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This paper presents a fully reconfigurable lowpower filter that is composed of a cascade of floating-gate transistor-capacitor (FGT-C) biquadratic sections suitable for biomedical applications. The proposed FGT-C filter provides both the lowpass and bandpass outputs to the following stage with all filter parameters reconfigurable, including the gains, natural frequency, quality factor, DC levels for input and outputs. The filter topology exhibits good modularity so that the biquadratic sections can be cascaded and scaled up to implement highorder frequency responses easily with efficient area and power consumption. A prototype chip has been fabricated in a 0.35 m 2P4M CMOS process and each FGT-C biquadratic filter occupies an area of 0.0313 mm 2 . From measurement results, the filter consumes 118.4 nW of power with a dynamic range of 45.5 dB while operating at 1.8 V power supply with a 10 kHz bandwidth.

People with quadriplegia cannot move their body and limbs freely, making them unable to interact normally with their environment. This article aims to improve the life quality of quadriplegia patients through a development of a system to help them interact with their surroundings. A novel algorithm to classify human gestures is proposed in this article. The algorithm is developed as the core of an assistive technology system in the form of a human interface device, which utilizes electromyograph as its sensor. The system utilizes a wearable electromyograph with a custom software as the signal capturing and processing tool. The electrodes of the electromyograph are placed on certain positions on the face, corresponding to the locations of the major muscles that govern certain facial gestures. The signals are then processed using a novel algorithm that employs hidden Markov model and improved particle swarm optimization to classify the gesture. Based on the gestures, a custom command can be assigned for different conditions. The accuracy of the system is 96.25% for five gestures classification.

In recent years, Chinese has become one of the most popular languages globally. The demand for automatic Chinese sentence correction has gradually increased. This research can be adopted to Chinese language learning to reduce the cost of learning and feedback time, and help writers check for wrong words. The traditional way to do Chinese sentence correction is to check if the word exists in the predefined dictionary. However, this kind of method cannot deal with semantic error. As deep learning becomes popular, an artificial neural network can be applied to understand the sentence’s context to correct the semantic error. However, there are still many issues that need to be discussed. For example, the accuracy and the computation time required to correct a sentence are still lacking, so maybe it is still not the time to adopt the deep learning based Chinese sentence correction system to large-scale commercial applications. Our goal is to obtain a model with better accuracy and computation time. Combining recurrent neural network and Bidirectional Encoder Representations from Transformers (BERT), a recently popular model, known for its high performance and slow inference speed, we introduce a hybrid model which can be applied to Chinese sentence correction, improving the accuracy and also the inference speed. Among the results, BERT-GRU has obtained the highest BLEU Score in all experiments. The inference speed of the transformer-based original model can be improved by 1131% in beam search decoding in the 128-word experiment, and greedy decoding can also be improved by 452%. The longer the sequence, the larger the improvement.

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In the last decade, deep learning has enjoyed its spotlight as the game-changing addition to smart farming and precision agriculture. Such development has been predominantly observed in developed countries, while on the other hand, in developing countries most farmers especially ones with smallholder farms have not enjoyed such wide and deep adoption of this new technologies. In this paper we attempt to improve the image classification part of smart farming and precision agriculture. Agricultural commodities tend to possess certain textural details on their surfaces which we attempt to exploit. In this work, we propose a deep learning based approach called Selective Context Adaptation Network (SCANet). SCANet performs feature enhancement strategy by leveraging level-wise information and employing context selection mechanism. In exploiting contextual correlation feature of the crop images our proposed approach demonstrates the effectiveness of the context selection mechanism. Our proposed scheme achieves 88.72% accuracy and outperforms the existing approaches. Our model is evaluated on the cocoa bean dataset constructed from the real cocoa bean industry scene in Indonesia.

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