Search results for: fall detection

Authors: Sehreen Moorat, Mussarat Lakho

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A method of artificial intelligence using digital mammograms data has been proposed in this paper for detection of breast cancer. Many researchers have developed techniques for the early detection of breast cancer; the early diagnosis helps to save many lives. The detection of breast cancer through mammography is effective method which detects the cancer before it is felt and increases the survival rate. In this paper, we have purposed image processing technique for enhancing the image to detect the graphical table data and markings. Texture features based on Gray-Level Co-Occurrence Matrix and intensity based features are extracted from the selected region. For classification purpose, neural network based supervised classifier system has been used which can discriminate between benign and malignant. Hence, 68 digital mammograms have been used to train the classifier. The obtained result proved that automated detection of breast cancer is beneficial for early diagnosis and increases the survival rates of breast cancer patients. The proposed system will help radiologist in the better interpretation of breast cancer.

Keywords: medical imaging, cancer, processing, neural network

Procedia PDF Downloads 242

Authors: Noureddine Mohtaram, Jeremy Patrix, Jerome Verny

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As an enormous number of online services have been developed into web applications, security problems based on web applications are becoming more serious now. Most intrusion detection systems rely on each request to find the cyber-attack rather than on user behavior, and these systems can only protect web applications against known vulnerabilities rather than certain zero-day attacks. In order to detect new attacks, we analyze the HTTP protocols of web servers to divide them into two categories: normal attacks and malicious attacks. On the other hand, the quality of the results obtained by deep learning (DL) in various areas of big data has given an important motivation to apply it to cybersecurity. Deep learning for attack detection in cybersecurity has the potential to be a robust tool from small transformations to new attacks due to its capability to extract more high-level features. This research aims to take a new approach, deep learning to cybersecurity, to classify these two categories to eliminate attacks and protect web servers of the defense sector which encounters different web traffic compared to other sectors (such as e-commerce, web app, etc.). The result shows that by using a machine learning method, a higher accuracy rate, and a lower false alarm detection rate can be achieved.

Keywords: anomaly detection, HTTP protocol, logs, cyber attack, deep learning

Procedia PDF Downloads 188

Authors: Sonali Banrjee, Swarup Kumar Mitra, Aritra Acharyya

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A piano note recognition method has been proposed by the authors in this paper. The authors have used a comprehensive method for onset detection of each note present in a piano piece followed by segmented short-time Fourier transform (STFT) for the identification of piano notes. The performance evaluation of the proposed method has been carried out in different harsh noisy environments by adding different levels of additive white Gaussian noise (AWGN) having different signal-to-noise ratio (SNR) in the original signal and evaluating the note detection error rate (NDER) of different piano pieces consisting of different number of notes at different SNR levels. The NDER is found to be remained within 15% for all piano pieces under consideration when the SNR is kept above 8 dB.

Keywords: AWGN, onset detection, piano note, STFT

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Authors: S. Jagadeesh Kumar

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Face detection and recognition is an authoritative technology for image database management, video surveillance, and human computer interface (HCI). Face recognition is a rapidly nascent method, which has been extensively discarded in forensics such as felonious identification, tenable entree, and custodial security. This paper recommends an erudite technique using curvature analysis (CA) that has less false positives incidence, operative in different light environments and confiscates the artifacts that are introduced during image acquisition by ring correction in polar coordinate (RCP) method. This technique affronts mean and median filtering technique to remove the artifacts but it works in polar coordinate during image acquisition. Investigational fallouts for face detection and recognition confirms decent recitation even in diagonal orientation and stance variation.

Keywords: curvature analysis, ring correction in polar coordinate method, face detection, face recognition, human computer interaction

Procedia PDF Downloads 263

Authors: In-Geun Lim, Sung-Woong Ra

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As high resolution satellite images can be used, lots of studies are carried out for exploiting these images in various fields. This paper proposes the method based on mathematical morphology for extracting the ‘horse's hoof shaped object’. This proposed method can make an automatic object detection system to track the meaningful object in a large satellite image rapidly. Mathematical morphology process can apply in binary image, so this method is very simple. Therefore this method can easily extract the ‘horse's hoof shaped object’ from any images which have indistinct edges of the tracking object and have different image qualities depending on filming location, filming time, and filming environment. Using the proposed method by which ‘horse's hoof shaped object’ can be rapidly extracted, the performance of the automatic object detection system can be improved dramatically.

