Search results for: quickest change detection
9815 Developing Artificial Neural Networks (ANN) for Falls Detection
Authors: Nantakrit Yodpijit, Teppakorn Sittiwanchai
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The number of older adults is rising rapidly. The world’s population becomes aging. Falls is one of common and major health problems in the elderly. Falls may lead to acute and chronic injuries and deaths. The fall-prone individuals are at greater risk for decreased quality of life, lowered productivity and poverty, social problems, and additional health problems. A number of studies on falls prevention using fall detection system have been conducted. Many available technologies for fall detection system are laboratory-based and can incur substantial costs for falls prevention. The utilization of alternative technologies can potentially reduce costs. This paper presents the new design and development of a wearable-based fall detection system using an Accelerometer and Gyroscope as motion sensors for the detection of body orientation and movement. Algorithms are developed to differentiate between Activities of Daily Living (ADL) and falls by comparing Threshold-based values with Artificial Neural Networks (ANN). Results indicate the possibility of using the new threshold-based method with neural network algorithm to reduce the number of false positive (false alarm) and improve the accuracy of fall detection system.Keywords: aging, algorithm, artificial neural networks (ANN), fall detection system, motion sensorsthreshold
Procedia PDF Downloads 4889814 Multiscale Edge Detection Based on Nonsubsampled Contourlet Transform
Authors: Enqing Chen, Jianbo Wang
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It is well known that the wavelet transform provides a very effective framework for multiscale edges analysis. However, wavelets are not very effective in representing images containing distributed discontinuities such as edges. In this paper, we propose a novel multiscale edge detection method in nonsubsampled contourlet transform (NSCT) domain, which is based on the dominant multiscale, multidirection edge expression and outstanding edge location of NSCT. Through real images experiments, simulation results demonstrate that the proposed method is better than other edge detection methods based on Canny operator, wavelet and contourlet. Additionally, the proposed method also works well for noisy images.Keywords: edge detection, NSCT, shift invariant, modulus maxima
Procedia PDF Downloads 4859813 Implementation of Edge Detection Based on Autofluorescence Endoscopic Image of Field Programmable Gate Array
Authors: Hao Cheng, Zhiwu Wang, Guozheng Yan, Pingping Jiang, Shijia Qin, Shuai Kuang
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Autofluorescence Imaging (AFI) is a technology for detecting early carcinogenesis of the gastrointestinal tract in recent years. Compared with traditional white light endoscopy (WLE), this technology greatly improves the detection accuracy of early carcinogenesis, because the colors of normal tissues are different from cancerous tissues. Thus, edge detection can distinguish them in grayscale images. In this paper, based on the traditional Sobel edge detection method, optimization has been performed on this method which considers the environment of the gastrointestinal, including adaptive threshold and morphological processing. All of the processes are implemented on our self-designed system based on the image sensor OV6930 and Field Programmable Gate Array (FPGA), The system can capture the gastrointestinal image taken by the lens in real time and detect edges. The final experiments verified the feasibility of our system and the effectiveness and accuracy of the edge detection algorithm.Keywords: AFI, edge detection, adaptive threshold, morphological processing, OV6930, FPGA
Procedia PDF Downloads 2249812 Detection of Epinephrine in Chicken Serum at Iron Oxide Screen Print Modified Electrode
Authors: Oluwole Opeyemi Dina, Saheed E. Elugoke, Peter Olutope Fayemi, Omolola E. Fayemi
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This study presents the detection of epinephrine (EP) at Fe₃O₄ modified screen printed silver electrode (SPSE). The iron oxide (Fe₃O₄) nanoparticles were characterized with UV-visible spectroscopy, Fourier-Transform infrared spectroscopy (FT-IR) and Scanning electron microscopy (SEM) prior to the modification of the SPSE. The EP oxidation peak current (Iap) increased with an increase in the concentration of EP as well as the scan rate (from 25 - 400 mVs⁻¹). Using cyclic voltammetry (CV), the relationship between Iap and EP concentration was linear over a range of 3.8 -118.9 µM and 118.9-175 µM with a detection limit of 41.99 µM and 83.16 µM, respectively. Selective detection of EP in the presence of ascorbic acid was also achieved at this electrode.Keywords: screenprint electrode, iron oxide nanoparticle, epinephrine, serum, cyclic voltametry
Procedia PDF Downloads 1629811 Same-Day Detection Method of Salmonella Spp., Shigella Spp. and Listeria Monocytogenes with Fluorescence-Based Triplex Real-Time PCR
Authors: Ergun Sakalar, Kubra Bilgic
