The Experts below are selected from a list of 327 Experts worldwide ranked by ideXlab platform
Gregory W Mccarty - One of the best experts on this subject based on the ideXlab platform.
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Acquisition of NIR-Green-Blue Digital Photographs from Unmanned Aircraft for Crop Monitoring
Remote Sensing, 2010Co-Authors: E. Raymond Hunt, Stephen J. Fujikawa, David S. Linden, Craig S. T. Daughtry, W. Dean Hively, Gregory W MccartyAbstract:Payload size and weight are critical factors for small Unmanned Aerial Vehicles (UAVs). Digital color-infrared photographs were acquired from a single 12-Megapixel Camera that did not have an internal hot-mirror filter and had a red-light-blocking filter in front of the lens, resulting in near-infrared (NIR), green and blue images. We tested the UAV-Camera system over two variably-fertilized fields of winter wheat and found a good correlation between leaf area index and the green normalized difference vegetation index (GNDVI). The low cost and very-high spatial resolution associated with the Camera-UAV system may provide important information for site-specific agriculture.
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Acquisition of NIR-green-blue digital photographs from unmanned aircraft for crop monitoring
Remote Sensing, 2010Co-Authors: E. Raymond Hunt, Jr., W. Dean Hively, Stephen J. Fujikawa, David S. Linden, Craig S. T. Daughtry, Gregory W MccartyAbstract:Payload size and weight are critical factors for small Unmanned Aerial Vehicles (UAVs). Digital color-infrared photographs were acquired from a single 12-Megapixel Camera that did not have an internal hot-mirror filter and had a red-light-blocking filter in front of the lens, resulting in near-infrared (NIR), green and blue images. We tested the UAV-Camera system over two variably-fertilized fields of winter wheat and found a good correlation between leaf area index and the green normalized difference vegetation index (GNDVI). The low cost and very-high spatial resolution associated with the Camera-UAV system may provide important information for site-specific agriculture. © 2010 by the authors.
Ramón Mollá - One of the best experts on this subject based on the ideXlab platform.
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An Augmented Reality App for Therapeutic Education and Suitable for Mobile Devices with Different Features
2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT), 2019Co-Authors: Andres-marcelo Calle-bustos, M. Carmen Juan, Francisco Abad, Ramón MolláAbstract:Patients with chronic diseases can improve their quality of life with therapeutic education. Augmented Reality (AR) can be used to develop therapeutic tools. This paper presents a study in which an AR app to support therapeutic education in diabetes was used. The app supports the learning about carbohydrate portions contained in different foods. The virtual food is shown on a real dish. Sixty-six children with diabetes participated in our study. The children were divided into three groups that used three different mobile devices. The main differences between the three devices were Camera resolution and display size. We used two tablets with similar display size and different Camera resolution (2-Megapixel and 8-Megapixel), and a smartphone with about half of the diagonal display size of the tablets and with an 8-Megapixel Camera. The results indicated that the children learned about carbohydrate estimation by using our app. There were no statistically significant differences for the knowledge acquired, or for the perceived usability and satisfaction among the three groups. This result points out that our AR app is effective as a therapeutic education tool independently of the features of the device used.
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ICALT - An Augmented Reality App for Therapeutic Education and Suitable for Mobile Devices with Different Features
2019 IEEE 19th International Conference on Advanced Learning Technologies (ICALT), 2019Co-Authors: Andres-marcelo Calle-bustos, M. Carmen Juan, Francisco Abad, Ramón MolláAbstract:Patients with chronic diseases can improve their quality of life with therapeutic education. Augmented Reality (AR) can be used to develop therapeutic tools. This paper presents a study in which an AR app to support therapeutic education in diabetes was used. The app supports the learning about carbohydrate portions contained in different foods. The virtual food is shown on a real dish. Sixty-six children with diabetes participated in our study. The children were divided into three groups that used three different mobile devices. The main differences between the three devices were Camera resolution and display size. We used two tablets with similar display size and different Camera resolution (2-Megapixel and 8-Megapixel), and a smartphone with about half of the diagonal display size of the tablets and with an 8-Megapixel Camera. The results indicated that the children learned about carbohydrate estimation by using our app. There were no statistically significant differences for the knowledge acquired, or for the perceived usability and satisfaction among the three groups. This result points out that our AR app is effective as a therapeutic education tool independently of the features of the device used.
Richard Hartley - One of the best experts on this subject based on the ideXlab platform.
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DICTA - Portable Multi-Megapixel Camera with Real-Time Recording and Playback
2009 Digital Image Computing: Techniques and Applications, 2009Co-Authors: Peter Carr, Richard HartleyAbstract:We are interested in the problem of automatically tracking football players, subject to the constraint that only one vantage point is available. Tracking algorithms benefit from seeing the entire playing field, as one does not have to worry about objects entering and leaving the field of view. However, the image of the entire field must be of sufficient resolution to allow each of the players to be identified automatically. To achieve this desired video data, several high definition video Cameras are used to record a football match from a single vantage point. The Cameras are oriented to cover the entire playing field, and their images combined to create a single high-resolution video feed. The user is able to pan and zoom in real-time within the unified video stream while it is playing. The system is achieved by distributing tasks across a network of computers and only processing data that will be visible to the user.