Keywords: facility detection, satellite image, object, mathematical morphology

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Authors: Abdulrahman S. Alturki, John S. Loomis

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This paper discusses a corner detection algorithm for camera calibration. Calibration is a necessary step in many computer vision and image processing applications. Robust corner detection for an image of a checkerboard is required to determine intrinsic and extrinsic parameters. In this paper, an algorithm for fully automatic and robust X-corner detection is presented. Checkerboard corner points are automatically found in each image without user interaction or any prior information regarding the number of rows or columns. The approach represents each X-corner with a quadratic fitting function. Using the fact that the X-corners are saddle points, the coefficients in the fitting function are used to identify each corner location. The automation of this process greatly simplifies calibration. Our method is robust against noise and different camera orientations. Experimental analysis shows the accuracy of our method using actual images acquired at different camera locations and orientations.

Keywords: camera calibration, corner detector, edge detector, saddle points

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Authors: Kashika P. H.

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The development of computer vision systems has been greatly aided by the efficient and precise detection of images and videos. Although the ability to recognize and comprehend images is a strength of the human brain, employing technology to tackle this issue is exceedingly challenging. In the past few years, the use of Deep Learning algorithms to treat object detection has dramatically expanded. One of the key issues in the realm of image recognition is the recognition and detection of certain notable people from randomly acquired photographs. Face recognition uses a way to identify, assess, and compare faces for a variety of purposes, including user identification, user counting, and classification. With the aid of an accessible deep learning-based API, this article intends to recognize various faces of people and their facial descriptors more accurately. The purpose of this study is to locate suitable individuals and deliver accurate information about them by using the Amazon Rekognition system to identify a specific human from a vast image dataset. We have chosen the Amazon Rekognition system, which allows for more accurate face analysis, face comparison, and face search, to tackle this difficulty.

Keywords: Amazon rekognition, API, deep learning, computer vision, face detection, text detection

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Authors: Richard O’Riordan, Saritha Unnikrishnan

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Autonomous Vehicles (AVs) are incorporating an increasing number of ADAS features, including automated lane-keeping systems. In recent years, many research papers into lane detection algorithms have been published, varying from computer vision techniques to deep learning methods. The transition from lower levels of autonomy defined in the SAE framework and the progression to higher autonomy levels requires increasingly complex models and algorithms that must be highly reliable in their operation and functionality capacities. Furthermore, these algorithms have no room for error when operating at high levels of autonomy. Although the current research details existing computer vision and deep learning algorithms and their methodologies and individual results, the research also details challenges faced by the algorithms and the resources needed to operate, along with shortcomings experienced during their detection of lanes in certain weather and lighting conditions. This paper will explore these shortcomings and attempt to implement a lane detection algorithm that could be used to achieve improvements in AV lane detection systems. This paper uses a pre-trained LaneNet model to detect lane or non-lane pixels using binary segmentation as the base detection method using an existing dataset BDD100k followed by a custom dataset generated locally. The selected roads will be modern well-laid roads with up-to-date infrastructure and lane markings, while the second road network will be an older road with infrastructure and lane markings reflecting the road network's age. The performance of the proposed method will be evaluated on the custom dataset to compare its performance to the BDD100k dataset. In summary, this paper will use Transfer Learning to provide a fast and robust lane detection algorithm that can handle various road conditions and provide accurate lane detection.

Keywords: ADAS, autonomous vehicles, deep learning, LaneNet, lane detection

Procedia PDF Downloads 79

Authors: Xinghang Lv, Tao Peng, Jia Chen, Junping Liu, Xinrong Hu, Ruhan He, Minghua Jiang, Wenli Cao

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As one of the most dangerous vulnerabilities, effective detection of buffer overflow vulnerabilities is extremely necessary. Traditional detection methods are not accurate enough and consume more resources to meet complex and enormous code environment at present. In order to resolve the above problems, we propose the method for Buffer overflow detection based on Abstract syntax tree, Control flow graph, and Data dependency graph (BodeACD) in C/C++ programs with source code. Firstly, BodeACD constructs the function samples of buffer overflow that are available on Github, then represents them as code representation sequences, which fuse control flow, data dependency, and syntax structure of source code to reduce information loss during code representation. Finally, BodeACD learns vulnerability patterns for vulnerability detection through deep learning. The results of the experiments show that BodeACD has increased the precision and recall by 6.3% and 8.5% respectively compared with the latest methods, which can effectively improve vulnerability detection and reduce False-positive rate and False-negative rate.

