Search results for: aerial imaging and detection

Authors: Ashwani Kumar

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Use of NIR light for imaging the biological tissue and to quantify its optical properties is a good choice over other invasive methods. Optical tomography involves two steps. One is the forward problem and the other is the reconstruction problem. The forward problem consists of finding the measurements of transmitted light through the tissue from source to detector, given the spatial distribution of absorption and scattering properties. The second step is the reconstruction problem. In X-ray tomography, there is standard method for reconstruction called filtered back projection method or the algebraic reconstruction methods. But this method cannot be applied as such, in optical tomography due to highly scattering nature of biological tissue. A hybrid algorithm for reconstruction has been implemented in this work which takes into account the highly scattered path taken by photons while back projecting the forward data obtained during Monte Carlo simulation. The reconstructed image suffers from blurring due to point spread function.

Keywords: NIR light, tissue, blurring, Monte Carlo simulation

Procedia PDF Downloads 494

Authors: Nijole Savickiene, Antonella Di Sotto, Gabriela Mazzanti, Rasa Starselskyte, Silvia Di Giacomo, Annabella Vitalone

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Plant lectins are non-enzymic and non-immune origin proteins that specifically recognize and bind to various sugar structures and possess the activity to agglutinate cells and/or precipitate polysaccharides and glycoconjugates. The emerging evidences showed that plant lectins contribute not only to tumour cell recognition but also to cell adhesion and localization, to signal transduction, to mitogenic cytotoxicity and apoptosis. Among chitin-binding lectins, the Urtica dioica agglutinin (UDA), which is a complex of different isoforms, has been poorly studied for its biological activity. In this context and according to the increasing interest for lectins as novel antitumor drugs, present paper aimed at evaluating the potential antimutagenic activity of a lectin-like glycoprotein-enriched fraction from aerial part of Urtica dioica L. Aim: to evaluate the potential chemopreventive properties of a protein - lectin fraction from the aerial part of Urtica dioica. Materials and methods: Protein – lectin fraction has been tested for the antimutagenic activity in bacteria (50–800 mg/plate; Ames test by the preincubation method) and for the cytotoxicity on human hepatoma HepG2 cells (0.06–2 mg/mL; 24 and 48 h incubation). Results: Protein – lectin fraction from stinging nettle was not cytotoxic on HepG2 cells up to 2 mg/mL; conversely, it exhibited a strong antimutagenic activity against the mutagen 2-aminoanthracene (2AA) in all strains tested (maximum inhibition of 56.78 and 61% in TA98, TA100, and WP2uvrA strains, respectively, at 800 mg/plate). Discussion and conclusions: Protein – lectin fraction from Urtica dioica L. possesses antimutagenic and radical scavenging properties. Being 2AA a pro-carcinogenic agent, we hypothesize that the antimutagenicity of it can be due to the inhibition of CYP450-isoenzymes, involved in the mutagen bioactivation.

Keywords: lectins, antimutagenicity, chemoprevention, Urtica dioica

Procedia PDF Downloads 426

Authors: Po-Hui Yang, Jing-Min Chen, Po-Yu Kuo, Chia-Chun Wu

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This paper proposed a high resolution Vernier Delay Line (VDL) measurement circuit with coarse and fine detection mechanism, which improved the trade-off problem between high resolution and less delay cells in traditional VDL circuits. And the measuring time of proposed measurement circuit is also under the high resolution requests. At first, the testing range of input signal which proposed high resolution delay line is detected by coarse detection VDL. Moreover, the delayed input signal is transmitted to fine detection VDL for measuring value with better accuracy. This paper is implemented at 0.18μm process, operating frequency is 100 MHz, and the resolution achieved 2.0 ps with only 16-stage delay cells. The test range is 170ps wide, and 17% stages saved compare with traditional single delay line circuit.

Keywords: vernier delay line, D-type flip-flop, DFF, metastable phenomenon

Procedia PDF Downloads 597

Authors: S. Guruprasad, M. Z. Kurian, H. N. Suma

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Medical imaging modalities are becoming life saving components. These modalities are very much essential to doctors for proper diagnosis, treatment planning and follow up. Some modalities provide anatomical information such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), X-rays and some provides only functional information such as Positron Emission Tomography (PET). Therefore, single modality image does not give complete information. This paper presents the fusion of structural information in CT and functional information present in PET image. This fused image is very much essential in detecting the stages and location of abnormalities and in particular very much needed in oncology for improved diagnosis and treatment. We have implemented and compared image fusion techniques like pyramid, wavelet, and principal components fusion methods along with hybrid method of DWT and PCA. The performances of the algorithms are evaluated quantitatively and qualitatively. The system is implemented and tested by using MATLAB software. Based on the MSE, PSNR and ENTROPY analysis, PCA and DWT-PCA methods showed best results over all experiments.

