This work develops a speech recognition system that uses two procedures of proposed noise detection and combined noise reduction. The system can be used in applications that require interactive robots to recognize the contents of speech that includes ambient noise. The system comprises two stages, which are the threshold-based noise detection and the noise reduction procedure. In the first stage, the proposed system automatically determines when to enhance the quality of speech based on the signal-to-noise ratio (SNR) values of the collected speech at all times. In the second stage, independent component analysis (ICA) and subspace speech enhancement (SSE) are employed for noise reduction. Experimental results reveal that the SNR values of the enhanced speech exceed those of the received noisy speech by approximately 20 dB to 25 dB. The noise reduction procedure improves the speech recognition rates by around 15% to 25%. The experimental results indicate that the proposed system can reduce the effect of noise in numerous noisy environments and improve the quality of speech for recognition purposes.

Road segmentation is beneficial to build a vision-controllable mission-oriented self-driving bot, e.g., the Self-Driving Sweeping Bot, or SDSB, for working in restricted areas. Using road segmentation, the bot itself and physical facilities may be protected and the sweeping efficiency of the SDSB promoted. However, roads in the real world are generally exposed to intricate noise conditions as a result of changing weather and climate effects; these include sunshine spots, shadowing caused by trees or physical facilities, traffic obstacles and signs, and cracks or sealing signs resulting from long-term road usage, as well as different types of road materials, such as cement or asphalt; all of these factors greatly influence the effectiveness of road segmentation. In this work, we investigate the extension of Primordial U-Net by the proposed EnRDeA U-Net, which uses an input channel applying a Residual U-Net block as an encoder and an attention gate in the output channel as a decoder, to validate a dataset of intricate road noises. In addition, we carry out a detailed analysis of the nets’ features and segmentation performance to validate the intricate noises dataset on three U-Net extensions, i.e., the Primordial U-Net, Residual U-Net, and EnRDeA U-Net. Finally, the nets’ structures, parameters, training losses, performance indexes, etc., are presented and discussed in the experimental results.

This study implements Fuzzy logic-based obstacle avoidance and human tracking on an omnidirectional mobile system for service robots. The mobile system could be separated and combined with the robot which can be controlled remotely and switched to go forward and avoid obstacles in an indoor environment automatically. The system is able to track and go to the user according to the user's position. The omnidirectional wheel was adapted in the power system to perform translating and spinning movements. The translating movement enables the robot to avoid obstacles faster and flexibly in paths. With the spinning movement, the robot can quickly find the direction of the object. Finally, the experiments show that the proposed system has good performance in service environments.

Mental care has become crucial with the rapid growth of economy and technology. However, recent movements, such as green technologies, place more emphasis on environmental issues than on mental care. Therefore, this study presents an emerging technology called orange computing for mental care applications. Orange computing refers to health, happiness, and physiopsychological care computing, which focuses on designing algorithms and systems for enhancing body and mind balance. The representative color of orange computing originates from a harmonic fusion of passion, love, happiness, and warmth. A case study on a human-machine interactive and assistive system for emotion care was conducted in this study to demonstrate the concept of orange computing. The system can detect emotional states of users by analyzing their facial expressions, emotional speech, and laughter in a ubiquitous environment. In addition, the system can provide corresponding feedback to users according to the results. Experimental results show that the system can achieve an accurate audiovisual recognition rate of 81.8% on average, thereby demonstrating the feasibility of the system. Compared with traditional questionnaire-based approaches, the proposed system can offer real-time analysis of emotional status more efficiently.

This brief presents a fixed-point architecture based on a reconfigurable scheme for integrating several commonly used mathematical operations of speech signal processing. The proposed design can perform two transcendental mathematical operations called logarithm and powering, and three commonly used computations with similar operations named polynomial calculation, filtering, and windowing. By analyzing the adopted algorithms of the above five operations, a simplified computing unit is designed. This unit can combine six types of operations by reconfiguring the data paths, and the same multiply-add architecture can be reused for reducing the redundant usage of logic gates. The experimental results reveal that the proposed design can work at a 200-MHz clock rate, and its gate count only has 11.9k. Compared with the results of the floating-point function, the median errors of the proposed design for computing the powering and logarithmic functions are 0.57% and 0.11%, respectively. Such results indicate that this simple architecture can be effectively used in most speech processing applications.