Keywords: vulnerability detection, abstract syntax tree, control flow graph, data dependency graph, code representation, deep learning

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Authors: Mohammed Saleem Tariq

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The enthusiasm for gluten avoidance in a growing market is met by improvements in sensitive detection methods for analysing gluten content. Paradoxically, manufacturers employ no such systems in the production process but continue to market their product as gluten free, a significant risk posed to an undetermined coeliac population. The paper resonates with an immunological response that causes gastrointestinal scarring and villous atrophy with the conventional description of personal injury. The current developing regime in the UK however, it is discussed, has avoided creating specific rules to provide an adequate level of protection for this type of vulnerable ‘characteristic’. Due to the struggle involved with identifying an appropriate cause of action, this paper analyses whether a claim brought in misrepresentation, negligence and/or under the Consumer Protect Act 1987 could be sustained. A necessary comparison is then made with the approach adopted by the Americans with Disability Act 1990 which recognises this chronic disease as a disability. The ongoing failure to introduce a level of protection which matches that afforded to those who fall into any one of the ‘protected characteristics’ under the Equality Act 2010, is inconceivable given the outstanding level of legal vulnerability.

Keywords: coeliac, litigation, misrepresentation, negligence

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Authors: Atinkut Atinafu Yilma, Eyob Messele Sefene

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Anomaly detection is one of the essential mechanisms to control and reduce production loss, especially in today's smart manufacturing. Quick anomaly detection aids in reducing the cost of production by minimizing the possibility of producing defective products. However, developing an anomaly detection model that can rapidly detect a production change is challenging. This paper proposes Gated Recurrent Unit (GRU) combined with Random Forest (RF) to detect anomalies in the production process in real-time quickly. The GRU is used as a feature detector, and RF as a classifier using the input features from GRU. The model was tested using various synthesis and real-world datasets against benchmark methods. The results show that the proposed GRU-RF outperforms the benchmark methods with the shortest time taken to detect anomalies in the production process. Based on the investigation from the study, this proposed model can eliminate or reduce unnecessary production costs and bring a competitive advantage to manufacturing industries.

Keywords: anomaly detection, multivariate time series data, smart manufacturing, gated recurrent unit network, random forest

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Authors: Maryam Heidari, James H. Jones Jr.

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Millions of online posts about different topics and products are shared on popular social media platforms. One use of this content is to provide crowd-sourced information about a specific topic, event or product. However, this use raises an important question: what percentage of information available through these services is trustworthy? In particular, might some of this information be generated by a machine, i.e., a bot, instead of a human? Bots can be, and often are, purposely designed to generate enough volume to skew an apparent trend or position on a topic, yet the consumer of such content cannot easily distinguish a bot post from a human post. In this paper, we introduce a model for social media bot detection which uses Bidirectional Encoder Representations from Transformers (Google Bert) for sentiment classification of tweets to identify topic-independent features. Our use of a Natural Language Processing approach to derive topic-independent features for our new bot detection model distinguishes this work from previous bot detection models. We achieve 94\% accuracy classifying the contents of data as generated by a bot or a human, where the most accurate prior work achieved accuracy of 92\%.

Keywords: bot detection, natural language processing, neural network, social media

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Authors: H. Bakhshi, E. Khayyamian

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Cognitive radios have been recognized as one of the most promising technologies dealing with the scarcity of the radio spectrum. In cognitive radio systems, secondary users are allowed to utilize the frequency bands of primary users when the bands are idle. Hence, how to accurately detect the idle frequency bands has attracted many researchers’ interest. Detection performance is sensitive toward noise power and gain fluctuation. Since signal to noise ratio (SNR) between primary user and secondary users are not the same and change over the time, SNR and noise power estimation is essential. In this paper, we present a cooperative spectrum sensing algorithm using SNR estimation to improve detection performance in the real situation.

