Soybean fungal diseases such as Blight, Frogeye leaf spot and Brown Spot are a significant threat to soybean plant due to the severe symptoms and lack of treatments. Traditional diagnosis of the thease diseases relies on disease symptom identification based on neaked eye observation by pathalogiest, which can lead to a high rate of false-recognition. This work present a novel system, utilizing multiclass support vector machine and KNN classifiers, for detection and classification of soybean diseases using color images of diseased leaf samples. Images of healthy and diseased leaves affected by Blight, Frogeye leaf spot and Brown Spot were acquired by a digital camera. The acquired images are preprocessed using image enhancement techniques. The background of each image was removed by a thresholding method and the Region of Interest (ROI) is obtained. Color-based segmentation technique based on K-means clustering is applied to the region of interest for partitioning the diseased region. The severity of disease is estimated by quantifying a number of pixels in the diseased region and in total leaf region. Different color features of segmented diseased leaf region were extracted using RGB color space and texture features were extracted using Gray Level Co-occurrence Matrix (GLCM) to compose a feature database. Finally, the support vector machine (SVM) and K-Nearest Negbiour (KNN) classifiers are used for classifying the disease. This proposed classifers system is capable to classify the types of blight, brown spot, frogeye leaf spot diseases and Healthy samples with an accuracy of 87.3% and 83.6 % are achieved.

Regression analysis, in statistic a modelling, is a set of statical processes that can be used to estimate the relationship between a dependent variable, commonly known as the outcome or response, and more independent variables generally called predictors of covariant. On the other hand, autoregression, which is based on regression equations, is a sequential model that uses time to predict the next step data from the previous step. Given the importance of accurate modelling and reliable predictions. in this paper we have analyzed the most popular methods used for data prediction. Nonlinear autoregressive methods were introduced, and then the machine deep learning approach was used to apply prediction based on a selected input data set. The mean square error was calculated for various artificial neural networks architecture to reach the optimal architecture, which minimized the error. Different artificial neural network (ANN) architectures were trained, tested, and validated using various regressive models, a recommendation was raised according to the obtained and analyzed experimental results. It was shown that using the concepts of machine deep learning will enhance the response of the prediction model.

This paper presents a compact size ultra-wideband (UWB) patch antenna operating over frequency range of 3.02-13.84 GHz and has dual-band notched characteristics for WiMAX and WLAN wireless narrow-band applications. The designed antenna has a monopole nature constructed on a FR4-epoxy substrate with a microstrip transmission line feed style and has a partial ground plane configuration. For developing a dual-band notch performance, a mirror L-shaped connected together from the top has been etched near to the feed line terminal connected to the patch for suppressing the WiMAX operating band. Furthermore, a spilt ring resonator (SRR) slot configuration has been notched on the lower part of the patch for eliminating the WLAN frequency band. The proposed antenna has been designed using CST-EM software package. All the dimensions introduced in the proposed antenna have been optimized to obtain the proper antenna performance. The antenna characteristics have been investigated in terms of the impedance bandwidth, surface current distribution, maximum gain, radiation efficiency, and radiation pattern. The simulation results demonstrate that the antenna has good performance for UWB services.

The main objective of this paper is to design and implement a remote sensing based home automation system using Arduino Uno Kit as a main controller for this scheme. The proposed system has two operational scenarios. The first scenario is to control the home devices from anywhere by using a smart phone with remote sensors and internet technologies controlling by the user, it is named a manual-scenario. The second scenario is to monitor and control the home devices by an automatic management between the remote sensors and the main controller Arduino Uno Kit', it is called an automatic scenario. To make and easy deal for a user the Matlab GUI platform is designed as a good interface to control and monitor the system. The proposed system is shown to be a simple, cost effective and flexible that making it a suitable and a good candidate for the smart home future.

In this paper, a semi-circular ultra-wideband antenna has been modified according to the 2×1 and 2×2 MIMO scenarios. The proposed antennas were designed based on the FR-4 substrate material with dimensions of 36×50mm and 60×60mm for 2×1 and 2×2 scenarios respectively. Simulation results show that a gain improvement of the proposed MIMO antennas from 1 to 2.5dB has been achieved in comparison with the single patch antenna. The radiation pattern for the original and the proposed 2×2 MIMO antennas are exhibited. The main advantage of the proposed antennas is that the gain improves without the need to increase the operating power. This makes the proposed MIMO antennas suitable to be used for UWB antenna applications.