Search results for: acoustic source detection

Authors: Hang Thuy Tran, Hanh Hong Pham, Ha Thanh Hoa

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From 2009 up to now, the water quantity and water quality of the Mekong River, located in the South of Vietnam, have been significantly reduced. The phenomenon happened as a result of climate change and human activities. The Mekong River is an international source of water, flowing across the borders of 6 countries, with Vietnam downstream. Activities to block the flow or build dams to construct hydroelectricity or diversion in upstream countries are either the direct cause or the risk of further deterioration of the water quality and quantity of the Mekong River, as evidenced by two phenomena which are a saline intrusion and transboundary water pollution. The protection of the crucial source of water is done through bilateral and multilateral cooperation mechanisms, notably the Mekong River Commission, established by members of the Agreement on the Cooperation for the Sustainable Development of the Mekong River Basin 1995. In this document, under Article 7, the 'no-harm' principle requires member states to take appropriate measures to prevent causing substantial damage to other member states. This principle has been practiced through the work of a number of committees established by the commission. However, the content of the rules is undetailed, lacks an implementation monitoring mechanism, and has an unreasonable dispute solution. With such difficulties, the provisions in the principle of no-harm are not adequate to protect the Mekong River's water resources in the current context.

Keywords: no-harm principle, transboundary water pollution, Mekong Commission, international source of water

Procedia PDF Downloads 174

Authors: H. Aldewachi, M. Hines, M. McCulloch, N. Woodroofe, P. Gardiner

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DPP-IV is an enzyme whose expression is affected in a variety of diseases, therefore, has been identified as possible diagnostic or prognostic marker for various tumours, immunological, inflammatory, neuroendocrine, and viral diseases. Recently, DPP-IV enzyme has been identified as a novel target for type II diabetes treatment where the enzyme is involved. There is, therefore, a need to develop sensitive and specific methods that can be easily deployed for the screening of the enzyme either as a tool for drug screening or disease marker in biological samples. A variety of assays have been introduced for the determination of DPP-IV enzyme activity using chromogenic and fluorogenic substrates, nevertheless these assays either lack the required sensitivity especially in inhibited enzyme samples or displays low water solubility implying difficulty for use in vivo samples in addition to labour and time-consuming sample preparation. In this study, novel strategies based on exploiting the high extinction coefficient of gold nanoparticles (GNPs) are investigated in order to develop fast, specific and reliable enzymatic assay by investigating synthetic peptide sequences containing a DPP IV cleavage site and coupling them to GNPs. The DPP IV could be detected by colorimetric response of peptide capped GNPs (P-GNPS) that could be monitored by a UV-visible spectrophotometer or even naked eyes, and the detection limit could reach 0.01 unit/ml. The P-GNPs, when subjected to DPP IV, showed excellent selectivity compared to other proteins (thrombin and human serum albumin) , which led to prominent colour change. This provided a simple and effective colorimetric sensor for on-site and real-time detection of DPP IV.

Keywords: gold nanoparticles, synthetic peptides, colorimetric detection, DPP-IV enzyme

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Authors: Mounir Zekkaoui, Abdelhadi Fennan

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The software evolution control requires a deep understanding of the changes and their impact on different system heterogeneous artifacts. And an understanding of descriptive knowledge of the developed software artifacts is a prerequisite condition for the success of the evolutionary process. The implementation of an evolutionary process is to make changes more or less important to many heterogeneous software artifacts such as source code, analysis and design models, unit testing, XML deployment descriptors, user guides, and others. These changes can be a source of degradation in functional, qualitative or behavioral terms of modified software. Hence the need for a unified approach for extraction and representation of different heterogeneous artifacts in order to ensure a unified and detailed description of heterogeneous software artifacts, exploitable by several software tools and allowing to responsible for the evolution of carry out the reasoning change concerned.

Keywords: heterogeneous software artifacts, software evolution control, unified approach, meta model, software architecture

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Authors: Hyun-Woo Cho

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Automatic process monitoring schemes are designed to give early warnings for unusual process events or abnormalities as soon as possible. For this end, various techniques have been developed and utilized in various industrial processes. It includes multivariate statistical methods, representation skills in reduced spaces, kernel-based nonlinear techniques, etc. This work presents a nonlinear empirical monitoring scheme for batch type production processes with incomplete process measurement data. While normal operation data are easy to get, unusual fault data occurs infrequently and thus are difficult to collect. In this work, noise filtering steps are added in order to enhance monitoring performance by eliminating irrelevant information of the data. The performance of the monitoring scheme was demonstrated using batch process data. The results showed that the monitoring performance was improved significantly in terms of detection success rate of process fault.

