With the evolution of science and technology, monitoring human reactions and activities have become really easy and smooth. These new technologies have the potential to revolutionize the domain of safety and security ...
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With the evolution of science and technology, monitoring human reactions and activities have become really easy and smooth. These new technologies have the potential to revolutionize the domain of safety and security in different realms of the society. Surveillance being the key factor of security measures has been elevated to a whole new level with the advancement in signal processing techniques. This paper basically focuses on the implementation of a smart surveillance system using signal processing and embedded tools which is applied in automobiles to ultimately develop the holistic driver assistance system. Earlier methods were based on physiological and analog data, but the present day scenario demands a smarter and digitalized working system so as to employ integrity and compatibility with other smart sub-systems like mobile phones and tablets. Transportation as we all know is one of the key sectors in the society. But the safety and security measures which people implement for their homes is not being employed for their vehicles. Apart from the vehicular anti-theft burglar systems, driver monitoring systems are also crucial to the lives of the driver and the passengers. Hence, this paper consists of three inter-linked modules which are the driver fatigue detection, alcohol content detection and vehicular crash detection along with control to monitor the driver's physiological state that can affect the vehicular control. A variety of input extraction hardware tools and software algorithms have been utilized in a collaborative way to implement this process.
A new approach based on the fuzzy set theory is proposed, which allows one to quantify the value of information. Different approaches to definition and calculation of basic concepts of information theory are considere...
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A new approach based on the fuzzy set theory is proposed, which allows one to quantify the value of information. Different approaches to definition and calculation of basic concepts of information theory are considered, in particular, amount of information and its evaluation based on statistical considerations (classical approach), theory of algorithms (algorithmic approach), and pattern recognition theory (image approach). Approaches to processing of fuzzy information under incomplete definition of the vector of input attributes based on fuzzy set theory are proposed. Their analysis is carried out, the limits of their use and the fields of efficient application are established.
Synthetic-aperture radar is usually a complex software and hardware system. It allows obtaining images in radio range, comparable in resolution with optical systems. The advantage of radio waves is that the images are...
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Synthetic-aperture radar is usually a complex software and hardware system. It allows obtaining images in radio range, comparable in resolution with optical systems. The advantage of radio waves is that the images are of high quality, despite cloudiness and dark time. The development of algorithms for such systems is a rather complex process. Mathematical modeling applied in purpose to reduce costs. In this paper, we give an overview of early created systems. We discuss the methods for calculating the scattered electromagnetic field. We choose methods that are most suitable for simulating a synthetic aperture radar. Combination of different approximation methods allows us to process large scenes. We take into account the various effects that arise when propagating radio waves. Also, we describe algorithms for a synthesis of radar images. In particular, we consider range-migration algorithm and time-frequency processing algorithm. We show that the frequency-time processing algorithm is preferable for synthesis a radio image in the X-band due to its speed. In opposite, the range-migration effect in P-band is too strong to ignore it. The time-frequency algorithm gives not focused image with serious artifacts. It is better to use the range-migration algorithm for P-band.
imageprocessing (IP) applications have become popular with the advent of efficient algorithms and low-cost CMOS cameras with high resolution. However, IP applications are compute-intensive, consume a lot of energy an...
imageprocessing (IP) applications have become popular with the advent of efficient algorithms and low-cost CMOS cameras with high resolution. However, IP applications are compute-intensive, consume a lot of energy and have long processing times. image approximation has been proposed by recent works for an energy-efficient design of these applications. It also reduces the impact of long processing times. The challenge here is that the IP applications often work as a part of bigger closed-loop control systems, e. g. advanced driver assistance system (ADAS). The impact of image approximations that tolerate certain error on these image-based control (IBC) systems is very important. However, there is a lack of tool support to evaluate the performance of such closed-loop IBC systems when the IP is approximated. In this work, we study the impact of algorithmic approximation on the quality-of-control for IBC systems. We propose a framework for performance evaluation of image approximation on a closed-loop automotive IBC system. Our framework is written in C++ and uses v-REP as the simulation environment. For the simulation, v-REP runs as a server and the C++ module as a client in synchronous mode. We show the effectiveness of our framework using a vision-based lateral control example. Our results show that approximate computing allows to improve the processing time up to a factor of 3.5. The measurements on our framework allowed us to develop a thorough understanding on the impact of approximation and achieve an overall quality-of-control improvement of up to 50%, when using approximate computing.
This paper presents a study related to feature extractors (detectors and descriptors) for v-SLAM applications. A review is performed with regards to well-known and optimized features descriptors like ORB, BRIEF, SURF,...
