9,357 research outputs found

    Graduate Catalog of Studies, 2023-2024

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    Audio-visual multi-modality driven hybrid feature learning model for crowd analysis and classification

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    The high pace emergence in advanced software systems, low-cost hardware and decentralized cloud computing technologies have broadened the horizon for vision-based surveillance, monitoring and control. However, complex and inferior feature learning over visual artefacts or video streams, especially under extreme conditions confine majority of the at-hand vision-based crowd analysis and classification systems. Retrieving event-sensitive or crowd-type sensitive spatio-temporal features for the different crowd types under extreme conditions is a highly complex task. Consequently, it results in lower accuracy and hence low reliability that confines existing methods for real-time crowd analysis. Despite numerous efforts in vision-based approaches, the lack of acoustic cues often creates ambiguity in crowd classification. On the other hand, the strategic amalgamation of audio-visual features can enable accurate and reliable crowd analysis and classification. Considering it as motivation, in this research a novel audio-visual multi-modality driven hybrid feature learning model is developed for crowd analysis and classification. In this work, a hybrid feature extraction model was applied to extract deep spatio-temporal features by using Gray-Level Co-occurrence Metrics (GLCM) and AlexNet transferrable learning model. Once extracting the different GLCM features and AlexNet deep features, horizontal concatenation was done to fuse the different feature sets. Similarly, for acoustic feature extraction, the audio samples (from the input video) were processed for static (fixed size) sampling, pre-emphasis, block framing and Hann windowing, followed by acoustic feature extraction like GTCC, GTCC-Delta, GTCC-Delta-Delta, MFCC, Spectral Entropy, Spectral Flux, Spectral Slope and Harmonics to Noise Ratio (HNR). Finally, the extracted audio-visual features were fused to yield a composite multi-modal feature set, which is processed for classification using the random forest ensemble classifier. The multi-class classification yields a crowd-classification accurac12529y of (98.26%), precision (98.89%), sensitivity (94.82%), specificity (95.57%), and F-Measure of 98.84%. The robustness of the proposed multi-modality-based crowd analysis model confirms its suitability towards real-world crowd detection and classification tasks

    Review of the applications of principles of insect hearing to microscale acoustic engineering challenges

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    When looking for novel, simple, and energy-efficient solutions to engineering problems, nature has proved to be an incredibly valuable source of inspiration. The development of acoustic sensors has been a prolific field for bioinspired solutions. With a diverse array of evolutionary approaches to the problem of hearing at small scales (some widely different to the traditional concept of "ear"), insects in particular have served as a starting point for several designs. From locusts to moths, through crickets and mosquitoes among many others, the mechanisms found in nature to deal with small-scale acoustic detection and the engineering solutions they have inspired are reviewed. The present article is comprised of three main sections corresponding to the principal problems faced by insects, namely frequency discrimination, which is addressed by tonotopy, whether performed by a specific organ or directly on the tympana; directionality, with solutions including diverse adaptations to tympanal structure; and detection of weak signals, through what is known as active hearing. The three aforementioned problems concern tiny animals as much as human-manufactured microphones and have therefore been widely investigated. Even though bioinspired systems may not always provide perfect performance, they are sure to give us solutions with clever use of resources and minimal post-processing, being serious contenders for the best alternative depending on the requisites of the problem

    Non-Intrusive Disaggregation of Advanced Metering Infrastructure Signals for Demand-Side Management

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    As intermittent renewable energy generation resources become more prevalent, innovative ways to manage the electric grid are sought. In the past, much of the grid balancing effort has been focused on the supply side or on demand-side management of large commercial or industrial electricity customers. Today, with the increase in enabling technologies such as Internet-connected appliances, home energy management systems, and advanced metering infrastructure (AMI) smart meters, residential demand-side management is also a possibility. For a utility to assess the potential capacity of residential demand-side flexibility, power data from controllable appliances from a large sample of houses is required. These data may be collected by installing time- and cost-intensive monitoring equipment at every site, or, alternatively, by disaggregating the signals communicated to the utility by AMI meters. In this study, non-intrusive load monitoring algorithms are used to disaggregate low-resolution real power signals from AMI smart meters. Disaggregation results using both supervised and unsupervised versions of a graph signal processing (GSP) -based algorithm are presented. The effects of varying key parameters in each GSP algorithm, including scaling factor, sequence, and classifier threshold are also presented, and limitations of the algorithm based on energy use patterns are discussed. FM values greater than 0.8 were achieved for the electric resistance water heater and electric vehicle charger using the unsupervised GSP algorithm. The disaggregated signals are then used to develop energy forecasting models for predicting the load of controllable appliances over a given demand response period. ARIMA, SVR, and LSTM forecasting methods were evaluated and compared to a baseline model developed using the mean hourly power draw values. The minimum MAAPE was achieved for the water heater, with an approximate range of 10 < MAAPE < 20. The total energy flexibility of each appliance and the associated uncertainty of the combined disaggregation and forecast are characterized to assess the feasibility of this approach for demand-side management applications. The framework presented in this study may be used to characterize the ability of signals to be disaggregated from a larger dataset of AMI data, based on the whole-house signal characteristics. This analysis can aid grid managers in assessing the viability of selected devices, such as the water heater, for demand response activities.Ph.D

    Science and Innovations for Food Systems Transformation

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    This Open Access book compiles the findings of the Scientific Group of the United Nations Food Systems Summit 2021 and its research partners. The Scientific Group was an independent group of 28 food systems scientists from all over the world with a mandate from the Deputy Secretary-General of the United Nations. The chapters provide science- and research-based, state-of-the-art, solution-oriented knowledge and evidence to inform the transformation of contemporary food systems in order to achieve more sustainable, equitable and resilient systems