Keywords: batch process, monitoring, measurement, kernel method

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Authors: Renata Markiewicz-Żukowska, Patryk Nowakowski, Sylwia K. Naliwajko, Jakub M. Bołtryk, Katarzyna Socha, Anna Puścion-Jakubik, Jolanta Soroczyńska, Maria H. Borawska

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Selenium (Se) is an essential element for human health. As an integral part of glutathione peroxidase, it has antioxidant, anti-inflammatory and anticancer activities. Unfortunately, Se dietary intake is often insufficient, especially in regions where the soil is low in Se. Therefore, in search for good sources of Se, the content of this element in food products should be monitored. Food product can be considered as a source of Se when its standard portion covers above 15% of recommended daily allowance. In the case of nuts, 42g is recognized as the standard portion. The aim of this study was to determine the Se content in nuts and to answer the question of whether the studied nuts can be considered as a source of Se in the diet. The material for the study consisted of 10 types of nuts (12 samples of each one): almonds, Brazil nuts, cashews, hazelnuts, macadamia nuts, peanuts, pecans, pine nuts, pistachios and walnuts. The nuts were mineralized using microwave technique (Berghof, Germany). The content of Se was determined by atomic absorption spectrometry method with electrothermal atomization in a graphite tube with Zeeman background correction (Hitachi, Japan). The accuracy of the method was verified on certified reference material: Simulated Diet D. The statistical analysis was performed using Statistica v. 13.0 software. Statistical significance was determined at p < 0.05 level. The highest content of Se was found in Brazil nuts (4566.21 ± 3393.9 µg/kg) and the lowest in almonds (36.07 ± 18.8 µg/kg). A standard portion (42g) of almonds, brazil nuts, cashews, hazelnuts, macadamia nuts, peanuts, pecans, pine nuts, pistachios and walnuts covers the recommended daily allowance for Se respectively in: 2, 192, 28, 2, 16, 7, 4, 3, 12, 6%. Brazil nuts, cashews and macadamia nuts can be considered as a good source of Se in diet.

Keywords: atomic absorption spectrometry, diet, nuts, selenium

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Authors: Walaa M.Abd-Elhafiez, Wajeb Gharibi

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In this paper a novel color image compression technique for efficient storage and delivery of data is proposed. The proposed compression technique started by RGB to YCbCr color transformation process. Secondly, the canny edge detection method is used to classify the blocks into edge and non-edge blocks. Each color component Y, Cb, and Cr compressed by discrete cosine transform (DCT) process, quantizing and coding step by step using adaptive arithmetic coding. Our technique is concerned with the compression ratio, bits per pixel and peak signal to noise ratio, and produce better results than JPEG and more recent published schemes (like, CBDCT-CABS and MHC). The provided experimental results illustrate the proposed technique which is efficient and feasible in terms of compression ratio, bits per pixel and peak signal to noise ratio.

Keywords: image compression, color image, q-coder, quantization, edge-detection

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Authors: Chinh Nguyen Nhu Bao, Hien To Thi, Norimichi Takenaka

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Formaldehyde (HCHO) concentrations in rainwater including in tropical storms in Sakai City, Osaka, Japan have been measured continuously during rain event by developed chemiluminescence method. The level of formaldehyde was ranged from 15 µg/L to 500 µg/L. The high concentration of HCHO in rainwater is related to the wind direction from the south and west sides of Sakai City where manufactures related to chemicals, oil-refinery, and steel. The in-situ irradiated experiment on rainwater sample was conducted to prove the aqueous phase photo-production of HCHO and the degradation of HCHO. In the daytime, the aqueous phase photolysis is the source of HCHO in rainwater (4.52 ± 5.74 µg/L/h for UV light source in-situ condition, 2.84-8.96 µg/L/h under sunlight). However, in the night time, the degradation is the function of microorganism.

Keywords: chemiluminescence, formaldehyde, rainwater, typhoon

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Authors: Hu Ding, Kai Liu, Guoan Tang

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The Chinese Loess Plateau suffers from serious gully erosion induced by natural and human causes. Gully features detection including gully-affected area and its two dimension parameters (length, width, area et al.), is a significant task not only for researchers but also for policy-makers. This study aims at gully-affected area detection in three catchments of Chinese Loess Plateau, which were selected in Changwu, Ansai, and Suide by using unmanned aerial vehicle (UAV). The methodology includes a sequence of UAV data generation, image segmentation, feature calculation and selection, and random forest classification. Two experiments were conducted to investigate the influences of segmentation strategy and feature selection. Results showed that vertical and horizontal root-mean-square errors were below 0.5 and 0.2 m, respectively, which were ideal for the Loess Plateau region. The segmentation strategy adopted in this paper, which considers the topographic information, and optimal parameter combination can improve the segmentation results. Besides, the overall extraction accuracy in Changwu, Ansai, and Suide achieved was 84.62%, 86.46%, and 93.06%, respectively, which indicated that the proposed method for detecting gully-affected area is more objective and effective than traditional methods. This study demonstrated that UAV can bridge the gap between field measurement and satellite-based remote sensing, obtaining a balance in resolution and efficiency for catchment-scale gully erosion research.