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Faster detection and characterization of pathogens are the basis of the evoid from foodborne pathogens. Salmonella spp., Shigella spp. and Listeria monocytogenes are common foodborne bacteria that are among the most life-threatining. It is important to rapid and accurate detection of these pathogens to prevent food poisoning and outbreaks or to manage food chains. The present work promise to develop a sensitive, species specific and reliable PCR based detection system for simultaneous detection of Salmonella spp., Shigella spp. and Listeria monocytogenes. For this purpose, three genes were picked out, ompC for Salmonella spp., ipaH for Shigella spp. and hlyA for L. monocytogenes. After short pre-enrichment of milk was passed through a vacuum filter and bacterial DNA was exracted using commercially available kit GIDAGEN®(Turkey, İstanbul). Detection of amplicons was verified by examination of the melting temperature (Tm) that are 72° C, 78° C, 82° C for Salmonella spp., Shigella spp. and L. monocytogenes, respectively. The method specificity was checked against a group of bacteria strains, and also carried out sensitivity test resulting in under 10² CFU mL⁻¹ of milk for each bacteria strain. Our results show that the flourescence based triplex qPCR method can be used routinely to detect Salmonella spp., Shigella spp. and L. monocytogenes during the milk processing procedures in order to reduce cost, time of analysis and the risk of foodborne disease outbreaks.Keywords: evagreen, food-born bacteria, pathogen detection, real-time pcr
Procedia PDF Downloads 2409810 Latency-Based Motion Detection in Spiking Neural Networks
Authors: Mohammad Saleh Vahdatpour, Yanqing Zhang
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Understanding the neural mechanisms underlying motion detection in the human visual system has long been a fascinating challenge in neuroscience and artificial intelligence. This paper presents a spiking neural network model inspired by the processing of motion information in the primate visual system, particularly focusing on the Middle Temporal (MT) area. In our study, we propose a multi-layer spiking neural network model to perform motion detection tasks, leveraging the idea that synaptic delays in neuronal communication are pivotal in motion perception. Synaptic delay, determined by factors like axon length and myelin insulation, affects the temporal order of input spikes, thereby encoding motion direction and speed. Overall, our spiking neural network model demonstrates the feasibility of capturing motion detection principles observed in the primate visual system. The combination of synaptic delays, learning mechanisms, and shared weights and delays in SMD provides a promising framework for motion perception in artificial systems, with potential applications in computer vision and robotics.Keywords: neural network, motion detection, signature detection, convolutional neural network
Procedia PDF Downloads 759809 Inverter IGBT Open–Circuit Fault Detection Using Park's Vectors Enhanced by Polar Coordinates
Authors: Bendiabdellah Azzeddine, Cherif Bilal Djamal Eddine
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The three-phase power converter voltage structure is widely used in many power applications but its failure can lead to partial or total loss of control of the phase currents and can cause serious system malfunctions or even a complete system shutdown. To ensure continuity of service in all circumstances, effective and rapid techniques of detection and location of inverter fault is to be implemented. The present paper is dedicated to open-circuit fault detection in a three-phase two-level inverter fed induction motor. For detection purpose, the proposed contribution addresses the Park’s current vectors associated to a polar coordinates calculation tool to compute the exact value of the fault angle corresponding directly to the faulty IGBT switch. The merit of the proposed contribution is illustrated by experimental results.Keywords: diagnosis, detection, Park’s vectors, polar coordinates, open-circuit fault, inverter, IGBT switch
Procedia PDF Downloads 3929808 Comparative Analysis of Edge Detection Techniques for Extracting Characters
Authors: Rana Gill, Chandandeep Kaur
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Segmentation of images can be implemented using different fundamental algorithms like edge detection (discontinuity based segmentation), region growing (similarity based segmentation), iterative thresholding method. A comprehensive literature review relevant to the study gives description of different techniques for vehicle number plate detection and edge detection techniques widely used on different types of images. This research work is based on edge detection techniques and calculating threshold on the basis of five edge operators. Five operators used are Prewitt, Roberts, Sobel, LoG and Canny. Segmentation of characters present in different type of images like vehicle number plate, name plate of house and characters on different sign boards are selected as a case study in this work. The proposed methodology has seven stages. The proposed system has been implemented using MATLAB R2010a. Comparison of all the five operators has been done on the basis of their performance. From the results it is found that Canny operators produce best results among the used operators and performance of different edge operators in decreasing order is: Canny>Log>Sobel>Prewitt>Roberts.Keywords: segmentation, edge detection, text, extracting characters
Procedia PDF Downloads 4239807 A Dynamic Ensemble Learning Approach for Online Anomaly Detection in Alibaba Datacenters
Authors: Wanyi Zhu, Xia Ming, Huafeng Wang, Junda Chen, Lu Liu, Jiangwei Jiang, Guohua Liu
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Anomaly detection is a first and imperative step needed to respond to unexpected problems and to assure high performance and security in large data center management. This paper presents an online anomaly detection system through an innovative approach of ensemble machine learning and adaptive differentiation algorithms, and applies them to performance data collected from a continuous monitoring system for multi-tier web applications running in Alibaba data centers. We evaluate the effectiveness and efficiency of this algorithm with production traffic data and compare with the traditional anomaly detection approaches such as a static threshold and other deviation-based detection techniques. The experiment results show that our algorithm correctly identifies the unexpected performance variances of any running application, with an acceptable false positive rate. This proposed approach has already been deployed in real-time production environments to enhance the efficiency and stability in daily data center operations.Keywords: Alibaba data centers, anomaly detection, big data computation, dynamic ensemble learning
