16813459
Scenario-based multitasking for real-time object-oriented models
Abstract Contemporary embedded systems quite often employ extremely complicated software consisting of a number of interrelated components, and this has made object-oriented design methodologies widely used in practice. To implement an object-oriented model in given target hardware, it is imperative to derive a set of tasks from the designed objects. This process of determining tasks and the events they handle greatly influences the real-time performance of the resultant system including response times and real-time guarantees. However, the innate discrepancies between objects and tasks make this exceedingly difficult, and many developers are forced to find their task sets through trial and error. In this paper, we propose Scenario-based Implementation Synthesis Architecture (SISA), an architecture consisting of a method for deriving a task set from a given object-oriented model and the development tools and run-time system architecture to support the method. A system developed with SISA guarantees the optimal response time for each event while deriving the smallest possible number of tasks. We have fully implemented SISA by extending the RoseRT development tool and applied it to an existing industrial PBX (private branch exchange) system. The experimental results show that SISA outperforms the best known conventional techniques by reducing maximum response times an average of 30.3%.
14413427
Memory Footprint Reduction with Quasi-Static Shared Libraries in MMU-less Embedded Systems
Despite a rapid decrease in the price of solid state memory devices, system memory is still a very precious resource in embedded systems. The use of shared libraries is known to be effective in significantly reducing memory usage. Unfortunately, many resource-constrained embedded systems lack MMU, making it extremely difficult to support this technique. To address this problem, we propose a novel shared library scheme called the quasi-static shared library. In quasi-static shared libraries, global symbols are bound to pseudo-addresses at linking time and the actual physical addresses are bound at loading time. This scheme is made possible by emulating MMU’s memory mapping feature with a Data Section Base Register (DSBR) and a Data Section Base Table (DSBT). Quasi-static shared libraries do not require symbol tables which take up time and space at runtime. We have implemented the proposed scheme in a commercial ADSL (Asymmetric Digital Subscriber Line) home network gateway and conducted a series of experiments measuring its memory usage and performance overhead. The result is drastic: a 35% reduction in flash memory usage and a 10% reduction in RAM usage. These results were achieved with only a negligible performance penalty of less than 4%. Even though this scheme was applied to uClinux-based embedded systems, it can be used for any MMU-less real-time operating system.
14990532
Weaving Aspects into Real-Time Operating System Design Using Object-Oriented Model Transformation
Despite of the proliferation of object-oriented and component technology, their application to real-time operating systems (RTOS) has been limited since most design concerns in RTOSes crosscut software components and these are critical to deliver required performance and functionality. Aspect-Oriented Programming (AOP) is a very effective means to solve the crosscutting problem. However, we have observed the following limitations of the current AOP framework: (1) the current text-based AOP languages cannot clearly show how aspects are weaved together, (2) their granularity is too coarse to capture all aspects in an RTOS, (3) it is difficult to control the weaving process, since aspect weavers are usually hard-coded. In this paper, we propose a new AOP framework that provides (1) a graphical aspect programming environment that visualizes aspects, crosscutting classes, and method structures, (2) a new aspect model that supports a sub-method level granularity where an aspect is defined as a set of classes, and (3) an aspect weaving process specified by an object-oriented meta-model transformation. Since our aspect-oriented programming framework improves the expressiveness of the crosscutting concerns of RTOSes and automates aspect weaver generation, it can enhance RTOS customization.
5714667
Extending software communications architecture for QoS support in SDR signal processing
The software communications architecture (SCA) is middleware for providing component interfaces and dynamic reconfigurability to software define radio (SDR) modems and it is widely adopted by the SDR forum. Although its main application domains include real-time signal processing, SCA lacks QoS support. We thus propose a QoS-enabled extension of SCA called Q-SCA. Our extended architecture incorporates a new waveform model, extended domain profiles that describe QoS requirements, and a modified application instantiation process that supports both admission control and resource allocation. We show the viability of Q-SCA through full implementation and experiments.
15702195
OCR in a Hierarchical Feature Space
This paper describes hierarchical OCR, a character recognition methodology that achieves high speed and accuracy by using a multiresolution and hierarchical feature space. Features at different resolutions, from coarse to fine-grained, are implemented by means of a recursive classification scheme. Typically, recognizers have to balance the use of features at many resolutions (which yields a high accuracy), with the burden on computational resources in terms of storage space and processing time. We present in this paper, a method that adaptively determines the degree of resolution necessary in order to classify an input pattern. This leads to optimal use of computational resources. The hierarchical OCR dynamically adapts to factors such as the quality of the input pattern, its intrinsic similarities and differences from patterns of other classes it is being compared against, and the processing time available. Furthermore, the finer resolution is accorded to only certain "zones" of the input pattern which are deemed important given the classes that are being discriminated. Experimental results support the methodology presented. When tested on standard NIST data sets, the hierarchical OCR proves to be 300 times faster than a traditional K-nearest-neighbor classification method, and 10 times taster than a neural network method. The comparison uses the same feature set for all methods. Recognition rate of about 96 percent is achieved by the hierarchical OCR. This is at par with the other two traditional methods.