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Portable Multi-Megapixel Camera with Real-Time Recording and Playback
2009 Digital Image Computing: Techniques and Applications, 2009Co-Authors: Peter Carr, Richard HartleyAbstract:We are interested in the problem of automatically tracking football players, subject to the constraint that only one vantage point is available. Tracking algorithms benefit from seeing the entire playing field, as one does not have to worry about objects entering and leaving the field of view. However, the image of the entire field must be of sufficient resolution to allow each of the players to be identified automatically. To achieve this desired video data, several high definition video Cameras are used to record a football match from a single vantage point. The Cameras are oriented to cover the entire playing field, and their images combined to create a single high-resolution video feed. The user is able to pan and zoom in real-time within the unified video stream while it is playing. The system is achieved by distributing tasks across a network of computers and only processing data that will be visible to the user.
Vijay Raghunathan - One of the best experts on this subject based on the ideXlab platform.
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Approximate Circuits - Approximate Systems: Synergistically Approximating Sensing, Computing, Memory, and Communication Subsystems for Energy Efficiency
Approximate Circuits, 2018Co-Authors: Arnab Raha, Vijay RaghunathanAbstract:Emerging application domains exhibit the property of intrinsic error resilience that enables new avenues for energy optimization of computing systems, namely the introduction of a small amount of approximations during system operation in exchange for substantial energy savings. Almost all prior work in the area of approximate computing has focused on individual subsystems of a computing system, e.g., the computational subsystem or the memory subsystem. Since they focus only on individual subsystems, these techniques are unable to exploit the large energy-saving opportunities that stem from adopting a full-system perspective and approximating multiple subsystems of a computing platform simultaneously in a coordinated manner. Towards this end, this chapter introduces the concept of an Approximate System that performs joint approximations across different subsystems, leading to significant energy benefits compared to approximating individual subsystems in isolation. We use the example of a smart Camera system that executes various computer vision and image processing applications to illustrate how the sensing, memory, processing, and communication subsystems can all be approximated synergistically. The approximate smart Camera system was implemented using an Altera Stratix IV GX FPGA development board, a Terasic TRDB-D5M 5 Megapixel Camera module, a Terasic RFS WiFi module, and a 1 GB DDR3 DRAM SODIMM module. Experimental results obtained using six application benchmarks demonstrate that the proposed full-system approximation methodology achieves significant energy savings of 1.8 × to 5.5 × on average over individual subsystem-level approximations for minimal (
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Approximating Beyond the Processor: Exploring Full-System Energy-Accuracy Tradeoffs in a Smart Camera System
IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2018Co-Authors: Arnab Raha, Vijay RaghunathanAbstract:The intrinsic error resilience exhibited by emerging application domains enables new avenues for energy optimization of computing systems, namely, the introduction of a small amount of approximations during system operation in exchange for substantial energy savings. Prior work in the area of approximate computing has focused on individual subsystems of a computing system, for example, the computational subsystem or the memory subsystem. Since they focus only on individual subsystems, these techniques are unable to exploit the large energy-saving opportunities that stem from adopting a full-system perspective and approximating multiple subsystems of a computing platform simultaneously in a coordinated manner. This paper proposes a systematic methodology to perform joint approximations across different subsystems, leading to significant energy benefits compared to approximating individual subsystems in isolation. We use the example of a smart Camera system that executes various computer vision and image processing applications to illustrate how the sensing, memory, processing, and communication subsystems can all be approximated synergistically. We demonstrate our proposed methodology using two variants of a smart Camera system: 1) a compute-intensive smart Camera system, AxSYScomp, where the error-resilient application executes locally within the Camera and produces the final application output, and 2) a communication-intensive smart Camera system, AxSYScomp, that sends the captured image to a remote cloud server, where the error-resilient application is executed and the final output is generated. We have implemented such an approximate smart Camera system using an Altera Stratix IV GX FPGA development board, a Terasic TRDB-D5M 5-Megapixel Camera module, a Terasic RFS WiFi module, and a 1-GB DDR3 dynamic random access memory small outline dual in-line memory module (SODIMM). Experimental results obtained using six application benchmarks demonstrate significant energy savings (around 7.5x for AxSYScomp and 4x on average for AxSYScomp) for minimal (
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Towards full-system energy-accuracy tradeoffs: A case study of an approximate smart Camera system*
