Efficient Architecture of Variable Size HEVC 2D-DCT for FPGA Platforms

This study presents a design of two-dimensional (2D) discrete cosine transform (DCT) hardware architecture dedicated for High Efficiency Video Coding (HEVC) in field programmable gate array (FPGA) platforms. The proposed methodology efficiently proceeds 2D-DCT computation to fit internal components and characteristics of FPGA resources. A four-stage circuit architecture is developed to implement the proposed methodology. This architecture supports variable size of DCT computation, including 4×4, 8×8, 16×16, and 32×32. The proposed architecture has been implemented in System Verilog and synthesized in various FPGA platforms. Compared with existing related works in literature, this proposed architecture demonstrates significant advantages in hardware cost and performance improvement. The proposed architecture is able to sustain 4K@30fps ultra high definition (UHD) TV real-time encoding applications with a reduction of 31-64% in hardware cost

Improving the Efficiency of UAV Communication Channels in the Context of Electronic Warfare

The article is devoted to the development of a method for increasing the efficiency of communication channels of unmanned aerial vehicles (UAVs) in the conditions of electronic warfare (EW). The author analyses the threats that may be caused by the use of electronic warfare against autonomous UAVs. A review of some technologies that can be used to create original algorithms for countering electronic warfare and increasing the autonomy of UAVs on the battlefield is carried out. The structure of modern digital communication systems is considered. The requirements of unmanned aerial vehicle manufacturers for onboard electronic equipment are analyzed, and the choice of the hardware platform of the target radio system is justified. The main idea and novelty of the proposed method are highlighted. The creation of a model of a cognitive radio channel for UAVs is considered step by step. The main steps of modeling the spectral activity of electronic warfare equipment are proposed. The main criteria for choosing a free spectral range are determined. The type of neural network for use in the target cognitive radio system is substantiated. The idea of applying adaptive coding in UAV communication channels using multicomponent turbo codes in combination with neural networks, which are simultaneously used for cognitive radio, has been further developed

Metrics to evaluate compressions algorithms for RAW SAR data

Modern synthetic aperture radar (SAR) systems have size, weight, power and cost (SWAP-C) limitations since platforms are becoming smaller, while SAR operating modes are becoming more complex. Due to the computational complexity of the SAR processing required for modern SAR systems, performing the processing on board the platform is not a feasible option. Thus, SAR systems are producing an ever-increasing volume of data that needs to be transmitted to a ground station for processing. Compression algorithms are utilised to reduce the data volume of the raw data. However, these algorithms can cause degradation and losses that may degrade the effectiveness of the SAR mission. This study addresses the lack of standardised quantitative performance metrics to objectively quantify the performance of SAR data-compression algorithms. Therefore, metrics were established in two different domains, namely the data domain and the image domain. The data-domain metrics are used to determine the performance of the quantisation and the associated losses or errors it induces in the raw data samples. The image-domain metrics evaluate the quality of the SAR image after SAR processing has been performed. In this study three well-known SAR compression algorithms were implemented and applied to three real SAR data sets that were obtained from a prototype airborne SAR system. The performance of these algorithms were evaluated using the proposed metrics. Important metrics in the data domain were found to be the compression ratio, the entropy, statistical parameters like the skewness and kurtosis to measure the deviation from the original distributions of the uncompressed data, and the dynamic range. The data histograms are an important visual representation of the effects of the compression algorithm on the data. An important error measure in the data domain is the signal-to-quantisation-noise ratio (SQNR), and the phase error for applications where phase information is required to produce the output. Important metrics in the image domain include the dynamic range, the impulse response function, the image contrast, as well as the error measure, signal-to-distortion-noise ratio (SDNR). The metrics suggested that all three algorithms performed well and are thus well suited for the compression of raw SAR data. The fast Fourier transform block adaptive quantiser (FFT-BAQ) algorithm had the overall best performance, but the analysis of the computational complexity of its compression steps, indicated that it is has the highest level of complexity compared to the other two algorithms. Since different levels of degradation are acceptable for different SAR applications, a trade-off can be made between the data reduction and the degradation caused by the algorithm. Due to SWAP-C limitations, there also remains a trade-off between the performance and the computational complexity of the compression algorithm.Dissertation (MEng)--University of Pretoria, 2019.Electrical, Electronic and Computer EngineeringMEngUnrestricte

Implementation GStreamer framework with face detection system for Unmanned Aerial Vehicle

This article provides an experimental development of unmanned aerial vehicle (UAV), and the method by which a video stream from the UAV is transmitted through the pipeline, using GStreamer framework, which in turn takes up in the ground station. Preprocessed data video link will defined the face using Viola Jones method through OpenCV library. During research was modeled drone with the flight controller based hardware computing platform "Raspberry Pi" and "Arduino" with complex algorithms based on Open Source projects "ArduQuad, Arducopter, MultiWii" on the C ++ programming language, Python. A pipe through which is passed a raw data stream videos via WiFi c UAV ground station network

Mobile prototyping platforms for remote engineering applications

This paper describes a low-cost mobile communication platform as a universal rapid-prototyping system, which is based on the Quadrocopter concept. At the Integrated Hardware and Software Systems Group at the Ilmenau University of Technology these mobile platforms are used to motivate bachelor and master students to study Computer Engineering sciences. This could be done by increasing their interest in technical issues, using this platform as integral part of a new ad-hoc lab to demonstrate different aspects in the area of Mobile Communication as well as universal rapid prototyping nodes to investigate different mechanisms for self-organized mobile communication systems within the International Graduate School on Mobile Communications. Beside the three fields of application, the paper describes the current architecture concept of the mobile prototyping platform as well as the chosen control mechanism and the assigned sensor systems to fulfill all the required tasks

Modeling and performance analysis of a UAV-based sensor network for improved ATR

Automatic Target Recognition (ATR) is computer processing of images or signals acquired by sensors with the purpose to identify objects of interest (targets). This technology is a critical element for surveillance missions. Over the past several years there has been an increasing trend towards fielding swarms of unattended aerial vehicles (UAVs) operating as sensor networks in the air. This trend offers opportunities of integration ATR systems with a UAV-based sensor network to improve the recognition performance. This dissertation addresses some of design issues of ATR systems, explores recognition capabilities of sensor networks in the presence of various distortions and analyzes the limiting recognition performance of sensor networks.;We assume that each UAV is equipped with an optical camera. A model based recognition method for single and multiple frames is introduced. A complete ATR system, including detection, segmentation, recognition and clutter rejection, is designed and tested using synthetic and realistic images. The effects of environmental conditions on target recognition are also investigated.;To analyze and predict ATR performance of a recognition sensor network, a general methodology from information theory view point is used. Given the encoding method, the recognition system is analyzed using a recognition channel. The concepts of recognition capacity, error exponents and probability of outage are defined and derived for a PCA-based ATR system. Both the case of a single encoded image and the case of encoded correlated multiple frames are analyzed. Numerical evaluations are performed. Finally we discuss the joint recognition and communication problems. Three scenarios of a two node recognition sensor network are analyzed. The communication and recognition performances for each scenario are evaluated numerically

Vision Based Localization for Multiple UAVs and Mobile Robots

Master'sMASTER OF ENGINEERIN