Procedia PDF Downloads 1919806 Medical Image Watermark and Tamper Detection Using Constant Correlation Spread Spectrum Watermarking
Authors: Peter U. Eze, P. Udaya, Robin J. Evans
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Data hiding can be achieved by Steganography or invisible digital watermarking. For digital watermarking, both accurate retrieval of the embedded watermark and the integrity of the cover image are important. Medical image security in Teleradiology is one of the applications where the embedded patient record needs to be extracted with accuracy as well as the medical image integrity verified. In this research paper, the Constant Correlation Spread Spectrum digital watermarking for medical image tamper detection and accurate embedded watermark retrieval is introduced. In the proposed method, a watermark bit from a patient record is spread in a medical image sub-block such that the correlation of all watermarked sub-blocks with a spreading code, W, would have a constant value, p. The constant correlation p, spreading code, W and the size of the sub-blocks constitute the secret key. Tamper detection is achieved by flagging any sub-block whose correlation value deviates by more than a small value, ℇ, from p. The major features of our new scheme include: (1) Improving watermark detection accuracy for high-pixel depth medical images by reducing the Bit Error Rate (BER) to Zero and (2) block-level tamper detection in a single computational process with simultaneous watermark detection, thereby increasing utility with the same computational cost.Keywords: Constant Correlation, Medical Image, Spread Spectrum, Tamper Detection, Watermarking
Procedia PDF Downloads 1819805 Meyer Wavelet Transform and Jaccard Deep Q-Net for Small Object Classification Using Multi-Modal Images
Authors: Mian Muhammad Kamal
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Accurate detection of small objects is extremely essential in critical applications like military reconnaissance and emergency rescue. However, owing to the low resolution, occlusion, and background interference, small object detection is a tedious process. One of the most appropriate approaches is to combine the data available in multimodal images to enhance the detection ability. This paper proposes a small object detection technique using three kinds of multimodal images, such as Hyperspectral-Multispectral (HS-MS), HS-Synthetic Aperture Radar (HS-SAR), and HS-SAR-Digital Surface Model (HS-SAR-DSM). The detection is accomplished by utilizing the Jaccard Deep Q-Net (JDQN) that is created by the incorporation of the Jaccard similarity measure and Deep Q-Network (DQN) using Regression modeling. Further, a Deep Maxout Network (DMN) is used for fusing the detected outputs obtained from each modality so as to generate the final output. Moreover, the supremacy of the proposed JDQN in detecting small objects is established by the utilization of metrics, like accuracy, Mean Squared Error (MSE), precision, and Root MSE (RMSE), and experimentation reveals that the JDQN recorded superior accuracy of 0.907, normalized MSE of 0.448, precision of 0.904, and normalized RMSE of 0.670.Keywords: small object detection, Multimodality, deep learning, Jaccard deep q-net, deep maxout network
Procedia PDF Downloads 89804 Immuno-field Effect Transistor Using Carbon Nanotubes Network – Based for Human Serum Albumin Highly Sensitive Detection
Authors: Muhamad Azuddin Hassan, Siti Shafura Karim, Ambri Mohamed, Iskandar Yahya
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Human serum albumin plays a significant part in the physiological functions of the human body system (HSA).HSA level monitoring is critical for early detection of HSA-related illnesses. The goal of this study is to show that a field effect transistor (FET)-based immunosensor can assess HSA using high aspect ratio carbon nanotubes network (CNT) as a transducer. The CNT network were deposited using air brush technique, and the FET device was made using a shadow mask process. Field emission scanning electron microscopy and a current-voltage measurement system were used to examine the morphology and electrical properties of the CNT network, respectively. X-ray photoelectron spectroscopy and Fourier transform infrared spectroscopy were used to confirm the surface alteration of the CNT. The detection process is based on covalent binding interactions between an antibody and an HSA target, which resulted in a change in the manufactured biosensor's drain current (Id).In a linear range between 1 ng/ml and 10zg/ml, the biosensor has a high sensitivity of 0.826 mA (g/ml)-1 and a LOD value of 1.9zg/ml.HSA was also identified in a genuine serum despite interference from other biomolecules, demonstrating the CNT-FET immunosensor's ability to quantify HSA in a complex biological environment.Keywords: carbon nanotubes network, biosensor, human serum albumin
Procedia PDF Downloads 1339803 A Microfluidic Biosensor for Detection of EGFR 19 Deletion Mutation Targeting Non-Small Cell Lung Cancer on Rolling Circle Amplification
Authors: Ji Su Kim, Bo Ram Choi, Ju Yeon Cho, Hyukjin Lee
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Epidermal growth factor receptor (EGFR) 19 deletion mutation gene is over-expressed in carcinoma patient. EGFR 19 deletion mutation is known as typical biomarker of non-small cell lung cancer (NSCLC), which one section in the coding exon 19 of EGFR is deleted. Therefore, there have been many attempts over the years to detect EGFR 19 deletion mutation for replacing conventional diagnostic method such as PCR and tissue biopsy. We developed a simple and facile detection platform based on Rolling Circle Amplification (RCA), which provides highly amplified products in isothermal amplification of the ligated DNA template. Limit of detection (~50 nM) and a faster detection time (~30 min) could be achieved by introducing RCA.Keywords: EGFR19, cancer, diagnosis, rolling circle amplification (RCA), hydrogel
