12470418
Genetic Fuzzy Discretization for Classification Problems
Many real-world classification algorithms can not be applied unless the continuous attributes are discretized and the interval discretization methods are used in many machine learning techniques. It is hard to determine the intervals for the discretization of numerical attributes that has an infinite number of candidates. And interval discretization methods are based on a crisp set, a value in a continuous attribute must belong to only one interval. They are often not proper for describing a value located around the boundaries of intervals. Fuzzy partioning is an attractive method for those cases in classification problems. An important decision in fuzzy partitioning is about the positions of interval boundaries and the degrees of overlapping in the fuzzy sets. We optimize the parameters that specify fuzzy partitioning by genetic algorithms.
253336
Parameter Optimization by a Genetic Algorithm for a Pitch Tracking System
The emergence of multimedia data in databases requires adequate methods for information retrieval. In a music data retrieval system by humming, the first stage is to extract exact pitch periods from a flow of signals. Due to the complexity of speech signals, it is difficult to make a robust and practical pitch tracking system. We adopt genetic algorithm in optimizing the control parameters for note segmentation and pitch determination. We applied the results to HumSearch, a commercialized product, as a pitch tracking engine. Experimental results showed that the proposed engine notably improved the performance of the existing engine in HumSearch.
14946468
Address assignment in DSP code generation - an integrated approach
One of the important issues in embedded system design is to optimize program code for the microprocessor to be stored in ROM. In this paper, we propose an integrated approach to the DSP address-code generation problem for minimizing the number of addressing instructions. Unlike previous works, in which code scheduling and offset assignment are performed sequentially without any interaction between them, our work tightly couples offset assignment problem with code scheduling to exploit scheduling on minimizing addressing instructions more effectively. We accomplish this by developing a fast but accurate two-phase assignment procedure which, for a sequence of code schedules, finds a sequence of memory layouts with minimum addressing instructions. Experimental results with benchmark DSP programs show improvements an average of 5.8% in the whole code size over the existing methods.
5197608
Address assignment combined with scheduling in DSP code generation
One of the important issues in embedded system design is to optimize program code for the microprocessor to be stored in ROM. In this paper, we propose an integrated approach to the DSP address code generation problem for minimizing the number of addressing instructions. Unlike previous works in which code scheduling and offset assignment are performed sequentially without any interaction between them, our work tightly couples offset assignment problem with code scheduling to exploit scheduling on minimizing addressing instructions more effectively. We accomplish this by developing a fast but accurate two-phase procedure which, for a sequence of code schedules, finds a sequence of memory layouts with minimum addressing instructions. Experimental results with benchmark DSP programs show improvements of 13%--33% in the address code size over Solve-SOA/GOA [7].
1627364
DC-DC converter-aware power management for battery-operated embedded systems
Most digital systems are equipped with DC-DC converters to supply various levels of voltages from batteries to logic devices. DC-DC converters maintain legal voltage ranges regardless of the load current variation as well as battery voltage drop. Although the efficiency of DC-DC converters is changed by the output voltage level and the load current, most existing power management techniques simply ignore the efficiency variation of DC-DC converters. However, without a careful consideration of the efficiency variation of DC-DC converters, finding a true optimal power management is impossible. In this work, we solve the problem of energy minimization with the consideration of the characteristics of power consumption of DC-DC converter. Specifically, the contributions of our work are: (1) We analyze the effects of the efficiency variation of DC-DC converters on a single task execution in DVS (dynamic voltage scaling) scheme, and propose a technique, called DC/spl I.bar/DVS, of DC-DC converter-aware energy-minimal DVS; (2) DC/spl I.bar/DVS is then extended to combine the effects of DC-DC converters with the procedures of general DVS techniques with multiple tasks; (3) Conversely, we propose a technique, called DC/spl I.bar/CONF, of generating a DC-DC converter that is best suited, in terms of total energy efficiency, to the intended application, and (4) finally, we complete our integrated framework DC-lp, which is based on DC/spl I.bar/DVS and DC/spl I.bar/CONF, that attempts to solve the DC-DC converter configuration selection problem and the DVS problem simultaneously. To show the effectiveness of the proposed techniques, a set of experimental results is provided. In summary, it is shown that DC-lp is able to save 16.0% /spl sim/ 22.1% of energy on the average, which otherwise was dissipated in the previous power management schemes with no consideration of DC-DC converter efficiency variation.
154999
Identifying Sources of Opinions with Conditional Random Fields and Extraction Patterns
Recent systems have been developed for sentiment classification, opinion recognition, and opinion analysis (e.g., detecting polarity and strength). We pursue another aspect of opinion analysis: identifying the sources of opinions, emotions, and sentiments. We view this problem as an information extraction task and adopt a hybrid approach that combines Conditional Random Fields (Lafferty et al., 2001) and a variation of AutoSlog (Riloff, 1996a). While CRFs model source identification as a sequence tagging task, AutoSlog learns extraction patterns. Our results show that the combination of these two methods performs better than either one alone. The resulting system identifies opinion sources with 79.3% precision and 59.5% recall using a head noun matching measure, and 81.2% precision and 60.6% recall using an overlap measure.
195882206
Memory layout techniques for variables utilizing efficient DRAM access modes in embedded system design
The delay of memory access is one of the major bottlenecks in embedded systems' performance. In software compilation, it is known that there are high variations in memory access delay depending on the ways of storing/retrieving the variables in code to/from the memories. In this paper, we propose an effective storage assignment technique for variables to maximize the use of memory bandwidth. Specifically, we study the problem of DRAM memory layout for storing the nonarray variables in code to achieve a maximum utilization of page and/or burst modes for the memory accesses. The contributions of our work are, for each of page and burst modes: (1) We prove that the problem is NP-hard; (2) We propose an exact formulation of the problem and efficient memory layout algorithms, called Solve-MLP for the page mode and Solve-MLB for the burst mode; From experiments with a set of benchmark programs, we confirm that our proposed techniques use on average 20.0% and 9.9% more page accesses and 54.0% and 86.6% more burst accesses than those by OFU (the order of first use) and the technique in P.R. Panda et al. (1997), respectively.