Keywords: unmanned aerial vehicle (UAV), object-analysis image analysis, gully erosion, gully-affected area, Loess Plateau, random forest

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Authors: Animut Meseret Simachew

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Parallel Code Phase Search Acquisition (PCSA) Algorithm has been considered as a promising method in GPS software receivers for detection and estimation of the accurate correlation peak between the received Global Positioning System (GPS) signal and locally generated replicas. GPS signal acquisition in highly dense multipath environments is the main research challenge. In this work, we proposed a robust variable step-size (RVSS) PCSA algorithm based on fast frequency transform (FFT) filtering technique to mitigate short time delay multipath signals. Simulation results reveal the effectiveness of the proposed algorithm over the conventional PCSA algorithm. The proposed RVSS-PCSA algorithm equalizes the received carrier wiped-off signal with locally generated C/A code.

Keywords: adaptive PCSA, detection and estimation, GPS signal acquisition, GPS software receiver

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Authors: Patrick Cormier, Cesar Meylan, Matt Jensen, Dana Agar-Newman, Chloe Werle, Ming-Chang Tsai, Marc Klimstra

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Sprint-derived horizontal force-velocity-power (FVP) profiles can be developed with adequate validity and reliability with satellite (GPS/GNSS) systems. However, FVP metrics are sensitive to small nuances in data processing procedures such that minor differences in defining the onset and end of the sprint could result in different FVP metric outcomes. Furthermore, in team-sports, there is a requirement for rapid analysis and feedback of results from multiple athletes, therefore developing standardized and automated methods to improve the speed, efficiency and reliability of this process are warranted. Thus, the purpose of this study was to compare different methods of sprint end detection on the development of FVP profiles from 10Hz GPS/GNSS data through goodness-of-fit and intertrial reliability statistics. Seventeen national team female soccer players participated in the FVP protocol which consisted of 2x40m maximal sprints performed towards the end of a soccer specific warm-up in a training session (1020 hPa, wind = 0, temperature = 30°C) on an open grass field. Each player wore a 10Hz Catapult system unit (Vector S7, Catapult Innovations) inserted in a vest in a pouch between the scapulae. All data were analyzed following common procedures. Variables computed and assessed were the model parameters, estimated maximal sprint speed (MSS) and the acceleration constant τ, in addition to horizontal relative force (F₀), velocity at zero (V₀), and relative mechanical power (Pmax). The onset of the sprints was standardized with an acceleration threshold of 0.1 m/s². The sprint end detection methods were: 1. Time when peak velocity (MSS) was achieved (zero acceleration), 2. Time after peak velocity drops by -0.4 m/s, 3. Time after peak velocity drops by -0.6 m/s, and 4. When the integrated distance from the GPS/GNSS signal achieves 40-m. Goodness-of-fit of each sprint end detection method was determined using the residual sum of squares (RSS) to demonstrate the error of the FVP modeling with the sprint data from the GPS/GNSS system. Inter-trial reliability (from 2 trials) was assessed utilizing intraclass correlation coefficients (ICC). For goodness-of-fit results, the end detection technique that used the time when peak velocity was achieved (zero acceleration) had the lowest RSS values, followed by -0.4 and -0.6 velocity decay, and 40-m end had the highest RSS values. For intertrial reliability, the end of sprint detection techniques that were defined as the time at (method 1) or shortly after (method 2 and 3) when MSS was achieved had very large to near perfect ICC and the time at the 40 m integrated distance (method 4) had large to very large ICCs. Peak velocity was reached at 29.52 ± 4.02-m. Therefore, sport scientists should implement end of sprint detection either when peak velocity is determined or shortly after to improve goodness of fit to achieve reliable between trial FVP profile metrics. Although, more robust processing and modeling procedures should be developed in future research to improve sprint model fitting. This protocol was seamlessly integrated into the usual training which shows promise for sprint monitoring in the field with this technology.