Procedia PDF Downloads 2499802 An Analytical Survey of Construction Changes: Gaps and Opportunities
Authors: Ehsan Eshtehardian, Saeed Khodaverdi
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This paper surveys the studies on construction change and reveals some of the potential future works. A full-scale investigation of change literature, including change definitions, types, causes and effects, and change management systems, is accomplished to explore some of the coming change trends. It is tried to pick up the critical works in each section to deduct a true timeline of construction changes. The findings show that leaping from best practice guides in late 1990s and generic process models in the early 2000s to very advanced modeling environments in the mid-2000s and the early 2010s have made gaps along with opportunities for change researchers in order to develop some more easy and applicable models. Another finding is that there is a compelling similarity between the change and risk prediction models. Therefore, integrating these two concepts, specifically from proactive management point of view, may lead to a synergy and help project teams avoid rework. Also, the findings show that exploitation of cause-effect relationship models, in order to facilitate the dispute resolutions, seems to be an interesting field for future works.Keywords: construction change, change management systems, dispute resolutions, change literature
Procedia PDF Downloads 2929801 Feedforward Neural Network with Backpropagation for Epilepsy Seizure Detection
Authors: Natalia Espinosa, Arthur Amorim, Rudolf Huebner
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Epilepsy is a chronic neural disease and around 50 million people in the world suffer from this disease, however, in many cases, the individual acquires resistance to the medication, which is known as drug-resistant epilepsy, where a detection system is necessary. This paper showed the development of an automatic system for seizure detection based on artificial neural networks (ANN), which are common techniques of machine learning. Discrete Wavelet Transform (DWT) is used for decomposing electroencephalogram (EEG) signal into main brain waves, with these frequency bands is extracted features for training a feedforward neural network with backpropagation, finally made a pattern classification, seizure or non-seizure. Obtaining 95% accuracy in epileptic EEG and 100% in normal EEG.Keywords: Artificial Neural Network (ANN), Discrete Wavelet Transform (DWT), Epilepsy Detection , Seizure.
Procedia PDF Downloads 2129800 Biologically Inspired Small Infrared Target Detection Using Local Contrast Mechanisms
Authors: Tian Xia, Yuan Yan Tang
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In order to obtain higher small target detection accuracy, this paper presents an effective algorithm inspired by the local contrast mechanism. The proposed method can enhance target signal and suppress background clutter simultaneously. In the first stage, a enhanced image is obtained using the proposed Weighted Laplacian of Gaussian. In the second stage, an adaptive threshold is adopted to segment the target. Experimental results on two changeling image sequences show that the proposed method can detect the bright and dark targets simultaneously, and is not sensitive to sea-sky line of the infrared image. So it is fit for IR small infrared target detection.Keywords: small target detection, local contrast, human vision system, Laplacian of Gaussian
Procedia PDF Downloads 4609799 Cognitive Methods for Detecting Deception During the Criminal Investigation Process
Authors: Laid Fekih
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Background: It is difficult to detect lying, deception, and misrepresentation just by looking at verbal or non-verbal expression during the criminal investigation process, as there is a common belief that it is possible to tell whether a person is lying or telling the truth just by looking at the way they act or behave. The process of detecting lies and deception during the criminal investigation process needs more studies and research to overcome the difficulties facing the investigators. Method: The present study aimed to identify the effectiveness of cognitive methods and techniques in detecting deception during the criminal investigation. It adopted the quasi-experimental method and covered a sample of (20) defendants distributed randomly into two homogeneous groups, an experimental group of (10) defendants be subject to criminal investigation by applying cognitive techniques to detect deception and a second experimental group of (10) defendants be subject to the direct investigation method. The tool that used is a guided interview based on models of investigative questions according to the cognitive deception detection approach, which consists of three techniques of Vrij: imposing the cognitive burden, encouragement to provide more information, and ask unexpected questions, and the Direct Investigation Method. Results: Results revealed a significant difference between the two groups in term of lie detection accuracy in favour of defendants be subject to criminal investigation by applying cognitive techniques, the cognitive deception detection approach produced superior total accuracy rates both with human observers and through an analysis of objective criteria. The cognitive deception detection approach produced superior accuracy results in truth detection: 71%, deception detection: 70% compared to a direct investigation method truth detection: 52%; deception detection: 49%. Conclusion: The study recommended if practitioners use a cognitive deception detection technique, they will correctly classify more individuals than when they use a direct investigation method.Keywords: the cognitive lie detection approach, deception, criminal investigation, mental health
