While current computers have shown to be particular useful for arithmetic and logic implementations, their accuracy and efficiency for applications such as e.g. face, object and speech recognition, are not that impres...
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ISBN:
(纸本)9781467398893
While current computers have shown to be particular useful for arithmetic and logic implementations, their accuracy and efficiency for applications such as e.g. face, object and speech recognition, are not that impressive, especially when compared to what the human brain can do. Machine learning algorithms have been useful, especially for these type of applications, as they operate in a similar way to the human brain, by learning the data provided and storing it for future recognition. Until now, there has been a strong focus on developing the process of data storage and retrieval, merely neglecting the value of the provided information and the amount of data required to store. Hence, currently all information provided is stored, because it is difficult for the machine to decide which information needs to be stored. Consequently, large amounts of data are stored, which then affects the processing of the data. Thus, this paper investigates the opportunity to reduce data storage through the use of differentiation and combine it with an existing similarity detection algorithm. The differentiation isWhile current computers have shown to be particular useful for arithmetic and logic implementations, their accuracy and efficiency for applications such as e.g. face, object and speech recognition, are not that impressive, especially when compared to what the human brain can do. Machine learning algorithms have been useful, especially for these type of applications, as they operate in a similar way to the human brain, by learning the data provided and storing it for future recognition. Until now, there has been a strong focus on developing the process of data storage and retrieval, merely neglecting the value of the provided information and the amount of data required to store. Hence, currently all information provided is stored, because it is difficult for the machine to decide which information needs to be stored. Consequently, large amounts of data are stored, which then
This article focuses on an original approach aiming the processing of low-altitude aerial sequences taken from an helicopter (or drone) and presenting a road traffic. Proposed system attempts to extract vehicles from ...
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This article focuses on an original approach aiming the processing of low-altitude aerial sequences taken from an helicopter (or drone) and presenting a road traffic. Proposed system attempts to extract vehicles from acquired sequences. Our approach begins with detecting the primitives of sequence images. At the time of this step of segmentation, the system computes dominant motion for each pair of images. This motion is computed using wavelets analysis on optical flow equation and robust techniques. Interesting areas (areas not affected by the dominant motion) are detected thanks to a Markov hierarchical model. Primitives stemming from segmentation and interesting areas are used to build a graph on which partitioning process is executed. This graph gathers only the primitives (considered as nodes) witch belong to the interesting areas. Nodes are interconnected by Perceptive Criteria. To extract the important elements of the sequence (vehicles), a bi-partition of this graph using Normalized Cuts technique takes place. Finally, parameters of proposed algorithm are chosen thanks to a learning stage for which we use Genetic algorithms.
A large portion of imageprocessing applications often come with stringent requirements regarding performance, energy efficiency, and power. FPGAs have proven to be among the most suitable architectures for algorithms...
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A large portion of imageprocessing applications often come with stringent requirements regarding performance, energy efficiency, and power. FPGAs have proven to be among the most suitable architectures for algorithms that can be processed in a streaming pipeline. Yet, designing imaging systems for FPGAs remains a very time consuming task. High-Level Synthesis, which has significantly improved due to recent advancements, promises to overcome this obstacle. In particular, Altera OpenCL is a handy solution for employing an FPGA in a heterogeneous system as it covers all device communication. However, to obtain efficient hardware implementations, extreme code modifications, contradicting OpenCL's data-parallel programming paradigm, are necessary. In this work, we explore the programming methodology that yields significantly better hardware implementations for the Altera Offline Compiler. We furthermore designed a compiler back end for a domain-specific source-to-source compiler to leverage the algorithm description to a higher level and generate highly optimized OpenCL code. Moreover, we advanced the compiler to support arbitrary bit width operations, which are fundamental to hardware designs. We evaluate our approach by discussing the resulting implementations throughout an extensive application set and comparing them with example designs, provided by Altera. In addition, as we can derive multiple implementations for completely different target platforms from the same domain-specific language source code, we present a comparison of the achieved implementations in contrast to GPU implementations.
The paper reveals a two-processor Central processing Unit (CPU) designed for Programmable Logic Controllers (PLC). The CPU is made up of a 1-bit (bit-type) processor and a 32-bit (word-type) processor. The both proces...
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The paper reveals a two-processor Central processing Unit (CPU) designed for Programmable Logic Controllers (PLC). The CPU is made up of a 1-bit (bit-type) processor and a 32-bit (word-type) processor. The both processors are operated totally independently from one another and information between them is exchanged via a exchange memory module. The 1-bit processor collaborates with edge detectors whilst the 32-bit processor is dedicated for implementation of timers and counters as well as enables execution of arithmetic operations on 32-bit integers and floating point variables. The both processors have been developed as specialized structures capable of executing control routines developed in the Instruction List (IL) programming language in line with requirements of the EN 61131-3 standard. The study presents a new approach to development of the exchange memory module and edge detectors as well as implementation of timers and counters. It is the approach that combines both software and hardware solutions, which makes them more efficient in terms of time performance as compared to already known solutions. Finally the CPU was designed with use of the hardware description language and then implemented in FPGA resources. This paper discloses results of the synthesis and execution times achieved for both individual instructions and benchmarks routines running on the newly designed CPU. The accomplished results make it possible to conclude that the CPU can be considered as an alternative solution to other units available from the market whilst the capability to execute algorithms developed in strict conformity to EN 61131-3 standard is its advantage.
