Search results for: objects detection

Authors: Ansah Iris Baffour, Dong-Ho Kim, Sung-Gyu Park

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The ongoing COVID-19 pandemic, caused by the SARS-CoV-2 virus and is gradually shifting to an endemic phase which implies the outbreak is far from over and will be difficult to eradicate. Global cooperation has led to unified precautions that aim to suppress epidemiological spread (e.g., through travel restrictions) and reach herd immunity (through vaccinations); however, the primary strategy to restrain the spread of the virus in mass populations relies on screening protocols that enable rapid on-site diagnosis of infections. Herein, we employed surface enhanced Raman spectroscopy (SERS) for the rapid detection of SARS-CoV-2 lysate on an Au-modified Au nanodimple(AuND)electrode. Through in situone-pot Au electrodeposition on the AuND electrode, Au-lysate nanocomposites were synthesized, generating3D internal hotspots for large SERS signal enhancements within 30 s of the deposition. The capture of lysate into newly generated plasmonic nanogaps within the nanocomposite structures enhanced metal-spike protein contact in 3D spaces and served as hotspots for sensitive detection. The limit of detection of SARS-CoV-2 lysate was 5 x 10-2 PFU/mL. Interestingly, ultrasensitive detection of the lysates of influenza A/H1N1 and respiratory syncytial virus (RSV) was possible, but the method showed ultimate selectivity for SARS-CoV-2 in lysate solution mixtures. We investigated the practical application of the approach for rapid on-site diagnosis by detecting SARS-CoV-2 lysate spiked in normal human saliva at ultralow concentrations. The results presented demonstrate the reliability and sensitivity of the assay for rapid diagnosis of COVID-19.

Keywords: label-free detection, nanocomposites, SARS-CoV-2, surface-enhanced raman spectroscopy

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Authors: Yuta Nakano, Kozo Kajiwara, Atsushi Hori, Takeshi Fujita

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In association with the wave of electric vehicles (EV), low energy consumption systems have become more and more important. One of the key technologies to realize low energy consumption is a dynamic vision sensor (DVS), or we can call it an event sensor, neuromorphic vision sensor and so on. This sensor has several features, such as high temporal resolution, which can achieve 1 Mframe/s, and a high dynamic range (120 DB). However, the point that can contribute to low energy consumption the most is its sparsity; to be more specific, this sensor only captures the pixels that have intensity change. In other words, there is no signal in the area that does not have any intensity change. That is to say, this sensor is more energy efficient than conventional sensors such as RGB cameras because we can remove redundant data. On the other side of the advantages, it is difficult to handle the data because the data format is completely different from RGB image; for example, acquired signals are asynchronous and sparse, and each signal is composed of x-y coordinate, polarity (two values: +1 or -1) and time stamp, it does not include intensity such as RGB values. Therefore, as we cannot use existing algorithms straightforwardly, we have to design a new processing algorithm to cope with DVS data. In order to solve difficulties caused by data format differences, most of the prior arts make a frame data and feed it to deep learning such as Convolutional Neural Networks (CNN) for object detection and recognition purposes. However, even though we can feed the data, it is still difficult to achieve good performance due to a lack of intensity information. Although polarity is often used as intensity instead of RGB pixel value, it is apparent that polarity information is not rich enough. Considering this context, we proposed to use the timestamp information as a data representation that is fed to deep learning. Concretely, at first, we also make frame data divided by a certain time period, then give intensity value in response to the timestamp in each frame; for example, a high value is given on a recent signal. We expected that this data representation could capture the features, especially of moving objects, because timestamp represents the movement direction and speed. By using this proposal method, we made our own dataset by DVS fixed on a parked car to develop an application for a surveillance system that can detect persons around the car. We think DVS is one of the ideal sensors for surveillance purposes because this sensor can run for a long time with low energy consumption in a NOT dynamic situation. For comparison purposes, we reproduced state of the art method as a benchmark, which makes frames the same as us and feeds polarity information to CNN. Then, we measured the object detection performances of the benchmark and ours on the same dataset. As a result, our method achieved a maximum of 7 points greater than the benchmark in the F1 score.

Keywords: event camera, dynamic vision sensor, deep learning, data representation, object recognition, low energy consumption

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Authors: Jorge Maestre Vidal, Ana Lucila Sandoval Orozco, Luis Javier García Villalba

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With the increase in popularity of mobile devices, new and varied forms of malware have emerged. Consequently, the organizations for cyberdefense have echoed the need to deploy more effective defensive schemes adapted to the challenges posed by these recent monitoring environments. In order to contribute to their development, this paper presents a malware detection strategy for mobile devices based on sequence alignment algorithms. Unlike the previous proposals, only the system calls performed during the startup of applications are studied. In this way, it is possible to efficiently study in depth, the sequences of system calls executed by the applications just downloaded from app stores, and initialize them in a secure and isolated environment. As demonstrated in the performed experimentation, most of the analyzed malicious activities were successfully identified in their boot processes.

