at every region in the clutch is used to determine the temperatures at various elements when band contact occurs between the rubbing surfaces during the operation of an automotive clutch. Temperature calculations have been made for contact areas of different band width on the two clutch facings and the results obtained compared with those attained when complete contact occurs. Both single and repeated engagements made at regular intervals are considered.
A System Using a Mechanical Passive Arm for Position Measurement that Is Suitable for Inspection and Intervention Tasks Requiring Precise Positioning

R emotely operated underwater vehicles (ROVs) play an important role in underwater technology. ROVs have been used for the past three decades in the offshore oil industry. They are essential at depths were human diving is impractical. This is the case of the Brazilian Marlim oil field, which has wells deeper than 1500 m. ROVs are used for constructing production facilities, for inspection, and for intervention. ROVs are linked to a surface vessel by a tether cable that powers the ROV and transmits command signals and video images. Human pilots, aboard the surface vessel, usually operate the ROV through joysticks that command ROV thrusters. Video cameras and other sensors (e.g., depth-meter and compass) are the usual on-board navigation instruments. Acoustic systems (long baseline and short baseline) are often employed as auxiliary navigation systems. In many operations it is required that the ROV keeps its position close to some underwater structure (station keeping). This is usual in intervention operations involving ROV manipulators (e.g., opening a valve). Those operations are time consuming, difficult, and tedious for the ROV pilot. The multivariable (six degree-of-freedom) and nonlinear nature of the ROV dynamics makes manual station keeping difficult. The pilot must continuously correct the ROV position to counteract the water current and the reaction forces caused by the motion of the manipulator. In this article we describe an automatic dynamic positioning system for ROVs (DPSROV) that is based on a mechanical passive arm (PA) measurement system. The PA system was selected from a group of candidate measurement systems, including long baseline, short baseline, inertial, speed logs, and taut-wire. The selection was based on several relevant criteria; namely, precision, construction cost, and operational facilities. We describe the DPSROV hardware and software; outline the dynamic model of the ROV and the position control system; discuss a conventional P-PI linear controller and the variable structure model-reference adaptive controller (VS-MRAC), which were implemented in order to show that the DPSROV can execute control algorithms of such diverse complexities and resource requirement; and present some experimental results obtained with the DPSROV. In a comparative study [1], we have concluded that the PA is the most reliable system for precise ROV dynamic positioning in a short-range workspace (few meters).
Fruit Quality Recognition using Deep Learning Algorithm

Fruit classification is essential in various industrial settings, such as factories, supermarkets, and other places. Fruit classification may also be beneficial to persons with unique nutritional needs who utilize it to choose the proper fruits. Manual sorting was formerly used for fruit classification is time-consuming and requires continual human presence. Many fruit classification machine learning techniques have been proposed in the past. Deep learning may be a powerful engine for generating actionable results in today’s reality because of its detection and classification abilities. As a result, a convolutional neural network was employed to construct an effective fruit classification model. It makes use of the fruits 360 dataset, which contains 131 different fruit and vegetable varieties. In this paper, we used three fruits, divided into three categories: good, raw, and damaged. The model was made in Keras. It had been trained for 50 epochs and had a 95% accuracy rate.
Electronic crosstalk correction for terra long wave infrared photovoltaic bands

The MODerate-resolution Imaging Spectroradiometer (MODIS) is one of the primary instruments in the National Aeronautics and Space Administration (NASA) Earth Observing System (EOS). The first MODIS instrument was launched in December 1999 on-board the Terra spacecraft. MODIS has 36 bands, among which 27-30 are Long Wave Infrared (LWIR) PhotoVoltaic (PV) bands covering a wavelength range from 6.72 μm to 9.73 μm. It has been found that there is severe contamination in Terra band 27 from other three bands due to crosstalk of signals among them. The crosstalk effect induces strong striping in the Earth View (EV) images and causes large long-term drift in the EV Brightness Temperature (BT) in the band. An algorithm using a linear approximation derived from on-orbit lunar observations has been developed to correct the crosstalk effect for band 27. It was demonstrated that the crosstalk correction can substantially reduce the striping in the EV images and significantly remove the long-term drift in the EV BT. In this paper, it is shown that other three LWIR PV bands are also contaminated by the crosstalk of signals among themselves. The effect induces strong striping artifacts and large long-term drifts in these bands as similarly observed in band 27. The crosstalk correction algorithm previously developed is applied to correct the crosstalk effect. It is demonstrated that the crosstalk correction successfully reduces the striping in the EV images and removes long-term drifts in the EV BT in bands 28-30 as was done similarly for band 27. The crosstalk correction algorithm can thus substantially improve both the image quality and radiometric accuracy of the LWIR PV bands Level 1B (L1B) products. The algorithm can be applied to other MODIS bands and/or other remote sensors that exhibit an electronic crosstalk effect.