Comparing Unmanned Aerial Vehicle(UAV) video image patches is a fundamental task in UAV video image *** main difficulty lies in the wide variety of appearance changes in images taken under different UAV imaging condit...
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Comparing Unmanned Aerial Vehicle(UAV) video image patches is a fundamental task in UAV video image *** main difficulty lies in the wide variety of appearance changes in images taken under different UAV imaging conditions, such as jitter, changing points of view, frequent undefined motion, illumination changes and *** usual algorithms which are based on the hand-craft features and independently predefined similarity metrics, cannot deal with these factors *** by recent successes on learning deep feature representations and feature similarity metric, a method which jointly models and learns these two objects is proposed ***, comparing UAV video image patches is deemed as a binary classification problem and a Convolutional Neural Network(CNN) based comparing system is *** is composed of three parts:(1) two stream CNNs,(2) one similarity metric network,(3) one softmax *** jointly learn the CNNs and similarity metric, the available standard natural image datasets are employed and two new datasets representing typical satellite and UAV imaging scenes are ***, over the datasets from different imaging scenes, the transfer joint learning of the proposed comparing system is *** primary experimental results show that the proposed method can significantly outperform the recent results of the hand-craft feature based comparing methods.
This study presents a new algorithm to adaptively detect change points of functional connectivity networks in the brain. It uses scans from resting-state functional magnetic resonance imaging (rsfMRI) which is one of ...
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ISBN:
(纸本)9781457702198
This study presents a new algorithm to adaptively detect change points of functional connectivity networks in the brain. It uses scans from resting-state functional magnetic resonance imaging (rsfMRI) which is one of the major tools to investigate intrinsic brain functionality. Different regions of the resting brain form networks that change states within a few seconds to minutes. The change points of these networks are different in normal and disordered brain functions and their understanding can help in identification of brain disorders. These changes arise from many unknown factors and extraction of these change points is one of the the major challenges in the absence of any ground truth. Our algorithm detects these change points adaptively by computing sum of absolute sign differences of adjacent images in rsfMRI scans using measures from image and video processing. We demonstrate the effectiveness of the proposed algorithm and show that these change points can be detected reliably in both task-based and resting-state networks. The outcomes also point to new directions for future work.
A significant hurdle of accurate needle tip placement in percutaneous needle-based prostate interventions is unmodeled needle deflection and tissue deformation during insertion. This paper introduces a robotic platfor...
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A significant hurdle of accurate needle tip placement in percutaneous needle-based prostate interventions is unmodeled needle deflection and tissue deformation during insertion. This paper introduces a robotic platform for developing synergistic, cooperatively controlled needle insertion algorithms decoupled from closed-loop image-guided needle steering. Shared control of the surgical workspace through human-robot synergy creates a balance between the accuracy of robotic autonomy while still providing ultimate control of the procedure to the physician. Validation tests were performed using camera-based image-guided feedback control of needle steering with cooperative hands-on needle insertion. Locations were targeted inside a transparent gelatin phantom with an average total error of 2.68 ± 0.34mm and in-plane error of 2.59 ± 0.30mm.
Signal source separation is an important aspect of both measurement and signature intelligence (MASINT) as well as modern commercial smart grid applications. In some MASINT scenarios, real-time signal processing is re...
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ISBN:
(纸本)9781509037827
Signal source separation is an important aspect of both measurement and signature intelligence (MASINT) as well as modern commercial smart grid applications. In some MASINT scenarios, real-time signal processing is required in a small physical form factor, which can become prohibitive with the high sample rates required of modern signal identification algorithms. We develop a new Fast Fourier Transform (FFT) algorithm, the Aliased FFT (AFFT), that exploits spectral redundancy and sparsity in the signal to drastically reduce the number of computations required from O(N logN), for the normal FFT, to one that approaches the idealized O(K logK) for a K-sparse signal. We also develop a signal source separation algorithm using harmonically aligned signal projections (HASP) that is useful for transforming a one-dimensional signal of interest (SOI) into a two-dimensional image in such a way to facilitate automated feature extraction. An automated feature extractor is described which can accurately achieve super-resolution far beyond that of the traditional FFT by again taking advantage of the harmonic structure present in the SOI. We demonstrate HASP and this feature extractor for the application of load disaggregation-identifying the presence of certain types of devices connected to a power system.
The comparison of the modern microwave screening systems is given in this paper in the aspect of employing mono-static or multi-static antenna configuration, with their numerical models described and applied in the pr...
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ISBN:
(纸本)9781509060948
The comparison of the modern microwave screening systems is given in this paper in the aspect of employing mono-static or multi-static antenna configuration, with their numerical models described and applied in the presented numerical experiments. The phenomenological model of scattering from human body and foreign objects is used to obtain the radar signal, which is processed according to the given mono-static or multi-static signal processingalgorithms. The model of scattering objects is based on point scatterer approximation of their surfaces. The numerical simulations are performed for human body and foreign objects to obtain detailed radar images for mono-static and two multi-static antenna configurations at a single and multiple frequencies. According to the results of numerical simulations, the same quality of radar images, visually assessed by achievable plan view resolution and the level of artifacts, can be obtained by significantly lesser number of antenna elements in the case of multi-static antenna configuration. It was shown that extending the frequency band from 10 to 16 GHz substantially increases the contrast of the foreign objects placed over the human body.
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