Keywords: cognitive radio, cooperative spectrum sensing, energy detection, SNR estimation, spectrum sensing, rayleigh fading channel

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Authors: Mohamed H. Kosba, Heba A. Ibrahim, H. Rozita

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Statement of the Problem: The life expectancy for transfusion-dependent thalassemia patients has increased dramatically with iron-chelation therapy and other modern management modalities. In these patients, the most dominant maxillofacial manifestations are protrusion of zygomatic bones and premaxilla due to the hyperplasia of bone marrow. The purpose of this study is to determine the prevalence of malocclusion and orthodontic treatment needs according to the Dental Aesthetic Index (DAI) among Malay transfusion-dependent thalassemia patients. Orientation: This is a cross-sectional study consist of 43 Malay transfusion-dependent thalassemia patients, 22 males, and 19 females with the mean age of 15.9 years old (SD 3.58). The subjects were selected randomly from patients attending Paediatrics and Internal Medicine Clinic at Hospital USM and Hospital Sultana Bahiyah. The subjects were assessed for malocclusion according to Angle’s classification, and orthodontic treatment needs using DAI. The results show that 22 of the subjects (51.1%) have class II malocclusion, 12 subjects (28%) have class І, while 9 subjects (20.9%) have class Ⅲ. The assessment of orthodontic treatment needs to reveal 22 cases (51.1%) fall in the normal/minor needs category, 12 subjects (28%) fall in the severe and very severe category, while 9 subjects (20.9%) fall in the definite category. Conclusion & Significance: Half of Malay transfusion-dependent thalassemia patients have Class Ⅱmalocclusion. About 28% had malocclusion and required orthodontic treatment. This research shows that Malay transfusion-dependent thalassemia may require orthodontic management; earlier intervention to reduce the complexity of the treatment later, suggesting functional appliance as a suitable treatment option for them, a twin block appliance together with headgear to restrict maxillary growth suggested for management. The current protocol implemented by the Malaysian Ministry of Health for the management of these patients seems to be sufficient since the result shows that about 28% require orthodontic treatment need, according to DAI.

Keywords: prevalence, DAI, thalassaemia, angle classification

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Authors: Sheldon Liu, Gordon Wang, Lei Liu, Xuefeng Liu

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Anomaly detection, also referred to as one-class classification, plays a crucial role in identifying product images that deviate from the expected distribution. This study introduces Data-centric Anomaly Detection with Diffusion Models (DCADDM), presenting a systematic strategy for data collection and further diversifying the data with image generation via diffusion models. The algorithm addresses data collection challenges in real-world scenarios and points toward data augmentation with the integration of generative AI capabilities. The paper explores the generation of normal images using diffusion models. The experiments demonstrate that with 30% of the original normal image size, modeling in an unsupervised setting with state-of-the-art approaches can achieve equivalent performances. With the addition of generated images via diffusion models (10% equivalence of the original dataset size), the proposed algorithm achieves better or equivalent anomaly localization performance.

Keywords: diffusion models, anomaly detection, data-centric, generative AI

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Authors: Jui-Chien Hsieh

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Background:12-lead electrocardiography(ECG) is one of frequently-used tools to detect atrial fibrillation (AF), which might degenerate into life-threaten stroke, in clinical Practice. Based on this study, the AF detection by the clinically-used 12-lead ECG device has only 0.73~0.77 positive predictive value (ppv). Objective: It is on great demand to develop a new algorithm to improve the precision of AF detection using 12-lead ECG. Due to the progress on artificial intelligence (AI), we develop an ECG deep model that has the ability to recognize AF patterns and reduce false-positive errors. Methods: In this study, (1) 570-sample 12-lead ECG reports whose computer interpretation by the ECG device was AF were collected as the training dataset. The ECG reports were interpreted by 2 senior cardiologists, and confirmed that the precision of AF detection by the ECG device is 0.73.; (2) 88 12-lead ECG reports whose computer interpretation generated by the ECG device was AF were used as test dataset. Cardiologist confirmed that 68 cases of 88 reports were AF, and others were not AF. The precision of AF detection by ECG device is about 0.77; (3) A parallel 4-layer 1 dimensional convolutional neural network (CNN) was developed to identify AF based on limb-lead ECGs and chest-lead ECGs. Results: The results indicated that this model has better performance on AF detection than traditional computer interpretation of the ECG device in 88 test samples with 0.94 ppv, 0.98 sensitivity, 0.80 specificity. Conclusions: As compared to the clinical ECG device, this AI ECG model promotes the precision of AF detection from 0.77 to 0.94, and can generate impacts on clinical applications.

