The development of the internet of things (IoT) and the internet of robotics (IoR) are becoming more and more involved with our daily lives. It serves a variety of tasks some of them are essential to us. The main objective of SRR is to develop a surveillance system for detecting suspicious and targeted places for users without any loss of human life. This paper shows the design and implementation of a robotic surveillance platform for real-time monitoring with the help of image processing, which can explorer places of difficult access or high risk. The robotic live streaming is via two cameras, the first one is fixed straight on the road and the second one is dynamic with tilt-pan ability. All cameras have image processing capabilities to analyze, detect and track objects plus few other graphical functions. The components mentioned above built on top of the four-wheel vehicle system with high torque to provide mobility on rough terrain. This work is based on Raspberry Pi and can be controlled over Wi-Fi locally or publicly over the internet. The results show making a high potential, relatively low price robot with lots of features and functions that can perform multiple tasks simultaneously, all are crucial to surveillance and monitoring problems, controlled by a user from far distances and for a long time.

Enhancement <span>and color correction of images play an important role and can be considered as one of the fundamental and basic operations in image analysis for the purpose of spee<a name="_GoBack"></a>ding up the diagnosis of the medical images. Improving the quality and contrast of the medical image is the basic requirement for clinicians for obtaining an accurate and accurate medical diagnosis. Thus, getting a clear X-ray image reduces the effort and time-wasting. In this study a new idea will be applied for improving image contrast of the collected COVID-19 X-ray images, this idea is based on using Wiener filter, multilevel of histogram equalization (HE) technique with OpenCV library and then using contrast limited adaptive histogram equalization (CLAHE) techniques with OpenCV library. The proposed methodology programmed in MATLAB software and then implemented using Rasperry Pi 3 model B. The size and resolution of images are different as inputted images and this difference succeeded in proving the strength of the proposed idea. The collected X-ray images have undergone experiential evaluations which clearly showed the effective performance of the proposed methodology.</span>

The Internet of Things (IoT) technology and smart systems are playing a major role in the advanced developments in the world that take place nowadays, especially in multiple privilege systems. There are many smart systems used in daily human life to serve them and facilitate their tasks, such as alarm systems that work to prevent unwanted events or face detection and recognition systems. The main idea of this work is to capture live video using a connected Pi camera, save it, and unlock the electric strike door in several ways; either automatically by displaying a live video connected via USB webcam using a deep learning algorithm of facial recognition and OpenCV or by RFID technology, as well as by detecting abnormal entrance with a ringing buzzer. In addition, this system is made in Python language and based on the Raspberry pi 4 B model that can be viewed and controlled by connecting to a screen or Wi-Fi locally or publicly over the Internet from any other smart device, such as a laptop or mobile phone, by installing the VNC application or the remote desktop connection application. The results obtained showed the efficiency and performance of this system through remote control to display, detect, and identify the persons who are authorized to permit the electronic lock/unlock door (E-Door) at a relatively low cost with the implementation of many functions in real-time.

<span lang="EN-US">The need for robotics systems has become an urgent necessity in various fields, especially in video surveillance and live broadcasting systems. The main goal of this work is to design and implement a rover robotic monitoring system based on raspberry pi 4 model B to control this overall system and display a live video by using a webcam (USB camera) as well as using you only look once algorithm-version five (YOLOv5) to detect, recognize and display objects in real-time. This deep learning algorithm is highly accurate and fast and is implemented by Python, OpenCV, PyTorch codes and the Context Object Detection Task (COCO) 2020 dataset. This robot can move in all directions and in different places especially in undesirable places to transmit live video with a moving camera and process it by the YOLOv5 model. Also, the robot system can receive images, videos, or YouTube links and process them with YOLOv5. Raspberry Pi is controlled remotely by connecting to the network through Wi-Fi locally or publicly using the internet with a remote desktop connection application. The results were very satisfactory and proved the high-performance efficiency of the robot.</span>

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