Keywords: android, information security, intrusion detection systems, malware, mobile devices

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Authors: Aika Umemuro, Mitsuru Sato, Mizuki Narita, Saya Hori, Saya Sakurai, Tomomi Nakayama, Ayano Nakazawa, Toshihiro Ogura

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Mammography requires the detection of very small calcifications, and physicians search for microcalcifications by magnifying the images as they read them. The mouse is necessary to zoom in on the images, but this can be tiring and distracting when many images are read in a single day. Therefore, an image magnification system combining an eye-detector and a simple electroencephalograph (EEG) scanner was devised, and its operability was evaluated. Two experiments were conducted in this study: the measurement of eye-detection error using an eye-detector and the measurement of the time required for image magnification using a simple EEG scanner. Eye-detector validation showed that the mean distance of eye-detection error ranged from 0.64 cm to 2.17 cm, with an overall mean of 1.24 ± 0.81 cm for the observers. The results showed that the eye detection error was small enough for the magnified area of the mammographic image. The average time required for point magnification in the verification of the simple EEG scanner ranged from 5.85 to 16.73 seconds, and individual differences were observed. The reason for this may be that the size of the simple EEG scanner used was not adjustable, so it did not fit well for some subjects. The use of a simple EEG scanner with size adjustment would solve this problem. Therefore, the image magnification system using the eye-detector and the simple EEG scanner is useful.

Keywords: EEG scanner, eye-detector, mammography, observers

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Authors: Hao Mi, Ming Yang, Tian-yue Yang

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Ultrasonic infrared nondestructive testing is a kind of testing method with high speed, accuracy and localization. However, there are still some problems, such as the detection requires manual real-time field judgment, the methods of result storage and viewing are still primitive. An intelligent non-destructive detection system based on embedded linux is put forward in this paper. The hardware part of the detection system is based on the ARM (Advanced Reduced Instruction Set Computer Machine) core and an embedded linux system is built to realize image processing and defect detection of thermal images. The CLAHE algorithm and the Butterworth filter are used to process the thermal image, and then the boa server and CGI (Common Gateway Interface) technology are used to transmit the test results to the display terminal through the network for real-time monitoring and remote monitoring. The system also liberates labor and eliminates the obstacle of manual judgment. According to the experiment result, the system provides a convenient and quick solution for industrial non-destructive testing.

Keywords: remote monitoring, non-destructive testing, embedded Linux system, image processing

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Authors: Usama Shahid

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Be it written documents, news columns, or students' essays, verifying the content can be a time-consuming task. Apart from the spelling and grammar mistakes, the proofreader is also supposed to verify whether the content included in the essay or document is relevant or not. The irrelevant content in any document or essay is referred to as off-topic text and in this paper, we will address the problem of off-topic text detection from a document using machine learning techniques. Our study aims to identify the off-topic content from a document using Echo state network model and we will also compare data with other models. The previous study uses Convolutional Neural Networks and TFIDF to detect off-topic text. We will rearrange the existing datasets and take new classifiers along with new word embeddings and implement them on existing and new datasets in order to compare the results with the previously existing CNN model.

Keywords: off topic, text detection, eco state network, machine learning

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Authors: Zhang Xingnan, Huang Jingjia, Feng Yue, Burra Venkata Durga Kumar

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This paper proposes a comprehensive approach to mitigate ROP (Return-Oriented Programming) attacks by combining internal operating system protection mechanisms and hardware-assisted techniques. Through extensive literature review, we identify the effectiveness of ASLR (Address Space Layout Randomization) and LBR (Last Branch Record) in preventing ROP attacks. We present a process involving buffer overflow detection, hardware-assisted ROP attack detection, and the use of Turing detection technology to monitor control flow behavior. We envision a specialized tool that views and analyzes the last branch record, compares control flow with a baseline, and outputs differences in natural language. This tool offers a graphical interface, facilitating the prevention and detection of ROP attacks. The proposed approach and tool provide practical solutions for enhancing software security.