Keywords: 12-lead ECG, atrial fibrillation, deep learning, convolutional neural network

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Authors: Lina Pan

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In non-Gaussian clutter of a spherically invariant random vector, in the cases that a certain estimated covariance matrix could become singular, the adaptive target detection of high-range-resolution radar is addressed. Firstly, the restricted maximum likelihood (RML) estimates of unknown covariance matrix and scatterer amplitudes are derived for non-Gaussian clutter. And then the RML estimate of texture is obtained. Finally, a novel detector is devised. It is showed that, without secondary data, the proposed detector outperforms the existing Kelly binary integrator.

Keywords: non-Gaussian clutter, covariance matrix estimation, target detection, maximum likelihood

Procedia PDF Downloads 447

Authors: Nir Nissim, Ran Yahalom, Tomer Lancewiki, Yuval Elovici, Boaz Lerner

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Background: Attackers increasingly take advantage of innocent users who tend to use USB devices casually, assuming these devices benign when in fact they may carry an embedded malicious behavior or hidden malware. USB devices have many properties and capabilities that have become the subject of malicious operations. Many of the recent attacks targeting individuals, and especially organizations, utilize popular and widely used USB devices, such as mice, keyboards, flash drives, printers, and smartphones. However, current detection tools, techniques, and solutions generally fail to detect both the known and unknown attacks launched via USB devices. Significance: We propose USBWARE, a project that focuses on the vulnerabilities of USB devices and centers on the development of a comprehensive detection framework that relies upon a crucial attack repository. USBWARE will allow researchers and companies to better understand the vulnerabilities and attacks associated with USB devices as well as providing a comprehensive platform for developing detection solutions. Methodology: The framework of USBWARE is aimed at accurate detection of both known and unknown USB-based attacks by a process that efficiently enhances the framework's detection capabilities over time. The framework will integrate two main security approaches in order to enhance the detection of USB-based attacks associated with a variety of USB devices. The first approach is aimed at the detection of known attacks and their variants, whereas the second approach focuses on the detection of unknown attacks. USBWARE will consist of six independent but complimentary detection modules, each detecting attacks based on a different approach or discipline. These modules include novel ideas and algorithms inspired from or already developed within our team's domains of expertise, including cyber security, electrical and signal processing, machine learning, and computational biology. The establishment and maintenance of the USBWARE’s dynamic and up-to-date attack repository will strengthen the capabilities of the USBWARE detection framework. The attack repository’s infrastructure will enable researchers to record, document, create, and simulate existing and new USB-based attacks. This data will be used to maintain the detection framework’s updatability by incorporating knowledge regarding new attacks. Based on our experience in the cyber security domain, we aim to design the USBWARE framework so that it will have several characteristics that are crucial for this type of cyber-security detection solution. Specifically, the USBWARE framework should be: Novel, Multidisciplinary, Trusted, Lightweight, Extendable, Modular and Updatable and Adaptable. Major Findings: Based on our initial survey, we have already found more than 23 types of USB-based attacks, divided into six major categories. Our preliminary evaluation and proof of concepts showed that our detection modules can be used for efficient detection of several basic known USB attacks. Further research, development, and enhancements are required so that USBWARE will be capable to cover all of the major known USB attacks and to detect unknown attacks. Conclusion: USBWARE is a crucial detection framework that must be further enhanced and developed.

