Current Search: Detectors (x)
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Title
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MEASUREMENT, ANALYSIS, CLASSIFICATION AND DETECTION OF GUNSHOT AND GUNSHOT-LIKE SOUNDS.
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Creator
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Baliram, Rajesh Singh, Zhuang, Hanqi, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
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Abstract/Description
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The recent uptick in senseless shootings in otherwise quiet and relatively safe environments is powerful evidence of the need, now more than ever, to reduce these occurrences. Artificial intelligence (AI) can play a significant role in deterring individuals from attempting these acts of violence. The installation of audio sensors can assist in the proper surveillance of surroundings linked to public safety, which is the first step toward AI-driven surveillance. With the increasing popularity...
Show moreThe recent uptick in senseless shootings in otherwise quiet and relatively safe environments is powerful evidence of the need, now more than ever, to reduce these occurrences. Artificial intelligence (AI) can play a significant role in deterring individuals from attempting these acts of violence. The installation of audio sensors can assist in the proper surveillance of surroundings linked to public safety, which is the first step toward AI-driven surveillance. With the increasing popularity of machine learning (ML) processes, systems are being developed and optimized to assist personnel in highly dangerous situations. In addition to saving innocent lives, supporting the capture of the responsible criminals is part of the AI algorithm that can be hosted in acoustic gunshot detection systems (AGDSs). Although there has been some speculation that these AGDSs produce a higher false positive rate (FPR) than reported in their specifications, optimizing the dataset used for the model’s training and testing will enhance its performance. This dissertation proposes a new gunshot-like sound database that can be incorporated into a dataset for improved training and testing of a ML gunshot detection model. Reduction of the sample bias (that is, a bias in ML caused by an incomplete database) is achievable. The Mel frequency cepstral coefficient (MFCC) feature extraction process was utilized in this research. The uniform manifold and projection (UMAP) algorithm revealed that the MFCCs of this newly created database were the closest sounds to a gunshot sound, as compared to other gunshot-like sounds reported in literature. The UMAP algorithm reinforced the outcome derived from the calculation of the distances of the centroids of various gunshot-like sounds in MFCCs’ clusters. Further research was conducted into the feature reduction aspect of the gunshot detection ML model. Reducing a feature set to a minimum, while also maintaining a high accuracy rate, is a key parameter of a highly efficient model. Therefore, it is necessary for field deployed ML applications to be computationally light weight and highly efficient. Building on the discoveries of this research can lead to the development of highly efficient gunshot detection models.
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Date Issued
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2022
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PURL
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http://purl.flvc.org/fau/fd/FA00014110
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Subject Headings
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Firearms, Sound, Detectors, Machine learning
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Format
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Document (PDF)
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Title
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Atmospheric corrosion sensor studies in accelerated and natural environments.
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Creator
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Richard, Sebastien Laurent., Florida Atlantic University, Granata, Richard D., College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
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Abstract/Description
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Tests in a cyclic chamber and in real atmospheric conditions resulted in the development of an improved corrosion coulometer sensor. First tests showed that it responded well in a reproduced environment but not satisfactorily in a real one, although it seemed to present a good correlation with the weather observations. However, these tests allowed a small time step data analysis of atmospheric corrosion and therefore an improved knowledge of this process. Also discussed are the possible ways...
Show moreTests in a cyclic chamber and in real atmospheric conditions resulted in the development of an improved corrosion coulometer sensor. First tests showed that it responded well in a reproduced environment but not satisfactorily in a real one, although it seemed to present a good correlation with the weather observations. However, these tests allowed a small time step data analysis of atmospheric corrosion and therefore an improved knowledge of this process. Also discussed are the possible ways of retrieving the corrosion coulometer data wirelessly, thus allowing a real-time analysis of atmospheric corrosion on steel structures. Ideas are proposed for improving both the sensor and the electronic package to make the system an efficient monitor of atmospheric corrosion.
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Date Issued
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2003
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PURL
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http://purl.flvc.org/fcla/dt/13041
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Subject Headings
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Corrosion and anti-corrosives, Voltameters, Detectors
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Format
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Document (PDF)
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Title
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HUMAN ACTIVITY RECOGNITION: INTEGRATING SENSOR FUSION AND ARTIFICIAL INTELLIGENCE.
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Creator
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Alanazi, Munid, Ilyas, Mohammad, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
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Abstract/Description
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Human Activity Recognition (HAR) plays a crucial role in various applications, including healthcare, fitness tracking, security, and smart environments, by enabling the automatic classification of human actions based on sensor and visual data. This dissertation presents a comprehensive exploration of HAR utilizing machine learning, sensor-based data, and Fusion approaches. HAR involves classifying human activities over time by analyzing data from sensors such as accelerometers and gyroscopes....