Keywords: operating system, ROP attacks, returning-oriented programming attacks, ASLR, LBR, CFI, DEP, code randomization, hardware-assisted CFI

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Authors: Beema Akbar, Varun P. Gopi, V. Suresh Babu

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Colon cancer causes the deaths of about half a million people every year. The common method of its detection is histopathological tissue analysis, it leads to tiredness and workload to the pathologist. A novel method is proposed that combines both structural and statistical pattern recognition used for the detection of colon cancer. This paper presents a comparison among the different classifiers such as Multilayer Perception (MLP), Sequential Minimal Optimization (SMO), Bayesian Logistic Regression (BLR) and k-star by using classification accuracy and error rate based on the percentage split method. The result shows that the best algorithm in WEKA is MLP classifier with an accuracy of 83.333% and kappa statistics is 0.625. The MLP classifier which has a lower error rate, will be preferred as more powerful classification capability.

Keywords: colon cancer, histopathological image, structural and statistical pattern recognition, multilayer perception

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Authors: Martin Pumera

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Graphene materials are being widely used in electrochemistry due to their versatility and excellent properties as platforms for biosensing. Here we present current trends in the electrochemical biosensing of pesticides and other toxic compounds. We explore two fundamentally different designs, (i) using graphene and other 2-D nanomaterials as an electrochemical platform and (ii) using these nanomaterials in the laboratory on chip design, together with paramagnetic beads. More specifically: (i) We explore graphene as transducer platform with very good conductivity, large surface area, and fast heterogeneous electron transfer for the biosensing. We will present the comparison of these materials and of the immobilization techniques. (ii) We present use of the graphene in the laboratory on chip systems. Laboratory on the chip had a huge advantage due to small footprint, fast analysis times and sample handling. We will show the application of these systems for pesticide detection and detection of other toxic compounds.

Keywords: graphene, 2D nanomaterials, biosensing, chip design

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Authors: M. Naveed Akhter, Jawad Aslam, Omer Gillani

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To aid and rehabilitate increasing number of patients suffering from spinal cord injury (SCI) and stroke, a lightweight, wearable, and 3D printable exoskeletal glove has been developed. Unlike previously developed metal or fabric-based exoskeletons, this research presents the development of soft exoskeletal glove made of thermoplastic polyurethane (TPU). The drive mechanism consists of a single motor-driven antagonistic tendon to perform extension or flexion of middle and index finger. The tendon-based differential drive has been incorporated to allow for grasping of irregularly shaped objects. The design features easy 3D-printability with TPU without a need for supports. The overall weight of the glove and the actuation unit is approximately 500g. Performance of the glove was tested on a custom test-bench with integrated load cells, and the grip strength was tested to be around 30N per finger while grasping objects of irregular shape.

Keywords: 3D printable, differential drive, exoskeletal glove, rehabilitation, single motor driven

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Authors: Jamison Duckworth, Shankarachary Ragi

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Detection and segmentation of wildfire smoke plumes from remote sensing imagery are being pursued as a solution for early fire detection and response. Smoke plume detection can be automated and made robust by the application of artificial intelligence methods. Specifically, in this study, the deep learning approach Mask Region-based Convolutional Neural Network (RCNN) is being proposed to learn smoke patterns across different spectral bands. This method is proposed to separate the smoke regions from the background and return masks placed over the smoke plumes. Multispectral data was acquired using NASA’s Earthdata and WorldView and services and satellite imagery. Due to the use of multispectral bands along with the three visual bands, we show that Mask R-CNN can be applied to distinguish smoke plumes from clouds and other landscape features that resemble smoke.

Keywords: deep learning, mask-RCNN, smoke plumes, spectral bands

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Authors: Li Hui, Riyadh Hindi

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Concrete structures frequently suffer from crack formation, a critical issue that can significantly reduce their lifespan by allowing damaging agents to enter. Traditional methods of crack detection depend on manual visual inspections, which heavily relies on the experience and expertise of inspectors using tools. In this study, a more efficient, computer vision-based approach is introduced by using the lightweight MobileNetV2 neural network. A dataset of 40,000 images was used to develop a specialized crack evaluation algorithm. The analysis indicates that MobileNetV2 matches the accuracy of traditional CNN methods but is more efficient due to its smaller size, making it well-suited for mobile device applications. The effectiveness and reliability of this new method were validated through experimental testing, highlighting its potential as an automated solution for crack detection in concrete structures.

Keywords: Concrete crack, computer vision, deep learning, MobileNetV2 neural network

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Authors: Liang Wang, Beste F. Yuksel, David Guy Brizan

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A computational model of affect that can distinguish between spontaneous and posed smiles with no errors on a large, popular data set using deep learning techniques is presented in this paper. A Long Short-Term Memory (LSTM) classifier, a type of Recurrent Neural Network, is utilized and compared to human classification. Results showed that while human classification (mean of 0.7133) was above chance, the LSTM model was more accurate than human classification and other comparable state-of-the-art systems. Additionally, a high accuracy rate was maintained with small amounts of training videos (70 instances). The derivation of important features to further understand the success of our computational model were analyzed, and it was inferred that thousands of pairs of points within the eyes and mouth are important throughout all time segments in a smile. This suggests that distinguishing between a posed and spontaneous smile is a complex task, one which may account for the difficulty and lower accuracy of human classification compared to machine learning models.