Keywords: image fusion, pyramid, wavelets, principal component analysis

Procedia PDF Downloads 284

Authors: Cristina Vatamanu, Doina Cosovan, Dragos Gavrilut, Henri Luchian

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In the past few years, the amount of malicious software increased exponentially and, therefore, machine learning algorithms became instrumental in identifying clean and malware files through semi-automated classification. When working with very large datasets, the major challenge is to reach both a very high malware detection rate and a very low false positive rate. Another challenge is to minimize the time needed for the machine learning algorithm to do so. This paper presents a comparative study between different machine learning techniques such as linear classifiers, ensembles, decision trees or various hybrids thereof. The training dataset consists of approximately 2 million clean files and 200.000 infected files, which is a realistic quantitative mixture. The paper investigates the above mentioned methods with respect to both their performance (detection rate and false positive rate) and their practicability.

Keywords: ensembles, false positives, feature selection, one side class algorithm

Procedia PDF Downloads 292

Authors: Mohammad Tarek Al Muallim, Ozhan Duzenli, Ceyhun Ilguy

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Harbor protection systems are so important. The need for automatic protection systems has increased over the last years. Diver detection active sonar has great significance. It used to detect underwater threats such as divers and autonomous underwater vehicle. To automatically detect such threats the sonar image is processed by algorithms. These algorithms used to detect, track and classify of underwater objects. In this work, divers tracking and classification algorithm is improved be proposing a robust wake detection method. To detect objects the sonar images is normalized then segmented based on fixed threshold. Next, the centroids of the segments are found and clustered based on distance metric. Then to track the objects linear Kalman filter is applied. To reduce effect of noise and creation of false tracks, the Kalman tracker is fine tuned. The tuning is done based on our active sonar specifications. After the tracks are initialed and updated they are subjected to a filtering stage to eliminate the noisy and unstable tracks. Also to eliminate object with a speed out of the diver speed range such as buoys and fast boats. Afterwards the result tracks are subjected to a classification stage to deiced the type of the object been tracked. Here the classification stage is to deice wither if the tracked object is an open circuit diver or a close circuit diver. At the classification stage, a small area around the object is extracted and a novel wake detection method is applied. The morphological features of the object with his wake is extracted. We used support vector machine to find the best classifier. The sonar training images and the test images are collected by ARMELSAN Defense Technologies Company using the portable diver detection sonar ARAS-2023. After applying the algorithm to the test sonar data, we get fine and stable tracks of the divers. The total classification accuracy achieved with the diver type is 97%.

Keywords: harbor protection, diver detection, active sonar, wake detection, diver classification

Procedia PDF Downloads 238

Authors: Mulugeta K. Tefera, Xiaolong Yang, Jian Liu

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Detection and tracking of moving objects are very important in many application contexts such as detection and recognition of people, visual surveillance and automatic generation of video effect and so on. However, the task of detecting a real shape of an object in motion becomes tricky due to various challenges like dynamic scene changes, presence of shadow, and illumination variations due to light switch. For such systems, once the moving object is detected, tracking is also a crucial step for those applications that used in military defense, video surveillance, human computer interaction, and medical diagnostics as well as in commercial fields such as video games. In this paper, an object presents in dynamic background is detected using adaptive mixture of Gaussian based analysis of the video sequences. Then the detected moving object is tracked using the region based moving object tracking and inter-frame differential mechanisms to address the partial overlapping and occlusion problems. Firstly, the detection algorithm effectively detects and extracts the moving object target by enhancing and post processing morphological operations. Secondly, the extracted object uses region based moving object tracking and inter-frame difference to improve the tracking speed of real-time moving objects in different video frames. Finally, the plotting method was applied to detect the moving objects effectively and describes the object’s motion being tracked. The experiment has been performed on image sequences acquired both indoor and outdoor environments and one stationary and web camera has been used.

Keywords: background modeling, Gaussian mixture model, inter-frame difference, object detection and tracking, video surveillance

Procedia PDF Downloads 477

Authors: Bright Chukwunwike Uzuegbunam, Wojciech Paslawski, Hans Agren, Christer Halldin, Wolfgang Weber, Markus Luster, Thomas Arzberger, Behrooz Hooshyar Yousefi

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There is a need to develop a PET tracer that will enable to diagnosis and track the progression of Alpha-synucleinopathies (Parkinson’s disease [PD], dementia with Lewy bodies [DLB], multiple system atrophy [MSA]) in living subjects over time. Alpha-synuclein aggregates (a-syn), which are present in all the stages of disease progression, for instance, in PD, are a suitable target for in vivo PET imaging. For this reason, we have developed some promising a-syn tracers based on a disarylbisthiazole (DABTA) scaffold. The precursors are synthesized via a modified Hantzsch thiazole synthesis. The precursors were then radiolabeled via one- or two-step radiofluorination methods. The ligands were initially screened using a combination of molecular dynamics and quantum/molecular mechanics approaches in order to calculate the binding affinity to a-syn (in silico binding experiments). Experimental in vitro binding assays were also performed. The ligands were further screened in other experiments such as log D, in vitro plasma protein binding & plasma stability, biodistribution & brain metabolite analyses in healthy mice. Radiochemical yields were up to 30% - 72% in some cases. Molecular docking revealed possible binding sites in a-syn and also the free energy of binding to those sites (-28.9 - -66.9 kcal/mol), which correlated to the high binding affinity of the DABTAs to a-syn (Ki as low as 0.5 nM) and selectivity (> 100-fold) over Aβ and tau, which usually co-exist with a-synin some pathologies. The log D values range from 2.88 - 2.34, which correlated with free-protein fraction of 0.28% - 0.5%. Biodistribution experiments revealed that the tracers are taken up (5.6 %ID/g - 7.3 %ID/g) in the brain at 5 min (post-injection) p.i., and cleared out (values as low as 0.39 %ID/g were obtained at 120 min p.i. Analyses of the mice brain 20 min p.i. Revealed almost no radiometabolites in the brain in most cases. It can be concluded that in silico study presents a new venue for the rational development of radioligands with suitable features. The results obtained so far are promising and encourage us to further validate the DABTAs in autoradiography, immunohistochemistry, and in vivo imaging in non-human primates and humans.