Keywords: USB, device, cyber security, attack, detection

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Authors: Felipe A. Guth, Shane Ward, Kevin McDonnell

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In the precision agriculture area, one of the main tasks is the automated detection of diseases in crops. Machine Learning algorithms have been studied in recent decades for such tasks in view of their potential for improving economic outcomes that automated disease detection may attain over crop fields. The latest generation of deep learning convolution neural networks has presented significant results in the area of image classification. In this way, this work has tested the implementation of an architecture of deep learning convolution neural network for the detection of diseases in different types of crops. A data augmentation strategy was used to meet the requirements of the algorithm implemented with a deep learning framework. Two test scenarios were deployed. The first scenario implemented a neural network under images extracted from a controlled environment while the second one took images both from the field and the controlled environment. The results evaluated the generalisation capacity of the neural networks in relation to the two types of images presented. Results yielded a general classification accuracy of 59% in scenario 1 and 96% in scenario 2.

Keywords: convolutional neural networks, deep learning, disease detection, precision agriculture

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Authors: Zuzana Chaloupková, Zdeňka Marková, Václav Ranc, Radek Zbořil

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Prostate cancer is now one of the most serious oncological diseases in men with an incidence higher than that of all other solid tumors combined. Diagnosis of prostate cancer usually involves detection of related genes or detection of marker proteins, such as PSA. One of the new potential markers is PSMA (prostate specific membrane antigen). PSMA is a unique membrane bound glycoprotein, which is considerably overexpressed on prostate cancer as well as neovasculature of most of the solid tumors. Commonly applied methods for a detection of proteins include techniques based on immunochemical approaches, including ELISA and RIA. Magnetically assisted surface enhanced Raman spectroscopy (MA-SERS) can be considered as an interesting alternative to generally accepted approaches. This work describes a utilization of MA-SERS in a detection of PSMA in human blood. This analytical platform is based on magnetic nanocomposites Fe3O4@Ag, functionalized by a low-molecular selector labeled as JB303. The system allows isolating the marker from the complex sample using application of magnetic force. Detection of PSMA is than performed by SERS effect given by a presence of silver nanoparticles. This system allowed us to analyze PSMA in clinical samples with limits of detection lower than 1 ng/mL.

Keywords: diagnosis, cancer, PSMA, MA-SERS, Ag nanoparticles

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Authors: A. Mohammadpour, Ebrahim Najafi Kajabad, Ghazale Ipakchi

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Presently, computer networks’ security rise in importance and many studies have also been conducted in this field. By the penetration of the internet networks in different fields, many things need to be done to provide a secure industrial and non-industrial network. Fire walls, appropriate Intrusion Detection Systems (IDS), encryption protocols for information sending and receiving, and use of authentication certificated are among things, which should be considered for system security. The aim of the present study is to use the outcome of several algorithms, which cause decline in IDS errors, in the way that improves system security and prevents additional overload to the system. Finally, regarding the obtained result we can also detect the amount and percentage of more sub attacks. By running the proposed system, which is based on the use of multi-algorithmic outcome and comparing that by the proposed single algorithmic methods, we observed a 78.64% result in attack detection that is improved by 3.14% than the proposed algorithms.

Keywords: intrusion detection systems, clustering, k-means, k-medoids, SV clustering, ensemble

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Authors: Juneseok You, Kuewhan Jang, Chanho Park, Jaeyeong Choi, Hyunjun Park, Sehyun Shin, Changsoo Han, Sungsoo Na

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Nanomaterials occur toxic effects to human being or ecological systems. Some sensors have been developed to detect toxic materials and the standard for toxic materials has been established. Zinc oxide nanowire (ZnO NW) is known for toxic material. By ionizing in cell body, ionized Zn ions are overexposed to cell components, which cause critical damage or death. In this paper, we detected ZnO NW in water using QCM (Quartz Crystal Microbalance) and ssDNA (single strand DNA). We achieved 30 minutes of response time for real time detection and 100 pg/mL of limit of detection (LOD).

