The spatial Sigma-Delta architecture can be used to reduce the quantization noise and thus improve the effective resolution of few-bit analog-to-digital converters (ADCs) for certain spatial frequencies of interest. T...
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ISBN:
(数字)9798350354058
ISBN:
(纸本)9798350354065
The spatial Sigma-Delta architecture can be used to reduce the quantization noise and thus improve the effective resolution of few-bit analog-to-digital converters (ADCs) for certain spatial frequencies of interest. This paper proposes a novel data detection scheme based on the variational Bayes (VB) inference framework for multiple-input multiple-output (MIMO) systems that utilize first-order spatial Sigma-Delta ADCs. We derive a closed-form expression to approximate the posterior distributions of the transmitted data symbols, which are then used for their estimation. Simulation results show that the proposed detection scheme achieves a detection performance comparable to unquantized systems and has a lower symbol error rate (SER) than the conventional quantized VB and linear minimum mean-squared error (LMMSE) methods. The effects of the azimuth range, and the antenna spacing and wavelength on the SER performance of all detection algorithms are also extensively analyzed.
We describe a new anomaly detection algorithm based on an electrical impedance tomography (EIT) technique. When only the boundary current and voltage measurements are available, it is not practically feasible to recon...
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We describe a new anomaly detection algorithm based on an electrical impedance tomography (EIT) technique. When only the boundary current and voltage measurements are available, it is not practically feasible to reconstruct accurate high-resolution cross-sectional resistivity images of a subject. In this paper, we focus our attention on the detection of the location and size of anomalies with resistivity values different from the background tissues. We show the performance of the algorithm from experimental results using a 32-channel EIT system and saline phantoms. The algorithm is applicable to the detection of cancerous tissues in the breast.
In this paper, a new collision detection algorithm based on simulated annealing algorithm which is effective in searching optimal solution is presented. Basic principle of this new collision detection algorithm is as ...
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In this paper, a new collision detection algorithm based on simulated annealing algorithm which is effective in searching optimal solution is presented. Basic principle of this new collision detection algorithm is as follows: The time variant parameter used for establishing objective function based on kinetic equation of missile and attacked objective is extracted firstly; Secondly, objective function adopted as condition of collision detection is constructed availing of extracted parameter; Finally, the optimal solution of objective function is got based on simulated annealing algorithm, and whether the collision happens is determined by the optimal solution. The simulated result proves this new algorithm is feasible.
An objects detection algorithm for color dynamic images from two cameras is proposed for a real surveillance system under low illumination. It provides automatic calculation of a Fuzzy Corresponding Map and color simi...
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An objects detection algorithm for color dynamic images from two cameras is proposed for a real surveillance system under low illumination. It provides automatic calculation of a Fuzzy Corresponding Map and color similarity for lower luminance conditions, which detects small chromatic regions in CCD camera images under lower illumination. Experimental detection results for two dynamic images from real surveillance cameras in a downtown area in Japan under low luminance conditions show that the proposed algorithm has 15% improved accuracy compared with the independent detection algorithm in the same false alarm rate, which implementability for severe surveillance situation is discussed. The proposed algorithm is being considered for use in a low cost surveillance system at a relatively poor security downtown (shopping mall) area in Japan.
Image segmentation is an important and challenging problem in an image analysis. Segmentation of objects in an image is even more difficult and computationally expensive. In this paper an unsupervised object based ima...
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Image segmentation is an important and challenging problem in an image analysis. Segmentation of objects in an image is even more difficult and computationally expensive. In this paper an unsupervised object based image segmentation that is mean shift clustering approach will be studied. One of the most important step is pre-processed image by a standard mean shift based segmentation, which preserves desirable discontinuities present in the image and guarantees over segmentation in the image in their Output. This type of mean shift segmentation technique which clusters the regions instead of image pixels mostly reduces the sensitivity to noise and hence enhances the overall segmentation performance. detection of circle is very important for initial stage of Mean Shift segmentation. It will first detect a circle with Circular Hough Transform and then with Modified Canny Edge detection Algorithm. The Modified Canny Edge detection Algorithm is very fast algorithm to detect circle from the images as compared to Circular Hough Transform.
There is currently a need in cochlear implants to develop speech coding algorithms that provide better access to pitch cues, known to be critical for music perception. Such algorithms require an estimate of the fundam...
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There is currently a need in cochlear implants to develop speech coding algorithms that provide better access to pitch cues, known to be critical for music perception. Such algorithms require an estimate of the fundamental frequency (F0). This paper presents the implementation of a real-time pitch (F0) detector on a Personal Digital Assistant (PDA). The pitch detection algorithm is based on the autocorrelation function and is implemented real-time on a Dell AXIM Pocket PC. Its performance, in terms of F0 accuracy, is compared against that obtained by the pitch detection algorithm used in STRAIGHT. The implementation details and real-time performance measurements are also provided.
In this paper a new skin detection method based on adaptive thresholds is proposed. Compared with the fixed threshold histogram method used widely, ours can find optimal thresholds to the different complex backgrounds...
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In this paper a new skin detection method based on adaptive thresholds is proposed. Compared with the fixed threshold histogram method used widely, ours can find optimal thresholds to the different complex backgrounds. Four clues are summarized from the skin probability distribution histogram (SPDH) to help search candidates of optimum thresholds, and an ANN classifier is trained to select the final optimum threshold. A color deviation histogram (CDH) is also proposed to eliminate confusing backgrounds and refine optimal thresholds. The selection process of optimal thresholds is fast thus appropriate for real-time applications since no iterative operation is involved. Experimental results show that the proposed method can achieve better performance than the fixed threshold histogram method.
Failure detection is a key technology to implement a high reliable system. It is usually based on overtime mechanism to determine whether a process is failure or not. With the development of network, old failure detec...
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Failure detection is a key technology to implement a high reliable system. It is usually based on overtime mechanism to determine whether a process is failure or not. With the development of network, old failure detectors without adaptive mechanism can not meet the requirements of QoS of application all the time. Adaptive failure detection requires that the failure detectors can dynamically adjust the detecting quality according to the requirements of applications and the variations of network. A new failure detection model based on the predicted message delay is proposed in this paper. An adaptive failure detection algorithm is discussed and realized, which is based on the prediction from historical messages delay time. Experimental results show that the algorithm can satisfy the userpsilas demand of QoS on the failure detector to some extent.
Histogram of Oriented Gradient (HOG) features are proved to be very effective for pedestrian detection in static image. However, most of the background information is wasted when the features are used to detect human ...
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Histogram of Oriented Gradient (HOG) features are proved to be very effective for pedestrian detection in static image. However, most of the background information is wasted when the features are used to detect human in video. Especially in complex environment, the non-eliminated background gradient will affect the detection results. To improve the overall detection performance, a new feature named Non-background HOG is proposed which created a cell map using GMM for the procedure of image gradient calculation in HOG algorithm. This new algorithm not only is capable of reducing the influence of background gradient, but also speeds up the extraction running time. Evaluation experiment demonstrated that the non-background HOG algorithm gives a better performance than classic HOG in pedestrian video detection.
This paper examines the feasibility of developing the fuzzy systems based automatic incident detection algorithms to improve the implementation of the real-time computerized freeway traffic management systems.
This paper examines the feasibility of developing the fuzzy systems based automatic incident detection algorithms to improve the implementation of the real-time computerized freeway traffic management systems.< >
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