Show moreHuman Activity Recognition (HAR) plays a crucial role in various applications, including healthcare, fitness tracking, security, and smart environments, by enabling the automatic classification of human actions based on sensor and visual data. This dissertation presents a comprehensive exploration of HAR utilizing machine learning, sensor-based data, and Fusion approaches. HAR involves classifying human activities over time by analyzing data from sensors such as accelerometers and gyroscopes. Recent advancements in computational technology and sensor availability have driven significant progress in this field, enabling the integration of these sensors into smartphones and other devices. The first study outlines the foundational aspects of HAR and reviews existing literature, highlighting the importance of machine learning applications in healthcare, athletics, and personal use. In the second study, the focus shifts to addressing challenges in handling large-scale, variable, and noisy sensor data for HAR systems. The research applies machine learning algorithms to the KU-HAR dataset, revealing that the LightGBM classifier outperforms others in key performance metrics such as accuracy, precision, recall, and F1 score. This study underscores the continued relevance of optimizing machine learning techniques for improved HAR systems. The study highlights the potential for future research to explore more advanced fusion techniques to fully leverage different data modalities for HAR. The third study focuses on overcoming common challenges in HAR research, such as varying smartphone models and sensor configurations, by employing data fusion techniques.
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Date Issued
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2024
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PURL
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http://purl.flvc.org/fau/fd/FA00014496
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Subject Headings
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Artificial intelligence, Human activity recognition, Detectors
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Format
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Document (PDF)
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Title
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A communication protocol for wireless sensor networks.
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Creator
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Callaway, Edgar Herbert, Jr., Florida Atlantic University, Shankar, Ravi, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
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Abstract/Description
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Many wireless network applications, such as wireless computing on local area networks, employ data throughput as a primary performance metric. The data throughput on such networks has therefore been increasing in recent years. However, there are other potential wireless network applications, such as industrial monitoring and control, consumer home automation, and military remote sensing, that have relaxed throughput requirements, often measured in bits/day. Such networks have power...
Show moreMany wireless network applications, such as wireless computing on local area networks, employ data throughput as a primary performance metric. The data throughput on such networks has therefore been increasing in recent years. However, there are other potential wireless network applications, such as industrial monitoring and control, consumer home automation, and military remote sensing, that have relaxed throughput requirements, often measured in bits/day. Such networks have power consumption and cost as primary performance metrics, rather than data throughput, and have been called wireless sensor networks. This work describes a physical layer, a data link layer, and a network layer design suitable for use in wireless sensor networks. To minimize node duty cycle and therefore average power consumption, while minimizing the symbol rate, the proposed physical layer employs a form of orthogonal multilevel signaling in a direct sequence spread spectrum format. Results of Signal Processing Worksystem (SPW, Cadence, Inc.) simulations are presented showing a 4-dB sensitivity advantage of the proposed modulation method compared to binary signaling, in agreement with theory. Since the proposed band of operation is the 2.4 GHz unlicensed band, interference from other services is possible; to address this, SPW simulations of the proposed modulation method in the presence of Bluetooth interference are presented. The processing gain inherent in the proposed spread spectrum scheme is shown to require the interferer to be significantly stronger than the desired signal before materially affecting the received bit error rate. The proposed data link layer employs a novel distributed mediation device (MD) technique to enable networked nodes to synchronize to each other, even when the node duty cycle is arbitrarily low (e.g., <0.1%). This technique enables low-cost devices, which may employ only low-stability time bases, to remain asynchronous to one another, becoming synchronized only when communication is necessary between them. Finally, a wireless sensor network design is presented. A cluster-type architecture is chosen; the clusters are organized in a hierarchical tree to simplify the routing algorithm. Results of several network performance metrics simulations, including the effects of the distributed MD dynamic synchronization scheme, are presented, including the average message latency, node duty cycle, and data throughput. The architecture is shown to represent a practical alternative for the design of wireless sensor networks.
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Date Issued
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2002
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PURL
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http://purl.flvc.org/fcla/dt/11991
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Subject Headings
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Wireless communication systems, Computer network protocols, Radio detectors
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Format
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Document (PDF)
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Title
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Studies of nanoparticle reinforced polymer coatings for trace gas detection.
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Creator
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Davis, Charles, Mahfuz, Hassan, College of Engineering and Computer Science, Department of Ocean and Mechanical Engineering
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Abstract/Description
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With the goal of improving chemical detection methods for buried improvised explosive devices (IED’s), the intention of this study is to show that functionalized nano-particles improve the sensing properties of a polymer applied to gas sensors. The approach was reinforcing the polymer, Nafion, with acid-functionalized carbon nanotubes (CNT’s). Ammonia was chosen as the analyte for its similarity to IED byproducts without the dangers of toxicity or explosion. Two sensor platforms were...
Show moreWith the goal of improving chemical detection methods for buried improvised explosive devices (IED’s), the intention of this study is to show that functionalized nano-particles improve the sensing properties of a polymer applied to gas sensors. The approach was reinforcing the polymer, Nafion, with acid-functionalized carbon nanotubes (CNT’s). Ammonia was chosen as the analyte for its similarity to IED byproducts without the dangers of toxicity or explosion. Two sensor platforms were investigated: Quartz crystal microbalances (QCM’s) and microcantilevers (MC’s). Preliminary evaluation of treated QCM’s, via frequency analyzer, showed improvements in sensitivity and fast reversal of adsorption; and suggested increased stability. Tests with coated MC’s also supported the findings of QCM tests. Amplitude response of MC’s was on average 4 times greater when the Nafion coating contained CNT’s. Quantitative QCM testing with gas-flow meters showed that with CNT inclusion: the average number of moles adsorbed increased by 35% (>1.2 times frequency response); sensitivity improved by 0.63 Hz/ppt on average; although the detection threshold decreased marginally; but reusability was much better after extended exposures to concentrated ammonia.