Keywords: alpha-synuclein aggregates, alpha-synucleinopathies, PET imaging, tracer development

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Authors: YingWei Tan, XueFeng Ding

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Attention-based voice activity detection models have gained significant attention in recent years due to their fast training speed and ability to capture a wide contextual range. The inclusion of multi-head style and position embedding in the attention architecture are crucial. Having multiple attention heads allows for differential focus on different parts of the sequence, while position embedding provides guidance for modeling dependencies between elements at various positions in the input sequence. In this work, we propose an approach by considering each head as a node, enabling the application of graph neural networks (GNN) to identify correlations among the different nodes. In addition, we adopt an implementation named rotary position embedding (RoPE), which encodes absolute positional information into the input sequence by a rotation matrix, and naturally incorporates explicit relative position information into a self-attention module. We evaluate the effectiveness of our method on a synthetic dataset, and the results demonstrate its superiority over the baseline CRNN in scenarios with low signal-to-noise ratio and noise, while also exhibiting robustness across different noise types. In summary, our proposed framework effectively combines the strengths of CNN and RNN (LSTM), and further enhances detection performance through the integration of graph neural networks and rotary position embedding.

Keywords: voice activity detection, CRNN, graph neural networks, rotary position embedding

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Authors: Raphael Tuor, Denis Lalanne

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The ATM research community is missing suitable tools to design, test, and validate new UI prototypes. Important stakes underline the implementation of both DSS and XAI methods into current systems. ML-based DSS are gaining in relevance as ATFM becomes increasingly complex. However, these systems only prove useful if a human can understand them, and thus new XAI methods are needed. The human-machine dyad should work as a team and should understand each other. We present xSky, a configurable benchmark tool that allows us to compare different versions of an ATC interface in conflict detection tasks. Our main contributions to the ATC research community are (1) a conflict detection task simulator (xSky) that allows to test the applicability of visual prototypes on scenarios of varying difficulty and outputting relevant operational metrics (2) a theoretical approach to the explanations of AI-driven trajectory predictions. xSky addresses several issues that were identified within available research tools. Researchers can configure the dimensions affecting scenario difficulty with a simple CSV file. Both the content and appearance of the XAI elements can be customized in a few steps. As a proof-of-concept, we implemented an XAI prototype inspired by the maritime field.

Keywords: air traffic control, air traffic simulation, conflict detection, explainable artificial intelligence, explainability, human-automation collaboration, human factors, information visualization, interpretability, trajectory prediction

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Authors: Michael Leontiev, Danil Zhilikov, Dmitry Lobanov, Lenar Klimov, Vyacheslav Chertan, Daniel Bobrov, Vladislav Maslov, Vasilii Vologdin, Ksenia Balabaeva

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Pharmaceutical manufacturing is a complex process, where each stage requires a high level of safety and sterility. Personal Protective Equipment (PPE) is used for this purpose. Despite all the measures of control, the human factor (improper PPE wearing) causes numerous losses to human health and material property. This research proposes a solid computer vision system for ensuring safety in pharmaceutical laboratories. For this, we have tested a wide range of state-of-the-art object detection methods. Composing previously obtained results in this sphere with our own approach to this problem, we have reached a high accuracy ([email protected]) ranging from 0.77 up to 0.98 in detecting all the elements of a common set of PPE used in pharmaceutical laboratories. Our system is a step towards safe medicine production.

