This paper presents a unified software and hardware wireless AI platform (USHWAP) for developing and evaluating machine learning in wireless systems. The platform integrates multi-software development such as MATLAB and Python with hardware platforms like FPGA and SDR, allowing for flexible and scalable device and edge computing application development. The USHWAP is implemented and validated using FPGAs and SDRs. Wireless signal classification, wireless LAN sensing, and rate adaptation are used as examples to showcase the platform's capabilities. The platform enables versatile development, including software simulation and real-time hardware implementation, offering flexibility and scalability for multiple applications. It is intended to be used by wireless-AI researchers to develop and evaluate intelligent algorithms in a laboratory environment.

MATLAB/Simulink is the de facto standard tool for the model-based development (MBD) of control software for automotive systems. A Simulink model developed in MBD for real automotive systems involves complex computation as well as tens of thousands of blocks. In this paper, we focus on decision coverage (DC), condition coverage (CC) and modified condition/decision coverage (MC/DC) criteria, and propose a Monte-Carlo test suite generation method for large and complex Simulink models. In the method, a candidate test case is generated by assigning random values to the parameters of signal templates with specific waveforms. We try to find contributable candidates in a plausible and understandable search space, specified by a set of templates. We implemented the method as a tool, and our experimental evaluation showed that the tool was able to generate test suites for industrial implementation models with higher coverages and shorter execution times than Simulink Design Verifier. Additionally, the tool includes a fast coverage measurement engine, which demonstrated better performance than Simulink Coverage in our experiments.

Parallel programs for distributed memory machines are not easy to create and maintain, especially when they involve sparse matrix computations. In this paper, we propose a program translation system for generating parallel sparse matrix computation codes utilizing PSBLAS. The purpose of the development of the system is to offer the user a convenient way to construct parallel sparse code based on PSBLAS. The system is build up on the idea of bridging the gap between the easy-to-read program representations and highly-tuned parallel executables based on existing parallel sparse matrix computation libraries. The system accepts a MATLAB program with annotations and generates subroutines for an SPMD-style parallel program which runs on distributed-memory machines. Experimental results on parallel machines show that the prototype of our system can generate fairly efficient PSBLAS codes for simple applications such as CG and Bi-CGSTAB programs.