Keywords: zinc oxide nanowire, QCM, ssDNA, toxic material, biosensor

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Authors: Craig Mahlasi

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The Subtropical Thicket Biome has been in peril of transformation. Estimates indicate that as much as 63% of the Subtropical Thicket Biome is severely degraded. Agricultural expansion is the main driver of transformation. While several studies have sought to document and map the long term transformations, there is a lack of information on disturbance events that allow for timely intervention by authorities. Furthermore, tools that seek to perform continuous land cover change detection are often developed for forests and thus tend to perform poorly in thicket ecosystems. This study investigates the utility of Earth Observation data for continuous land cover change detection in Subtropical Thicket ecosystems. Temporal Neural Networks are implemented on a time series of Sentinel-2 observations. The model obtained 0.93 accuracy, a recall score of 0.93, and a precision score of 0.91 in detecting Thicket disturbances. The study demonstrates the potential of continuous land cover change in Subtropical Thicket ecosystems.

Keywords: remote sensing, land cover change detection, subtropical thickets, near-real time

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Authors: R. Loukil, M. Chtourou, T. Damak

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In this work, we use the Fault detection and isolation and the Fault tolerant control based on sliding mode observer in order to introduce the well diagnosis of a nonlinear system. The robustness of the proposed observer for the two techniques is tested through a physical example. The results in this paper show the interaction between the Fault tolerant control and the Diagnosis procedure.

Keywords: fault detection and isolation FDI, fault tolerant control FTC, sliding mode observer, nonlinear system, robustness, stability

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Authors: Pyung Soo Kim, Eung Hyuk Lee, Mun Suck Jang

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In the current paper, a residual generation filter with finite memory structure is proposed for fault detection. The proposed finite memory residual generation filter provides the residual by real-time filtering of fault vector using only the most recent finite observations and inputs on the window. It is shown that the residual given by the proposed residual generation filter provides the exact fault for noise-free systems. Finally, to illustrate the capability of the proposed residual generation filter, numerical examples are performed for the discretized DC motor system having the multiple sensor faults.

Keywords: residual generation filter, finite memory structure, kalman filter, fast detection

Procedia PDF Downloads 674

Authors: Yin Zhang, Kai Qiao, Xiyang Zhi, Jinnan Gong, Jianming Hu

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In order to achieve effective detection of aerial targets over long distances from space-based platforms, the mechanism of interaction between the radiation characteristics of the aerial targets and the complex scene environment including the sunlight conditions, underlying surfaces and the atmosphere are analyzed. A large simulated database of space-based radiance images is constructed considering several typical aerial targets, target working modes (flight velocity and altitude), illumination and observation angles, background types (cloud, ocean, and urban areas) and sensor spectrums ranging from visible to thermal infrared. The target detectability is characterized by the signal-to-clutter ratio (SCR) extracted from the images. The influence laws of the target detectability are discussed under different detection bands and instantaneous fields of view (IFOV). Furthermore, the optimal center wavelengths and widths of the detection bands are suggested, and the minimum IFOV requirements are proposed. The research can provide theoretical support and scientific guidance for the design of space-based detection systems and on-board information processing algorithms.

Keywords: space-based detection, aerial targets, detectability analysis, scene environment

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Authors: Abdullah H. Özcan, Dilara Hisar, Yetkin Sayar, Cem Ünsalan

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In this study, an automated building and tree detection method is proposed using DSM data and true orthophoto image. A multiscale matched filtering is used on DSM data. Therefore, first watershed transform is applied. Then, Otsu’s thresholding method is used as an adaptive threshold to segment each watershed region. Detected objects are masked with NDVI to separate buildings and trees. The proposed method is able to detect buildings and trees without entering any elevation threshold. We tested our method on ISPRS semantic labeling dataset and obtained promising results.

Keywords: building detection, local maximum filtering, matched filtering, multiscale

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Authors: Jacky Liu, Dulani Jayasuriya, Ryan Elmore

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Match fixing and anomalous sports events have increasingly threatened the integrity of professional sports, prompting concerns about existing detection methods. This study addresses prior research limitations in match fixing detection, improving the identification of potential fraudulent matches by incorporating advanced anomaly detection techniques. We develop a novel method to identify anomalous matches and player performances by examining series of matches, such as playoffs. Additionally, we investigate bettors' potential profits when avoiding anomaly matches and explore factors behind unusual player performances. Our literature review covers match fixing detection, match outcome forecasting models, and anomaly detection methods, underscoring current limitations and proposing a new sports anomaly detection method. Our findings reveal anomalous series in the 2022 NBA playoffs, with the Phoenix Suns vs Dallas Mavericks series having the lowest natural occurrence probability. We identify abnormal player performances and bettors' profits significantly decrease when post-season matches are included. This study contributes by developing a new approach to detect anomalous matches and player performances, and assisting investigators in identifying responsible parties. While we cannot conclusively establish reasons behind unusual player performances, our findings suggest factors such as team financial difficulties, executive mismanagement, and individual player contract issues.