Keywords: automated, biomechanics, team-sports, sprint

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Authors: D. Alromi, A. Alansari, S. Alghamdi, E. Jambi

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As the need for water is increasing globally, and the available water resources are barely meeting the current quality of life and economy. Air conditioning (AC) condensate water could be explored as an alternative water source, which could be considered within the global calculations of the water supply. The objective of this study is to better understand the potential for recovery of condensate water from air conditioning systems. The results generated so far showed that the AC produces a high quantity of water, and data analysis revealed that the amount of water is positively and significantly correlated with the humidity (P <= 0.05). In the meantime, the amount of heavy metals has been measuring using ICP-OES. The results, in terms of quantity, clearly show that the AC can be used as an alternative source of water, especially in the regions characterized by high humidity. The results also showed that the amount of produced water depends on the type of AC.

Keywords: air conditioning systems, water quantity, water resources, wastewater

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Authors: Birmohan Singh, V.K.Jain

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Computer aided diagnosis systems provide vital opinion to radiologists in the detection of early signs of breast cancer from mammogram images. Masses and microcalcifications, architectural distortions are the major abnormalities. In this paper, a computer aided diagnosis system has been proposed for distinguishing abnormal mammograms with architectural distortion from normal mammogram. Four types of texture features GLCM texture, GLRLM texture, fractal texture and spectral texture features for the regions of suspicion are extracted. Support Vector Machine has been used as classifier in this study. The proposed system yielded an overall sensitivity of 96.47% and accuracy of 96% for the detection of abnormalities with mammogram images collected from Digital Database for Screening Mammography (DDSM) database.

Keywords: architecture distortion, mammograms, GLCM texture features, GLRLM texture features, support vector machine classifier

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Authors: Haidong Li, Weishou Shen, Guoping Lv, Tao Wang

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Aiming to overcome the limitation of the application of traditional remote sensing (RS) technique in the mine eco-environmental monitoring, in this paper, we first classified the eco-environmental damages caused by mining activities and then introduced the principle, classification and characteristics of the Light Detection and Ranging (LiDAR) technique. The potentiality of LiDAR technique in the mine eco-environmental monitoring was analyzed, particularly in extracting vertical structure parameters of vegetation, through comparing the feasibility and applicability of traditional RS method and LiDAR technique in monitoring different types of indicators. The application situation of LiDAR technique in extracting typical mine indicators, such as land destruction in mining areas, damage of ecological integrity and natural soil erosion. The result showed that the LiDAR technique has the ability to monitor most of the mine eco-environmental indicators, and exhibited higher accuracy comparing with traditional RS technique, specifically speaking, the applicability of LiDAR technique on each indicator depends on the accuracy requirement of mine eco-environmental monitoring. In the item of large mine, LiDAR three-dimensional point cloud data not only could be used as the complementary data source of optical RS, Airborne/Satellite LiDAR could also fulfill the demand of extracting vertical structure parameters of vegetation in large areas.

Keywords: LiDAR, mine, ecological damage, monitoring, traditional remote sensing technique

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Authors: Assma Azeroual, Karim Afdel, Mohamed El Hajji, Hassan Douzi

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Key frame extraction methods select the most representative frames of a video, which can be used in different areas of video processing such as video retrieval, video summary, and video indexing. In this paper we present a novel approach for extracting key frames from video sequences. The frame is characterized uniquely by his contours which are represented by the dominant blocks. These dominant blocks are located on the contours and its near textures. When the video frames have a noticeable changement, its dominant blocks changed, then we can extracte a key frame. The dominant blocks of every frame is computed, and then feature vectors are extracted from the dominant blocks image of each frame and arranged in a feature matrix. Singular Value Decomposition is used to calculate sliding windows ranks of those matrices. Finally the computed ranks are traced and then we are able to extract key frames of a video. Experimental results show that the proposed approach is robust against a large range of digital effects used during shot transition.

Keywords: FSDWT, key frame extraction, shot detection, singular value decomposition

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Authors: Minal Jain, Vinayak Malhotra

