Existing indoor positioning methods for visible-light communication systems require large database, powerful signal processing units, additional sensors, such as gyroscopes, or the receiver placed toward a certain ang...
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Existing indoor positioning methods for visible-light communication systems require large database, powerful signal processing units, additional sensors, such as gyroscopes, or the receiver placed toward a certain angle. These assumptions limit the applications of the indoor positioning systems in low-cost scenarios. In this paper, we propose a novel positioning framework based on the angle differences of arrival (ADOA) in 3-D coordinate systems, which can be used in receivers with image sensors or photodiodes. The proposed ADOA positioning does not require the receiver to be placed toward a certain angle and no additional sensor is required. Two positioning algorithms are proposed: one is based on the method of exhaustion (MEX), and the other is based on the least squares method (LSM). The MEX algorithm is analytically proved to be the optimal, while the LSM algorithm has much lower complexity. Both the upper and lower bounds are derived for the average discrepancy between the exact position and the estimated position. These performance bounds can facilitate the design of a light-emitting diode array. Experimental results show that the MEX algorithm can achieve an average error of 3.20 cm with a time cost of 0.36 s, and the LSM algorithm can achieve an average error of 14.66 cm with a time cost of 0.001 s.
Recently, software-defined satellite has become a research hotspot in the aerospace. Based on an advanced computing platform with open system architecture, researchers can upload software for specific tasks even the s...
ISBN:
(数字)9781728123486
ISBN:
(纸本)9781728123493
Recently, software-defined satellite has become a research hotspot in the aerospace. Based on an advanced computing platform with open system architecture, researchers can upload software for specific tasks even the satellite has been launched into space. This paper we have designed an on-orbit application for China's first software-defined satellite TianZhi-1, which use Android smartphone as a system platform. Two main tasks are focused on our work, one is to reduce data redundancy and the other is to compress the size of the software. First, a light-weight and extensible framework is designed to support different imageprocessingalgorithms. Following this, we propose a three-step approach for on-orbit valuable image extraction, include image denoising, stitching, and salient object extraction. Experiments on the real satellite achieve outstanding results.
algorithms with operations on large regular data structures such as imageprocessing can be highly accelerated when executed as hardware tasks in an FPGA fabric. The Dynamic Partial Reconfiguration (DPR) feature of ne...
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ISBN:
(纸本)9783319776101;9783319776095
algorithms with operations on large regular data structures such as imageprocessing can be highly accelerated when executed as hardware tasks in an FPGA fabric. The Dynamic Partial Reconfiguration (DPR) feature of new SRAM-based FPGA families allows a dynamic swapping and replacement of hardware tasks during runtime. Particularly embedded systems with processing chains that change over time or that are too large to be implemented in an FPGA fabric in parallel, benefit from DPR. In this paper we present a complete framework for hardware acceleration using DPR in the microkernel based Genode OS. This makes the DPR feature available not only for the high-performance computing field, but also for safety-critical applications. The new framework is evaluated for an exemplary imaging application running on a Xilinx Zynq-7000 SoC.
The current trend in automotive systems is to integrate more software applications into fewer ECU's to decrease the cost and increase efficiency. This means more applications share the same resources which in turn...
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The current trend in automotive systems is to integrate more software applications into fewer ECU's to decrease the cost and increase efficiency. This means more applications share the same resources which in turn can cause congestion on resources such as such as caches. Shared resource congestion may cause problems for time critical applications due to unpredictable interference among applications. It is possible to reduce the effects of shared resource congestion using cache partitioning techniques, which assign dedicated cache lines to different applications. We propose a cache partition controller called LLC-PC that uses the Palloc page coloring framework to decrease the cache partition sizes for applications during runtime. LLC-PC creates cache partitioning directives for the Palloc tool by evaluating the performance gained from increasing the cache partition size. We have evaluated LLC-PC using 3 different applications, including the SIFT imageprocessing algorithm which is commonly used for feature detection in vision systems. We show that LLC-PC is able to decrease the amount of cache size allocated to applications while maintaining their performance allowing more cache space to be allocated for other applications.
This paper presents a novel technique of factorization for 2-D non-separable quaternionic paraunitary filter banks (2-D NSQ-PUFB). Two-dimensional factorization structures called "16in-16out" and "64in-...
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ISBN:
(纸本)9788362065332
This paper presents a novel technique of factorization for 2-D non-separable quaternionic paraunitary filter banks (2-D NSQ-PUFB). Two-dimensional factorization structures called "16in-16out" and "64in-64out" respectively for 4-channel and 8-channel Q-PUFB based on the proposed technique are shown. The given structures can be mapped to parallel-pipeline processor architecture with a minimum latency time 2(N + 1) quaternion multiplication operations, where N is transform order of the Q-PUFB. The latency of parallel-pipeline processing does not depend on the size of the original image in contrast to the conventional 2-D transform. The coding gains CG(MD) of 2-D non-separable Q-PUFBs for the isotropic auto-correlation function model with the correlation factor rho = 0.95 are the following: CG(MD) = 13.4 dB for "16in-16out" structure and CG(MD) = 15.6 dB for "64in-64out" structure.
