36888588
Efficient connected-index finite-length arithmetic codes
In this paper, we propose a connected-index approach to construct efficient finite-length arithmetic codes by splitting the information of the last nonfitting symbol into the current and subsequent codewords. The proposed arithmetic codes, which limit the error propagation in about one block, require neither a post-appended end-of-block symbol, nor pre-affixed side-information, to characterize the number of encoded symbols. Hence, the proposed finite-length arithmetic codes can nearly achieve the coding efficiency attained by infinite-length arithmetic codes. With high coding efficiency, limited error-propagation, and the regular process, the proposed coding approach is suitable for information exchange with small packets in modern high-speed network systems.
17739128
Modified Hough Transforms for Object Feature Extraction
In this paper, we propose the use of modified Hough transforms to efficiently extract object feature parameters, which are usually contaminated by heavily noisy corrugation and discontinuity. The modified HT (MHT) is developed by introducing spatial and parameter weighting functions to improve the detection performance for the traditional Hough transform (HT), which generally fails to robustly detect natural object parameters. Using designed test patterns and real images, simulations show that the proposed weighting functions are helpful in detecting noise-corrupted object features. Due to its robustness, the MHT can be easily figured with a coarse-to-fine adaptive search mechanism to reduce the huge amount of computation for feature parameters extraction.
16111118
Energy-Efficient Continuous Isoline Queries in Sensor Networks
Environmental monitoring is a promising application for sensor networks. Many scenarios produce geographically correlated readings, making them visually interesting and good targets for the isoline query. This query depicts boundaries showing how values change in the network. Temporal and spatial suppression provide opportunities for reducing the cost of maintaining the query result. We combine both techniques for maximal benefit by monitoring node and edge constraints. A monitored node triggers a report if its value changes. A monitored edge triggers a report if the difference between its nodes’ values changes. The root collects reports and derives all node values, from which the query result is generated. We fully exploit this strategy in our algorithm, CONCH, which maintains the set of node and edge constraints that minimizes message traffic.
15187378
An efficient fault-tolerant distributed channel allocation algorithm for cellular networks
A channel allocation algorithm in a cellular network consists of two parts: a channel acquisition algorithm and a channel selection algorithm. Some of the previous works in this field focused on centralized approaches to allocating channels. But, centralized approaches are neither scalable nor reliable. Recently, distributed dynamic channel allocation algorithms have been proposed, and they have gained a lot of attention due to their high reliability and scalability. But, in most of the algorithms, the cell that wants to borrow a channel has to wait for replies from all its interference neighbors and, hence, is not fault-tolerant. In this paper, we propose a new algorithm that is fault-tolerant and makes full use of the available channels. It can tolerate the failure of mobile nodes as well as static nodes without any significant degradation in service.
3145676
A Genetic Algorithm with Adaptive Mutations and Family Competition for Training Neural Networks
In this paper, we present a new evolutionary technique to train three general neural networks. Based on family competition principles and adaptive rules, the proposed approach integrates decreasing-based mutations and self-adaptive mutations to collaborate with each other. Different mutations act as global and local strategies respectively to balance the trade-off between solution quality and convergence speed. Our algorithm is then applied to three different task domains: Boolean functions, regular language recognition, and artificial ant problems. Experimental results indicate that the proposed algorithm is very competitive with comparable evolutionary algorithms. We also discuss the search power of our proposed approach.
1146793
An empirical study of operating systems errors
We present a study of operating system errors found by automatic, static, compiler analysis applied to the Linux and OpenBSD kernels. Our approach differs from previous studies that consider errors found by manual inspection of logs, testing, and surveys because static analysis is applied uniformly to the entire kernel source, though our approach necessarily considers a less comprehensive variety of errors than previous studies. In addition, automation allows us to track errors over multiple versions of the kernel source to estimate how long errors remain in the system before they are fixed.We found that device drivers have error rates up to three to seven times higher than the rest of the kernel. We found that the largest quartile of functions have error rates two to six times higher than the smallest quartile. We found that the newest quartile of files have error rates up to twice that of the oldest quartile, which provides evidence that code "hardens" over time. Finally, we found that bugs remain in the Linux kernel an average of 1.8 years before being fixed.
1498767
Clustering by pattern similarity in large data sets
Clustering is the process of grouping a set of objects into classes of similar objects. Although definitions of similarity vary from one clustering model to another, in most of these models the concept of similarity is based on distances, e.g., Euclidean distance or cosine distance. In other words, similar objects are required to have close values on at least a set of dimensions. In this paper, we explore a more general type of similarity. Under the pCluster model we proposed, two objects are similar if they exhibit a coherent pattern on a subset of dimensions. For instance, in DNA microarray analysis, the expression levels of two genes may rise and fall synchronously in response to a set of environmental stimuli. Although the magnitude of their expression levels may not be close, the patterns they exhibit can be very much alike. Discovery of such clusters of genes is essential in revealing significant connections in gene regulatory networks. E-commerce applications, such as collaborative filtering, can also benefit from the new model, which captures not only the closeness of values of certain leading indicators but also the closeness of (purchasing, browsing, etc.) patterns exhibited by the customers. Our paper introduces an effective algorithm to detect such clusters, and we perform tests on several real and synthetic data sets to show its effectiveness.