Keywords: sterility and safety in pharmaceutical development, personal protective equipment, computer vision, object detection, monitoring in pharmaceutical development, PPE

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Authors: Ankit Kamboj, Shikha Talwar, Nilesh Powar

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To successfully deliver our products in the market, the employees need to be in a safe environment, especially in an industrial and manufacturing environment. The consequences of delinquency in wearing safety glasses while working in industrial plants could be high risk to employees, hence the need to develop a real-time automatic detection system which detects the persons (violators) not wearing safety glasses. In this study a convolutional neural network (CNN) algorithm called faster region based CNN (Faster RCNN) with rotated bounding box has been used for detecting safety glasses on persons; the algorithm has an advantage of detecting safety glasses with different orientation angles on the persons. The proposed method of rotational bounding boxes with a convolutional neural network first detects a person from the images, and then the method detects whether the person is wearing safety glasses or not. The video data is captured at the entrance of restricted zones of the industrial environment (manufacturing plant), which is further converted into images at 2 frames per second. In the first step, the CNN with pre-trained weights on COCO dataset is used for person detection where the detections are cropped as images. Then the safety goggles are labelled on the cropped images using the image labelling tool called roLabelImg, which is used to annotate the ground truth values of rotated objects more accurately, and the annotations obtained are further modified to depict four coordinates of the rectangular bounding box. Next, the faster RCNN with rotated bounding box is used to detect safety goggles, which is then compared with traditional bounding box faster RCNN in terms of detection accuracy (average precision), which shows the effectiveness of the proposed method for detection of rotatory objects. The deep learning benchmarking is done on a Dell workstation with a 16GB Nvidia GPU.

Keywords: CNN, deep learning, faster RCNN, roLabelImg rotated bounding box, safety goggle detection

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Authors: Fouzi Harrou, Ying Sun, Sofiane Khadraoui

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Anomaly detection based on Principal Component Analysis (PCA) was studied intensively and largely applied to multivariate processes with highly cross-correlated process variables. Monitoring metrics such as the Hotelling's T2 and the Q statistics are usually used in PCA-based monitoring to elucidate the pattern variations in the principal and residual subspaces, respectively. However, these metrics are ill suited to detect small faults. In this paper, the Exponentially Weighted Moving Average (EWMA) based on the Q and T statistics, T2-EWMA and Q-EWMA, were developed for detecting faults in the process mean. The performance of the proposed methods was compared with that of the conventional PCA-based fault detection method using synthetic data. The results clearly show the benefit and the effectiveness of the proposed methods over the conventional PCA method, especially for detecting small faults in highly correlated multivariate data.

Keywords: data-driven method, process control, anomaly detection, dimensionality reduction

Procedia PDF Downloads 299

Authors: Luís Miguel Cabeça Marques, Alberto Manuel Martinho Vale, José Pedro Miragaia Trancoso Vaz, Ana Sofia Baptista Fernandes, Rui Alexandre de Barros Coito, Tiago Miguel Prates da Costa

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The detection and localization of hidden radioactive sources are of significant importance in countering the illicit traffic of Special Nuclear Materials and other radioactive sources and materials. Radiation portal monitors are commonly used at airports, seaports, and international land borders for inspecting cargo and vehicles. However, these equipment can be expensive and are not available at all checkpoints. Consequently, the localization of SNM and other radioactive sources often relies on handheld equipment, which can be time-consuming. The current study presents the advantages of real-time analysis of gamma-ray count rate data from a mobile radiation detection system based on simulated data and field tests. The incorporation of profit functions and decision criteria to optimize the detection system's path significantly enhances the radiation field information and reduces survey time during cargo inspection. For source position estimation, a maximum likelihood estimation algorithm is employed, and confidence intervals are derived using the Fisher information. The study also explores the impact of uncertainties, baselines, and thresholds on the performance of the profit function. The proposed detection system, utilizing a plastic scintillator with silicon photomultiplier sensors, boasts several benefits, including cost-effectiveness, high geometric efficiency, compactness, and lightweight design. This versatility allows for seamless integration into any mobile platform, be it air, land, maritime, or hybrid, and it can also serve as a handheld device. Furthermore, integration of the detection system into drones, particularly multirotors, and its affordability enable the automation of source search and substantial reduction in survey time, particularly when deploying a fleet of drones. While the primary focus is on inspecting maritime container cargo, the methodologies explored in this research can be applied to the inspection of other infrastructures, such as nuclear facilities or vehicles.

Keywords: plastic scintillators, profit functions, path planning, gamma-ray detection, source localization, mobile radiation detection system, security scenario

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Authors: Nitin Dwivedi, Jigna Shah

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Brain tumor is metastatic neoplasm of central nervous system, in most of cases it is life threatening disease with low survival rate. Despite of enormous efforts in the development of therapeutics and diagnostic tools, the treatment of brain tumors and gliomas remain a considerable challenge in the area of neuro-oncology. The most reason behind of this the presence of physiological barriers including blood brain barrier and blood brain tumor barrier, lead to insufficient reach ability of therapeutic agents at the site of tumor, result of inadequate destruction of gliomas. So there is an indeed need empowerment of brain tumor imaging for better characterization and delineation of tumors, visualization of malignant tissue during surgery, and tracking of response to chemotherapy and radiotherapy. Multifunctional different generations of dendrimer offer an improved effort for potentiate drug delivery at the site of brain tumor and gliomas. So this article emphasizes the innovative dendrimer approaches in tumor targeting, tumor imaging and delivery of therapeutic agent.

