Search results for: community detection

Authors: Abdulrahman S. Alturki, John S. Loomis

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

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

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Authors: Minnie Y. Teng, Kathy Xie, Jarus Tal

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Rationale: The traditional approach to community programs for children with mental and/or developmental challenges often involves segregation from typically-developing peers. However, studies show that inclusive education improves children’s quality of life, self-concept, and long term health outcomes. Investigating factors that influence inclusion can thus have important implications in the design and facilitation of community programs such that all children - across a spectrum of needs and abilities - may benefit. Objectives: This study explores barriers and facilitators to inclusive community programming for children aged 0 to 12 with developmental/mental challenges. Methods: Using a participatory-action research methodology, semi-structured focus groups and interviews will be used to explore perspectives of sighted students, instructors, and staff. Data will be transcribed and coded thematically. Practice Implications or Results: By having a deeper understanding of the barriers and facilitators to inclusive programming in the community, researchers can work with the broader community to facilitate inclusion in children’s community programs. Conclusions: Expanding inclusive practices may improve the health and wellbeing of the pediatric populations with disabilities, which consistently reports lower levels of participation. These findings may help to identify gaps in existing practices and ways to approach them.

Keywords: aquatic programs, children, disabilities, inclusion, community programs

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Authors: Kathaleeya Chanda

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The objectives of the research on the development of printed media for public relations of community products in Bang Khonthi District, Samut Songkram Province were to; 1) develop printed media for public relations of community products in Bang Khonthi District, Samut Songkram Province and 2) study the satisfaction towards the printed media for public relations of community products in Bang Khonthi District, Samut Songkram Province. The study was on the satisfaction focused on content, illustration, design, and quality. The sampling group of this study included 30 local community producers selected by probability sampling and simple random sampling. The research procedure consisted of 4 phases; 1) problem and situation study, 2) the development of printed media for public relations, 3) research tool development, and 4) data collection. Questionnaires to evaluate the satisfaction of local community producers towards the printed media for public relation were used to collect data which were analysed to find percentage, arithmetic means, and standard deviation. The results showed that the informants reported their satisfaction on 4 aspects as follows: 1) very high satisfaction on the content, 2) high satisfaction on the illustration, 3) very high satisfaction on the design, and 4) very high satisfaction on the product quality. This can be concluded that the developed printed media for public relations of community products can be used effectively with high satisfaction.

Keywords: printed media, public relations, community products in Bang Khonthi district, Samut Songkram Province

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Authors: Kashika P. H.

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

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

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Authors: Ramzi Rihane, Yassine Benayed

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Epilepsy is a chronic neurological disorder characterized by recurrent, unprovoked seizures resulting from abnormal electrical activity in the brain. Timely and accurate detection of these seizures is essential for improving patient care. In this study, we leverage the UK Bonn University open-source EEG dataset and employ advanced deep-learning techniques to automate the detection of epileptic seizures. By extracting key features from both time and frequency domains, as well as Spectrogram features, we enhance the performance of various deep learning models. Our investigation includes architectures such as Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), 1D Convolutional Neural Networks (1D-CNN), and hybrid CNN-LSTM and CNN-BiLSTM models. The models achieved impressive accuracies: LSTM (98.52%), Bi-LSTM (98.61%), CNN-LSTM (98.91%), CNN-BiLSTM (98.83%), and CNN (98.73%). Additionally, we utilized a data augmentation technique called SMOTE, which yielded the following results: CNN (97.36%), LSTM (97.01%), Bi-LSTM (97.23%), CNN-LSTM (97.45%), and CNN-BiLSTM (97.34%). These findings demonstrate the effectiveness of deep learning in capturing complex patterns in EEG signals, providing a reliable and scalable solution for real-time seizure detection in clinical environments.

Keywords: electroencephalogram, epileptic seizure, deep learning, LSTM, CNN, BI-LSTM, seizure detection

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Authors: Kariman Abdelaziz Ahmed Ali Hamzawy

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Amidst the growing global challenges facing communities, from climate change to educational gaps, sustainable community education has emerged as a vital tool for ensuring comprehensive and enduring development. This research aims to explore effective strategies for sustainable community education that can lead to long-term impacts on local communities. The study begins by defining the concept of sustainable education within a community context and reviews the current literature on the topic. It then presents case studies from various communities around the world where sustainable educational strategies have been successfully implemented. These case studies illustrate how sustainable education can enhance community engagement, build local capacities, and improve quality of life in sustainable ways. The findings from these studies are analyzed to identify the key factors contributing to the success of sustainable educational programs. These factors include partnerships between different sectors (governmental, private, and community), the innovative use of technology, and the adaptation of educational curricula to meet the unique needs of the community. The research also offers practical recommendations on designing and implementing sustainable educational programs, emphasizing the integration of formal and informal education, promoting lifelong learning, and developing local resources. It addresses potential challenges and ways to overcome them to ensure the long-term sustainability of these programs. In conclusion, the research provides a future vision of the role of sustainable education in building resilient and prosperous communities and highlights the importance of investing in education as a key driver of sustainable development. This study contributes to the ongoing discussion on achieving lasting impact through sustainable community education and offers a practical framework for stakeholders to adopt and implement these strategies.

