انجمن کامپیوتر ایران

The evolution of optical technology has resulted in the emergence of several promising paradigms so as to realize the next generation Internet backbone infrastructure. Among all the existing switching techniques, optical burst switching (OBS) has shown to support the bursty nature of diverse IP traffic classes more efficiently. Nevertheless, with increase in realtime applications over the Internet, devising mechanisms that guarantee quality of service (QoS) and efficient bandwidth utilization within the OBS network has become a major endeavor. In the literature, resource allocation-based QoS management has been investigated as a possible solution to overcome this necessity. However, to the best of our knowledge, the resource-based technique has not been analytically studied for any arbitrary number of traffic classes taking both, wavelengths and fiber delay lines (FDLs) into account. In this paper, we present a novel mathematical model for the resource-based approach with two service classes namely, real-time (H) and non real-time (L) classes. We then extend the model to cover any number of classes. The effectiveness of the model is justified through simulation experiments.

Recent advancements in the field of multimedia and wireless communications have led to a wide array of application and services requiring high data processing rate at minimal power consumption. This new generation of data hungry portable devices requires power efficient hardware solutions where the operational specifications are as important as objective functionality. Conventional processing solutions like MIPS fall short on real time computational intensive operations due to large software overhead. This class of applications demands dedicated hardware units like Application Specific Processors (ASP) working as hardware accelerators for intensive data processing operations. In this paper we describe a novel Register Transfer Level (RTL) synthesis process of a power and throughput optimized ASP for a sample application. The ASP implemented on an FPGA, can serve as a hardware accelerator for system on chip (SOC) or as a standalone Application Specific Integrated Circuit (ASIC) at silicon level.

A combined texture- and color-based skin detection is proposed in this paper. Nonsubsampled contourlet transform is used to represent texture of the whole image. Local neighbor contourlet coefficients of a pixel are used as feature vectors to classify each pixel. Dimensionality reduction is addressed through principal component analysis (PCA) to remedy the curse of dimensionality in the training phase. Before texture classification, the pixel is tested to determine whether it is skin-colored. Therefore, the classifier is learned to discriminate skin and non-skin texture for skin colored regions. A multi-layer perceptron is then trained using the feature vectors in the PCA reduced space. The Markov property of images is addressed in post-processing to join separate neighbor skin detected regions. Comparison of the proposed method with other state-of-the-art methods shows a lower false positive rate with a little decrease in true positive rate.

In this paper, we present a novel hybrid algorithm for binarization of badly illuminated document images. This algorithm locally enhances the document image and makes the gray levels of text and background pixels separable. Afterward a simple global binarization algorithm binarizes the enhanced image. The enhancement process is a novel method that uses a separate transformation function to map the gray level of each pixel into a new domain. For each pixel, the transformation function is determined using its neighboring pixels gray level. The proposed binarization algorithm is robust for wide variety of degraded document images. Evaluation over a set of degraded document images illustrates the effectiveness of our proposed binarization algorithm.

One of the toughest challenges in medical diagnosis is uncertainty handling. The recognition of intestinal bacteria such as Salmonella and Shigella which cause typhoid fever and dysentery, respectively, is one such challenging problem for microbiologists. In this paper, we take an intelligent approach towards the bacteria classification problem by using five similarity measures of fuzzy sets (FSs) and intuitionistic fuzzy sets (IFSs) to examine their capabilities in encountering uncertainty in the medical pattern recognition. Finally, the recognition rates of the measures are calculated among which IFS Mitchel and Hausdorf similarity measures score the best results with 95.27% and 94.48% recognition rates, respectively. On the other hand, FS Euclidean distance yieldes only 85% recognition rate.

One of the most important phases of speaker indexing is speaker clustering which aims to find the number of speakers in a speech document and merge the speech segments corresponding to a single speaker. The most critical source of problem in speaker clustering is the speech segments duration which may be so short that proper segment modeling becomes hard to achieve. An alternative suggestion in these situations is to adapt global models with new data instead of building the speaker models from the ground. In this paper we investigate two adaptation techniques in eigen-voice space for improving clustering performance especially for shorter speech utterances. These techniques were embedded in a clustering framework and evaluated on a set of domestic conversational speech. We have also compared the proposed methods with some other known techniques. The experiments show a considerable improvement in speaker clustering performance.

In this paper we study combinatorial segment visibility, and show how to compute and maintain it as the observer moves in the plane. There are n non-overlapping convex objects in the plane, and we have a segment observer among them. We first consider static case of the problem, in which the observer and objects are static, and then we study dynamic case of the problem, in which the observer can move among obstacles.