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In the past years, most task scheduling algorithms consider either to minimize the application's completion time or to minimize the application's energy consumption subject to a deadline constraint. Few of them can achieve these two performance goals at the same time. In this paper, we propose an energyaware importance-ratio-based stochastic task scheduling (EISTS) algorithm, which makes a good balance between the makespan and energy consumption and achieves high system weighted improvement based on the importance-ratio of makespan toenergy consumption for heterogeneous parallel systems. Compared with other existing algorithms, our algorithm can achieve shorter application's completion time when the importance-ratio of makespan to energy consumption is high. On the contrary, our algorithm consumes less energy. To prove its validity, we compare our proposed algorithms with two existing scheduling algorithms(SHEFT and ECS) in terms of system weighted improvement, makespan, and energy consumption respectively. The results of simulations show that the system weighted improvement of our algorithm and those two compared algorithms are 37%, 17%, and 18% respectively, which clearly demonstrates that ourEISTS algorithm outperforms them significantly. In addition, our algorithm is also very competitive in terms of both makespan and energy consumption.

In a subtropical region, the application of remote sensing in geological survey and mapping is limited by the dense vegetation and soil cover and poor data availability due to clouds and rain. This study emphasizes the detection of structural and lithological features underneath a vegetation canopy using shuttle imaging radar C/X band synthetic aperture radar (SIR-C/X-SAR) data. Zhao Qing test site in southern China was selected for this study. The SIR-C/X-SAR data have advantages including all weather, all time imaging capability, high altitude, and wide swath along with the expanded information from multifrequency and multipolarization that improves the geological interpretibility, especially in a moist, vegetated region. This paper presents interpretations of structures, beds, and lithostratigraphic units from the shuttle imaging radar data, and makes an assessment of SIR-C/X-SAR data for geological mapping.

In this paper, a segment extraction algorithm based on polygonal approximation for on-line Chinese characters recognition (OLCCR) is presented. With this method, the point with the smallest interior angle is detected and the whole stroke is split into two adjacent curves by this point, which is called as a cut-off point or an inflexion. To each of the two curves, the same step is performed to detect the cut-off points respectively. The same operations are performed iteratively until the smallest interior angle in all the curves is larger than an appointed threshold value. All the cut-off points and the start-end points compose the stroke and every pair of adjacent points constructs a segment. Experiments proved that this method has the advantages of less computing complexity and better approximating effect then other methods. An OLCCR system with this segment extraction algorithm has achieved the speed of 20/s and the recognition rate of 97.2%.

In this paper, a simple and efficient method for coarse classification of Chinese characters is presented. In the new approach, a Chinese character is characterized with its outmost and second-peripheral segment features, each of which is expressed as four string vectors. The outmost and second-peripheral segment features of a Chinese word describe the structure of segments in top, bottom, left and right directions respectively. In addition, A scoring-based coarse classification scheme is devised in forming candidate set. Ten sets of Chinese characters are tested and the average number of characters in candidate sets is 100 and error rate is about 2%. Experimental results show that the approach is feasible in coarse classification of Chinese characters.

In this paper, a segment extraction algorithm based on polygonal approximation for on-line Chinese characters recognition (OLCCR) is presented. With this method, the point with the smallest interior angle is detected and the whole stroke is split into two adjacent curves by this point-which is called as a cut-off point or an inflexion. To each of the two curves, the same step is performed to detect the cut-off points respectively. The same operations are performed iteratively until the smallest interior angle in all the curves is larger than an appointed threshold value. All the cut-off points and the start-end points compose the stroke and every pair of adjacent points constructs a segment. After segments have been extracted, finite state machines is used to check whether the adjacent segments need combination thus redundant segments can be reduced. Experiments proved that this method has the advantages of less computing complexity and better approximating effect than other methods. An OLCCR system with this segment extraction and combination algorithm has achieved the speed of 20/s and the recognition rate of 97.2%.

In this paper, a new partial area matching method based on support vector machine (SVM) for distinguishing similar on-line handwritten Chinese characters is presented. With this new method, the similar characters are divided into several classes according to the difference among their structure features. After the candidate character set is obtained by the former recognizing steps, the SVM is used to pick up the most accurately matched character from the set. Experiments showed that the similar Chinese characters can be distinguished effectively and the recognition rate for the first rank Chinese characters can be improved with this method.