Current deep learning convolutional neural network (DCNN) -based hand gesture detectors with acute precision demand incredibly high-performance computing power. Although DCNN-based detectors are capable of accurate classification, the sheer computing power needed for this form of classification makes it very difficult to run with lower computational power in remote environments. Moreover, classical DCNN architectures have a fixed number of input dimensions, which forces preprocessing, thus making it impractical for real-world applications. In this research, a practical DCNN with an optimized architecture is proposed with DCNN filter/node pruning, and spatial pyramid pooling (SPP) is introduced in order to make the model input dimension-invariant. This compact SPP-DCNN module uses $65\%$ fewer parameters than traditional classifiers and operates almost $3\times$ faster than classical models. Moreover, the new improved proposed algorithm, which decodes gestures or sign language finger-spelling from videos, gave a benchmark highest accuracy with the fastest processing speed. This proposed method paves the way for various practical and applied hand gesture input-based human-computer interaction (HCI) applications.

In modern smart factories, quality estimation is vital for maximum productivity. However, quality estimation by definition relies on an imbalanced dataset, as most smart factories are highly efficient. In this research, we propose a...

We proposed the automatic sound scene control system using the image sensor network for preserving the constant sound scene without respect to the users' movement. In the proposed system, the image sensor network detects the human location in the multichannel playback environment and the SSC (sound scene control) module automatically controls the sound scene of the multichannel audio signals according to the estimated human location which is the angle information. To estimate the direction of the human face, we used the normalized RGB (red, green, and blue) and the HSV (hue, saturation, and value) calculated from the images obtained by the image sensor network. The direction of the human face can be easily decided as the image sensor to capture the image with the highest number of pixels to satisfy the thresholds of the normalized RGB and the HSV. The estimated direction of the human face is directly fed to the SSC module, and the controlled sound scene can be simply generated. Experimental results show that the image sensor network successfully detected the human location with the accuracy of about 98% and the controlled sound scene by the SSC according to the detected human location was perceived as the original sound scene with the accuracy of 95%.