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Ever increasing demand for data transfer speed and consequently bandwidth poses new challenges. Digital modulation techniques have contributed a lot to increase the capacity, data rates and quality of network. WCDMA is a high potential technique being rapidly adopted by the industry. This paper presents a simulation technique to enable select better digital modulation scheme for WCDMA. The paper highlights the results of this simulation for BPSK, QPSK, 8-PSK modulation models over AWGN fading channel for WCDMA.Keyword: WCDMA, spread spectrum DSSS, spreading, despreading, AWGN, BER. I. BACKGROUNDThe fusion of mobility with communication has completely revolutionized everyday life with technology advancing from one generation to the next. First generation provided only voice services while the second bought in more facilities like fax, data and messaging. Hunger for more demanding services like multimedia became unquenchable giving rise to increased bit rate being the prime feature of third and next generations.Evolution of wireless began with wireless analog networks termed as first generation. It provided merely voice services while the second generation (2G) added medium bit rate data services. GSM and CDMA were the main 2G technologies. Applications like video calling, online shopping etc. began to emerge which 2G failed to cater, as it needed higher speed services. This resulted in evolution of 2.5G and 3G. 2.5G used GPRS and EDGE technologies to enhance the data capacity of GSM. With both voice and data traffic moving on the system, the need was felt to increase the data rates further. Then by use of sophisticated coding, the data rates could be increased to 384 kbps [1]. High data rate and spectral efficiency thus were main drives of 3G.

Smart agriculture has become crucial in meeting the increasing dietary needs of a growing population, particularly in countries where agriculture has significant economic impact. Deep learning techniques, such as convolutional neural networks and recurrent neural networks, have been extensively researched and applied in agriculture in recent years. In this study, recent research articles on deep learning in agriculture over the past five years are analyzed to identify key contributions and challenges. The study has also explored agriculture parameters monitored by the internet of things and used them to train the deep learning algorithms for analysis. The study compares various factors across different studies, including the agriculture area of focus, dataset used, deep learning model and framework, data preprocessing and augmentation methods, and accuracy of results.

Plants growth is crucial to an agricultural industry, and to the economy of a nation. Consequently, taking care of plants is essential. Like humans, plants are susceptible to several bacterial, fungal, and viral diseases. To avoid the plants from being destroyed, prompt disease detection and treatment are crucial. The goal of this chapter is to introduce plant disease detection and classification using deep learning and transfer learning based pre-trained models. Using images of leaves, the models can identify several plant ailments. The models, namely- Convolutional Neural Network (CNN), MobileNet, and VGG16 are applied for plant disease identification. To train them, the PlantVillage dataset is used, and to enhance the sample size, the dataset is augmented. For experimentation purposes, images of both healthy and damaged plants are taken. Experiment results reveal that VGG16 has outperformed CNN and MobileNet models for the detection of tomato, potato, and apple plant diseases. The accuracy of the VGG16 model is 0.89, 0.92, and 0.95 for the tomato, potato, and apple plant diseases respectively.