Keywords: affective computing, affect detection, computer vision, deep learning, human-computer interaction, machine learning, posed smile detection, spontaneous smile detection

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Authors: Zheng Yuxun

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This review critically assesses the advancements and prospective developments in defect detection methodologies within the semiconductor industry, an essential domain that significantly affects the operational efficiency and reliability of electronic components. As semiconductor devices continue to decrease in size and increase in complexity, the precision and efficacy of defect detection strategies become increasingly critical. Tracing the evolution from traditional manual inspections to the adoption of advanced technologies employing automated vision systems, artificial intelligence (AI), and machine learning (ML), the paper highlights the significance of precise defect detection in semiconductor manufacturing by discussing various defect types, such as crystallographic errors, surface anomalies, and chemical impurities, which profoundly influence the functionality and durability of semiconductor devices, underscoring the necessity for their precise identification. The narrative transitions to the technological evolution in defect detection, depicting a shift from rudimentary methods like optical microscopy and basic electronic tests to more sophisticated techniques including electron microscopy, X-ray imaging, and infrared spectroscopy. The incorporation of AI and ML marks a pivotal advancement towards more adaptive, accurate, and expedited defect detection mechanisms. The paper addresses current challenges, particularly the constraints imposed by the diminutive scale of contemporary semiconductor devices, the elevated costs associated with advanced imaging technologies, and the demand for rapid processing that aligns with mass production standards. A critical gap is identified between the capabilities of existing technologies and the industry's requirements, especially concerning scalability and processing velocities. Future research directions are proposed to bridge these gaps, suggesting enhancements in the computational efficiency of AI algorithms, the development of novel materials to improve imaging contrast in defect detection, and the seamless integration of these systems into semiconductor production lines. By offering a synthesis of existing technologies and forecasting upcoming trends, this review aims to foster the dialogue and development of more effective defect detection methods, thereby facilitating the production of more dependable and robust semiconductor devices. This thorough analysis not only elucidates the current technological landscape but also paves the way for forthcoming innovations in semiconductor defect detection.

Keywords: semiconductor defect detection, artificial intelligence in semiconductor manufacturing, machine learning applications, technological evolution in defect analysis

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Authors: Shehret Tilvaldyev, Jorge Flores-Garay, Alfredo Villanueva, Erwin Martinez, Lazaro Rico

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The efficiency of modern transportation is severely compromised by the prevalence of turbulent drag. The high level of turbulent skin-friction occurring, e.g., on the surface of an aircraft, automobiles or the carriage of a high-speed train, is responsible for excess fuel consumption and increased carbon emissions. The environmental, political, and economic pressure to improve fuel efficiency and reduce carbon emissions associated with transportation means that reducing turbulent skin-friction drag is a pressing engineering problem. The dynamic pressure of subsonic airflow around solid objects creates lift, but also induces drag force. This paper is presenting the results of laboratory experiments, investigating appearance and magnitude of dynamic pressure in micro scale of subsonic air flow around right cylinder and symmetrical airfoil.

Keywords: airflow, dynamic pressure, micro scale, symmetric object

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Authors: Mehdi Moradi Sarmeidani, Peyman Keyhani, Hasan Momtaz

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Chlamydophila psittaci is a avian pathogen that may cause respiratory disorders in humans. Conjunctival and cloacal swabs from 54 captive psittacine birds presented at veterinary clinics were collected to determine the prevalence of C. psittaci in domestic birds in Isfahan. Samples were collected during 2014 from a total of 10 different species of parrots, with African gray(33), Cockatiel lutino(3), Cockatiel gray(2), Cockatiel cinnamon(1), Pearl cockatiel(6), Timneh African grey(1), Ringneck parakeet(2), Melopsittacus undulatus(1), Alexander parakeet(2), Green Parakeet(3) being the most representative species sampled. C. psittaci was detected in 27 (50%) birds using molecular detection (PCR) method. The detection of this bacterium in captive psittacine birds shows that there is a potential risk for human whom has a direct contact and there is a possibility of infecting other birds.