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Solid Propellant ignition consists of rapid and complex events comprising of heat generation and transfer of heat with spreading of flames over the entire burning surface area. Proper combustion and thus propulsion depends heavily on the modes of heat transfer characteristics and cavity volume. Fire safety is an integral component of a successful rocket flight failing to which may lead to overall failure of the rocket. This leads to enormous forfeiture in resources viz., money, time, and labor involved. When the propellant is ignited, thrust is generated and the casing gets heated up. This heat adds on to the propellant heat and the casing, if not at proper orientation starts burning as well, leading to the whole rocket being completely destroyed. This has necessitated active research efforts emphasizing a comprehensive study on the inter-energy relations involved for effective utilization of the solid rocket motors for better space missions. Present work is focused on one of the major influential aspects of this detrimental burning which is the presence of an external heat source, in addition to a potential heat source which is already ignited. The study is motivated by the need to ensure better combustion and fire safety presented experimentally as a simplified small-scale mode of a rocket carrying a solid propellant inside a cavity. The experimental setup comprises of a paraffin wax candle as the pilot fuel and incense stick as the external heat source. The candle is fixed and the incense stick position and location is varied to investigate the find the influence of the pilot heat source. Different configurations of the external heat source presence with separation distance are tested upon. Regression rates of the pilot thin solid fuel are noted to fundamentally understand the non-linear heat and mass transfer which is the governing phenomenon. An attempt is made to understand the phenomenon fundamentally and the mechanism governing it. Results till now indicate non-linear heat transfer assisted with the occurrence of flaming transition at selected critical distances. With an increase in separation distance, the effect is noted to drop in a non-monotonic trend. The parametric study results are likely to provide useful physical insight about the governing physics and utilization in proper testing, validation, material selection, and designing of solid rocket motors with enhanced safety.

Keywords: combustion, propellant, regression, safety

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Authors: Prodipta Bhowmik, Poulami Saha, Preety Mehra, Yogesh Soni, Triloki Nath Jha

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In India, the smart city concept is growing day by day. So, for smart city development, a better traffic management and monitoring system is a very important requirement. Nowadays, road accidents increase due to more vehicles on the road. Reckless driving is mainly responsible for a huge number of accidents. So, an efficient traffic management system is required for all kinds of roads to control the traffic speed. The speed limit varies from road to road basis. Previously, there was a radar system but due to high cost and less precision, the radar system is unable to become favorable in a traffic management system. Traffic management system faces different types of problems every day and it has become a researchable topic on how to solve this problem. This paper proposed a computer vision and machine learning-based automated system for multiple vehicle detection, tracking, and speed estimation of vehicles using image processing. Detection of vehicles and estimating their speed from a real-time video is tough work to do. The objective of this paper is to detect vehicles and estimate their speed as accurately as possible. So for this, a real-time video is first captured, then the frames are extracted from that video, then from that frames, the vehicles are detected, and thereafter, the tracking of vehicles starts, and finally, the speed of the moving vehicles is estimated. The goal of this method is to develop a cost-friendly system that can able to detect multiple types of vehicles at the same time.

Keywords: OpenCV, Haar Cascade classifier, DLIB, YOLOV3, centroid tracker, vehicle detection, vehicle tracking, vehicle speed estimation, computer vision

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Authors: Siu Hua Chang, Ayub Md Som, Jagannathan Krishnan

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The kinetics of Cu(II) transport through a bulk liquid membrane with different membrane materials was investigated in this work. Three types of membrane materials were used: Fresh cooking oil, waste cooking oil, and kerosene each of which was mixed with di-2-ethylhexylphosphoric acid (carrier) and tributylphosphate (modifier). Kinetic models derived from the kinetic laws of two consecutive irreversible first-order reactions were used to study the facilitated transport of Cu(II) across the source, membrane, and receiving phases of bulk liquid membrane. It was found that the transport kinetics of Cu(II) across the source phase was not affected by different types of membrane materials but decreased considerably when the membrane materials changed from kerosene, waste cooking oil to fresh cooking oil. The rate constants of Cu(II) removal and recovery processes through the bulk liquid membrane were also determined.

Keywords: transport kinetics, Cu(II), bulk liquid membrane, waste cooking oil

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Authors: Artur Krukowski, Emmanouela Vogiatzaki

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The article presents results from the AF3 project “Advanced Forest Fire Fighting” focused on Unmanned Aircraft Systems (UAS)-based 3D surveillance and 3D area mapping using high-resolution photogrammetric methods from multispectral imaging, also taking advantage of the 3D scanning techniques from the SCAN4RECO project. We also present a proprietary embedded sensor system used for the detection of fire ignitions in the forest using near-infrared based scanner with weight and form factors allowing it to be easily deployed on standard commercial micro-UAVs, such as DJI Inspire or Mavic. Results from real-life pilot trials in Greece, Spain, and Israel demonstrated added-value in the use of UAS for precise and reliable detection of forest fires, as well as high-resolution 3D aerial modeling for accurate quantification of human resources and equipment required for firefighting.