Procedia PDF Downloads 639798 Advancing in Cricket Analytics: Novel Approaches for Pitch and Ball Detection Employing OpenCV and YOLOV8
Authors: Pratham Madnur, Prathamkumar Shetty, Sneha Varur, Gouri Parashetti
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In order to overcome conventional obstacles, this research paper investigates novel approaches for cricket pitch and ball detection that make use of cutting-edge technologies. The research integrates OpenCV for pitch inspection and modifies the YOLOv8 model for cricket ball detection in order to overcome the shortcomings of manual pitch assessment and traditional ball detection techniques. To ensure flexibility in a range of pitch environments, the pitch detection method leverages OpenCV’s color space transformation, contour extraction, and accurate color range defining features. Regarding ball detection, the YOLOv8 model emphasizes the preservation of minor object details to improve accuracy and is specifically trained to the unique properties of cricket balls. The methods are more reliable because of the careful preparation of the datasets, which include novel ball and pitch information. These cutting-edge methods not only improve cricket analytics but also set the stage for flexible methods in more general sports technology applications.Keywords: OpenCV, YOLOv8, cricket, custom dataset, computer vision, sports
Procedia PDF Downloads 639797 Analysis of Maize Yield under Climate Change, Adaptations in Varieties and Planting Date in Northeast China in Recent Thirty Years
Authors: Zhan Fengmei Yao, Hui Li, Jiahua Zhang
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The Northeast China (NEC) was the most important agriculture areas and known as the Golden-Maize-Belt. Based on observed crop data and crop model, we design four simulating experiments and separate relative impacts and contribution under climate change, planting date shift, and varieties change as well change of varieties and planting date. Without planting date and varieties change, maize yields had no significant change trend at Hailun station located in the north of NEC, and presented significant decrease by 0.2-0.4 t/10a at two stations, which located in the middle and the south of NEC. With planting date change, yields showed a significant increase by 0.09 - 0.47 t/10a. With varieties change, maize yields had significant increase by 1.8~ 1.9 t/10a at Hailun and Huadian stations, but a non-significant and low increase by 0.2t /10a at Benxi located in the south of NEC. With change of varieties and planting date, yields presented a significant increasing by 0.53-2.0 t/10a. Their contribution to yields was -25% ~ -55% for climate change, 15% ~ 35% for planting date change, and 20% ~110% for varieties change as well 30% ~135% for varieties with planting date shift. It found that change in varieties and planting date were highest yields and were responsible for significant increases in maize yields, varieties was secondly, and planting date was thirdly. It found that adaptation in varieties and planting date greatly improved maize yields, and increased yields annual variability. The increase of contribution with planting date and varieties change in 2000s was lower than in 1990s. Yields with the varieties change and yields with planting date and varieties change all showed a decreasing trend at Huadian and Benxi since 2002 or so. It indicated that maize yields increasing trend stagnated in the middle and south of NEC, and continued in the north of NEC.Keywords: climate change, maize yields, varieties, planting date, impacts
Procedia PDF Downloads 3519796 Development of Cost-effective Sensitive Methods for Pathogen Detection in Community Wastewater for Disease Surveillance
Authors: Jesmin Akter, Chang Hyuk Ahn, Ilho Kim, Jaiyeop Lee
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Global pandemic coronavirus disease (COVID-19) caused by Severe acute respiratory syndrome SARS-CoV-2, to control the spread of the COVID-19 pandemic, wastewater surveillance has been used to monitor SARS-CoV2 prevalence in the community. The challenging part is establishing wastewater surveillance; there is a need for a well-equipped laboratory for wastewater sample analysis. According to many previous studies, reverse transcription-polymerase chain reaction (RT-PCR) based molecular tests are the most widely used and popular detection method worldwide. However, the RT-qPCR based approaches for the detection or quantification of SARS-CoV-2 genetic fragments ribonucleic acid (RNA) from wastewater require a specialized laboratory, skilled personnel, expensive instruments, and a workflow that typically requires 6 to 8 hours to provide results for just minimum samples. Rapid and reliable alternative detection methods are needed to enable less-well-qualified practitioners to set up and provide sensitive detection of SARS-CoV-2 within wastewater at less-specialized regional laboratories. Therefore, scientists and researchers are conducting experiments for rapid detection methods of COVID-19; in some cases, the structural and molecular characteristics of SARS-CoV-2 are unknown, and various strategies for the correct diagnosis of COVID-19 have been proposed by research laboratories, which are presented in the present study. The ongoing research and development of these highly sensitive and rapid technologies, namely RT-LAMP, ELISA, Biosensors, GeneXpert, allows a wide range of potential options not only for SARS-CoV-2 detection but also for other viruses as well. The effort of this study is to discuss the above effective and regional rapid detection and quantification methods in community wastewater as an essential step in advancing scientific goals.Keywords: rapid detection, SARS-CoV-2, sensitive detection, wastewater surveillance