Keywords: sustainable education, community education, Community engagement, local capacity building, educational technology

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Authors: Richard O’Riordan, Saritha Unnikrishnan

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

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

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

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

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

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Authors: Atinkut Atinafu Yilma, Eyob Messele Sefene

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

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

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Authors: Maryam Heidari, James H. Jones Jr.

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

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

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Authors: Bernard Owusu Asare

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This study investigates the implementation and efficacy of community policing within the Ghana Police Service, with a focus on its impact on local communities. Emphasizing the goal of creating safer environments and improving the overall quality of life, the research engages opinion leaders from selected communities in Ghana, as well as members of the police force stationed within these communities. Employing a semi-structured interview guide as the primary research instrument, data collection involves face-to-face interviews conducted at respondents' residences and policing centers. The preliminary findings underscore the pivotal role of collaborative efforts between community elders and police personnel in the successful execution of community policing initiatives. Furthermore, the study identifies gainful employment for the youth as a key determinant of effective policing, highlighting the interconnectedness of socioeconomic factors with law enforcement outcomes. The study further reveals that access to the internet emerges as a factor influencing both policing practices and the overall quality of life within these communities. By contextualizing the dynamics of community policing in the local Ghanaian context, this research aims to contribute valuable insights to the ongoing discourse on effective law enforcement strategies and their impact on community well-being.

Keywords: community, policing, police service, Ghana

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Authors: H. Bakhshi, E. Khayyamian

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

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

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

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

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

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Authors: Ngono Mindzeng Terencia

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Community based tourism, as a sustainable tourism approach, has been adopted as a tool for development among local communities in third world countries with income generation as the main driver. However, an analysis of community based tourism and development brings to light another driving force which is paramount to development strategies in the difficult conditions of third world countries: this driving force is “place revitalization”. This paper seeks to assess the relevance of “place revitalization” to the enhancement of development within the challenging context of developing countries. The research provides a community based tourism model to development in third world countries through a three step process based on awareness, mentoring and empowerment at the local level. It also tries to examine how effectively this model can address the development problems faced by the local communities of third world countries. The case study for this research is the Bamiléké region of Cameroon, the breeding ground of community based tourism initiatives and a region facing the difficulties of third world countries that are great impediments to community based tourism.

Keywords: awareness, empowerment, local communities, mentoring, place revitalization, third world countries

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Authors: Jui-Chien Hsieh

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

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

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Authors: Lina Pan

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

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

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

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

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

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

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

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

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Authors: Wu Chao

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The urban design of the new district in China is different from other existing cities at the city planning level, including Beijing, Shanghai, Guangzhou, etc. And the urban problems of these super-cities are same as many big cities around the world. The goal of the new district construction plan is to enable people to live comfortably, to improve the well-being of residents, and to create a way of life different from that of other urban communities. To avoid the emergence of the super community, the idea of "decentralization" is taken as the overall planning idea, and the function and form of each community are set up with a homogeneous allocation of resources so that the community can grow naturally. Similar to the growth of vines in nature, each community groups are independent and connected through roads, with clear community boundaries that limit their unlimited expansion. With a community contained 20,000 people as a case, the community is a mixture for living, production, office, entertainment, and other functions. Based on the development of the Internet, to create more space for public use, and can use data to allocate resources in real time. And this kind of shared space is the main part of the activity space in the community. At the same time, the transformation of spatial function can be determined by the usage feedback of all kinds of existing space, and the use of space can be changed by the changing data. Take the residential unit as the basic building function mass, take the lower three to four floors of the building as the main flexible space for use, distribute functions such as entertainment, service, office, etc. For the upper living space, set up a small amount of indoor and outdoor activity space, also used as shared space. The transformable space of the bottom layer is evenly distributed, combined with the walking space connected the community, the service and entertainment network can be formed in the whole community, and can be used in most of the community space. With the basic residential unit as the replicable module, the design of the other residential units runs through the idea of decentralization and the concept of the vine community, and the various units are reasonably combined. At the same time, a small number of office buildings are added to meet the special office needs. The new functional mixed community can change many problems of the present city in the future construction, at the same time, it can keep its vitality through the adjustment function of the Internet.

Keywords: decentralization, mixed functional community, shared space, spatial usage data

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

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

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

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Authors: Alvin Manuel Vazhayil, Chaozhong Tan, Karl M. Wantzen

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As local community participation in river restoration projects is increasingly recognized to be crucial for sustainable outcomes, researchers are exploring factors that motivate community participation globally. In India, while there is consensus in literature on the importance of community engagement in river restoration projects, research on what drives local communities to participate is limited, especially given the societal and economic challenges common in the Global South. This study addresses this gap by exploring the drivers of community participation in the local river restoration initiatives of the "Now Let Me Flow" campaign in Kerala, India. The project aimed to restore 87,000 kilometers of streams through the middle-ground governance approach that integrated bottom-up community efforts with top-down governmental support. The fieldwork involved interviews with 26 key agents, including local leaders, policy practitioners, politicians, and environmental activists associated with the project, and the collection of secondary data from 12 documents including project reports and news articles. The data was analyzed in NVivo (NVivo 11 Plus for Windows, version 11.3.0.773) using thematic analysis which included two cycles of systematic coding. The findings reveal two main drivers influencing community participation: top-down actions from local governments, and bottom-up drivers within the community. The study highlights the importance of local stakeholder collaboration, support of local governments, and local community engagement in successful river restoration projects. These findings are consistent with other empirical studies on participatory environmental problem-solving globally. The results offer crucial insights for policymakers and governments to better design and implement effective and sustainable participatory river restoration projects.