Keywords: forest wildfires, surveillance, fuel volume estimation, firefighting, ignition detectors, 3D modelling, UAV

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Authors: Morsy Ahmed Morsy Ismail

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In this paper, the structure of a coherent homodyne receiver for the Binary Phase Shift Keying (BPSK) Optical Code Division Multiple Access (OCDMA) network is introduced based on the Multi-Length Weighted Modified Prime Code (ML-WMPC) for multimedia applications. The Bit Error Rate (BER) of this homodyne detection is evaluated as a function of the number of active users and the signal to noise ratio for different code lengths according to the multimedia application such as audio, voice, and video. Besides, the Mach-Zehnder interferometer is used as an external phase modulator in homodyne detection. Furthermore, the Multiple Access Interference (MAI) and the receiver noise in a shot-noise limited regime are taken into consideration in the BER calculations.

Keywords: OCDMA networks, bit error rate, multiple access interference, binary phase-shift keying, multimedia

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Authors: Amritesh Srivastava, Gaurav Sharma

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The purpose of this project is to develop an open source object storage system that is highly durable, scalable and reliable. There are two representative systems in cloud computing: Google and Amazon. Their storage systems for Google GFS and Amazon S3 provide high reliability, performance and stability. Our proposed system is highly inspired from Amazon S3. We are using Hadoop Distributed File System (HDFS) Java API to implement our system. We propose the architecture of object storage system based on Hadoop. We discuss the requirements of our system, what we expect from our system and what problems we may encounter. We also give detailed design proposal along with the abstract source code to implement it. The final goal of the system is to provide REST based access to our object storage system that exists on top of HDFS.

Keywords: Hadoop, HBase, object storage, REST

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Authors: Tanmay Bisen, Aastha Shayla, Susham Biswas

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Oil spills from tankers can cause significant harm to the environment and local communities, as well as have economic consequences. Early predictions of oil spills can help to minimize these impacts. Our proposed system uses machine learning and neural networks to predict potential oil spills by monitoring data from ship Automatic Identification Systems (AIS). The model analyzes ship movements, speeds, and changes in direction to identify patterns that deviate from the norm and could indicate a potential spill. Our approach not only identifies anomalies but also predicts spills before they occur, providing early detection and mitigation measures. This can prevent or minimize damage to the reputation of the company responsible and the country where the spill takes place. The model's performance on the MV Wakashio oil spill provides insight into its ability to detect and respond to real-world oil spills, highlighting areas for improvement and further research.

Keywords: Anomaly Detection, Oil Spill Prediction, Machine Learning, Image Processing, Graph Neural Network (GNN)

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Authors: Zhongzhen Luo, Saeid Habibi, Martin v. Mohrenschildt

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Self-driving vehicle require a high level of situational awareness in order to maneuver safely when driving in real world condition. This paper presents a LiDAR based real time perception system that is able to process sensor raw data for multiple target detection and tracking in dynamic environment. The proposed algorithm is nonparametric and deterministic that is no assumptions and priori knowledge are needed from the input data and no initializations are required. Additionally, the proposed method is working on the three-dimensional data directly generated by LiDAR while not scarifying the rich information contained in the domain of 3D. Moreover, a fast and efficient for real time clustering algorithm is applied based on a radially bounded nearest neighbor (RBNN). Hungarian algorithm procedure and adaptive Kalman filtering are used for data association and tracking algorithm. The proposed algorithm is able to run in real time with average run time of 70ms per frame.

Keywords: lidar, segmentation, clustering, tracking

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Authors: B. Siva Kumar Reddy, B. Lakshmi

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Cognitive Radio is a turning out technology that empowers viable usage of the spectrum. Energy Detector-based Sensing is the most broadly utilized spectrum sensing strategy. Besides, it is a lot of generic as receivers does not like any information on the primary user's signals, channel data, of even the sort of modulation. This paper puts forth the execution of energy detection sensing for AM (Amplitude Modulated) signal at 710 KHz, FM (Frequency Modulated) signal at 103.45 MHz (local station frequency), Wi-Fi signal at 2.4 GHz and WiMAX signals at 6 GHz. The OFDM/OFDMA based WiMAX physical layer with convolutional channel coding is actualized utilizing USRP N210 (Universal Software Radio Peripheral) and GNU Radio based Software Defined Radio (SDR). Test outcomes demonstrated the BER (Bit Error Rate) augmentation with channel noise and BER execution is dissected for different Eb/N0 (the energy per bit to noise power spectral density ratio) values.