Procedia PDF Downloads 799795 Design and Fabrication of Optical Nanobiosensors for Detection of MicroRNAs Involved in Neurodegenerative Diseases
Authors: Mahdi Rahaie
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MicroRNAs are a novel class of small RNAs which regulate gene expression by translational repression or degradation of messenger RNAs. To produce sensitive, simple and cost-effective assays for microRNAs, detection is in urgent demand due to important role of these biomolecules in progression of human disease such as Alzheimer’s, Multiple sclerosis, and some other neurodegenerative diseases. Herein, we report several novel, sensitive and specific microRNA nanobiosensors which were designed based on colorimetric and fluorescence detection of nanoparticles and hybridization chain reaction amplification as an enzyme-free amplification. These new strategies eliminate the need for enzymatic reactions, chemical changes, separation processes and sophisticated equipment whereas less limit of detection with most specify are acceptable. The important features of these methods are high sensitivity and specificity to differentiate between perfectly matched, mismatched and non-complementary target microRNAs and also decent response in the real sample analysis with blood plasma. These nanobiosensors can clinically be used not only for the early detection of neuro diseases but also for every sickness related to miRNAs by direct detection of the plasma microRNAs in real clinical samples, without a need for sample preparation, RNA extraction and/or amplification.Keywords: hybridization chain reaction, microRNA, nanobiosensor, neurodegenerative diseases
Procedia PDF Downloads 1459794 An Aptasensor Based on Magnetic Relaxation Switch and Controlled Magnetic Separation for the Sensitive Detection of Pseudomonas aeruginosa
Authors: Fei Jia, Xingjian Bai, Xiaowei Zhang, Wenjie Yan, Ruitong Dai, Xingmin Li, Jozef Kokini
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Pseudomonas aeruginosa is a Gram-negative, aerobic, opportunistic human pathogen that is present in the soil, water, and food. This microbe has been recognized as a representative food-borne spoilage bacterium that can lead to many types of infections. Considering the casualties and property loss caused by P. aeruginosa, the development of a rapid and reliable technique for the detection of P. aeruginosa is crucial. The whole-cell aptasensor, an emerging biosensor using aptamer as a capture probe to bind to the whole cell, for food-borne pathogens detection has attracted much attention due to its convenience and high sensitivity. Here, a low-field magnetic resonance imaging (LF-MRI) aptasensor for the rapid detection of P. aeruginosa was developed. The basic detection principle of the magnetic relaxation switch (MRSw) nanosensor lies on the ‘T₂-shortening’ effect of magnetic nanoparticles in NMR measurements. Briefly speaking, the transverse relaxation time (T₂) of neighboring water protons get shortened when magnetic nanoparticles are clustered due to the cross-linking upon the recognition and binding of biological targets, or simply when the concentration of the magnetic nanoparticles increased. Such shortening is related to both the state change (aggregation or dissociation) and the concentration change of magnetic nanoparticles and can be detected using NMR relaxometry or MRI scanners. In this work, two different sizes of magnetic nanoparticles, which are 10 nm (MN₁₀) and 400 nm (MN₄₀₀) in diameter, were first immobilized with anti- P. aeruginosa aptamer through 1-Ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDC)/N-hydroxysuccinimide (NHS) chemistry separately, to capture and enrich the P. aeruginosa cells. When incubating with the target, a ‘sandwich’ (MN₁₀-bacteria-MN₄₀₀) complex are formed driven by the bonding of MN400 with P. aeruginosa through aptamer recognition, as well as the conjugate aggregation of MN₁₀ on the surface of P. aeruginosa. Due to the different magnetic performance of the MN₁₀ and MN₄₀₀ in the magnetic field caused by their different saturation magnetization, the MN₁₀-bacteria-MN₄₀₀ complex, as well as the unreacted MN₄₀₀ in the solution, can be quickly removed by magnetic separation, and as a result, only unreacted MN₁₀ remain in the solution. The remaining MN₁₀, which are superparamagnetic and stable in low field magnetic field, work as a signal readout for T₂ measurement. Under the optimum condition, the LF-MRI platform provides both image analysis and quantitative detection of P. aeruginosa, with the detection limit as low as 100 cfu/mL. The feasibility and specificity of the aptasensor are demonstrated in detecting real food samples and validated by using plate counting methods. Only two steps and less than 2 hours needed for the detection procedure, this robust aptasensor can detect P. aeruginosa with a wide linear range from 3.1 ×10² cfu/mL to 3.1 ×10⁷ cfu/mL, which is superior to conventional plate counting method and other molecular biology testing assay. Moreover, the aptasensor has a potential to detect other bacteria or toxins by changing suitable aptamers. Considering the excellent accuracy, feasibility, and practicality, the whole-cell aptasensor provides a promising platform for a quick, direct and accurate determination of food-borne pathogens at cell-level.Keywords: magnetic resonance imaging, meat spoilage, P. aeruginosa, transverse relaxation time
Procedia PDF Downloads 1449793 Detection of Keypoint in Press-Fit Curve Based on Convolutional Neural Network
Authors: Shoujia Fang, Guoqing Ding, Xin Chen
