21308144
EmoEars: An Emotion Recognition System for Mandarin Speech
In this paper, an emotion recognition system for mandarin speech is presented. Five basic human emotions including angry, fear, happy, neutral and sad are investigated. The recognizer is based on neural network with OCON and ACON architecture. Some novel feature selection methods are also added as optional tool to enhance the efficiency and classification accuracy. The system can train speaker dependent emotion speech model through online emotional utterance recording. Experiment results show that emotion can be recognized through neural network model, the best mean accuracy is 86.7%. In addition, the feature selection module is effective to reduce the compute load and increase the generalization ability of the recognizer.
14850996
Effects of network characteristics on task performance in a desktop CVE system
The influences of network characteristics on the task performance in a desktop collaborative virtual environment (CVE) system were studied. In the experiment, two participants worked together to accomplish a collaborative task and a network emulator was utilized to simulate real network characteristics. The task used in the experiment was designed to be highly interactive and had rigid requirements on network characteristics (e.g. latency and packet loss). The objective performance and subjective assessment were recorded and analyzed quantitatively as a function of network latency and packet loss rate. The experiment results show that network latency has no distinct effect on task performance when network latency is less than 200 ms and lightly affect task performance when network latency is between 200 and 600 ms; packet loss has no effect on task performance when there are 40 state update messages received per second and lightly affect task performance when there are between 15 and 40 state update messages received per second.
41643555
A Fuzzy Dead Reckoning Algorithm for Distributed Interactive Applications
A fuzzy Dead Reckoning (DR) algorithm for distributed interactive applications is proposed in this paper. Since fixed threshold cannot adequately handle the dynamic relationships between moving entities, some multi-level threshold DR algorithms were proposed in the past few years. In these algorithms the level of threshold is adaptively adjusted based on the distance between entities. The proposed fuzzy DR algorithm is based on multi-level threshold DR algorithm and takes all properties of entity (e.g. position, size and view angle etc.) into consideration when adjusting the level of threshold. This algorithm employs fuzzy correlation degree to measure the relationships between entities and determine the level of threshold for DR algorithm. Fuzzy consistent relation is used to distribute weight for each property. Simulation results indicate that fuzzy DR algorithm can achieve a considerable reduction in the number of state update messages while maintaining adequate accuracy in extrapolation.
206977606
Priority Based Overlay Multicast for Distributed Interactive Applications
Distributed Interactive Applications (DIAs) is different with other network-based applications, since the data should be delivered to the receiver node according to its priority. Among the existing overlay multicast protocols the "priority-based directed minimum spanning tree" (PST) is designed for DIAs. But in PST the available bandwidth (avail-bw) is used inefficiently and the system becomes unstable when its scale increases. This paper proposed a novel overlay multicast protocol named Priority Based Overlay Multicast (POM), which uses the entities’ priority, links’ delay and node’s avail-bw to build multicast tree. By using POM the nodes with high priority receive the data in short delay, while the nodes with low priority tolerate a long delay in order to balance the available bandwidth usage among the nodes. The simulation results show that POM uses the avail-bw efficiently and keeps the system stable when its scale increases. Keywords: DIAs; Priority; Available bandwidth.
11794423
An adaptive consistency maintenance approach for replicated continuous applications
Consistency maintenance is a key problem to replicated continuous applications and dead reckoning (DR) algorithm is widely used in this area. In order to eliminate short-term inconsistency caused by DR algorithm, local-lag approach was proposed. For the purpose of simplicity, fixed lag value is used by local-lag approach and the performance of the approach is greatly deteriorated. In this paper, an adaptive consistency maintenance approach, which is based on local-lag approach, is proposed. The new approach can adjust lag value according to the estimated typical network latency. In order to ensure the criterion of consistency correctness, a lag value adjustment method is also proposed. Through two experiments, the performance of the new approach is evaluated and the results indicate that it is feasible and can greatly improve the performance of the application.
