Abstract:
Differential spatial modulation (DSM) was recently proposed to overcome the challenge of channel estimation in spatial modulation (SM). In this letter, we propose a gray code order of antenna index permutations for DSM. To facilitate the implementation, the well-known Trotter-Johnson ranking and unranking algorithms are adopted, which results in similar computational complexity to the existing DSM that uses the lexicographic order. The coding gain achieved by the proposed gray code order over the existing lexicographic order is also analyzed and verified via simulations, which reveals a maximum of about 1.2dB for the case of four transmit antennas. Based on the gray coding framework, we further propose a diversity-enhancing scheme named intersected gray (I-gray) code order for DSM, where the permutations of active antenna indices are selected directly from the odd (even) positions of the full permutations in the gray code order. From analysis and simulations, it is shown that the I-gray code order can harvests an additional diversity order at the expense of only one information bit loss for each transmission with respect to the gray code order.

Abstract:
The paper presents some aspects of the (gray level) image binarization methods used in artificial vision systems. It is introduced a new approach of gray level image binarization for artificial vision systems dedicated to industrial automation temporal thresholding. In the first part of the paper are extracted some limitations of using the global optimum thresholding in gray level image binarization. In the second part of this paper are presented some aspects of the dynamic optimum thresholding method for gray level image binarization. Starting from classic methods of global and dynamic optimal thresholding of the gray level images in the next section are introduced the concepts of temporal histogram and temporal thresholding. In the final section are presented some practical aspects of the temporal thresholding method in artificial vision applications form the moving scene in robotic automation class; pointing out the influence of the acquisition frequency on the methods results.

Abstract:
Image Segmentation is a technique of partitioning the original image into some distinct classes. Many possible solutions may be available for segmenting an image into a certain number of classes, each one having different quality of segmentation. In our proposed method, multilevel thresholding technique has been used for image segmentation. A new approach of Cuckoo Search (CS) is used for selection of optimal threshold value. In other words, the algorithm is used to achieve the best solution from the initial random threshold values or solutions and to evaluate the quality of a solution correlation function is used. Finally, MSE and PSNR are measured to understand the segmentation quality.

Abstract:
This paper gave an athematic mode of 0-1 knapsack problem, and modified the binary coding to establish a gray coded hybrid genetic algorithm used greedy algorithm to handle with the constraint conditions, And this paper proposed a value density operator to the individual, which could improve the search effciency, used the elitism mechanism to accelerate the convergence process. The numerical experiment proves the affectivity of the algorithm.

Abstract:
An interleaving coded multi-threshold scheduling (ICMTS) algorithm is proposed in this paper. Since the ICMTS algorithm uses the interleaving coded thresholds of two stage queues as the scheduling weights, it can systematically evaluate the scheduling demands of both the input queues and the crosspoint queues. By segmenting the queue length as multiple thresholds, the hardware resource of this algorithm can be largely decreased. It is proved that a CICQ (combined input-crosspoint-queued) switch operating with the ICMTS algorithm can achieve 100% throughput with a speedup of two. To facilitate hardware implementation, a simplified maximal ICMTS scheme is also presented with a time complexity of O(logN). Simulation results show that even the simplified ICMTS scheme can obtain better performance than the existing algorithms.

Abstract:
As a generalization of 1D Otsu algorithm, 2D Otsu algorithm considers both the gray value of a pixel and the average gray value of its neighborhood, thus is more robust to noise. By constructing look-up tables recursively, its fast algorithm reduces its complexity from OL4 to OL2. Based on the decomposition of 2D Otsu algorithm, a method of calculating the optimal threshold of two 1D Otsu algorithms independently, instead of the optimal threshold of 2D Otsu algorithm, is proposed. When the hypothesis of original 2D Otsu algorithm holds, we point out that the threshold computed by our method is exactly the same as that of 2D Otsu algorithm, while the computational complexity is reduced to OL. As for real images, the hypothesis of 2D Otsu algorithm always fails, whereas experimental results show that the proposed threshold algorithm still outperforms original 2D Otsu algorithm. Without losing the robustness to noise, this method needs less time and space, and produces a comparable or better segmentation result.

Abstract:
This paper presents an algorithm for threshold employing gray level arithmetic mean based on relevant conception in probability theory.The method shows a good performance while aiming at binarizing result,time cost,computational complexity and applicability.It has been applied in PCB detecting system and a satisfactory result has been attained.

Abstract:
As to the problem to locate and extract the text region of the news in video frames,this paper proposed an effective methodology for caption location and extraction.It used the gray-scale difference statistics and local gray-scale histogram to locate the text region of the video frame,then applied improved two-dimensional maximum entropy threshold to extract the segmented text area to obtain a binarized image.Finally,analyzed the abilities of several algorithms by comparison for text location and optical ch...

Abstract:
为了解决快中子编码成像中复杂源图像定义与模拟效率的问题,研究了基于深度优先合并方法的复杂源图像定义方法。采用栅元合并技术来减小源定义所需栅元数目,同时保证蒙特卡洛模拟中源抽样的效率,从而提高复杂源图像编码成像模拟效率。模拟源为二值E字母时,源合并后模拟所得编码像的计算结果最小误差较源合并前没有变化且满足统计要求,计算时间则减少且为源合并前模拟时间的1/5;对16、64和256灰度阶E字母源进行了编码像的模拟计算,模拟结果的最小误差小于1％,符合重建研究的需要;采用3种重建算法对“西安交通大学校徽”复杂二值源的定义和模拟进行了源区重建,进而验证了基于深度优先合并的源定义方法的正确性。该方法可望为增进聚变源区所历复杂过程诊断的适应性提供一种切实可行的技术途径。 To solve the difficulties in complex gray？？scale source definition and simulation efficiency, a strategy for defining complex gray？？scale source based on the depth？？first merger is proposed. A technology of cells merging is used to reduce the number of cells for the source definition and ensure the sampling efficiency of source in Monte Carlo simulation, thus the simulation efficiency of coded imaging of the complex gray？？scale source can be increased. When the simulated source is the binary letter E source, the error of simulated coded image of the merged source remains same as that of the source without merging and meets the requirement of the reconstruction for statistics, while the computing time for merged source is reduced to 1/5 of the source without merging. The 16, 64 and 256 gray？？scale letter E sources are defined by the definition method of complex gray？？scale source based on the depth？？first merger, and the corresponding simulations of coded image are also carried out. The errors of simulation results reach less than 1％, which meet the requirement of the reconstruction for statistics. The complex binary source of Xi’an Jiaotong University school badge is defined with the proposed strategy for complex gray？？scale source based on the depth？？first merger and the corresponding simulation of coded image is carried out, then the reconstructions of the source by three different methods are realized. The strategy for defining complex gray？？scale source based on the depth？？first merger is verified, so it can be expected to provide a feasible technological approach to enhance the adaptability of diagnosis of the complex capsule implosion process

Abstract:
In this paper, Segmented Cellular Neural Network-Cellular Neural Network Combined Trellis Coded Quantization / Modulation (SCNN-CNN CTCQ/TCM) scheme is introduced. Here, a gray scaled image is lowered to 3 bit using our proposed Segmented Cellular Neural Network approach (SCNN) and then passed through a new CNN based structure which models combined trellis coded quantization / modulation. The performance of our combined scheme has been analyzed over Rician fading channel. Computer simulations studies confirm the analytical upper bound curves.