Distributed Evaluation of an Iterative Function for All Object Pairs on a SIMD Hypercube

An efficient distributed algorithm for evaluating an iterative function on all pairwise combinations of C objects on an SIMD hypercube is presented. The algorithm achieves uniform load distribution and minimal, completely local interprocessor communication

Automatic PCB Inspection Systems

There are more than 50 process steps required to fabricate a printed circuit board (PCB). To ensure quality, human operators simply inspect the work visually against prescribed standards. The decisions made by this labor intensive, and therefore costly, procedure often also involve subjective judgements. Automatic inspection systems remove the subjective aspects and provide fast, quantitative dimensional assessments. Machine vision may answer the manufacturing industry\u27s need to improve product quality and increase productivity. The major limitation of existing inspection systems is that all the algorithms need a special hardware platform to achieve the desired real-time speeds. This makes the systems extremely expensive. Any improvements in speeding up the computation process algorithmically could reduce the cost of these systems drastically. However, they remain a better option than increasingly error prone, and slow manual human inspectio

Workstation Clusters for Parallel Computing

Workstation clusters have become an increasingly popular alternative to traditional parallel supercomputers for many workloads requiring high performance computing. The use of parallel computing for scientific simulations has increased tremendously in the last ten years, and parallel implementations of scientific simulation codes are now in widespread use. There are two dominant parallel hardware/software architectures in use today: distributed memory, and shared memory. Systems implementing shared memory provide cooperating processes with a shared memory address space that can be accessed by all processors. In shared memory systems, parallel processing occurs through the use of shared data structures, or through emulation of message passing semantics in software. Distributed memory systems are composed of a number of interconnected computational nodes, which do not share memory, but can communicate with each other through a high-performance network of some kind. Parallelism is achieved on distributed memory systems with multiple copies of the parallel program running on different nodes, sending messages to each other to coordinate computations. The messages used in a distributed memory parallel program typically contain application data, synchronization information, and other data that controls the execution of the parallel program

RMESH Algorithms for Parallel String Matching

String matching problem received much attention over the years due to its importance in various applications such as text/file comparison, DNA sequencing, search engines, and spelling correction. Especially with the introduction of search engines dealing with tremendous amount of textual information presented on the world wide web and the research on DNA sequencing, this problem deserves special attention and any algorithmic or hardware improvements to speed up the process will benefit these important applications. In this paper, we present three algorithms for string matching on reconfigurable mesh architectures. Given a text T of length n and a pattern P of length m, the first algorithm finds the exact matching between T and P in O(1) time on a 2-dimensional RMESH of size (n-m+1) * m. The second algorithm finds the approximate matching between T and P in O(k) time on a 2D RMESH, where k is the maximum edit distance between T and P. The third algorithm allows only the replacement operation in the calculation of the edit distance and finds an approximate matching between T and P in constant-time on a 3D RMESH

A Systolic Algorithm to Process Compressed Binary Images

A new systolic algorithm which computes image differences in run-length encoded (RLE) format is described. The binary image difference operation is commonly used in many image processing applications including automated inspection systems, character recognition, fingerprint analysis, and motion detection. The efficiency of these operations can be improved significantly with the availability of a fast systolic system that computes the image difference as described in this paper It is shown that for images with a high similarity measure, the time complexity of the systolic algorithm is small and in some cases constant with respect to the image size. The time for the systolic algorithm is proportional to the difference between the number of runs in the two images, while the time for the sequential algorithm is proportional to the total number of runs in the two images together A formal proof of correctness for the algorithm is also given

Systolic Algorithm for Processing RLE Images

Image difference operation is commonly used in on-line automated printed circuit board (PCB) inspection systems as well as many other image processing applications. In this paper, we describe a new systolic algorithm and its system architecture which computes image differences in run-length encoded (RLE) format. The efficiency of this operation greatly affects the overall performance of the inspection system. It is shown that, for images with a high similarity measure, the time complexity of the systolic algorithm is a small constant. A formal proof of correctness for the algorithm is also given in the paper

A Systolic Image Difference Algorithm for RLE-Compressed Images

A new systolic algorithm which computes image differences in run-length encoded (RLE) format is described. The binary image difference operation is commonly used in many image processing applications including automated inspection systems, character recognition, fingerprint analysis, and motion detection. The efficiency of these operations can be improved significantly with the availability of a fast systolic system that computes the image difference as described in this paper. It is shown that for images with a high similarity measure, the time complexity of the systolic algorithm is small and, in some cases, constant with respect to the image size. A formal proof of correctness for the algorithm is also given

A Model-Based Approach for Compression of Fingerprint Images

We propose a new fingerprint image compression scheme based on the hybrid model of an image. Our scheme uses the essential steps of a typical automated fingerprint identification system (AFIS) such as enhancement, binarization and thinning to encode fingerprint images. The decoding process is based on reconstructing a hybrid surface by using the gray values on ridges and valleys. In this compression scheme, the ridge skeleton is coded efficiently by using differential chain codes. The valley skeleton is derived from the ridge skeleton and the gray values along the ridge and valley skeletons are encoded using the discrete cosine transform. The error between the original and the replica is also encoded to increase the quality. One advantage of our approach is that original features such as end points and bifurcation points can be extracted directly from compressed image even for a very high compression ratio. Another advantage is that the proposed scheme can be integrated to a typical AFIS easily. The algorithm has been applied to various fingerprint images, and high compression ratios like 63:1 have been obtained. A comparison to wavelet/scalar quantization (WSQ) has been also made

Detection of Skin Tumor Boundaries in Color Images

A simple and yet effective method for finding the borders of tumors is presented as an initial step towards the diagnosis of skin tumors from their color images. The method makes use of an adaptive color metric from the red, green, and blue planes that contains information for discriminating the tumor from the background. Using this suitable coordinate transformation, the image is segmented. The tumor portion is then extracted from the segmented image and borders are drawn. Experimental results that verify the effectiveness of this approach are give

Neural Network Diagnosis of Malignant Melanoma from Color Images

Malignant melanoma is the deadliest form of all skin cancers. Approximately 32,000 new cases of malignant melanoma were diagnosed in 1991 in the United States, with approximately 80% of patients expected to survive 5 years. Fortunately, if detected early, even malignant melanoma may be treated successfully, Thus, in recent years, there has been rising interest in the automated detection and diagnosis of skin cancer, particularly malignant melanoma. Here, the authors present a novel neural network approach for the automated separation of melanoma from 3 benign categories of tumors which exhibit melanoma-like characteristics. The approach uses discriminant features, based on tumor shape and relative tumor color, that are supplied to an artificial neural network for classification of tumor images as malignant or benign. With this approach, for reasonably balanced training/testing sets, the authors are able to obtain above 80% correct classification of the malignant and benign tumors on real skin tumor images