The massive development of IoT, Big Data and other technologies has led to security concerns with respect to data protection. It has become imperative to develop solutions to protect our data, such as images, texts, and audios from unauthorized access. This work presents an encrypted image transmission scheme based on a chaotic dynamic configuration of two synchronized spherical chaotic attractors of 3 dimension in a parent-child topology. We synchronized the future evolution of the chaotic systems starting with different initial conditions using the Hamiltonian observer-based approach and then utilized the resulting phase space points as the pseudo-random numbers for securing image transmitted through the communication channel. The scheme designed is realized and implemented on the Multiprocessor System-on- Chip (MPSoC) platform by harnessing the easy and synthesizable programming features of Python with MPSoC. The image is transmitted through the state variables x1, x2, and x3, and analyzed using the two statistical techniques namely, information entropy and correlation analysis where the result shows the full recovery of the image that was transmitted through the state variables.
The problem of detecting the vegetation index utilizing UAVs has been addressed in multiple articles in the literature, in which many special hardware and thermal or infrared cameras are adapted to improve its detection. The Vegetation Index is determined as a parameter calculated from the reflectance values at different wavelengths of the vegetation and is particularly sensitive to the vegetation cover. This article seeks to identify the vegetation index from its biophysical parameters. We help ourselves with artificial intelligence algorithms and machine learning algorithms. A semi-physical model was designed to estimate the ecosystem and establish the vegetation index correctly. The results will be validated by remote sensing. Finally, an ecological model will be developed to simulate the environmental impact on vegetation patterns and geographic plains. The proposed model successfully imitated the urban effect. Given these results, it was possible to predict better the impact of changing seasons in a defined geographic area.
In today's digital world, the secure transmission of sensitive information such as images is of paramount importance. Image data often contains private and sensitive information, so protecting it from unauthorized access and interception becomes a critical challenge. This article shows an encrypted image transmission scheme based on a chaotic dynamic configuration of multiple displacements supported by the saturated nonlinear function (SNLF) and implemented on the multipurpose system-on-chip (MPSoC) platform using Python. The main activity was the realization of a chaotic SNLF space system on the Xilinx FPGA PYNQ-Z1 (MPSoC) board by programming by interactive Python on Jupyter Notebook, with the purpose of implementing a secure communication system. The main contribution is the successful synchronization of a system with several chaotic attractors in a master-slave topology. Another important contribution is the rapid implementation on the PYNQ-Z1 FPGA of the robust secure communication system capable of resisting comprehensive attacks based on chaotic space attractor SNLF. Among the results obtained is to encrypt an image, in grayscale and RGB, with chaos and transmission key in the transmission system, send the encrypted image through its state variables and from them reconstruct the encrypted image by which the receiving system recovers the sent image without loss of information. By achieving this digital image processing architecture in MPSoC, it be possible to program and synthesize other types of algorithms for digital processing and real-time video application.
Modern computing systems are good for tasks that are difficult for humans, even for high-performance computing. Automation and artificial intelligence combined are disciplines, which emit to humans, generating structured data in real-time and transferable. This combination of systems is also compatible with new neuromorphic processor architectures. Neuromorphic computing is completely redesigned in its architecture to the conventional computer model, both in hardware and software. The Procedure that was carried out was the collection of faces with some emotion, the information was also generated for the real-life database, in which the classification of faces, and emotions was carried out using the theory of Robert Plutchik and with an artificial neural network spike, when combined with the previous concepts and tools, signals are obtained, which as a human would act and that does not work like a conventional computer. The result obtained from the classification of faces and emotions was made in a neural network whose performance is affected only when the contrast falls below 3%, it was also found that some images were 0.005%. Regarding noise resistance, the images withstood 50% noise, in which the performance of the network was not affected. Although neuromorphic computing is willing to simulate the human brain using artificial biological neural networks, it is also used in the classification of objects, and pattern recognition, with proposed image processing techniques, obtaining acceptable performance.
This article show a nonlinear dynamical system capable of generating spatial attractors. The main activity is the realization of a spatial chaotic attractor on Xilinx FPGA board PYNQ-Z1 by programming Python used Jupyter, with a focus on the implementation of a secure communication system. The first contribution is the successful synchronization of two chaotic attractors systems, in VHDL program, in a master-slave topology. The second important contribution is the FPGA realization of a secure communication system based on a spatial chaotic attractor, which involves encrypting grayscale and RGB images with chaos and broadcast key in the transmission system, sending the encrypted image through of the state variables and reconstruct the encrypted image then in the receiving system recovers sent imagen.
A combination between research lines of robotics and artificial intelligence and using computer vision, this consists of using robotic systems that can recognize and understand images and scenes, generally integrate the Areas: detection of objects image recognition and Image Generation Object detection is sophisticated and more in robotics due to countless applications that can develop through image processing. This article shows the implementation of the NVIDIA® Jetson development card in a remote control unmanned aerial system (RPAS) for object recognition, based on focal loss. Hence, it is a challenge to obtain results, which it faces to develop it will show in the expected final solution.
Technology development has allowed each day we have devices that can contain all the functionality of a Digital System on a single chip (SoC) and they have a very high scale of integration (VLSI) hundreds of millions of gates at very low costs. As well as the design, verification and synthesis tools offered by the development factories those make these SoC and FPGA components. This companies offer Integrated Development Environments with software tools to perform from the specification of the Design to its synthesis in C.I. and its verification in industry standard languages as Verilog and VHDL. This paper shows the advantages in design, verification, synthesis and testing that can be obtained by using HDL languages such as CHISEL, MyHDL for the processing of video processing in Real-Times and demonstrate its main advantages in both learning time and costs.
KEYWORDS: Image processing, Video, Cameras, Digital signal processing, Video processing, Image analysis, Sensors, Detection and tracking algorithms, Field programmable gate arrays, Prototyping
This article shows the application of the advantages offered by the SURF algorithm for the detection of points of interest in the video images, which are monitored in real time, of the concrete units that form a breakwater. This procedure of monitoring and analysis of the images allows determining the displacement suffered by the elements or shells of the breakwater and consequently the damages submerge the breakwaters. This technique is applied in modeling studies in hydraulic coastal laboratories. Damage can be weighted as a percentage of the total number of armor units on the slope of the breakwater, per unit of area covered by video camera monitoring and digital image processing with the SURF algorithm to determine the movements of the elements of the housing properly and efficiently.
In computational vision has a high computational cost, although, some algorithms had been implemented to get image features, that allow assorting, object and face recognition and so on. Some solutions have been developed in computers, DSP and GPU those that are not optimal with time. In order to improve the performance of these algorithms, we are implementing the SURF algorithm in embedded systems (FPGA) and applied to non-controller environments that require real-time response. In this work we development a SURF algorithm in order to improve time processing in video and image processing, we use an FPGA to apply that algorithm, we compare the time processing with different devices and the features found it into the images, this features will be invariant to scale, rotation and lighting, the SURF algorithm localize the interest points (features), its is using in facial recognition, object detection, stereo vision and so on. This algorithm has a high computational cost because of use a lot of data, in order to reduce the high cost we implemented LUTs and reduce time with code. With this work we try to find the best way to implement the algorithm into embedded systems, in order to use in non-controller environments and robots autonomous.
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