    Local Activism in Urban Neighborhood Governance

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    This study investigates local activism in politically challenging contexts, focusing on Cairo. In such contexts, active resident groups strive for urban improvement, while governance arrangements often disregard citizen involvement in urban and public affairs. Cairo presents an exemplary case of local activism in a politically challenging and under-researched context. The study explores the characteristics, roles, and interrelations of active resident groups with local governance arrangements and their deviations from existing literature. It employs a qualitative methodology with observations and semi-structured interviews with local officials and active residents from nine different districts. The study uses Facebook to select, observe, and analyze the activities of multiple active resident groups and contributes to theoretical frameworks for analyzing local activism in complex contexts. It reveals the dominant and absent roles and the governance dimensions (un)attainable by active residents. It also traces the sources of limited local activism in the existing governance arrangements in Cairo, highlighting the importance and difficulty of changing governance arrangements in Egypt. The study broadens our understanding of local activism in the Global South beyond dominant forms of activism.

    Weather or not? The role of international sanctions and climate on food prices in Iran

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    IntroductionThe scarcity of resources have affected food production, which has challenged the ability of Iran to provide adequate food for the population. Iterative and mounting sanctions on Iran by the international community have seriously eroded Iran's access to agricultural technology and resources to support a growing population. Limited moisture availability also affects Iran's agricultural production. The aim of this study was to analyze the influence of inflation, international sanctions, weather disturbances, and domestic crop production on the price of rice, wheat and lentils from 2010 to 2021 in Iran.MethodData were obtained from the statistical yearbooks of the Ministry of Agriculture in Iran, Statistical Center of Iran, and the Central Bank of Iran. We analyzed econometric measures of food prices, including CPI, food inflation, subsidy reform plan and sanctions to estimate economic relationships. After deflating the food prices through CPI and detrending the time series to resolve the non-linear issue, we used monthly Climate Hazards group Infrared Precipitation with Stations (CHIRPS) precipitation data to analyze the influence of weather disturbances on food prices.Results and discussionThe price of goods not only provides an important indicator of the balance between agricultural production and market demand, but also has strong impacts on food affordability and food security. This novel study used a combination of economic and climate factors to analyze the food prices in Iran. Our statistical modeling framework found that the monthly precipitation on domestic food prices, and ultimately food access, in the country is much less important than the international sanctions, lowering Iran's productive capability and negatively impacting its food security

    International Academic Symposium of Social Science 2022

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    This conference proceedings gathers work and research presented at the International Academic Symposium of Social Science 2022 (IASSC2022) held on July 3, 2022, in Kota Bharu, Kelantan, Malaysia. The conference was jointly organized by the Faculty of Information Management of Universiti Teknologi MARA Kelantan Branch, Malaysia; University of Malaya, Malaysia; Universitas Pembangunan Nasional Veteran Jakarta, Indonesia; Universitas Ngudi Waluyo, Indonesia; Camarines Sur Polytechnic Colleges, Philippines; and UCSI University, Malaysia. Featuring experienced keynote speakers from Malaysia, Australia, and England, this proceeding provides an opportunity for researchers, postgraduate students, and industry practitioners to gain knowledge and understanding of advanced topics concerning digital transformations in the perspective of the social sciences and information systems, focusing on issues, challenges, impacts, and theoretical foundations. This conference proceedings will assist in shaping the future of the academy and industry by compiling state-of-the-art works and future trends in the digital transformation of the social sciences and the field of information systems. It is also considered an interactive platform that enables academicians, practitioners and students from various institutions and industries to collaborate

    AN ADAPTIVE MULTIPLE-OBJECT TRACKING ARCHITECTURE FOR LONG-DURATION VIDEOS WITH VARIABLE TARGET DENSITY

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    Multiple-Object Tracking (MOT) methods are used to detect targets in individual video frames, e.g., vehicles, people, and other objects, and then record each unique target’s path over time. Current state-of-the-art approaches are extremely complex because most rely on extracting and comparing visual features at every frame to track each object. These approaches are geared toward high-difficulty-tracking scenarios, e.g., crowded airports, and require expensive dedicated hardware, e.g., Graphics Processing Units. In hardware-constrained applications, researchers are turning to older, less complex MOT methods, which reveals a serious scalability issue within the state-of-the-art. Crowded environments are a niche application for MOT, i.e., there are far more residential areas than there are airports. Given complex approaches are not required for low-difficulty-tracking scenarios, i.e., video showing mainly isolated targets, there is an opportunity to utilize more efficient MOT methods for these environments. Nevertheless, little recent research has focused on developing more efficient MOT methods. This thesis describes a novel MOT method, ClusterTracker, that is built to handle variable-difficulty-tracking environments an order of magnitude faster than the state-of-the-art. It achieves this by avoiding visual features and using quadratic-complexity algorithms instead of the cubic-complexity algorithms found in other trackers. ClusterTracker performs spatial clustering on object detections from short frame sequences, treats clusters as tracklets, and then connects successive tracklets with high bounding-box overlap to form tracks. With recorded video, parallel processing can be applied to several steps of ClusterTracker. This thesis evaluates ClusterTracker’s baseline performance on several benchmark datasets, describes its intended operating environments, and identifies its weaknesses. Subsequent modifications patch these weaknesses while also addressing the scalability concerns of more complex MOT methods. The modified architecture uses clustering feedback to separate isolated targets from non-isolated targets, re-processing the latter with a more complex MOT method. Results show ClusterTracker is uniquely suited for such an approach and allows complex MOT methods to be applied to the challenging tracking situations for which they are intended
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