Keywords: chlamydophila psittaci, psittacine birds, PCR, Isfahan

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Authors: Mohamed Gaith, Zaid Haddadin, Abdulah Wahbe, Mahmoud Hamam, Mahmoud Qunees, Mohammad Al Khatib, Mohammad Bsaileh, Abd Al-Aziz Jaber, Ahmad Aqra’a

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The crack is one of the most common types of failure in pipelines that convey fluid, and early detection of the crack may assist to avoid the piping system from experiencing catastrophic damage, which would otherwise be fatal. The influence of flow velocity and the presence of a crack on the performance of a tapered simply supported pipe containing moving fluid is explored using the finite element approach in this study. ANSYS software is used to simulate the pipe as Bernoulli's beam theory. In this paper, the fluctuation of natural frequencies and matching mode shapes for various scenarios owing to changes in fluid speed and the presence of damage is discussed in detail.

Keywords: damage detection, finite element, tapered pipe, vibration characteristics

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Authors: Seyedamir Makinejadsanij

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One of the most important factors in the production of quality steel is to know the exact weight of steel in the steelmaking area. In this study, a calculation method is presented to estimate the exact weight of the melt as well as the objects transported by the overhead crane. Iran Alloy Steel Company's steelmaking area has three 90-ton cranes, which are responsible for transferring the ladles and ladle caps between 34 areas in the melt shop. Each crane is equipped with a Disomat Tersus weighing system that calculates and displays real-time weight. The moving object has a variable weight due to swinging, and the weighing system has an error of about +-5%. This means that when the object is moving by a crane, which weighs about 80 tons, the device (Disomat Tersus system) calculates about 4 tons more or 4 tons less, and this is the biggest problem in calculating a real weight. The k-means algorithm is an unsupervised clustering method that was used here. The best result was obtained by considering 3 centers. Compared to the normal average(one) or two, four, five, and six centers, the best answer is with 3 centers, which is logically due to the elimination of noise above and below the real weight. Every day, the standard weight is moved with working cranes to test and calibrate cranes. The results are shown that the accuracy is about 40 kilos per 60 tons (standard weight). As a result, with this method, the accuracy of moving weight is calculated as 99.95%. K-means is used to calculate the exact mean of objects. The stopping criterion of the algorithm is also the number of 1000 repetitions or not moving the points between the clusters. As a result of the implementation of this system, the crane operator does not stop while moving objects and continues his activity regardless of weight calculations. Also, production speed increased, and human error decreased.

Keywords: k-means, overhead crane, melt weight, weight estimation, swing problem

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Authors: Noha Seddik, Sherine Youssef, Mohamed Kholeif

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The automated epileptic seizure detection research field has emerged in the recent years; this involves analyzing the Electroencephalogram (EEG) signals instead of the traditional visual inspection performed by expert neurologists. In this study, a Support Vector Machine (SVM) that uses Weighted Permutation Entropy (WPE) as the input feature is proposed for classifying normal and seizure EEG records. WPE is a modified statistical parameter of the permutation entropy (PE) that measures the complexity and irregularity of a time series. It incorporates both the mapped ordinal pattern of the time series and the information contained in the amplitude of its sample points. The proposed system utilizes the fact that entropy based measures for the EEG segments during epileptic seizure are lower than in normal EEG.

Keywords: electroencephalogram (EEG), epileptic seizure detection, weighted permutation entropy (WPE), support vector machine (SVM)

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Authors: Hakan Bozdoğan

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The goal of this paper is to evaluate the species richness, diversity and structure of in different habitats in the Kahramanmaraş Province in Turkey by using a mathematical program called as Geo-Gebra Software. The Ascalaphidae family comprises the most visually remarkable members of the order Neuroptera due to large dimensions, aerial predatory behaviour and dragonfly-like (or even butterfly-like) habits, allowing an immediate recognition also for occasional observers. Otherwise, they are one of the more poorly known families of the order in respect to biology, ecology and especially larval morphology. This discrepancy appears particularly noteworthy considering that it is a fairly large family (ca. 430 species) widely distributed in tropical and temperate areas of the World. The use of Dynamic Geometry, Analytical Softwares provides researchers a great way of visualising mathematical objects and encourage them to carry out tasks to interact with such objects and add to support of their researching. In this study we implemented; Circle with Center Through Point, Perpendicular Line, Vectors and Rays, Segments and Locus to elucidate the ecological and habitat behaviours of Butterfly-like lacewings in an analytical plane by using Geo-Gebra.

