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In this research, we propose a heterogeneous anomaly-based intrusion detection system (HA-IDS) which is built on both of Field-Programmable Gate Array (FPGA) and Graphics Processing Unit (GPU) platforms. An essential anomaly-based IDS comprises of the two main components: Feature Construction Module (FC) to extract and collect network header information, and Classification Module (CM) to categorize...
This paper describes FPGA implementation of object recognition processor for HDTV resolution 30 fps video using the Sparse FIND feature. Two-stage feature extraction processing by HOG and Sparse FIND, a highly parallel classification in the support vector machine (SVM), and a block-parallel processing for RAM access cycle reduction are proposed to perform a real time object recognition with enormous...
Image is a visual representation of object, or a scene which can be seen by human eyes. Image can be created and stored in electronic form. Image is a two dimensional function f(x, y) where, x and y are spatial co-ordinates and amplitude of images is defined in intensity level of each pixel. Image fusion means merging relevant information from different images to create one new image which is more...
Efficient detection and reliable matching of image features constitute a fundamental task in computer vision. When real-time operation is required, the solution to this problem becomes a real challenge, because of increased processing requirements. Scale Invariant Feature Transform (SIFT) is considered as a stable and robust algorithm for the extraction of invariant features, however special hardware...
Patients with epilepsy (a central nervous system disorder) suffer from frequent seizures that occur at unpredictable times without any warning. Therefore, it is necessary to identify the occurrence of seizure in an epileptic patient and prevents patients from SUDEP (SUDDEN UNEXPLAINED DEATH IN EPILEPSY). Prediction of epileptic seizure through analysis of scalp EEG signal which is the measure of the...
Convolutional neural networks (CNNs) are used to solve many challenging machine learning problems. Interest in CNNs has led to the design of CNN accelerators to improve CNN evaluation throughput and efficiency. Importantly, the bandwidth demand from weight data transfer for modern large CNNs causes CNN accelerators to be severely bandwidth bottlenecked, prompting the need for processing images in...
As more and more human-machine interactive applications call for higher frame rate and lower delay to get a better experience, there is an inevitable need for high frame rate and ultra-low delay image processing system. Current existing works based on vision chip target on video with simple patterns or simple shapes in order to get a higher speed, which is reasonable in the first trial of this new...
Nowadays, many people are affected by the neuromuscular problems like stroke, Parkinson's disease etc. around the world. Brain Computer Interface plays a major role for the communication between the patient and the real time environment. In this proposed paper noninvasive BCI method is used to detect the consciousness of the patient who is in the unconscious state for a longer period using the P300...
The development of SLAM algorithms in the era of autonomous navigation and the growing demand for autonomous robot in place of human being, has put into question how to reduce the computational complexity and make use of these algorithms to operate in real time. Our work aims to take advantage of the high level synthesis (HLS) on FPGAs to design a real time SLAM application. Precisely, we evaluate...
The detection and matching of point features play an important role in most of the computer vision algorithms, such as; for 3-D reconstruction and robotics navigation, localization and mapping. Over the last years various detectors and descriptors have been proposed and successfully applied to the different applications. However, the developed detectors are based on computationally intensive algorithms,...
Face recognition is an important biometric tool due to contact independence. In real time scenarios such as criminal record databases, it is vital to provide the user with high accuracy results in reasonable time. Compared to the software counter parts, the existing hardware solutions on FPGAs provide higher accuracy. However, such systems are not scalable due to high resource utilization (i.e. number...
RANSAC is a popular and robust fitting algorithm. In the field of image processing, RANSAC can be successfully used to reject false correspondences between similar images. Due to its iterative nature, RANSAC is computationally demanding and time consuming. When the target application operates in real-time, conventional approaches based on personal computers usually fail to meet the requirements. In...
To cope with the enhanced luminosity of the beam delivered by the Large Hadron Collider (LHC) in 2020, the “A Toroidal LHC ApparatuS” (ATLAS) experiment has planned a major upgrade. As part of this, the trigger at Level1 based on calorimeter data will be upgraded to exploit fine-granularity readout using a new system of Feature Extractors, which each use different physics objects for the trigger selection...
This paper gives the hardware implementation of face detection on FPGA using Haar features. The design consisting of integral image generation which is used to compute the Haar features at a faster rate, has been illustrated. The classifiers are built using the AdaBoost algorithm which selects a minimum number of critical Haar features from a very large set. Also, parallel processing classifiers increase...
Large-scale convolutional neural network is a fundamental algorithmic building block in many computer vision and artificial intelligence applications that follow the deep learning principle. However, a typically-sized CNN is well known to be computationally intensive. This work presents a novel stochastic-based and scalable hardware architecture and circuit design that computes a large-scale CNN with...
In this paper, a novel corner feature extracting simultaneous localization and mapping (CFESLAM) algorithm is proposed, which employs a mechanism of corner feature extraction that regards only corners as the landmarks to ease the computational burden. To further improve the overall computational efficiency, the proposed CFESLAM is implemented by hardware on a FPGA platform. By doing so, the proposed...
Stereo image matching is one of the research areas in computer vision. In stereo image matching, technological developments advances from area based matching techniques to the feature based matching techniques. In this paper, we present a Harris corner detection algorithm for stereo image feature matching. This is an intensity based feature matching algorithm and it controls the strong and weak corners...
Aiming at the characteristics of SIFT (Scale Invariant Feature Transform) algorithm which has large amount of calculation and can be highly paralleled, we propose an optimized FPGA implementation so that it can be accelerated on hardware. In this method, we firstly simplify the process of filtering image and generating Gaussian pyramids through selecting appropriate parameters and hardware structure,...
Convolutional neural network (CNN), well-knownto be computationally intensive, is a fundamental algorithmicbuilding block in many computer vision and artificial intelligenceapplications that follow the deep learning principle. This workpresents a novel stochastic-based and scalable hardware architectureand circuit design that computes a convolutional neuralnetwork with FPGA. The key idea is to implement...
Convolutional Neural Network (CNN) has become a successful algorithm in the region of artificial intelligence and a strong candidate for many applications. However, for embedded platforms, CNN-based solutions are still too complex to be applied if only CPU is utilized for computation. Various dedicated hardware designs on FPGA and ASIC have been carried out to accelerate CNN, while few of them explore...
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