16379329
Genetic fuzzy discretization with adaptive intervals for classification problems
We propose a genetic fuzzy discretization method for continuous numerical attributes. Traditional discretization methods categorize the continuous attributes into a number of bins. Because they are made on crisp discretization, there exists considerable information loss. Fuzzy discretization allows overlapping intervals and reflects linguistic classification. However, the number of intervals, the boundaries of intervals, and the degrees of overlapping are intractable to get optimized. We use a genetic algorithm to optimize these parameters. Experimental results showed considerable improvement on the classification accuracy over a crisp discretization and a typical fuzzy discretization.
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.
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.
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].
14917866
Protected heap sharing for memory-constrained java environments
Multitasking is one of capabilities we often want to have in memory-constrained embedded systems. To support multiple address spaces within a small physical memory, a simple memory management frequently encounters the lack of available memory. Our paper presents an efficient heap memory management scheme that reduces memory footprints by adaptively sharing heaps among multiple tasks in JVM environments. We modified KVM from Sun Microsystems so that Java applications acquire or release heaps in a shared pool on an as-needed basis. To protect address spaces among tasks in the absence of virtual memory capabilities, we use memory protection units (MPUs) by incorporating them into our heap sharing scheme. Our experiments with J2ME MIDP applications show significant reductions by 33% on average, ranging from 6% to 50% in memory usage over the execution. The overheads of our scheme in garbage collection are kept low. The execution times in our scheme increase only by 0.2% on average.
4648734
Selective channel feedback mechanisms for wireless multichannel scheduling
Opportunistic scheduling can significantly improve wireless network performance by exploiting the feedback information that conveys the underlying channel condition. In emerging multichannel systems, the perchannel feedback induces a substantial amount of feedback overhead and requires high computational complexity. To reduce the feedback overhead, we consider an opportunistic feedback strategy that activates the channel feedback opportunistically according to the channel condition. Then, we combine the opportunistic feedback with the best-n channel feedback scheme where a mobile user chooses the best n channels and transfers this information to the base station. We analyze the throughput and the amount of channel feedback information for proportionally fair opportunistic scheduling under Rayleigh fading i.i.d. channels. The numerical results confirm that our partial feedback schemes achieve a remarkable reduction in the amount of feedback information without a significant throughput degradation, thereby saving the scarce wireless bandwidth and limited battery power
34887568
Memory access driven storage assignment for variables in embedded system design
41031857
Address code optimization using code scheduling for digital signal processors
We propose an effective address code generation algorithm for digital signal processor (DSP) to minimize the number of addressing instructions. Unlike previous works in which code scheduling and offset (address) assignment are performed sequentially without any interaction between them, our work tightly couples code scheduling with offset assignment to exploit scheduling on optimizing addressing instructions more effectively. We accomplish this by proposing a new code scheduling algorithm that leads to an efficient sequence of variable accesses, minimizing addressing instructions. Experimental results with benchmark DSP programs show average improvements of 23.7% and 47.1% in the address code size and a naive storage assignment algorithm, respectively.