Traffic sign detection plays an essential role in advanced driver assistance system (ADAS) or self-driving vehicles. Typically, deep neural networks are employed to analyze road scene images captured by an onboard camera. However, due to the significant variation in appearance of different traffic signs, the classification of high similarity patterns is still a challenging task. To address these issues, this paper presents an end-to-end traffic sign detection framework based on DETR. The proposed network incorporates data augmentation and negative sample learning to mitigate the problem of data imbalance and enhance the model recognition capability effectively. An UASPP module (Upsample Atrous Pyramid Pooling) is introduced to integrate multi-scale features and global information. In the experiments, the performance evaluation has demonstrated the improvement of mAP by 3.9% on TT100K and 36.3% on GTSDB compared to state-of-the-art methods. The code and datasets are available at https://github.com/chinglun/TS-DETR.

For the patients suffering from amyotrophic lateral sclerosis (ALS), they are encouraged to exercise their bodies routinely to prevent or delay the paralysis of muscles. This work proposes an electromyogram (EMG) based ALS rehabilitation system via playing computer games. The multi-channel EMG measuring system and the controlled interfaces to computer games were developed. According to the symptoms of disability, the controls from different muscles are designed. For ALS patients in the early stage, the EMG electrodes were placed on the forearm to detect the finger gestures; for ALS patients in the middle stage, the EMG signals of upper extremity were employed to detect the hand gestures and arm moving; for the late ALS stage, the EMG electrodes were placed on chin to detect the facial expression. A commercial video game as well as a self-modified computer game are utilized in our rehabilitation systems. We believe that the patients are more preferable to exercise their bodies in a form of entertainment.

Scratchpad memory (SPM) has been increasingly used in embedded systems due to its higher efficiency in terms of energy and area compared to that of ordinary cache. A hybrid on-chip memory architecture that combines SPM with a mini-cache has been proposed. One key issue for hybrid on-chip memory architectures is to reduce the number of off-chip memory accesses and energy consumption. Existing methods achieve this by moving the most frequently accessed data into SPM. However, these methods may be ineffective because the main source of off-chip memory accesses may not be the most frequently accessed data. Instead, most off-chip memory accesses are caused by cache misses, so reducing the latter will reduce the former. Cache misses are mainly caused by data contending for cache lines. Therefore, this paper proposes a contention-aware SPM allocation method for hybrid on-chip management. The number of cache misses for a page is used as a metric to determine whether a page should be moved to SPM. When the number of misses for a page exceeds a threshold, the page is moved to SPM, reducing cache contention. Experimental results show that the proposed method can reduce the energy delay product by 35% to 53% compared to a cache-only on-chip memory architecture and 19% to 31% compared to an existing hybrid on-chip memory architecture.