Unmanned Aerial Vehicle (UAV) technology is considered one of the important technologies at present, which is believed to solve many problems in the future, such as delivering small-weighted goods, agriculture, and civil fields. This research paper presents the mathematical model of a quadcopter that was developed as an example of an unmanned aerial vehicle. The MATLAB and Simulink environment create the model simulation and controller design. The present study proposes a cost-effective and easily deployable control system with a comparison that shows the difference between the controller made

Integrating machine learning with the Internet of Things (IoT) enables many useful applica-tions. For IoT applications that incorporate video content analysis (VCA), deep learning models are usually used due to their capacity to encode the high-dimensional spatial and temporal representations of videos. However, limited energy and computation resources present a major challenge. Video captioning is one type of VCA that describes a video with a sentence or a set of sentences. This work proposes an IoT-based deep learning-based framework for video captioning that can (1) Mine large open-domain

Today, automatic food/non-food classification became extremely important for many real-time applications, specifically since the pandemic of the COVID-19 virus. Such that the 'no food policy' now became applied more than ever to help decrease the spread of the COVID-19 virus. Consequently, many studies used deep neural networks for the food/non-food classification task, yet these deep neural networks were computationally expensive. As a result, in this paper, a lightweight Convolution Neural Network (CNN) is proposed and put into use for classifying foods and non-foods. Compared to prior

The increase in population, food demand, and the pollution levels of the environment are considered major problems of this era. For these reasons, the traditional ways of farming are no longer suitable for early and accurate detection of biotic stress. Recently, precision agriculture has been extensively used as a potential solution for the aforementioned problems using high resolution optical sensors and data analysis methods that are able to cope with the resolution, size and complexity of the signals from these sensors. In this paper, several methods of machine learning have been utilized

We consider a time-slotted primary system where both the primary channel and primary activity are modeled as two independent two-state Markov chains. The primary transmitter can be idle or busy, whereas the channel can be in erasure or not. Moreover, the sensing channel between the primary transmitter and secondary transmitter is modeled as a two-state Markov chain to represent two levels of sensing reliability. At the beginning of each time slot, the secondary transmitter may remain idle, transmit directly, or probe the channel and access the channel only if it is sensed to be free. At the

We consider a secondary user with energy harvesting capability. We design access schemes for the secondary user which incorporate random spectrum sensing and random access, and which make use of the primary automatic repeat request (ARQ) feedback. The sensing and access probabilities are obtained such that the secondary throughput is maximized under the constraints that both the primary and secondary queues are stable and that the primary queueing delay is kept lower than a specified value needed to guarantee a certain quality of service (QoS) for the primary user. We consider spectrum sensing

We consider a cognitive radio scenario with an energy harvesting secondary user (SU) attempting to access a primary channel randomly. We assume multipacket reception (MPR) capability and investigate a system in which the SU may or may not exploit the primary feedback messages. The access probabilities are obtained to maximize the secondary throughput under the constraints of primary queue stability and such that the primary queueing delay is kept below a specified value in order to guarantee a certain quality of service (QoS) for the primary user (PU). We investigate the impact of the energy

In this paper, we completely characterize the deterministic capacity region of a four-node relay network with no direct links between the nodes, where each node communicates with the three other nodes via a relay. Towards this end, we develop an upper bound on the deterministic capacity region, based on the notion of a one-sided genie. To establish achievability, we use the detour schemes that achieve the upper bound by routing specific bits via indirect paths instead of sending them directly. © 2013 IEEE.

Sensor Actor NETworks (SANET) represent a major component of ubiquitous service environments promising interesting solutions to a wide range of problems. Despite the obvious increase in the research activities proposing architectures and protocols for SANETs, we are still no where near the production of industrial-grade SANET software that can be relied upon for mission critical applications. The cost of programming, deploying and maintaining SANET environments is still highly prohibitive due to the lack of industrial tools capable of realizing adaptive SANET software in a cost effective way