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ISBN:
(纸本)9781728112329
This paper presents a study related to feature extractors (detectors and descriptors) for v-SLAM applications. A review is performed with regards to well-known and optimized features descriptors like ORB, BRIEF, SURF, ZEON and bio-inspired descriptors like FREAK or HOOFR. Combinations with different widely used corner detectors like FAST, AGAST, STAR and SURF is also evaluated in terms of execution time resulting for both detection and description tasks. Results are analyzed to consider only the best combination for an automotive application. Evaluation results highlight the bio-inspired approaches considering the compromise between imageprocessing accuracy and execution times.
A third-generation algorithm that implements the method of mirror noise images for extracting a useful signal from noise is developed. In the proposed algorithm initial processing of the initial signal is realized by ...
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A third-generation algorithm that implements the method of mirror noise images for extracting a useful signal from noise is developed. In the proposed algorithm initial processing of the initial signal is realized by means of a second-generation algorithm and the second half of the signal is then generated by a reversal of that segment of the signal where the noise is suppressed. The useful signal is practically invariant in the newly created signal while the noise signal is substantially distorted which in the course of subsequent filtration leads to an additional increase in the signal-to-noise ratio by comparison to narrow-band filtration of signals and the first- and second-generation algorithms. The algorithm is intended for information-measurement systems that function according to the principle, switched on - measured - switched off. It is shown that the new algorithm produces a substantial gain in signal-to-noise ratio by comparison with narrow-band filtration of signals and the first- and second-generation algorithms.
The evolution of modern technologies has made real-time and personalized monitoring immensely helpful to the geriatrics. With the increasing trendin the growth of the elderly population, it has become cumbersome for c...
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ISBN:
(纸本)9781665435253
The evolution of modern technologies has made real-time and personalized monitoring immensely helpful to the geriatrics. With the increasing trendin the growth of the elderly population, it has become cumbersome for caregivers to keep a check on seniors' day by day exercises physically and ceaselessly. Trends in their daily activities, such as eating, sleeping, sitting, standing, walking, drinking etc. can provide caregivers proper information with regard to seniors' health. Hence, the proposed idea is to devise a self-operating model to continuously scan the seniors' activities and to provide them with descriptive analysis using imageprocessing techniques and Deep Learning algorithms. The proposed methodology consists of the Human activity recognition and the classification of the Human activity using ResNet-101, vGG-16 and Inception v3 which are Convolutional Neural Network Architectures and to propose the best model based on its performance. The model will detect the elderly's activity, recognize the activity and finally provide the descriptive wellness analysis.
The data about the effect of image quality on accuracy of information parameters are presented. Studies on accuracy of measuring characteristics of objects in their images depending on image quality are described. Stu...
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Convolutional neural networks (CNN) are a very powerful tool for many different applications. This capability is highly demanded in the field of embedded systems for video surveillance, speech recognition, and image a...
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ISBN:
(纸本)9781665437745
Convolutional neural networks (CNN) are a very powerful tool for many different applications. This capability is highly demanded in the field of embedded systems for video surveillance, speech recognition, and image analysis. Due to its high computational intensity, the application of CNN is limited to real-time research areas where computational speed is extremely important. Therefore, an appropriate accelerator is required to fulfill the requirements of these limitations. On the one hand, GPUs are widely used to accelerate the CNN under high power dissipation. On the other hand, the trend for FPGA implementation is increasing rapidly due to its low power consumption and facile re-configurability. In this work, we evaluate the inference performance of 10 classification models and 9 object detection models using the OpenvINO toolkit. In addition, we analyzed the implementation of these models on the DE5a-Net DDR4 equipped with an Arria 10 GX FPGA. The results show that the performance of Full-Precision FP32 classification models on a heterogeneous architecture FPGA/CPU is on average 3.6X faster than the CPU.
video surveillance systems are a major multipurpose video data source for modern emergency management solutions. As their complexity and image quality grows, the requirements for data exchange channels, storage and pr...
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ISBN:
(数字)9781728193748
ISBN:
(纸本)9781728193755
video surveillance systems are a major multipurpose video data source for modern emergency management solutions. As their complexity and image quality grows, the requirements for data exchange channels, storage and processing units are becoming much more ***, a wide range of neural networks becomes a universal instrument for image analysis. Such algorithms require deployment of powerful HPC-class server *** this article, we propose a new adaptive approach to decentralized environment for video data storage and event-driven video processing for modern emergency management *** proposed solutions have been tested on different arrays of video data collected from various sources. These sources include city video surveillance systems, supermarket video surveillance systems, *** each of the listed examples, a discrete dynamic model has been developed based on video processing and event *** research resulted in a new data collection and analysis approach based on distributed video processing, reconstruction of 3D scenes and tactical situations detection.
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