انجمن کامپیوتر ایران

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.

This paper proposes an integrated framework for speaker indexing which includes both speaker segmentation and speaker clustering. Speaker indexing systems has wide domains of application with different requirements which make a general speaker indexing framework hard to accomplish. The main source of performance degradation in speaker indexing is the probable existence of short speech utterances which makes the speaker turns hard to distinguish and also exposes the segment modeling to data insufficiency. This paper introduces a speaker indexing framework with high average performance which uses Support Vector Machines (SVM) as the core approach. The main contribution of this framework is the SVM based clustering approach which makes the indexing more robust against the short speech segments. This framework is evaluated on a domestic conversational speech dataset and the results were satisfactory.

The developers of a system are accepted as one of the most important stakeholders of an Architecture Description. The Development Viewpoint is suggested to satisfy the needs of the developers throughout the development process via codeline organization descriptions, programming models, etc. However, the available models for such purposes, if any, barely cross informal natural language descriptions and checklists. This paper introduces the idea of enhancing the description of the Development viewpoint using lightweight Domain- Specific Languages and presents the application of such languages in two industrial case studies. This language enables the architect to provide the necessary guidelines that constrains the implementers during the development process and it is also used as a means to discover the deviation of the code from the architecture as the development goes on.