28122333
Using a Hash-Based Method with Transaction Trimming for Mining Association Rules
We examine the issue of mining association rules among items in a large database of sales transactions. Mining association rules means that, given a database of sales transactions, to discover all associations among items such that the presence of some items in a transaction will imply the presence of other items in the same transaction. The mining of association rules can be mapped into the problem of discovering large itemsets where a large itemset is a group of items that appear in a sufficient number of transactions. The problem of discovering large itemsets can be solved by constructing a candidate set of itemsets first, and then, identifying, within this candidate set, these itemsets that meet the large itemset requirement. Generally, this is done iteratively for each large k-itemset in increasing order of k, where a large k-itemset is a large itemset with k items. To determine large itemsets from a huge number of candidate sets in early iterations is usually the dominating factor for the overall data mining performance. To address this issue, we develop an effective algorithm for the candidate set generation. It is a hash-based algorithm and is especially effective for the generation of a candidate set for large 2-itemsets. Explicitly, the number of candidate 2-itemsets generated by the proposed algorithm is, in orders of magnitude, smaller than that by previous methods, thus resolving the performance bottleneck. Note that the generation of smaller candidate sets enables us to effectively trim the transaction database size at a much earlier stage of the iterations, thereby reducing the computational cost for later iterations significantly. The advantage of the proposed algorithm also provides us the opportunity of reducing the amount of disk I/O required. An extensive simulation study is conducted to evaluate performance of the proposed algorithm.
9022148
Parallel Searching in Generalized Monge Arrays
Abstract. This paper investigates the parallel time and processor complexities of several searching problems involving Monge, staircase-Monge, and Monge-composite arrays. We present array-searching algorithms for concurrent-read-exclusive-write (CREW) PRAMs, hypercubes, and several hypercubic networks. All these algorithms run in near-optimal time, and their processor-time products are all within an 
 $O (\lg n)$ factor of the worst-case sequential bounds. Several applications of these algorithms are also given. Two applications improve previous results substantially, and the others provide novel parallel algorithms for problems not previously considered.
16088118
An effective hash-based algorithm for mining association rules
In this paper, we examine the issue of mining association rules among items in a large database of sales transactions. The mining of association rules can be mapped into the problem of discovering large itemsets where a large itemset is a group of items which appear in a sufficient number of transactions. The problem of discovering large itemsets can be solved by constructing a candidate set of itemsets first and then, identifying, within this candidate set, those itemsets that meet the large itemset requirement. Generally this is done iteratively for each large k-itemset in increasing order of k where a large k-itemset is a large itemset with k items. To determine large itemsets from a huge number of candidate large itemsets in early iterations is usually the dominating factor for the overall data mining performance. To address this issue, we propose an effective hash-based algorithm for the candidate set generation. Explicitly, the number of candidate 2-itemsets generated by the proposed algorithm is, in orders of magnitude, smaller than that by previous methods, thus resolving the performance bottleneck. Note that the generation of smaller candidate sets enables us to effectively trim the transaction database size at a much earlier stage of the iterations, thereby reducing the computational cost for later iterations significantly. Extensive simulation study is conducted to evaluate performance of the proposed algorithm.
9959276
Injecting RBAC to secure a Web-based workflow system
Web-based workflow systems have recently received much attention because they can support dynamic business processes over heterogeneous computing systems. Most existing web-based workflow systems, however, provide minimal security services such as authentication of users and network security. In this paper we describes an experiment in injecting role-based access control (RBAC) into an existing web-based workflow system. Specifically, we ensure that each task can only be executed by users belonging to a specific role. In order to achieve this, we define a simplified RBAC model to meet our needs and describe the security architecture to be applied to an existing web-based workflow system. We describe our implementation using commercial off-the-shelf (COTS) technology to demonstrate the feasibility of this approach. Our implementation uses X.509v3 certificates with role attribute, and employs a user-pull style where the client requests a client certificate from the role-server and presents it to the workflow system. A major goal of our implementation is to have minimal changes to the existing web server and no changes to the browser. We also discuss alternative architecture such as server-pull with LDAP (Lightweight Directory Access Protocol).