205287010
Quality of service support in IEEE 802.11 wireless ad hoc networks
Abstract Supporting Quality of Service (QoS) in wireless networks is a challenging problem. The IEEE 802.11 LAN standard was developed primarily for elastic data applications. In order to support the transmission of real-time data, a polling-based scheme called the point coordination function (PCF) was introduced in IEEE 802.11. However, PCF was not able to meet the desired and practical service differentiation requirements to fulfill the need of real-time data. Therefore, Task Group E of the IEEE 802.11 working group released several IEEE 802.11e drafts, whose main task is to support QoS in IEEE 802.11 LANs. The polling scheme of PCF is extended in IEEE 802.11e into the more complex hybrid coordination function (HCF). We found that HCF has several performance issues that may affect its anticipated performance. In this paper, we address these issues and propose a QoS enhancement over PCF, called enhanced PCF (EPCF) that enables Wireless LAN to send a combination of voice, data and isochronous data packets using the current IEEE 802.11 PCF. First, we compare the performance of the proposed model (EPCF) with the HCF function of the IEEE 802.11e through simulation. Second, we extend the proposed model (EPCF) to work in a multihop wireless ad hoc mode and present the advantages and limitations in this case. Simulation results demonstrate an enhanced performance of our scheme over the legacy PCF and a comparable performance to the IEEE 802.11e HCF in terms of the average delay and system throughput. However, EPCF is much simpler than HCF, provides flow differentiation, and is easy to implement in the current IEEE 802.11 standard.
39527990
FPGA-based reconfigurable computing
9645285
Using scratchpad to exploit object locality in Java
Performance of modern computers is tied closely to the effective use of cache because of the continually increasing speed discrepancy between processors and main memory. We demonstrate that generational garbage collection employed by a system with cache and scratchpad memory can take advantage of the locality of small short-lived objects in Java and reduce memory traffic by as much as 20% when compared to a cache-only configuration. Converting half of the cache to scratchpad can be more effective at reducing memory traffic than doubling or even quadrupling the size of the cache for several of the applications in SPECjvm98.
1021056
A performance perspective on the Active Memory System
Abstract One major problem of using Java in real-time and embedded devices is the non-deterministic turnaround time of dynamic memory management systems (memory allocation and garbage collection). For the allocation, the non-determinism is often contributed by the time to perform searching, splitting, and coalescing. For the garbage collection, the turnaround time is usually determined by the size of the heap, the number of live objects, the number of object collected, and the amount of garbage collected. Even with the current state-of-the-art garbage collectors (generational and incremental schemes), they may or may not guarantee the worst-case latency. Moreover, such schemes often prolong overall garbage collection time. In this paper, the performance analysis of the proposed Active Memory Module (AMM) for embedded systems is presented. Unlike the software counterparts, the AMM can perform a memory allocation in a predictable and bounded fashion (14 cycles). Moreover, it can also yield a bounded sweeping time regardless of the number of live objects or heap size. By utilizing the proposed system, the overall speedup can be as high as 23% compared to the garbage collection system of the JDK 1.2.2 running in classic mode.
12198410
Scalable hardware-algorithm for mark-sweep garbage collection
The memory-intensive nature of object-oriented languages such as C++ and Java has created the need for high-performance dynamic memory management. Object-oriented applications often generate higher memory intensity in the heap region. Thus, a high-performance memory manager is needed to cope with such applications. As today's VLSI technology advances, it becomes increasingly attractive to map software algorithms such as malloc(), free() and garbage collection into hardware. This paper presents a hardware design of a sweeping function (for mark-and-sweep garbage collection) that fully utilizes the advantages of combinational logic. In our scheme, the bit sweep can detect and sweep the garbage in a constant time. Bit-map marking in software can improve the cache performance and reduce number of page faults; however, it often requires several instructions to perform a single mark. In our scheme, only one hardware instruction is required per mark. Moreover, since the complexity of the sweeping phase is often higher than the marking phase, the garbage collection time may be substantially improved. The hardware complexity of the proposed scheme (bit-sweeper) is O(n), where n represents the size of the bit map.
18001825
Architectural support for dynamic memory management
Recent advances in software engineering, such as graphical user interfaces and object-oriented programming, have caused applications to become more memory intensive. These applications tend to allocate dynamic memory prolifically. Moreover, automatic dynamic memory reclamation (garbage collection, GC) has become a popular feature in modern programming languages. As a result, the time consumed by dynamic storage management can be up to one-third of the program execution time. This illustrates the need for a high-performance memory management scheme. This paper presents a top-level design and evaluation of the proposed instruction extensions to facilitate heap management.