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Date Issued
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2013
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PURL
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http://purl.flvc.org/fau/fd/FA0004014
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Subject Headings
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Conducting polymers, Detectors -- Technological innovations, Explosives -- Detection, Nanocomposites (Materials), Nanostructured materials, Smart materials
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Format
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Document (PDF)
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Title
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Real Time Traffic Monitoring System from a UAV Platform.
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Creator
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Biswas, Debojit, Su, Hongbo, Florida Atlantic University, College of Engineering and Computer Science, Department of Civil, Environmental and Geomatics Engineering
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Abstract/Description
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Today transportation systems are facing big transitions all over the world. We created fly overs, roads under the ground, bridges over the river and ocean to get efficient access and to increase the road connectivity. Our transportation system is more intelligent than ever. Our traffic signaling system became adaptive. Our vehicles equipped with new gadgets and we developed new tools for more efficient analysis of traffic. Our research relies on existing traffic infrastructure to generate...
Show moreToday transportation systems are facing big transitions all over the world. We created fly overs, roads under the ground, bridges over the river and ocean to get efficient access and to increase the road connectivity. Our transportation system is more intelligent than ever. Our traffic signaling system became adaptive. Our vehicles equipped with new gadgets and we developed new tools for more efficient analysis of traffic. Our research relies on existing traffic infrastructure to generate better understanding of traffic. More specifically, this research focused on traffic and UAV cameras to extract information about the traffic. Our first goal was to create an automatic system to count the cars using traffic cameras. To achieve this goal, we implemented Background Subtraction Method (BSM) and OverFeat Framework. BSM compares consecutive frames to detect the moving objects. Because BSM only works for ideal lab conditions, therefor we implemented a Convolutional Neural Network (CNN) based classification algorithm called OverFeat Framework. We created different segments on the road in various lanes to tabulate the number of passing cars. We achieved 96.55% accuracy for car counting irrespective of different visibility conditions of the day and night. Our second goal was to find out traffic density. We implemented two CNN based algorithms: Single Shot Detection (SSD) and MobileNet-SSD for vehicle detection. These algorithms are object detection algorithms. We used traffic cameras to detect vehicles on the roads. We utilized road markers and light pole distances to determine distances on the road. Using the distance and count information we calculated density. SSD is a more resource intense algorithm and it achieved 92.97% accuracy. MobileNet-SSD is a lighter algorithm and it achieved 79.30% accuracy. Finally, from a moving platform we estimated the velocity of multiple vehicles. There are a lot of roads where traffic cameras are not available, also traffic monitoring is necessary for special events. We implemented Faster R-CNN as a detection algorithm and Discriminative Correlation Filter (with Channel and Spatial Reliability Tracking) for tracking. We calculated the speed information from the tracking information in our study. Our framework achieved 96.80% speed accuracy compared to manual observation of speeds.
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Date Issued
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2019
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PURL
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http://purl.flvc.org/fau/fd/FA00013188
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Subject Headings
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Traffic monitoring, Intelligent transportation systems, Neural networks (Computer science), Vehicle detectors, Unmanned aerial vehicles
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Format
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Document (PDF)
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Title
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Static error modeling of sensors applicable to ocean systems.
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Creator
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Ah-Chong, Jeremy Fred., Florida Atlantic University, An, Edgar
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Abstract/Description
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This thesis presents a method for modeling navigation sensors used on ocean systems and particularly on Autonomous Underwater Vehicles (AUV). An extended Kalman filter was previously designed for the implementation of the Inertial Navigation System (INS) making use of Inertial Measurement Unit (IMU), a magnetic compass, a GPS/DGPS system and a Doppler Velocity Log (DVL). Emphasis is put on characterizing the static sensor error model. A "best-fit ARMA model" based on the Aikake Information...
Show moreThis thesis presents a method for modeling navigation sensors used on ocean systems and particularly on Autonomous Underwater Vehicles (AUV). An extended Kalman filter was previously designed for the implementation of the Inertial Navigation System (INS) making use of Inertial Measurement Unit (IMU), a magnetic compass, a GPS/DGPS system and a Doppler Velocity Log (DVL). Emphasis is put on characterizing the static sensor error model. A "best-fit ARMA model" based on the Aikake Information Criterion (AIC), Whiteness test and graphical analyses were used for the model identification. Model orders and parameters were successfully estimated for compass heading, GPS position and IMU static measurements. Static DVL measurements could not be collected and require another approach. The variability of the models between different measurement data sets suggests online error model estimation.
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Date Issued
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2003
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PURL
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http://purl.flvc.org/fcla/dt/12977
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Subject Headings
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Underwater navigation, Kalman filtering, Error-correcting codes (Information theory), Detectors
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Format
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Document (PDF)