Keywords: blood brain barrier, dendrimer, gliomas, nanotechnology

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Authors: Vishnu Nagendra, Marten Van Der Veen, Stefania Giodini

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Effective humanitarian logistics operations are a cornerstone in the success of disaster relief operations. However, for effectiveness, they need to be demand driven and supported by adequate data for prioritization. Without this data operations are carried out in an ad hoc manner and eventually become chaotic. The current availability of geospatial data helps in creating models for predictive damage and vulnerability assessment, which can be of great advantage to logisticians to gain an understanding on the nature and extent of the disaster damage. This translates into actionable information on the demand for relief goods, the state of the transport infrastructure and subsequently the priority areas for relief delivery. However, due to the unpredictable nature of disasters, the accuracy in the models need improvement which can be done using remote sensing data from UAVs (Unmanned Aerial Vehicles) or satellite imagery, which again come with certain limitations. This research addresses the need for a framework to combine data from different sources to support humanitarian logistic operations and prediction models. The focus is on developing a workflow to combine data from satellites and UAVs post a disaster strike. A three-step approach is followed: first, the data requirements for logistics activities are made explicit, which is done by carrying out semi-structured interviews with on field logistics workers. Second, the limitations in current data collection tools are analyzed to develop workaround solutions by following a systems design approach. Third, the data requirements and the developed workaround solutions are fit together towards a coherent workflow. The outcome of this research will provide a new method for logisticians to have immediately accurate and reliable data to support data-driven decision making.

Keywords: unmanned aerial vehicles, damage prediction models, remote sensing, data driven decision making

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Authors: Sankarprasad Bhuniya, Sukhendu Maiti, Eun-Joong Kim, Hyunseung Lee, Jonathan L. Sessler, Kwan Soo Hong, Jong Seung Kim

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A new theranostic strategy is described. It is based on the use of an “all in one” prodrug, namely the biotinylated piperazine-rhodol conjugate 4a. This conjugate, which incorporates the anticancer drug SN-38, undergoes self-immolative cleavage when exposed to biological thiols. This leads to the tumor-targeted release of the active SN-38 payload along with fluorophore 1a. This release is made selective as the result of the biotin functionality. Fluorophore 1a is 32-fold more fluorescent than prodrug 4a. It permits the delivery and release of the SN-38 payload to be monitored easily in vitro and in vivo, as inferred from cell studies and ex vivo analyses of mice xenografts derived HeLa cells, respectively. Prodrug 4a also displays anticancer activity in the HeLa cell murine xenograft tumor model. On the basis of these findings we suggest that the present strategy, which combines within a single agent the key functions of targeting, release, imaging, and treatment, may have a role to play in cancer diagnosis and therapy.

Keywords: theranostic, prodrug, cancer therapy, fluorescence

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Authors: S. Zolghadri, H. Yousefnia, A. R. Jalilian

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Bone metastases are observed in a wide range of cancers leading to intolerable pain. While early detection can help the physicians in the decision of the type of treatment, various radiopharmaceuticals using phosphonates like ⁶⁸Ga-EDTMP have been developed. In this work, due to the importance of absorbed dose, human absorbed dose of this new agent was calculated for the first time based on biodistribution data in Wild-type rats. ⁶⁸Ga was obtained from ⁶⁸Ge/⁶⁸Ga generator with radionuclidic purity and radiochemical purity of higher than 99%. The radiolabeled complex was prepared in the optimized conditions. Radiochemical purity of the radiolabeled complex was checked by instant thin layer chromatography (ITLC) method using Whatman No. 2 paper and saline. The results indicated the radiochemical purity of higher than 99%. The radiolabelled complex was injected into the Wild-type rats and its biodistribution was studied up to 120 min. As expected, major accumulation was observed in the bone. Absorbed dose of each human organ was calculated based on biodistribution in the rats using RADAR method. Bone surface and bone marrow with 0.112 and 0.053 mSv/MBq, respectively, received the highest absorbed dose. According to these results, the radiolabeled complex is a suitable and safe option for PET bone imaging.

Keywords: absorbed dose, EDTMP, ⁶⁸Ga, rats

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Authors: Jeewan Rai, Kinzang, Yeshi Jigme Choden

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Accumulation of dust on solar panels impacts the overall efficiency and the amount of energy they produce. While various techniques exist for detecting dust to schedule cleaning, many of these methods use MATLAB image processing tools and other licensed software, which can be financially burdensome. This study will investigate the efficiency of a free open-source computer vision library using the YOLO algorithm. The proposed approach has been tested on images of solar panels with varying dust levels through an experiment setup. The experimental findings illustrated the effectiveness of using the YOLO-based image classification method and the overall dust detection approach with an accuracy of 90% in distinguishing between clean and dusty panels. This open-source solution provides a cost effective and accessible alternative to commercial image processing tools, offering solutions for optimizing solar panel maintenance and enhancing energy production.