Keywords: BER, Cognitive Radio, GNU Radio, OFDM, SDR, WiMAX

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Authors: Kenan Morani, Esra Kaya Ayana

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This study aimed to develop a novel pipeline for COVID-19 detection using a large and rigorously annotated database of computed tomography (CT) images. The pipeline consists of UNet-based segmentation, lung extraction, and a classification part, with the addition of optional slice removal techniques following the segmentation part. In this work, a batch normalization was added to the original UNet model to produce lighter and better localization, which is then utilized to build a full pipeline for COVID-19 diagnosis. To evaluate the effectiveness of the proposed pipeline, various segmentation methods were compared in terms of their performance and complexity. The proposed segmentation method with batch normalization outperformed traditional methods and other alternatives, resulting in a higher dice score on a publicly available dataset. Moreover, at the slice level, the proposed pipeline demonstrated high validation accuracy, indicating the efficiency of predicting 2D slices. At the patient level, the full approach exhibited higher validation accuracy and macro F1 score compared to other alternatives, surpassing the baseline. The classification component of the proposed pipeline utilizes a convolutional neural network (CNN) to make final diagnosis decisions. The COV19-CT-DB dataset, which contains a large number of CT scans with various types of slices and rigorously annotated for COVID-19 detection, was utilized for classification. The proposed pipeline outperformed many other alternatives on the dataset.

Keywords: classification, computed tomography, lung extraction, macro F1 score, UNet segmentation

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Authors: Helga Heylen

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With dogs saving wildlife in New Zealand since 1890 and governments in Africa, Australia and Canada trusting them to give the best results, Conservation Dogs Ireland want to introduce more detection dogs to protect Europe's native wildlife. Conservation detection dogs are fast, portable and endlessly trainable. They are a cost-effective, highly sensitive and non-invasive way to detect protected and invasive species and wildlife disease. Conservation dogs find targets up to 40 times faster than any other method. They give results instantly, with near-perfect accuracy. They can search for multiple targets simultaneously, with no reduction in efficacy The European Red List indicates the decline in biodiversity has been most rapid in the past 50 years, and the risk of extinction never higher. Just two examples of major threats dogs are trained to tackle are: (I)Japanese Knotweed (Fallopia Japonica), not only a serious threat to ecosystems, crops, structures like bridges and roads - it can wipe out the entire value of a house. The property industry and homeowners are only just waking up to the full extent of the nightmare. When those working in construction on the roads move topsoil with a trace of Japanese Knotweed, it suffices to start a new colony. Japanese Knotweed grows up to 7cm a day. It can stay dormant and resprout after 20 years. In the UK, the cost of removing Japanese Knotweed from the London Olympic site in 2012 was around £70m (€83m). UK banks already no longer lend on a house that has Japanese Knotweed on-site. Legally, landowners are now obliged to excavate Japanese Knotweed and have it removed to a landfill. More and more, we see Japanese Knotweed grow where a new house has been constructed, and topsoil has been brought in. Conservation dogs are trained to detect small fragments of any part of the plant on sites and in topsoil. (II)Zebra mussels (Dreissena Polymorpha) are a threat to many waterways in the world. They colonize rivers, canals, docks, lakes, reservoirs, water pipes and cooling systems. They live up to 3 years and will release up to one million eggs each year. Zebra mussels attach to surfaces like rocks, anchors, boat hulls, intake pipes and boat engines. They cause changes in nutrient cycles, reduction of plankton and increased plant growth around lake edges, leading to the decline of Europe's native mussel and fish populations. There is no solution, only costly measures to keep it at bay. With many interconnected networks of waterways, they have spread uncontrollably. Conservation detection dogs detect the Zebra mussel from its early larvae stage, which is still invisible to the human eye. Detection dogs are more thorough and cost-effective than any other conservation method, and will greatly complement and speed up the work of biologists, surveyors, developers, ecologists and researchers.

Keywords: native biodiversity, conservation detection dogs, invasive species, Japanese Knotweed, zebra mussel

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Authors: Sergey Ermolin, Olga Ermolin

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A Deep Convolution Neural Network with Triplet Loss is used to identify duplicate images in real-estate advertisements in the presence of image artifacts such as watermarking, cropping, hue/brightness adjustment, and others. The effects of batch normalization, spatial dropout, and various convergence methodologies on the resulting detection accuracy are discussed. For comparative Return-on-Investment study (per industry request), end-2-end performance is benchmarked on both Nvidia Titan GPUs and Intel’s Xeon CPUs. A new real-estate dataset from San Francisco Bay Area is used for this work. Sufficient duplicate detection accuracy is achieved to supplement other database-grounded methods of duplicate removal. The implemented method is used in a Proof-of-Concept project in the real-estate industry.