2017 54th ACM EDAC IEEE Design Automation Conference (DAC), 2017Co-Authors: Arnab Raha, Vijay RaghunathanAbstract:The intrinsic error resilience exhibited by emerging application domains enables a new dimension for energy optimization of computing systems, namely the introduction of a controlled amount of approximations during system operation in exchange for substantial energy savings. Prior work in the area of approximate computing has focused on individual subsystems of a computing system, e.g., the computational subsystem or the memory subsystem. Since they focus only on individual subsystems, these techniques are unable to exploit the large energy-saving opportunities that stem from adopting a full-system perspective and approximating multiple subsystems of a computing platform simultaneously in a coordinated manner. This paper proposes a systematic methodology to perform joint approximations across different subsystems, leading to significant energy benefits compared to approximating individual subsystems in isolation. We use the example of a smart Camera system that executes various computer vision and image processing applications to illustrate how the sensing, memory, and processing subsystems can all be approximated synergistically. We have implemented such an approximate smart Camera system using an Altera Stratix IV GX FPGA development board, a Terasic TRDBD5M 5 Megapixel Camera module, and a 1GB DDR3 SODIMM module. Experimental results obtained using six application benchmarks demonstrate significant energy savings (around 7.5× on average) for minimal (
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DAC - Towards Full-System Energy-Accuracy Tradeoffs: A Case Study of An Approximate Smart Camera System
Proceedings of the 54th Annual Design Automation Conference 2017, 2017Co-Authors: Arnab Raha, Vijay RaghunathanAbstract:The intrinsic error resilience exhibited by emerging application domains enables a new dimension for energy optimization of computing systems, namely the introduction of a controlled amount of approximations during system operation in exchange for substantial energy savings. Prior work in the area of approximate computing has focused on individual subsystems of a computing system, e.g., the computational subsystem or the memory subsystem. Since they focus only on individual subsystems, these techniques are unable to exploit the large energy-saving opportunities that stem from adopting a full-system perspective and approximating multiple subsystems of a computing platform simultaneously in a coordinated manner. This paper proposes a systematic methodology to perform joint approximations across different subsystems, leading to significant energy benefits compared to approximating individual subsystems in isolation. We use the example of a smart Camera system that executes various computer vision and image processing applications to illustrate how the sensing, memory, and processing subsystems can all be approximated synergistically. We have implemented such an approximate smart Camera system using an Altera Stratix IV GX FPGA development board, a Terasic TRDBD5M 5 Megapixel Camera module, and a 1GB DDR3 SODIMM module. Experimental results obtained using six application benchmarks demonstrate significant energy savings (around 7.5× on average) for minimal (< 1%) loss in application quality. Compared to approximating a single subsystem, the proposed full-system approximation methodology achieves additional energy benefits of 3.5× – 5.5× on average for minimal (< 1%) quality loss.
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Towards Full-System Energy-Accuracy Tradeoffs : A Case Study of An Approximate Smart Camera System
Dac 2017, 2017Co-Authors: Arnab Raha, Vijay RaghunathanAbstract:The intrinsic error resilience exhibited by emerging application do-mains enables a new dimension for energy optimization of computing systems, namely the introduction of a controlled amount of approx-imations during system operation in exchange for substantial energy savings. Prior work in the area of approximate computing has focused on individual subsystems of a computing system, e.g., the computa-tional subsystem or the memory subsystem. Since they focus only on individual subsystems, these techniques are unable to exploit the large energy-saving opportunities that stem from adopting a full-system per-spective and approximating multiple subsystems of a computing plat-form simultaneously in a coordinated manner. This paper proposes a systematic methodology to perform joint approximations across differ-ent subsystems, leading to significant energy benefits compared to ap-proximating individual subsystems in isolation. We use the example of a smart Camera system that executes various computer vision and im-age processing applications to illustrate how the sensing, memory, and processing subsystems can all be approximated synergistically. We have implemented such an approximate smart Camera system using an Altera Stratix IV GX FPGA development board, a Terasic TRDB-D5M 5 Megapixel Camera module, and a 1GB DDR3 SODIMM mod-ule. Experimental results obtained using six application benchmarks demonstrate significant energy savings (around 7.5⇥ on average) for minimal (< 1%) loss in application quality. Compared to approxi-mating a single subsystem, the proposed full-system approximation methodology achieves additional energy benefits of 3.5⇥ -5.5⇥ on average for minimal (< 1%) quality loss.
E. Raymond Hunt, Jr. - One of the best experts on this subject based on the ideXlab platform.
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Acquisition of NIR-green-blue digital photographs from unmanned aircraft for crop monitoring
Remote Sensing, 2010Co-Authors: E. Raymond Hunt, Jr., W. Dean Hively, Stephen J. Fujikawa, David S. Linden, Craig S. T. Daughtry, Gregory W MccartyAbstract:Payload size and weight are critical factors for small Unmanned Aerial Vehicles (UAVs). Digital color-infrared photographs were acquired from a single 12-Megapixel Camera that did not have an internal hot-mirror filter and had a red-light-blocking filter in front of the lens, resulting in near-infrared (NIR), green and blue images. We tested the UAV-Camera system over two variably-fertilized fields of winter wheat and found a good correlation between leaf area index and the green normalized difference vegetation index (GNDVI). The low cost and very-high spatial resolution associated with the Camera-UAV system may provide important information for site-specific agriculture. © 2010 by the authors.