Keywords: YOLO, openCV, dust detection, solar panels, computer vision, image processing

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Authors: Arnab Hazra

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Breath analysis is a quick, noninvasive and inexpensive technique for disease diagnosis can be used on people of all ages without any risk. Only a limited number of volatile organic compounds (VOCs) can be associated with the occurrence of specific diseases. These VOCs can be considered as disease markers or breath markers. Selective detection with specific concentration of breath marker in exhaled human breath is required to detect a particular disease. For example, acetone (C₃H₆O), ethanol (C₂H₅OH), ethane (C₂H₆) etc. are the breath markers and abnormal concentrations of these VOCs in exhaled human breath indicates the diseases like diabetes mellitus, renal failure, breast cancer respectively. Nanomaterial-based vapor sensors are inexpensive, small and potential candidate for the detection of breath markers. In practical measurement, selectivity is the most crucial issue where trace detection of breath marker is needed to identify accurately in the presence of several interfering vapors and gases. Current article concerns a novel technique for selective and lower ppb level detection of breath markers at very low temperature based on TiO₂ nanotube array based vapor sensor devices. Highly ordered and oriented TiO₂ nanotube array was synthesized by electrochemical anodization of high purity tatinium (Ti) foil. 0.5 wt% NH₄F, ethylene glycol and 10 vol% H₂O was used as the electrolyte and anodization was carried out for 90 min with 40 V DC potential. Au/TiO₂ Nanotube/Ti, sandwich type sensor device was fabricated for the selective detection of VOCs in low concentration range. Initially, sensor was characterized where resistive and capacitive change of the sensor was recorded within the valid concentration range for individual breath markers (or organic vapors). Sensor resistance was decreased and sensor capacitance was increased with the increase of vapor concentration. Now, the ratio of resistive slope (mR) and capacitive slope (mC) provided a concentration independent constant term (M) for a particular vapor. For the detection of unknown vapor, ratio of resistive change and capacitive change at any concentration was same to the previously calculated constant term (M). After successful identification of the target vapor, concentration was calculated from the straight line behavior of resistance as a function of concentration. Current technique is suitable for the detection of particular vapor from a mixture of other interfering vapors.

Keywords: breath marker, vapor sensors, selective detection, TiO₂ nanotube array

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Authors: Abdoulaye Diop, Nahid Emad, Thierry Winter, Mohamed Hilia

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Data assets protection is a crucial issue in the cybersecurity field. Companies use logical access control tools to vault their information assets and protect them against external threats, but they lack solutions to counter insider threats. Nowadays, insider threats are the most significant concern of security analysts. They are mainly individuals with legitimate access to companies information systems, which use their rights with malicious intents. In several fields, behavior anomaly detection is the method used by cyber specialists to counter the threats of user malicious activities effectively. In this paper, we present the step toward the construction of a user and entity behavior analysis framework by proposing a behavior anomaly detection model. This model combines machine learning classification techniques and graph-based methods, relying on linear algebra and parallel computing techniques. We show the utility of an ensemble learning approach in this context. We present some detection methods tests results on an representative access control dataset. The use of some explored classifiers gives results up to 99% of accuracy.

Keywords: cybersecurity, data protection, access control, insider threat, user behavior analysis, ensemble learning, high performance computing

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Authors: Shahrokh Barati, Reza Ramezani

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Due to the countries growing attention to the renewable energy producing, the demand for energy from renewable energy has gone up among the renewable energy sources; wind energy is the fastest growth in recent years. In this regard, in order to increase the availability of wind turbines, using of Fault Detection and Isolation (FDI) system is necessary. Wind turbines include of various faults such as sensors fault, actuator faults, network connection fault, mechanical faults and faults in the generator subsystem. Although, sensors and actuators have a large number of faults in wind turbine but have discussed fewer in the literature. Therefore, in this work, we focus our attention to design a sensor and actuator fault detection and isolation algorithm and Fault-tolerant control systems (FTCS) for Wind Turbine. The aim of this research is to propose a comprehensive fault detection and isolation system for sensors and actuators of wind turbine based on data-driven approaches. To achieve this goal, the features of measurable signals in real wind turbine extract in any condition. The next step is the feature selection among the extract in any condition. The next step is the feature selection among the extracted features. Features are selected that led to maximum separation networks that implemented in parallel and results of classifiers fused together. In order to maximize the reliability of decision on fault, the property of fault repeatability is used.

Keywords: FDI, wind turbines, sensors and actuators faults, renewable energy

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Authors: Rikson Gultom

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Hate Speech and Abusive language on social media is difficult to detect, usually, it is detected after it becomes viral in cyberspace, of course, it is too late for prevention. An early detection system that has a fairly good accuracy is needed so that it can reduce conflicts that occur in society caused by postings on social media that attack individuals, groups, and governments in Indonesia. The purpose of this study is to find an early detection model on Twitter social media using machine learning that has high accuracy from several machine learning methods studied. In this study, the support vector machine (SVM), Naïve Bayes (NB), and Random Forest Decision Tree (RFDT) methods were compared with the Support Vector machine with genetic algorithm (SVM-GA), Nave Bayes with genetic algorithm (NB-GA), and Random Forest Decision Tree with Genetic Algorithm (RFDT-GA). The study produced a comparison table for the accuracy of the hate speech and abusive language detection model, and presented it in the form of a graph of the accuracy of the six algorithms developed based on the Indonesian-language Twitter dataset, and concluded the best model with the highest accuracy.