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The quality of press-fit assembly is closely related to reliability and safety of product. The paper proposed a keypoint detection method based on convolutional neural network to improve the accuracy of keypoint detection in press-fit curve. It would provide an auxiliary basis for judging quality of press-fit assembly. The press-fit curve is a curve of press-fit force and displacement. Both force data and distance data are time-series data. Therefore, one-dimensional convolutional neural network is used to process the press-fit curve. After the obtained press-fit data is filtered, the multi-layer one-dimensional convolutional neural network is used to perform the automatic learning of press-fit curve features, and then sent to the multi-layer perceptron to finally output keypoint of the curve. We used the data of press-fit assembly equipment in the actual production process to train CNN model, and we used different data from the same equipment to evaluate the performance of detection. Compared with the existing research result, the performance of detection was significantly improved. This method can provide a reliable basis for the judgment of press-fit quality.Keywords: keypoint detection, curve feature, convolutional neural network, press-fit assembly
Procedia PDF Downloads 2219792 A Character Detection Method for Ancient Yi Books Based on Connected Components and Regressive Character Segmentation
Authors: Xu Han, Shanxiong Chen, Shiyu Zhu, Xiaoyu Lin, Fujia Zhao, Dingwang Wang
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Character detection is an important issue for character recognition of ancient Yi books. The accuracy of detection directly affects the recognition effect of ancient Yi books. Considering the complex layout, the lack of standard typesetting and the mixed arrangement between images and texts, we propose a character detection method for ancient Yi books based on connected components and regressive character segmentation. First, the scanned images of ancient Yi books are preprocessed with nonlocal mean filtering, and then a modified local adaptive threshold binarization algorithm is used to obtain the binary images to segment the foreground and background for the images. Second, the non-text areas are removed by the method based on connected components. Finally, the single character in the ancient Yi books is segmented by our method. The experimental results show that the method can effectively separate the text areas and non-text areas for ancient Yi books and achieve higher accuracy and recall rate in the experiment of character detection, and effectively solve the problem of character detection and segmentation in character recognition of ancient books.Keywords: CCS concepts, computing methodologies, interest point, salient region detections, image segmentation
Procedia PDF Downloads 1249791 A Bibliometric Analysis of Trends in Change Management Sciences
Authors: Thomas Lauer
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The paper aims to give an overview of change management research by using bibliometric methodology. Based on research papers of the last decade, which are listed on Research Gate, a multidimensional categorization is done. Considering categories like topic (e.g., success factors), industry, or research methodology, the development of the discipline is traced and, in a second step, confronted with external developments of the business environment, such as climate change, gen Z or COVID, to name a few. Based on these findings, a final evaluation concerning the thematical fit of previous research topics is also made, as well as a preview of likely future trends in change management sciences.Keywords: change management, bibliometrics, scientific trends, research topics
Procedia PDF Downloads 559790 Motion-Based Detection and Tracking of Multiple Pedestrians
Authors: A. Harras, A. Tsuji, K. Terada
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Tracking of moving people has gained a matter of great importance due to rapid technological advancements in the field of computer vision. The objective of this study is to design a motion based detection and tracking multiple walking pedestrians randomly in different directions. In our proposed method, Gaussian mixture model (GMM) is used to determine moving persons in image sequences. It reacts to changes that take place in the scene like different illumination; moving objects start and stop often, etc. Background noise in the scene is eliminated through applying morphological operations and the motions of tracked people which is determined by using the Kalman filter. The Kalman filter is applied to predict the tracked location in each frame and to determine the likelihood of each detection. We used a benchmark data set for the evaluation based on a side wall stationary camera. The actual scenes from the data set are taken on a street including up to eight people in front of the camera in different two scenes, the duration is 53 and 35 seconds, respectively. In the case of walking pedestrians in close proximity, the proposed method has achieved the detection ratio of 87%, and the tracking ratio is 77 % successfully. When they are deferred from each other, the detection ratio is increased to 90% and the tracking ratio is also increased to 79%.Keywords: automatic detection, tracking, pedestrians, counting
Procedia PDF Downloads 2519789 Comparative Analysis of Change in Vegetation in Four Districts of Punjab through Satellite Imagery, Land Use Statistics and Machine Learning
Authors: Mirza Waseem Abbas, Syed Danish Raza
