A tactile sensor array is a crucial component for applying physical sensors to a humanoid robot. This work focused on developing a palm-size tactile sensor array (56.0 mm × 56.0 mm) to apply object recognition for the humanoid robot hand. This sensor was based on a PCB technology operating with the piezoresistive principle. A conductive polymer composites sheet was used as a sensing element and the matrix array of this sensor was 16 × 16 pixels. The sensitivity of this sensor was evaluated and the sensor was installed on the robot hand. The tactile images, with resolution enhancement using bicubic interpolation obtained from 20 classes, were used to train and test 19 different DCNNs. InceptionResNetV2 provided superior performance with 91.82% accuracy. However, using the multimodal learning method that included InceptionResNetV2 and XceptionNet, the highest recognition rate of 92.73% was achieved. Moreover, this recognition rate improved when the object exploration was applied to demonstrate.

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This paper presents the classify personal shirt odor by electronic nose. Because body odor of a particular person varies, the researcher has picked up 4 volunteers to do exercise for 2 hours. After that bring their shirts to evaluate volatile organic compounds (VOCs) from the dirt on that shirts. The resistance of gas sensor array in the electronic nose will be changed depend on the amount VOCs when various kinds of gas sensor are combined together, the result will vary according to the property of each particular gas sensor. Then evaluate highest signal changing value of gas sensor and analyze by Principal Component Analysis (PCA). This technique is to identify the influence of the gas sensor array on the volunteer's shirts odor. From the experiment, we found that electronic nose can identify the odor of each shirt well, the data has been separated to 4 groups after analyze by PCA technique. It can extract data relation and percentage accumulation of PCA1 and PCA2 are 82.39.

The use of Address Vector Quantisation (VQ) in the compression of Linear Predictive coded (LPC) and Line Spectral Pairs (LSP) speech parameters in a speaker dependent system are examined. Four speakers are investigated two male and two female. The speech waveform is coded to LPC and LSP parameters using LPC techniques and is Vector Quantised using an unsupervised neural network, a Kohonen Self Organising Feature Map (KSOFM), to create a codebook of 128 entries. Address VQ is applied to the codebook and the data examined for recuning sequences to exploit redundancy. Preliminary results indicate that approximately 46% additional compression is achievable using this method. As Address VQ is a loss-less compression scheme, this reduction is achieved without any further reduction in speech quality.