Keywords: neuroptera, Ascalaphidae, geo-gebra software, habitat selectivity

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Authors: Akshat Kumar, Vidushi

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This paper identifies 27 KOIs, 26 of which are currently classified as candidates and one as false positives that have a high probability of being confirmed. For this purpose, 11 machine learning algorithms were implemented on the cumulative kepler dataset sourced from the NASA exoplanet archive; it was observed that the best-performing model was HistGradientBoosting and XGBoost with a test accuracy of 93.5%, and the lowest-performing model was Gaussian NB with a test accuracy of 54%, to test model performance F1, cross-validation score and RUC curve was calculated. Based on the learned models, the significant characteristics for confirm exoplanets were identified, putting emphasis on the object’s transit and stellar properties; these characteristics were namely koi_count, koi_prad, koi_period, koi_dor, koi_ror, and koi_smass, which were later considered to filter out the potential KOIs. The paper also calculates the Earth similarity index based on the planetary radius and equilibrium temperature for each KOI identified to aid in their classification.

Keywords: Kepler objects of interest, exoplanets, space exploration, machine learning, earth similarity index, transit photometry

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Authors: Yanchun Zuo, Yingao Liu, Wei Liu, Le Yu, Run Huang, Lixin Guo

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Electric field detection is important in many application scenarios. The traditional strategy is measuring the electric field with a man walking around in the area under test. This strategy cannot provide a satisfactory measurement accuracy. To solve the mentioned problem, an autopilot measurement system is divided. A mini-car is produced, which can travel in the area under test according to respect to the program within the CPU. The electric field measurement platform (EFMP) carries a central computer, two horn antennas, and a vector network analyzer. The mini-car stop at the sampling points according to the preset. When the car stops, the EFMP probes the electric field and stores data on the hard disk. After all the sampling points are traversed, an electric field map can be plotted. The proposed system can give an accurate field distribution description of the chamber.

Keywords: autopilot mini-car measurement system, electric field detection, field map, static zone measurement

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Authors: Faiza Belbachir, Thibault Schienhinski

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The massive development of online social networks allows users to post and share their opinions on various topics. With this huge volume of opinion, it is interesting to extract and interpret these information for different domains, e.g., product and service benchmarking, politic, system of recommendation. This is why opinion detection is one of the most important research tasks. It consists on differentiating between opinion data and factual data. The difficulty of this task is to determine an approach which returns opinionated document. Generally, there are two approaches used for opinion detection i.e. Lexical based approaches and Machine Learning based approaches. In Lexical based approaches, a dictionary of sentimental words is used, words are associated with weights. The opinion score of document is derived by the occurrence of words from this dictionary. In Machine learning approaches, usually a classifier is trained using a set of annotated document containing sentiment, and features such as n-grams of words, part-of-speech tags, and logical forms. Majority of these works are based on documents text to determine opinion score but dont take into account if these texts are really correct. Thus, it is interesting to exploit other information to improve opinion detection. In our work, we will develop a new way to consider the opinion score. We introduce the notion of trust score. We determine opinionated documents but also if these opinions are really trustable information in relation with topics. For that we use lexical SentiWordNet to calculate opinion and trust scores, we compute different features about users like (numbers of their comments, numbers of their useful comments, Average useful review). After that, we combine opinion score and trust score to obtain a final score. We applied our method to detect trust opinions in TRIPADVISOR collection. Our experimental results report that the combination between opinion score and trust score improves opinion detection.

Keywords: Tripadvisor, opinion detection, SentiWordNet, trust score

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Authors: Radhia Toujani, Jalel Akaichi

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Social Networks generally present a hierarchy of communities. To determine these communities and the relationship between them, detection algorithms should be applied. Most of the existing algorithms, proposed for hierarchical communities identification, are based on either agglomerative clustering or divisive clustering. In this paper, we present a hybrid hierarchical clustering approach for community detection based on both bottom-up and bottom-down clustering. Obviously, our approach provides more relevant community structure than hierarchical method which considers only divisive or agglomerative clustering to identify communities. Moreover, we performed some comparative experiments to enhance the quality of the clustering results and to show the effectiveness of our algorithm.

Keywords: agglomerative hierarchical clustering, community structure, divisive hierarchical clustering, hybrid hierarchical clustering, opinion mining, social network, social network analysis

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Authors: Bikshalu Kalagadda, Srikanth Rangu

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The important object of images segmentation is to extract objects with respect to some input features. One of the important methods for image segmentation is Level set method. Generally medical images and synthetic images with low contrast of pixel profile, for such images difficult to locate interested features in images. In conventional level set function, develops irregularity during its process of evaluation of contour of objects, this destroy the stability of evolution process. For this problem a remedy is proposed, a new hybrid algorithm is Clustering Level Set Evolution. Kernel fuzzy particles swarm optimization clustering with the Distance Regularized Level Set (DRLS) and Selective Binary, and Gaussian Filtering Regularized Level Set (SBGFRLS) methods are used. The ability of identifying different regions becomes easy with improved speed. Efficiency of the modified method can be evaluated by comparing with the previous method for similar specifications. Comparison can be carried out by considering medical and synthetic images.