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For many countries agriculture is still the major force driving the economy and a critically important socioeconomic sector, despite exceptional industrial development across the globe. In countries like Pakistan, this sector is considered the backbone of the economy, and most of the economic decision making revolves around agricultural outputs and data. Timely and accurate facts and figures about this vital sector hold immense significance and have serious implications for the long-term development of the economy. Therefore, any significant improvements in the statistics and other forms of data regarding agriculture sector are considered important by all policymakers. This is especially true for decision making for the betterment of crops and the agriculture sector in general. Provincial and federal agricultural departments collect data for all cash and non-cash crops and the sector, in general, every year. Traditional data collection for such a large sector i.e. agriculture, being time-consuming, prone to human error and labor-intensive, is slowly but gradually being replaced by remote sensing techniques. For this study, remotely sensed data were used for change detection (machine learning, supervised & unsupervised classification) to assess the increase or decrease in area under agriculture over the last fifteen years due to urbanization. Detailed Landsat Images for the selected agricultural districts were acquired for the year 2000 and compared to images of the same area acquired for the year 2016. Observed differences validated through detailed analysis of the areas show that there was a considerable decrease in vegetation during the last fifteen years in four major agricultural districts of the Punjab province due to urbanization (housing societies).Keywords: change detection, area estimation, machine learning, urbanization, remote sensing
Procedia PDF Downloads 2459788 Plastic Pipe Defect Detection Using Nonlinear Acoustic Modulation
Authors: Gigih Priyandoko, Mohd Fairusham Ghazali, Tan Siew Fun
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This paper discusses about the defect detection of plastic pipe by using nonlinear acoustic wave modulation method. It is a sensitive method for damage detection and it is based on the propagation of high frequency acoustic waves in plastic pipe with low frequency excitation. The plastic pipe is excited simultaneously with a slow amplitude modulated vibration pumping wave and a constant amplitude probing wave. The frequency of both the excitation signals coincides with the resonances of the plastic pipe. A PVP pipe is used as the specimen as it is commonly used for the conveyance of liquid in many fields. The results obtained are being observed and the difference between uncracked specimen and cracked specimen can be distinguished clearly.Keywords: plastic pipe, defect detection, nonlinear acoustic modulation, excitation
Procedia PDF Downloads 4449787 Aspects and Studies of Fractal Geometry in Automatic Breast Cancer Detection
Authors: Mrinal Kanti Bhowmik, Kakali Das Jr., Barin Kumar De, Debotosh Bhattacharjee
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Breast cancer is the most common cancer and a leading cause of death for women in the 35 to 55 age group. Early detection of breast cancer can decrease the mortality rate of breast cancer. Mammography is considered as a ‘Gold Standard’ for breast cancer detection and a very popular modality, presently used for breast cancer screening and detection. The screening of digital mammograms often leads to over diagnosis and a consequence to unnecessary traumatic & painful biopsies. For that reason recent studies involving the use of thermal imaging as a screening technique have generated a growing interest especially in cases where the mammography is limited, as in young patients who have dense breast tissue. Tumor is a significant sign of breast cancer in both mammography and thermography. The tumors are complex in structure and they also exhibit a different statistical and textural features compared to the breast background tissue. Fractal geometry is a geometry which is used to describe this type of complex structure as per their main characteristic, where traditional Euclidean geometry fails. Over the last few years, fractal geometrics have been applied mostly in many medical image (1D, 2D, or 3D) analysis applications. In breast cancer detection using digital mammogram images, also it plays a significant role. Fractal is also used in thermography for early detection of the masses using the thermal texture. This paper presents an overview of the recent aspects and initiatives of fractals in breast cancer detection in both mammography and thermography. The scope of fractal geometry in automatic breast cancer detection using digital mammogram and thermogram images are analysed, which forms a foundation for further study on application of fractal geometry in medical imaging for improving the efficiency of automatic detection.Keywords: fractal, tumor, thermography, mammography
Procedia PDF Downloads 3799786 The Case for Strategic Participation: How Facilitated Engagement Can Be Shown to Reduce Resistance and Improve Outcomes Through the Use of Strategic Models
Authors: Tony Mann
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This paper sets out the case for involving and engaging employees/workers/stakeholders/staff in any significant change that is being considered by the senior executives of the organization. It establishes the rationale, the approach, the methodology of engagement and the benefits of a participative approach. It challenges the new norm of imposing change for fear of resistance and instead suggests that involving people has better outcomes and a longer-lasting impact. Various strategic models are introduced and illustrated to explain how the process can be most effective. The paper highlights one model in particular (the Process Iceberg® Organizational Change model) that has proven to be instrumental in developing effective change. Its use is demonstrated in its various forms and explains why so much change fails to address the key elements and how we can be more productive in managing change. ‘Participation’ in change is too often seen as negative, expensive and unwieldy. The paper aims to show that another model: UIA=O+E, can offset the difficulties and, in fact, produce much more positive and effective change.Keywords: facilitation, stakeholders, buy-in, digital workshops
Procedia PDF Downloads 96