8895078
An Adaptive-Filtering Policy for Distributed Virtual Environments
In this paper, an adaptive-filtering policy for distributed virtual environments (DVEs) was brought forward. It took advantages of two traditional filtering policies (static-filtering policy and equidistance-transmission policy) and could adaptively select the fittest filtering policy corresponding to current motion characteristic of the virtual object in DVEs. An experiment was designed to compare this filtering policy with the two traditional filtering policies on task performance. Objective (task completion time, state updates transmitted and state updates per second) and subjective (filtering quality) evaluations were recorded in the experiment. The experiment results show that if mean state updates per second is equal or greater than 20 in the task, SUT and SUPS can be decreased by adaptive-filtering policy without task performance affected obviously. However, if mean state updates per second is around 10 in the task, accompanied with SUT and SUPS decreased by adaptive-filtering policy, task performance also deteriorated badly
12012725
An intelligent control system with a multi-objective self-exploration process
Abstract This paper proposes a novel approach based on artificial intelligence technologies (multi-objective Self-Exploration process based Intelligent Control System—mSEICS) for intelligent control systems. Not only can this system adapt to various environments, but it can also continually improve its performance. The mSEICS consists of four basic functions, controller, receptor, m-adaptor and advancer. A five-layer fuzzy neural network is applied to implement the controller. The receptor is used to evaluate the performance of system. The m-adaptor (multi-objective based adaptor) that comprises two elements, action explorer and rule generator, can generate a variety of new action sets in order to adapt to various environments. The Pareto optimality based multi-objective genetic algorithm is proposed to implement the action explorer to discover multiple action sets, and the rule generator is employed to transform the action set to fuzzy rules. In addition, the advancer consisting of action discoverer and rule generator is constructed to produce the novel action set to enhance the system efficiency. The parallel-simulated annealing approach is presented to realize the action discoverer. An application of the robotic path planning is applied to demonstrate the proposed model. The simulation results show that the mobile robot can reach the target successfully in various environments, and the proposed model is more efficient than the similar model.
5124709
Embedded hardware and software self-testing methodologies for processor cores
At-speed testing of GHz processors using external testers may not be technically and economically feasible. Hence, there is an emerging need for low-cost, high-quality self-test methodologies, which can be used by processors to test themselves at-speed. Currently, Built-In Self-Test (BIST) is the primary self-test methodology available and is widely used for testing embedded memory cores. In this paper, we report our experiences in applying a commercial BIST methodology to two processor cores and analyze the problems associated with the current hardware-based BIST methodologies. We propose a new software-based self-testing methodology for processors, which uses a software tester embedded in the processor memory as a vehicle for applying structural tests. The software tester consists of programs for test generation and test application. Prior to the test, structural tests are prepared for processor components in the form of self-test signatures. During the process of self-test, the test generation program expands the self-test signatures into test sets, and the test application program applies the tests to the components-under-test at the speed of the processor. Application of the novel software-based self-test method demonstrates its significant cost/fault coverage benefits and its ability to apply at-speed test while alleviating the need for high-speed testers.
18273444
A system of 3D hair style synthesis based on the wisp model
Hair plays an important role in the image of the human face. However, it is still difficult and time consuming to synthesize realistic 3D CG hair images. In this paper, we present an integrated system for 3D computer graphics (CG) hair image synthesis based on a trigonal prism wisp model. By applying the trigonal prism wisp model, 2D hair distribution maps and other CG techniques to our system, we can successfully and efficiently synthesize realistic CG hair style images. A 3D hair style editing tool (HET) is incorporated into the system to support the creation of 3D hair styles. We have also integrated this system with other research results to construct a system for 3D CG character synthesis.
8887048
Using a Soft Core in a SoC Design: Experiences with picoJava
Through our experience in synthesis, validation, test, and integration of the picoJava processor core in a system-on-chip (SoC) design we point out the challenges faced and issues to address in efficient reuse of a soft core.
207717559
Associative Clustering for Clusters of Arbitrary Distribution Shapes
A novel neural network, named Associative Clustering Neural Network (ACNN), is developed for clustering data whose underlying distribution shapes are arbitrary. ACNN is a dynamic model that collectively measures and updates the similarity of any two patterns through the interaction of a group of patterns. Such a new measure of similarity helps to achieve more robust clustering performance than using the existing measures that are staticly and individually based on the distances among the isolated pairwise data. The efficience of ACNN has been verified through the performance study.