Keywords: anomaly match detection, match fixing, match outcome forecasting, problematic players identification

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Authors: Bo Liu, Chi-Man Pun

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A novel technique for digital forgery detection by signal noise inconsistency is proposed in this paper. The forged area spliced from the other picture contains some features which may be inconsistent with the rest part of the image. Noise pattern and the level is a possible factor to reveal such inconsistency. To detect such noise discrepancies, the test picture is initially segmented into small pieces. The noise pattern and level of each segment are then estimated by using various filters. The noise features constructed in this step are utilized in energy-based graph cut to expose forged area in the final step. Experimental results show that our method provides a good illustration of regions with noise inconsistency in various scenarios.

Keywords: forgery detection, splicing forgery, noise estimation, noise

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Authors: Chor Yi Ng, Christine Kong, Loretta Teo, Stephen Yau, FC Cheung, TL Poon, Francis Lee

Abstract:

Purpose: Dynamic conformal arc (DCA) and intensity modulated radiotherapy (IMRT) are two treatment techniques commonly used for stereotactic radiosurgery/radiotherapy of cranial lesions. IMRT plans usually give better dose conformity while DCA plans have better dose fall off. Rapid dose fall off is preferred for radiotherapy of cranial lesions, but dose conformity is also important. For certain lesions, DCA plans have good conformity, while for some lesions, the conformity is just unacceptable with DCA plans, and IMRT has to be used. The choice between the two may not be apparent until each plan is prepared and dose indices compared. We described a deviation index (DI) which is a measurement of the deviation of the target shape from a sphere, and test its functionality to choose between the two techniques. Method and Materials: From May 2015 to May 2017, our institute has performed stereotactic radiotherapy for 105 patients treating a total of 115 lesions (64 DCA plans and 51 IMRT plans). Patients were treated with the Varian Clinac iX with HDMLC. Brainlab Exactrac system was used for patient setup. Treatment planning was done with Brainlab iPlan RT Dose (Version 4.5.4). DCA plans were found to give better dose fall off in terms of R50% (R50% (DCA) = 4.75 Vs R50% (IMRT) = 5.242) while IMRT plans have better conformity in terms of treatment volume ratio (TVR) (TVR(DCA) = 1.273 Vs TVR(IMRT) = 1.222). Deviation Index (DI) is proposed to better facilitate the choice between the two techniques. DI is the ratio of the volume of a 1 mm shell of the PTV and the volume of a 1 mm shell of a sphere of identical volume. DI will be close to 1 for a near spherical PTV while a large DI will imply a more irregular PTV. To study the functionality of DI, 23 cases were chosen with PTV volume ranged from 1.149 cc to 29.83 cc, and DI ranged from 1.059 to 3.202. For each case, we did a nine field IMRT plan with one pass optimization and a five arc DCA plan. Then the TVR and R50% of each case were compared and correlated with the DI. Results: For the 23 cases, TVRs and R50% of the DCA and IMRT plans were examined. The conformity for IMRT plans are better than DCA plans, with majority of the TVR(DCA)/TVR(IMRT) ratios > 1, values ranging from 0.877 to1.538. While the dose fall off is better for DCA plans, with majority of the R50%(DCA)/ R50%(IMRT) ratios < 1. Their correlations with DI were also studied. A strong positive correlation was found between the ratio of TVRs and DI (correlation coefficient = 0.839), while the correlation between the ratio of R50%s and DI was insignificant (correlation coefficient = -0.190). Conclusion: The results suggest DI can be used as a guide for choosing the planning technique. For DI greater than a certain value, we can expect the conformity for DCA plans to become unacceptably great, and IMRT will be the technique of choice.

Keywords: cranial lesions, dynamic conformal arc, IMRT, image guided radiotherapy, stereotactic radiotherapy

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