16368228
Color pattern recognition by quaternion correlation
It is popular to use the conventional correlation for pattern recognition. But when using the conventional correlation, the pattern should be the gray-level pattern. In this paper, we discuss how to use discrete quaternion correlation (DQCR) for the application of color pattern recognition. With the algorithm introduced here, we can detect the objects that have the same shape, color, and brightness as the reference pattern. Besides, we can also detect (a) the objects with the same shape, color, but different brightness, (b) the objects with the same shape, brightness, but different color, and (c) the objects just have the same shape as the reference. Our algorithm can classify the objects into 5 classes due to whether their shape, brightness, and color match those of the reference pattern. Besides, with our algorithm, the difference of the brightness and color can also be calculated at the same time.
17166482
Benchmarking the effectiveness of sequential pattern mining methods
Recently, there is an increasing interest in new intelligent mining methods to find more meaningful and compact results. In intelligent data mining research, accessing the quality and usefulness of the results from different mining methods is essential. However, there is no general benchmarking criteria to evaluate whether these new methods are indeed more effective compared to the traditional methods. Here we propose a novel benchmarking criteria that can systematically evaluate the effectiveness of any sequential pattern mining method under a variety of situations. The benchmark evaluates how well a mining method finds known common patterns in synthetic data. Such an evaluation provides a comprehensive understanding of the resulting patterns generated from any mining method empirically. In this paper, the criteria are applied to conduct a detailed comparison study of the support-based sequential pattern model with an approximate pattern model based on sequence alignment. The study suggests that the alignment model will give a good summary of the sequential data in the form of a set of common patterns in the data. In contrast, the support model generates massive amounts of frequent patterns with much redundancy. This suggests that the results of the support model require more post processing before it can be of actual use in real applications.
14320982
Sequential Pattern Mining in Multi-Databases via Multiple Alignment
To efficiently find global patterns from a multi-database, information in each local database must first be mined and summarized at the local level. Then only the summarized information is forwarded to the global mining process. However, conventional sequential pattern mining methods based on support cannot summarize the local information and is ineffective for global pattern mining from multiple data sources. In this paper, we present an alternative local mining approach for finding sequential patterns in the local databases of a multi-database. We propose the theme of approximate sequential pattern mining roughly defined as identifying patterns approximately shared by many sequences. Approximate sequential patterns can effectively summerize and represent the local databases by identifying the underlying trends in the data. We present a novel algorithm, ApproxMAP, to mine approximate sequential patterns, called consensus patterns, from large sequence databases in two steps. First, sequences are clustered by similarity. Then, consensus patterns are mined directly from each cluster through multiple alignment. We conduct an extensive and systematic performance study over synthetic and real data. The results demonstrate that ApproxMAP is effective and scalable in mining large sequences databases with long patterns. Hence, ApproxMAP can efficiently summarize a local database and reduce the cost for global mining. Furthremore, we present an elegant and uniform model to identify both high vote sequential patterns and exceptional sequential patterns from the collection of these consensus patterns from each local databases.
17498259
estWin: adaptively monitoring the recent change of frequent itemsets over online data streams
Knowledge embedded in a data stream is likely to be changed as time goes by. Consequently, identifying the recent change of the knowledge quickly can provide valuable information for the analysis of the data stream. However, most of mining algorithms or frequency approximation algorithms for a data stream do not able to extract the recent change of information in a data stream adaptively. This paper proposes a sliding window-based method that finds recently frequent itemsets over an online data stream adaptively. The size of a window defines a desired life-time of the information in a newly generated transaction. Consequently, only recently generated transactions in the range of the window are considered to find the frequent itemsets of a data stream.
14086063
A modular eigen subspace scheme for high-dimensional data classification
In this paper, a novel filter-based greedy modular subspace (GMS) technique is proposed to improve the accuracy of high-dimensional data classification. The proposed approach initially divides the whole set of high-dimensional features into several arbitrary number of highly correlated subgroups by performing a greedy correlation matrix reordering transformation for each class. These GMS can be treated as not only a preprocess of GMS filter-based classifiers but also a unique feature extractor to generate a particular feature subspaces for each different class presented in high-dimensional data. The similarity measures are next calculated by projecting the samples into different modular feature subspaces. Finally, a GMS filter-based architecture based on the mean absolute errors criterion is adopted to build a non-linear multi-class classifier. The proposed GMS filter-based classification scheme is developed to find non-linear boundaries of different classes for high-dimensional data. It not only significantly improves the classification accuracy but also dramatically reduces the computational complexity of feature extraction compared with the conventional principal components analysis. Experimental results demonstrate that the proposed GMS feature extraction method suits the GMS filter-based classifier best as a classification preprocess. It significantly improves the precision of high-dimensional data classification.