Keywords: community initiatives, drivers of participation, environmental governance, river restoration

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Authors: Sabina Ngodigha, Roland Gbarabe, Aiyebatonworio Austin

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A study of fisher’s knowledge (FK) and community-based fisheries management practices in River Nun estuary was conducted to assess the contribution of FK to fisheries resources conservation. A total of 390 fishers operates in the area of which 221 were interviewed based on having a minimum of 10 years of experience. Community-based fisheries management programme was introduced and implemented by fishermen’s union in 2010 for the sustainable management and conservation of fisheries resources. Local law introduced were: band on the use of mesh size of less than 5cm and band on chemical fishing. Defaulters were made to pay monetary fines ranging from #2,000 to #6,000 while fishers caught using chemicals to fish were arrested and landed over to the police for prosecution. The management method has enhanced conservation of fisheries resources which is a major source of livelihood for the people. Landings increased tremendously resulting in positive increase in the finances of the fishers. It is, therefore, pertinent to introduce community-based laws to check over exploitation of fisheries resources in the Niger Delta.

Keywords: community, conservation, fishers knowledge, local laws, management

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

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

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

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

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

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

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Authors: Craig Mahlasi

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

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

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

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

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

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Authors: Johannes Van Der Sandt

Abstract:

This quantitative study investigates different approaches of community singing, in building social capital in South Tyrol, Italy, and South Africa. The impact of the various approaches of community singing is examined by investigating the main components of social capital, namely, social norms and obligations, social networks and associations and trust, and how these components are manifested in two different societies. The research is based on the premise that community singing is an important agent for the development of social capital. It seeks to establish in what form community singing can best enhance the social capital of communities in South Tyrol that are undergoing significant changes in the ways in which social capital is generally being generated on account of demographic, economic, technological and cultural changes. South Tyrol and South Africa share some similarities in the management of their multi-cultural composition. By comparing the different approaches to community singing in two multi-cultural societies, it is hoped to gain insight, and an understanding of the connections between culture, social cohesion, identity and therefore to be able to add to the understanding of the building of social capital through community singing. Participation in music contributes to the growth of social capital in communities, this is amongst others the finding of an ever increasing amount of research. In sociological discourses on social capital generation, the dimension of community music making is recognized as an important factor. Trust and mutual cooperation are products when people listen to each other, when they work or play together, and when they care about each other. This is how social capital develops as an important shared resource. Scholars of Community Music still do not agree on a short and concise definition for Community Music. For the purpose of this research, the author concurs with the definition of Community Music of the Community Music Activity commission of the International Society of Music Education as having the following characteristics: decentralization, accessibility, equal opportunity, and active participation in music-making. These principles are social and political ones, and there can be no doubt that community music activity is more than a purely musical one. Trust, shared norms and values civic and community involvement, networks, knowledge resources, contact with families and friends, and fellowship are key components in fostering group cohesion and social capital development in a community. The research will show that there is no better place for these factors to flourish than in a community singing group. Through this comparative study, it is the aim to identify, analyze and explain similarities and differences in approaches to community across societies that find themselves in a rapid transition from traditional cultural to global cultural habits characterized by a plurality of orientation points, with the aim to gain a better understanding of the various directions South Tyrolean singing culture can take.

Keywords: community music, multicultural, singing, social capital

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

Abstract:

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

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

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Authors: Marshet Girmay

Abstract:

Local community involvement is widely considered vital for the sustainability of heritage management. Yet, it is often the case that heritage-related projects lag behind in community involvement. In the Semien Mountains the creation, first, and expansion, later, of the National Park has led to several conflicts with the local communities that for centuries have inhabited the area. Local communities have only been passive actors in the plans to expand the Park set up by UNESCO and by local decision makers. This paper investigates the causes that led the Gich community, one of the communities affected by the Park’s expansion, to refuse the resettlement plan offered by the authorities. Qualitative research methods were employed, including document analysis, community conference and interview of informants. The paper shows that although the local community of Gich was highly attached to the Park’s heritage assets, their level of involvement in the heritage management was very low due to shortcomings in the design and implementation of official policies. Therefore, their attitude towards the Park’s managers has been until the present one of mistrust and opposition. The paper recommends to policy-makers a series of measures more sensitive towards local communities, such as that the development agencies act as true communication facilitators and regional authorities nurture sincere relationships with the locals.

Keywords: Gich, heritage management, local communities, Semen Mountains, sustainability, UNESCO, world heritage site

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

Abstract:

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

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

Procedia PDF Downloads 144