Keywords: segmentation, clustering, level set function, re-initialization, Kernel fuzzy, swarm optimization

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Authors: Yanxue Shang, Jingbin Zeng

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Chlorobenzene homologues (CBHs) are a category of environmental pollutants that can not be ignored. They can stay in the environment for a long period and are potentially carcinogenic. The traditional degradation method of CBHs is dechlorination followed by sample preparation and analysis. This is not only time-consuming and laborious, but the detection and analysis processes are used in conjunction with large-scale instruments. Therefore, this can not achieve rapid and low-cost detection. Compared with traditional sensing methods, colorimetric sensing is simpler and more convenient. In recent years, chromaticity sensors based on fluorescence have attracted more and more attention. Compared with sensing methods based on changes in fluorescence intensity, changes in color gradients are easier to recognize by the naked eye. Accordingly, this work proposes to use single drop microextraction (SDME) technology to solve the above problems. After the dechlorination reaction was completed, the organic droplet extracts Cl⁻ and realizes fluorescence colorimetric sensing at the same time. This method was integrated sample processing and visual in-situ detection, simplifying the detection process. As a fluorescence colorimetric sensor material, CsPbBr₃ was encapsulated in MOF-5 to construct CsPbBr₃@MOF-5 fluorescence colorimetric composite. Then the fluorescence colorimetric sensor was constructed by dispersing the composite in SDME organic droplets. When the Br⁻ in CsPbBr₃ exchanges with Cl⁻ produced by the dechlorination reactions, it is converted into CsPbCl₃. The fluorescence color of the single droplet of SDME will change from green to blue emission, thereby realizing visual observation. Therein, SDME can enhance the concentration and enrichment of Cl⁻ and instead of sample pretreatment. The fluorescence color change of CsPbBr₃@MOF-5 can replace the detection process of large-scale instruments to achieve real-time rapid detection. Due to the absorption ability of MOF-5, it can not only improve the stability of CsPbBr₃, but induce the adsorption of Cl⁻. Simultaneously, accelerate the exchange of Br- and Cl⁻ in CsPbBr₃ and the detection process of Cl⁻. The absorption process was verified by density functional theory (DFT) calculations. This method exhibits exceptional linearity for Cl⁻ in the range of 10⁻² - 10⁻⁶ M (10000 μM - 1 μM) with a limit of detection of 10⁻⁷ M. Whereafter, the dechlorination reactions of different kinds of CBHs were also carried out with this method, and all had satisfactory detection ability. Also verified the accuracy by gas chromatography (GC), and it was found that the SDME we developed in this work had high credibility. In summary, the in-situ visualization method of dechlorination reaction detection was a combination of sample processing and fluorescence colorimetric sensing. Thus, the strategy researched herein represents a promising method for the visual detection of dechlorination reactions and can be extended for applications in environments, chemical industries, and foods.

Keywords: chlorobenzene homologues, colorimetric sensor, metal halide perovskite, metal-organic frameworks, single drop microextraction

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Authors: Suveen Kumar

Abstract:

Oral cancer (OC) is the sixth most death causing cancer in world which includes tumour of lips, floor of the mouth, tongue, palate, cheeks, sinuses, throat, etc. Conventionally, the techniques used for OC detection are toluidine blue staining, biopsy, liquid-based cytology, visual attachments, etc., however these are limited by their highly invasive nature, low sensitivity, time consumption, sophisticated instrument handling, sample processing and high cost. Therefore, we developed biosensing chips for non-invasive detection of OC via CYFRA-21-1 biomarker. CYFRA-21-1 (molecular weight: 40 kDa) is secreted in saliva of OC patients which is a non-invasive biological fluid with a cut-off value of 3.8 ng mL-1, above which the subjects will be suffering from oral cancer. Therefore, in first work, 3-aminopropyl triethoxy silane (APTES) functionalized zirconia (ZrO2) nanoparticles (APTES/nZrO2) were used to successfully detect CYFRA-21-1 in a linear detection range (LDR) of 2-16 ng mL-1 with sensitivity of 2.2 µA mL ng-1. Successively, APTES/nZrO2-RGO was employed to prevent agglomeration of ZrO2 by providing high surface area reduced graphene oxide (RGO) support and much wider LDR (2-22 ng mL-1) was obtained with remarkable limit of detection (LOD) as 0.12 ng mL-1. Further, APTES/nY2O3/ITO platform was used for oral cancer bioseneor development. The developed biosensor (BSA/anti-CYFRA-21-1/APTES/nY2O3/ITO) have wider LDR (0.01-50 ng mL-1) with remarkable limit of detection (LOD) as 0.01 ng mL-1. To improve the sensitivity of the biosensing platform, nanocomposite of yattria stabilized nanostructured zirconia-reduced graphene oxide (nYZR) based biosensor has been developed. The developed biosensing chip having ability to detect CYFRA-21-1 biomolecules in the range of 0.01-50 ng mL-1, LOD of 7.2 pg mL-1 with sensitivity of 200 µA mL ng-1. Further, the applicability of the fabricated biosensing chips were also checked through real sample (saliva) analysis of OC patients and the obtained results showed good correlation with the standard protein detection enzyme linked immunosorbent assay (ELISA) technique.

