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Nowadays, the demands on the usage of wireless network are increasing rapidly from year to year. Wireless network is a large scale of area where many nodes are connecting to each other to commun icate using a device. Primarily, wireless network also tend to be as a link to transmit and receive any multimedia such as image, sound, video, document and etc. In order to receive the transmitted media correctly, most type of media must be compressed before being transmitted and decompressed after being received by the device or else the device used must have the ability to read the media in a compressed way. In this paper, a hybrid compression of Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) with Huffman encoding technique are proposed for Wireless Sensor Network (WSN) application. Data compression is very useful to remove the redundant data and reduce the size of image. After conducting a comprehensive observation, it is found that hybrid compression is suitable due to the process consist of the combination of multiple compression techniques which suits for Wireless Sensor Network's application focusing on ZigBee platform.

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<span>With the advancement in technology, antennae are becoming a popular components to be used in various applications. Following the trend, a compact design of ultra-wideband (UWB) bistatic configuration of the antenna is presented in this paper using ground penetrating radar (GPR) technology specifically for detection applications. The antenna is first designed and simulated using defected ground structure (DGS) for impedance bandwidth with the obtained gain of around 6.2 dB and return losses from 3-16 GHz. Later the complete detection model is aimed to study and for this purpose CST is used to model human skin and performed an experiment based on antennas i.e. transmitter and receiver, obstacle and target, to study and analyze the received antenna reflections for detection purpose.</span>

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Due to its tasty and spicy fruit with nutritional qualities, chili is a demanding crop widely farmed around the world. Hence, it is essential to accurately determine the health status of chili for agricultural productivity. Recent years have seen impressive results in recognition fields due to deep learning approaches. However, deep learning models' networks need an abundant data to perform well and collecting enormous data for the networks is time-consuming and resource-intensive. A data augmentation method is proposed to overcome this problem. It was applied to a small dataset of healthy and diseased chili leaf by utilizing geometric transformation method. Eventually, two deep learning models of CNN and ResNet-18 were evaluated using augmented and original datasets. From a series of experiment, it can be concluded that the trained deep learning models using original and augmented datasets perform better with an average accuracy performance of 97%.

This research explores the use of the seismic surface wave technique which is called as a spectral analysis of surface wave (SASW) for investigating the shallow soil profile. The testing was conducted on soft ground located at Universiti Tun Hussein Onn Malaysia (UTHM). The testing was conducted using a new developed in-house seismic surface wave testing system. An impact source using 5 kg hammer is used to generate seismic energy and four differencesarrangement of the source to receiver distances to produce soil profile. The profile of phase velocity was obtained at a depth of 0.15 m to 1.8 m were between 68 m/s and 95 m/s. The results were calibrated with the hand vane shear test which is used to obtain the undrained shear strength and thus converted empirically to seismic velocity at 45 m/s and 95 m/s. The result shows good agreement between velocity obtained from the surface wave testing system and hand vane shear test. Therefore, the new developed in-house seismic surface wave system has been proven can be used to determine the seismic velocity at shallow depth.