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Implementation of a new instruction set architecture (ISA) is a non-trivial task which involves significant modifications to the system software, such as the compiler, the assembler, and the linker. This task also includes modifying and verifying functional and cycle accurate simulators to facilitate correct simulation and performance evaluation of programs under the new ISA. Isolating errors in these software components becomes extremely challenging and demands automated and semi-automated mechanisms since neither the compilation infrastructure nor the simulation infrastructure can be trusted as both parties have been heavily modified. Bootstrapping a new ISA is very common in embedded systems since there is a greater variety of embedded ISAs due to often not having a need to support backward compatibility of executables. In this paper, we present the tools and the verification mechanisms we have implemented to support the development of a number of related, but distinct ISAs. These ISAs are similar in complexity to the RISC-V ISA, and range from simple pipelined and superscalar processor ISAs, to a complete VLIW ISA. Our work in developing the system software and simulators for these ISAs demonstrate that a step-by-step semi-automated approach which relies on simple invariants can facilitate effective bootstrapping of the complete system software and the simulator infrastructure. 

Emotions significantly impact human physical and mental health, and, therefore, emotion recognition has been a popular research area in neuroscience, psychology, and medicine. In this paper, we preprocess the raw signals acquired by millimeter-wave radar to obtain high-quality heartbeat and respiration signals. Then, we propose a deep learning model incorporating a convolutional neural network and gated recurrent unit neural network in combination with human face expression images. The model achieves a recognition accuracy of 84.5% in person-dependent experiments and 74.25% in person-independent experiments. The experiments show that it outperforms a single deep learning model compared to traditional machine learning algorithms.