Keywords: abusive language, hate speech, machine learning, optimization, social media

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Authors: M. Mansouri, P. Jaklitsch, E. Teiniker

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SQL injection on web applications is a very popular kind of attack. There are mechanisms such as intrusion detection systems in order to detect this attack. These strategies often rely on techniques implemented at high layers of the application but do not consider the low level of system calls. The problem of only considering the high level perspective is that an attacker can circumvent the detection tools using certain techniques such as URL encoding. One technique currently used for detecting low-level attacks on privileged processes is the tracing of system calls. System calls act as a single gate to the Operating System (OS) kernel; they allow catching the critical data at an appropriate level of detail. Our basic assumption is that any type of application, be it a system service, utility program or Web application, “speaks” the language of system calls when having a conversation with the OS kernel. At this level we can see the actual attack while it is happening. We conduct an experiment in order to demonstrate the suitability of system call analysis for detecting SQL injection. We are able to detect the attack. Therefore we conclude that system calls are not only powerful in detecting low-level attacks but that they also enable us to detect high-level attacks such as SQL injection.

Keywords: Linux system calls, web attack detection, interception, SQL

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Authors: Gong Zhilin, Jing Yang, Jian Yin

Abstract:

The purpose of this paper is to develop a novel system for credit card fraud detection based on sequential modeling of data using hybrid deep learning models. The projected model encapsulates five major phases are pre-processing, imbalance-data handling, feature extraction, optimal feature selection, and fraud detection with an ensemble classifier. The collected raw data (input) is pre-processed to enhance the quality of the data through alleviation of the missing data, noisy data as well as null values. The pre-processed data are class imbalanced in nature, and therefore they are handled effectively with the K-means clustering-based SMOTE model. From the balanced class data, the most relevant features like improved Principal Component Analysis (PCA), statistical features (mean, median, standard deviation) and higher-order statistical features (skewness and kurtosis). Among the extracted features, the most optimal features are selected with the Self-improved Arithmetic Optimization Algorithm (SI-AOA). This SI-AOA model is the conceptual improvement of the standard Arithmetic Optimization Algorithm. The deep learning models like Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and optimized Quantum Deep Neural Network (QDNN). The LSTM and CNN are trained with the extracted optimal features. The outcomes from LSTM and CNN will enter as input to optimized QDNN that provides the final detection outcome. Since the QDNN is the ultimate detector, its weight function is fine-tuned with the Self-improved Arithmetic Optimization Algorithm (SI-AOA).

Keywords: credit card, data mining, fraud detection, money transactions

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Authors: Chhabi Nigam, S. Ramakrishnan

Abstract:

This paper brings out the analysis of the airborne Synthetic Aperture Radar (SAR) data to study the impact of Doppler centroid on Image quality and geocoding accuracy from the perspective of Stripmap mode of data acquisition. Although in Stripmap mode of data acquisition radar beam points at 90 degrees broad side (side looking), shift in the Doppler centroid is invariable due to platform motion. In-accurate estimation of Doppler centroid leads to poor image quality and image miss-registration. The effect of Doppler centroid is analyzed in this paper using multiple sets of data collected from airborne platform. Occurrences of ghost (ambiguous) targets and their power levels have been analyzed that impacts appropriate choice of PRF. Effect of aircraft attitudes (roll, pitch and yaw) on the Doppler centroid is also analyzed with the collected data sets. Various stages of the RDA (Range Doppler Algorithm) algorithm used for image formation in Stripmap mode, range compression, Doppler centroid estimation, azimuth compression, range cell migration correction are analyzed to find the performance limits and the dependence of the imaging geometry on the final image. The ability of Doppler centroid estimation to enhance the imaging accuracy for registration are also illustrated in this paper. The paper also tries to bring out the processing of low squint SAR data, the challenges and the performance limits imposed by the imaging geometry and the platform dynamics on the final image quality metrics. Finally, the effect on various terrain types, including land, water and bright scatters is also presented.

Keywords: ambiguous target, Doppler Centroid, image registration, Airborne SAR

Procedia PDF Downloads 218

Authors: A. T. Kuda, J. J. Dayya, A. Jimoh

Abstract:

This paper investigates the fault detection and Isolation technique of measurement data sets from a three tank system using analytical model-based temporal redundancy which is based on residual generation using parity equations/space approach. It further briefly outlines other approaches of model-based residual generation. The basic idea of parity space residual generation in temporal redundancy is dynamic relationship between sensor outputs and actuator inputs (input-output model). These residuals where then used to detect whether or not the system is faulty and indicate the location of the fault when it is faulty. The method obtains good results by detecting and isolating faults from the considered data sets measurements generated from the system.