We investigated the peculiarities of the terahertz pulse time-domain holography principle in the case of raster scanning with the balance detection system. The noise in this system represents a Skellam distribution mo...
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We investigated the peculiarities of the terahertz pulse time-domain holography principle in the case of raster scanning with the balance detection system. The noise in this system represents a Skellam distribution model, which differentiates it from systems based on a photoconductive antenna. We analyzed this Skellam model and provided both numerical and experimental investigations. We found that the variance of the noise in the balance detection system does not depend on the true signal. Complex-domain images obtained in this model are filtered by block-matching algorithms adapted for spatio-temporal and spatiospectral volumetric data. We presented a new cube complex-domain filter algorithm that uses block matching in all 3D data sets simultaneously in spatial and frequency coordinates. A combination of temporal and complex-domain filters allows us to expand the dynamic range of terahertz frequencies for which we can obtain amplitude/phase information. Experimental data demonstrate an improvement in the quality of the resultant images both in the time domain and complex-spectral domain. The simulation and experimental results are in good agreement. (C) 2019 Optical Society of America
To improve the performance of remote sensing images multiclass classification we propose two greedy algorithms of feature selection. The discriminant analysis criterion and regression coefficients are used as the meas...
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Interferometric Synthetic Aperture Radar (InSAR) is a remote sensing technology used for estimating the displacement of an object on the ground or the earth's surface itself. Persistent Scatterer-InSAR (PS-InSAR) ...
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Interferometric Synthetic Aperture Radar (InSAR) is a remote sensing technology used for estimating the displacement of an object on the ground or the earth's surface itself. Persistent Scatterer-InSAR (PS-InSAR) is a category of time series algorithms enabling high resolution monitoring. PS-InSAR relies on successful selection of points that appear stable across a set of satellite images taken over time. This paper presents PtSel, a new algorithm for selecting these points, a problem known as Persistent Scatterer Selection. The key advantage of PtSel over the key existing techniques is that it does not require model assumptions, yet preserves solution accuracy. Motivated by the abundance of parallelism the algorithm exposes, we have implemented it for GPUs. Our evaluation using real-world data shows that the GPU implementation not only offers superior performance but also scales linearly with GPU count and workload size. We compare the GPU implementation and a parallel CPU implementation: a consumer grade GPU offers 18x speedup over a 16-core Ivy Bridge Xeon System, while four GPUs offer 65x speedup. The GPU solution consumes 28x less energy than the CPU-only solution. Additionally, we present a comparison with the most widely used PS-interferometry software package StaMPS, in terms of point selection coverage and precision.
Automatic mouth detection can assist in controlling a robotic system with self-feeding of individuals with disability. To address this need we developed and evaluated algorithms that: 1) detect and track the mouth of ...
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ISBN:
(纸本)9781538659168
Automatic mouth detection can assist in controlling a robotic system with self-feeding of individuals with disability. To address this need we developed and evaluated algorithms that: 1) detect and track the mouth of an individual in real-time, and 2) classify if the mouth is open or closed. A k-nearest neighbors (KNN) clustering algorithm was used to classify and recognize the mouth’s posture. The KNN algorithm classified image frames using features extracted by four methods including a histogram of oriented gradients, Harris-Stephens algorithm, maximally stable extremal regions, and local binary patterns. The results of this study indicated a high classification accuracy (~87%) using 10-fold cross validation for three participants without disability. The study has shown that the algorithms can detect the mouth postures of a person in near real-time (<1s) while they have a robot-assisted meal in a social setting.
This paper focuses on solving a relevant and pressing safety issue on intercity roads. Two approaches were considered for solving the problem of traffic signs recognition;the approaches involved neural networks to ana...
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This paper focuses on solving a relevant and pressing safety issue on intercity roads. Two approaches were considered for solving the problem of traffic signs recognition;the approaches involved neural networks to analyze images obtained from a camera in the real-time mode. The first approach is based on a sequential imageprocessing. At the initial stage, with the help of color filters and morphological operations (dilatation and erosion), the area containing the traffic sign is located on the image, then the selected and scaled fragment of the image is analyzed using a feedforward neural network to determine the meaning of the found traffic sign. Learning of the neural network in this approach is carried out using a backpropagation method. The second approach involves convolution neural networks at both stages, i.e. when searching and selecting the area of the image containing the traffic sign, and when determining its meaning. Learning of the neural network in the second approach is carried out using the intersection over union function and a loss function. For neural networks to learn and the proposed algorithms to be tested, a series of videos from a dash cam were used that were shot under various weather and illumination conditions. As a result, the proposed approaches for traffic signs recognition were analyzed and compared by key indicators such as recognition rate percentage and the complexity of neural networks' learning process.
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