The rapid prototyping of a mixed-signal system-on-chip (SoC) has been enabled by reusing predesigned intellectual properties (IPs) and by integrating newly designed IP into the top design of SoC. The IPs have been designed on various hardware description levels, which leads to challenges in simulations that evaluate the prototyping. One traditional solution is to convert these heterogeneous IP models into equivalent models, that are described in a single description language. This conversion approach often requires manual rewriting of existing IPs, and this results in description loss during the model projection due to the absence of automatic conversion tools. The other solutions are co-simulation/emulation approaches that are based on the coupling of multiple simulators/emulators through connection modules. The conventional methods do not have formal theoretical backgrounds and an explicit interface for integrating the simulator into their solutions. In this paper, we propose a general co-simulation approach based on the high-level architecture (HLA) and a newly-defined programming language interface for interoperation (PLI-I) between heterogeneous IPs as a formal simulator interface. Based on the proposed PLI-I and HLA, we introduce formal procedures of integration and interoperation. To reduce integration costs, we split these procedures into two parts: a reusable common library and an additional model-dependent signal-to-event (SE) converter to handle differently abstracted in/out signals between the coupled IPs. During the interoperation, to resolve the different time-advance mechanisms and increase computation concurrency between digital and analog simulators, the proposed co-simulation approach performs an advanced HLA-based synchronization using the pre-simulation concepts. The case study shows the validation of interoperation behaviors between the heterogeneous IPs in mixed-signal SoC design, the reduced design effort in integrating, and the synchronization speedup using the proposed approach.

Variations in walking speed have a strong impact on gait-based person identification. We propose a method that is robust against walking-speed variations. It is based on a combination of cubic higher-order local auto-correlation (CHLAC), gait silhouette-based principal component analysis (GSP), and a statistical framework using hidden Markov models (HMMs). The CHLAC features capture the within-phase spatio-temporal characteristics of each individual, the GSP features retain more shape/phase information for better gait sequence alignment, and the HMMs classify the ID of each gait even when walking speed changes nonlinearly. We compared the performance of our method with other conventional methods using five different databases, SOTON, USF-NIST, CMU-MoBo, TokyoTech A and TokyoTech B. The proposed method was equal to or better than the others when the speed did not change greatly, and it was significantly better when the speed varied across and within a gait sequence.

Simulation based education and training, especially wargame simulations, are being used widely in the field of defense modeling and in simulation communities. In order to efficiently train students and trainees, the wargame simulations must have both high performance and high fidelity. In this paper, we discuss design and implementation issues for a prototype of a parallel and distributed wargame simulation system. This wargame simulation system is based on High Level Architecture (HLA) and employs some optimization to achieve both high performance and high fidelity in the simulation system. The results show that the proposed optimization method is effective when optimization is applied to 93.5% or less of the moving objects (PFs) within the range of detection (RofD) of both the red and blue teams. Specifically, when each team has 1000 PFs we found that if the percentage of PFs within RofD is less than 50% for both teams, our method is over two times better than for the situation where there is no optimization.

Although a number of car-traffic simulators have been developed for various purposes, none of the existing simulators enhance the simulation accuracy using sensor data or allow the system structure to re-configure the system structure depending on the application. Our goal was to develop a highly accurate, highly modular, flexible, and scalable micro-model car-traffic simulation system. The HLA (High Level Architecture) was applied to every system module as a standard interface between each module. This allows an efficient means for evaluating and validating a variety of micro-model simulation schemes. Our ongoing projects consist of running several identical simulations concurrently, with different parameter sets. By sending the results of these simulations to a manager module, which analyzes both the parameter sets and the simulated results, the manager module can evaluate the best-simulated results and determine the next action by comparing these results with the sensor data. In this system, the sensor data or the statistical data on the flow of traffic, obtained by monitoring real roads, is used to improve the simulation accuracy. Future systems are being planned to employ real time sensor data, where the input of the data occurs at almost real time speed. In this paper, we discuss the design of a HLA-based car-traffic simulation system and the construction of a sensor-data fusion algorithm. We also discuss our preliminary evaluation of the results obtained with this system. The results show that the proposed fusion algorithm can adjust the simulation accuracy to the logged sensor data within a difference of 5% (minimum 1.5%) in a specific time period. We also found that simulations with 500 different parameter sets can be executed within 5 minutes using 8 simulator modules.