Keywords: fault detection, fault isolation, disturbing influences, system failure, parity equation/relation, structured parity equations

Procedia PDF Downloads 302

Authors: Linjie Zhang, Baifen Ren, Xi Zhang, Genwang Liu

Abstract:

Ship detection is of great significance for both military and civilian applications. Synthetic aperture radar (SAR) with all-day, all-weather, ultra-long-range characteristics, has been used widely. In view of the low time resolution of low orbit SAR and the needs for high time resolution SAR data, GEO (Geosynchronous orbit) SAR is getting more and more attention. Since GEO SAR has short revisiting period and large coverage area, it is expected to be well utilized in marine ship targets monitoring. However, the height of the orbit increases the time of integration by almost two orders of magnitude. For moving marine vessels, the utility and efficacy of GEO SAR are still not sure. This paper attempts to find the feasibility of GEO SAR by giving a GEO SAR simulator of moving ships. This presented GEO SAR simulator is a kind of geometrical-based radar imaging simulator, which focus on geometrical quality rather than high radiometric. Inputs of this simulator are 3D ship model (.obj format, produced by most 3D design software, such as 3D Max), ship's velocity, and the parameters of satellite orbit and SAR platform. Its outputs are simulated GEO SAR raw signal data and SAR image. This simulating process is accomplished by the following four steps. (1) Reading 3D model, including the ship rotations (pitch, yaw, and roll) and velocity (speed and direction) parameters, extract information of those little primitives (triangles) which is visible from the SAR platform. (2) Computing the radar scattering from the ship with physical optics (PO) method. In this step, the vessel is sliced into many little rectangles primitives along the azimuth. The radiometric calculation of each primitive is carried out separately. Since this simulator only focuses on the complex structure of ships, only single-bounce reflection and double-bounce reflection are considered. (3) Generating the raw data with GEO SAR signal modeling. Since the normal ‘stop and go’ model is not available for GEO SAR, the range model should be reconsidered. (4) At last, generating GEO SAR image with improved Range Doppler method. Numerical simulation of fishing boat and cargo ship will be given. GEO SAR images of different posture, velocity, satellite orbit, and SAR platform will be simulated. By analyzing these simulated results, the effectiveness of GEO SAR for the detection of marine moving vessels is evaluated.

Keywords: GEO SAR, radar, simulation, ship

Procedia PDF Downloads 177

Authors: Ragini Verma, J. Ponmozhi

Abstract:

The oral cavity is home to a wide variety of microorganisms that lead to various diseases and even oral cancer. Despite advancements in the diagnosis and detection at the initial phase, the situation hasn’t improved much. Saliva-on-a-chip is an innovative point-of-care platform for early diagnosis of oral cancer and other oral diseases in live and dead cells using a microfluidic device with a current perspective. Some of the major challenges, like real-time imaging of the oral cancer microbes, high throughput values, obtaining a high spatiotemporal resolution, etc. were faced by the scientific community. Integrated microfluidics and microscopy provide powerful approaches to studying the dynamics of oral pathology, microbe interaction, and the oral microenvironment. Here we have developed a saliva-on-chip (salivary microbes) device to monitor the effect on oral cancer. Adhesion of cancer-causing F. nucleatum; subsp. Nucleatum and Prevotella intermedia in the device was observed. We also observed a significant reduction in the oral cancer growth rate when mortality and morbidity were induced. These results show that this approach has the potential to transform the oral cancer and early diagnosis study.

Keywords: microfluidic device, oral cancer microbes, early diagnosis, saliva-on-chip

Procedia PDF Downloads 101

Authors: Binh T. T. Nguyen, Zhenyu Li, Eric Yap, Yi Zhang, Ai-Qun Liu

Abstract:

Silicon-photonic-sensor system is an emerging class of analytical technologies that use evanescent field wave to sensitively measure the slight difference in the surrounding environment. The wavelength shift induced by local refractive index change is used as an indicator in the system. These devices can be served as sensors for a wide variety of chemical or biomolecular detection in clinical and environmental fields. In our study, a system including a silicon-based micro-ring resonator, microfluidic channel, and optical processing is designed, fabricated for biomolecule detection. The system is demonstrated to detect Clostridium botulinum type A neurotoxin (BoNT) in different water sources. BoNT is one of the most toxic substances known and relatively easily obtained from a cultured bacteria source. The toxin is extremely lethal with LD50 of about 0.1µg/70kg intravenously, 1µg/ 70 kg by inhalation, and 70µg/kg orally. These factors make botulinum neurotoxins primary candidates as bioterrorism or biothreat agents. It is required to have a sensing system which can detect BoNT in a short time, high sensitive and automatic. For BoNT detection, silicon-based micro-ring resonator is modified with a linker for the immobilization of the anti-botulinum capture antibody. The enzymatic reaction is employed to increase the signal hence gains sensitivity. As a result, a detection limit to 30 pg/mL is achieved by our silicon-photonic sensor within a short period of 80 min. The sensor also shows high specificity versus the other type of botulinum. In the future, by designing the multifunctional waveguide array with fully automatic control system, it is simple to simultaneously detect multi-biomaterials at a low concentration within a short period. The system has a great potential to apply for online, real-time and high sensitivity for the label-free bimolecular rapid detection.

Keywords: biotoxin, photonic, ring resonator, sensor

Procedia PDF Downloads 117