Keywords: visual recognition, convolutional neural networks, triplet loss, spatial batch normalization with dropout, duplicate removal, advertisement technologies, performance benchmarking

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Authors: Christopher Baiyegunhi, Kuiwu Liu, Oswald Gwavava

Abstract:

Petrography of the sandstones of Ecca Group, Karoo Supergroup in the Eastern Cape Province of South Africa have been investigated on composition, provenance and influence of weathering conditions. Petrographic studies based on quantitative analysis of the detrital minerals revealed that the sandstones are composed mostly of quartz, feldspar and lithic fragments of metamorphic and sedimentary rocks. The sandstones have an average framework composition of 24.3% quartz, 19.3% feldspar, 26.1% rock fragments, and 81.33% of the quartz grains are monocrystalline. These sandstones are generally very fine to fine grained, moderate to well sorted, and subangular to subrounded in shape. In addition, they are compositionally immature and can be classified as feldspathic wacke and lithic wacke. The absence of major petrographically distinctive compositional variations in the sandstones perhaps indicate homogeneity of their source. As a result of this, it is inferred that the transportation distance from the source area was quite short and the main mechanism of transportation was by river systems to the basin. The QFL ternary diagrams revealed dissected and transitional arc provenance pointing to an active margin and uplifted basement preserving the signature of a recycled provenance. This is an indication that the sandstones were derived from a magmatic arc provenance. Since magmatic provenance includes transitional arc and dissected arc, it also shows that the source area of the Ecca sediments had a secondary sedimentary and metasedimentary rocks from a marginal belt that developed as a result of rifting. The weathering diagrams and semi-quantitative weathering index indicate that the Ecca sandstones are mostly from a plutonic source area, with climatic conditions ranging from arid to humid. The compositional immaturity of the sandstones is suggested to be due to weathering or recycling and low relief or short transport from the source area. The detrital modal compositions of these sandstones are related to back arc to island and continental margin arc. The origin and deposition of the Ecca sandstones are due to low-moderate weathering, recycling of pre-existing rocks, erosion and transportation of debris from the orogeny of the Cape Fold Belt.

Keywords: petrography, tectonic setting, provenance, Ecca Group, Karoo Basin

Procedia PDF Downloads 411

Authors: Xun Li, Haojie Wang

Abstract:

Federated learning is a kind of distributed training and centralized training mode, which is of great value in the protection of user privacy. In order to solve the problem that the model is vulnerable to backdoor attacks in federated learning, a backdoor attack detection method based on a weighted k-medoids algorithm is proposed. First of all, this paper collates the update parameters of the client to construct a vector group, then uses the principal components analysis (PCA) algorithm to extract the corresponding feature information from the vector group, and finally uses the improved k-medoids clustering algorithm to identify the normal and backdoor update parameters. In this paper, the backdoor is implanted in the federation learning model through the model replacement attack method in the simulation experiment, and the update parameters from the attacker are effectively detected and removed by the defense method proposed in this paper.

Keywords: federated learning, backdoor attack, PCA, k-medoids, backdoor defense

Procedia PDF Downloads 93

Authors: M. Kucukali Ozturk, B. Nergis, C. Candan

Abstract:

Air permeability plays an important role for applications such as filtration, thermal and acoustic insulation. The study discussed in this paper was conducted in an attempt to investigate air permeability property of various combinations of nonwovens. The PROWHITE air permeability tester was used for the measurement of the air permeability of the samples in accordance with the relevant standards and a comparative study of the results were made. It was found that the fabric mass per unit area was closely related to the air-permeability. The air permeability decreased with the increase in mass per unit area. Additionally, the air permeability of nonwoven fabrics decreased with the increase in thickness. Moreover, air permeability of multilayered SMS nonwoven structures was lower than those of single layered ones.

Keywords: air permeability, mass per unit area, nonwoven structure, polypropylene nonwoven, thickness

Procedia PDF Downloads 329

Authors: Asowata Osamede, Trudy Sutherland

Abstract:

The purpose of this paper is to assess the available power from a Renewable Energy Source (off-grid photovoltaic (PV) panel) in the Southern Hemisphere using anisotropic model. Direct solar radiation is the driving force in photovoltaics. In a basic PV panels in the Southern Hemisphere, Power conversion is eminent, and this is achieved by the PV cells converting solar energy into electrical energy. In this research, the results was determined for a 6 month period from September 2022 through February 2023. Preliminary results, which include Normal Probability plot, data analysis - R2 value, effective conversion-time per week and work-time per day, indicate a favorably comparison between the empirical results and the simulation results.

Keywords: power-conversion, mathematical model, PV panels, DC-DC converters, direct solar radiation

Procedia PDF Downloads 69