Keywords: non-invasive, oral cancer, nanomaterials, biosensor, biochip

Procedia PDF Downloads 127

Authors: Le Fu, Rui Wu, Gang Feng Liu, Jie Zhao

Abstract:

Normally, impedance control methods are based on a model that connects a spring and damper in parallel. The series connection, namely the Maxwell model, has emerged as a counterpart and draw the attention of robotics researchers. In the theoretical analysis, it turns out that the two pattern are both equivalents to some extent, but notable differences of response characteristics exist, especially in the effect of damping viscosity. However, this novel impedance control design is lack of validation on realistic robot platforms. In this study, stability analysis and experimental evaluation are achieved using a 3-fingered Barrett® robotic hand BH8-282 endowed with tactile sensing, mounted on a torque-controlled lightweight and collaborative robot KUKA® LBR iiwa 14 R820. Object handover and incoming objects catching tasks are executed for validation and analysis. Experimental results show that the series connection pattern has much better performance in natural impact or shock absorption, which indicate promising applications in robots’ safe and physical interaction with humans and objects in various environments.

Keywords: impedance control, Maxwell model, force control, dexterous manipulation

Procedia PDF Downloads 497

Authors: Evan Lowhorn, Rocio Alba-Flores

Abstract:

Traditional classification Convolutional Neural Networks (CNN) attempt to classify an image in its entirety. This becomes problematic when trying to perform classification with a drone’s camera in real-time due to unpredictable backgrounds. Object detectors with bounding boxes can be used to isolate individuals and other items, but the original backgrounds remain within these boxes. These basic detectors have been regularly used to determine what type of object an item is, such as “person” or “dog.” Recent advancement in computer vision, particularly with human imaging, is keypoint detection. Human keypoint detection goes beyond bounding boxes to fully isolate humans and plot points, or Regions of Interest (ROI), on their bodies within an image. ROIs can include shoulders, elbows, knees, heads, etc. These points can then be related to each other and used in deep learning methods such as pose estimation. For drone control based on human motions, poses, or signals using the onboard camera, it is important to have a simple method for pilot identification among multiple individuals while also giving the pilot fine control options for the drone. To achieve this, the OpenPose keypoint detection network was used with body and hand keypoint detection enabled. OpenPose supports the ability to combine multiple keypoint detection methods in real-time with a single network. Body keypoint detection allows simple poses to act as the pilot identifier. The hand keypoint detection with ROIs for each finger can then offer a greater variety of signal options for the pilot once identified. For this work, the individual must raise their non-control arm to be identified as the operator and send commands with the hand on their other arm. The drone ignores all other individuals in the onboard camera feed until the current operator lowers their non-control arm. When another individual wish to operate the drone, they simply raise their arm once the current operator relinquishes control, and then they can begin controlling the drone with their other hand. This is all performed mid-flight with no landing or script editing required. When using a desktop with a discrete NVIDIA GPU, the drone’s 2.4 GHz Wi-Fi connection combined with OpenPose restrictions to only body and hand allows this control method to perform as intended while maintaining the responsiveness required for practical use.

Keywords: computer vision, drone control, keypoint detection, openpose

Procedia PDF Downloads 184

Authors: Ibrahim Shaker, Amr El Hossany, Moustafa Osman, Mohamed El Raey

Abstract:

This paper will explore integration model between GIS–SCADA system and enclosure quantification model to approach the impact of failure-safe event. There are real demands to identify spatial objects and improve control system performance. Nevertheless, the employed methodology is predicting electro-mechanic operations and corresponding time to environmental incident variations. Open processing, as object systems technology, is presented for integration enclosure database with minimal memory size and computation time via connectivity drivers such as ODBC:JDBC during main stages of GIS–SCADA connection. The function of Geographic Information System is manipulating power distribution in contrast to developing issues. In other ward, GIS-SCADA systems integration will require numerical objects of process to enable system model calibration and estimation demands, determine of past events for analysis and prediction of emergency situations for response training.

Keywords: air dispersion model, environmental management, SCADA systems, GIS system, integration power system

Procedia PDF Downloads 368