Vehicle-based pedestrian detection system receives more and more attentions in road safety applications of the modern intelligent transportation system. However, the existed detection algorithms are too computing exte...
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Vehicle-based pedestrian detection system receives more and more attentions in road safety applications of the modern intelligent transportation system. However, the existed detection algorithms are too computing extensive for single core vehicle-based processors. As the promising multi-core architecture provides both energy efficient and powerful computing solutions, it is relevant to evaluate the up-to-date pedestrian detection algorithm on such novel platforms. This paper implemented a popular template based pedestrian detection algorithm on such platforms and gave out computing bottlenecks by profiling. Furthermore, we proposed an application aware multithread accelerating technique. Experimental results showed that our design can achieve nearly 2 - 4x speedups on multi-core processors for critical function blocks, which reduces total execution time ranging from 26 to 41%.
Aimed at the problem that many AID algorithms have lower detection rate and higher false alarming rate, this paper proposed a kind of AID algorithms for freeways based on SVM. The eigenvector reflecting traffic state ...
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Aimed at the problem that many AID algorithms have lower detection rate and higher false alarming rate, this paper proposed a kind of AID algorithms for freeways based on SVM. The eigenvector reflecting traffic state was designed according to selected traffic measures that can be provided by many kinds of traffic sensors. AID algorithms were designed based on different sorts of SVM models and tested and compared with simulated data. The results showed that the performances of proposed methods are better than selected classic AID algorithms
Self-gating (SG) is a cardiac MRI technique to synchronize data acquisition to the cardiac cycle based upon MR signal triggers as opposed to conventional ECG triggers. Fourteen healthy subjects underwent cardiac MRI s...
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Self-gating (SG) is a cardiac MRI technique to synchronize data acquisition to the cardiac cycle based upon MR signal triggers as opposed to conventional ECG triggers. Fourteen healthy subjects underwent cardiac MRI scans in four different orientations: two chamber, three chamber, four chamber, and short axis. SG trigger times were computed using two methods, first difference and template matching, and ECG trigger times were also recorded for comparison. The root-mean-square (RMS) error was used to evaluate performance, defined as the variability relative to the mean difference between SG trigger times and ECG trigger times. The mean RMS error was lower for template matching than first difference approach for all scan orientations; the improvement in RMS error was statistically significant for all orientations except short axis. In conclusion, compared to the first difference approach, template matching improved the accuracy of trigger detection for two, three, and four chamber SG cardiac MRI scans.
A description is given of an implementation and performance evaluation of different deadlock prevention and detection algorithms. The algorithms are implemented in a locally distributed system. A number of experiments...
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A description is given of an implementation and performance evaluation of different deadlock prevention and detection algorithms. The algorithms are implemented in a locally distributed system. A number of experiments were executed in a distributed system for various lengths of file operation and different numbers of files. The performance of the system and of each algorithm is evaluated and discussed.< >
In this paper, three algorithms were choosen by reviewing the literature about pitch detection and implemented on the Matlab environment. Runtimes and error rates of these algorithms were compared with each other. The...
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In this paper, three algorithms were choosen by reviewing the literature about pitch detection and implemented on the Matlab environment. Runtimes and error rates of these algorithms were compared with each other. These algorithms are Harmonic Product Spectrum (HPS), Cepstral Pitch Tespit (CPD) and Autocorrelation Method. the observed results showed that the fastest algorithm is CPD and the slowest algorithm is HPS. The least error was achieved at 44.1 kHz sample rate for all algorithms. HPS algorithm become more successful with %99 success ratio in noiseless environment.
The onset of a potentially fatal arrhythmia is often preceded by abnormal morphologies in the QRS complex, the main feature in the electrocardiogram. However, these ectopic beats are difficult to detect as their shape...
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The onset of a potentially fatal arrhythmia is often preceded by abnormal morphologies in the QRS complex, the main feature in the electrocardiogram. However, these ectopic beats are difficult to detect as their shape is very similar to those found in a normal sinus rhythm. We show how an auto-associative multi-layer perceptron can be trained to detect normal beats only, so that the subtle abnormalities in the shape of ectopic beats become clearly identifiable. Details of how to train the network for use in a clinical environment are given utilising a new parameter, the variance ratio. Results for a study of the combination of algorithms to produce a robust ectopic beat detector are presented. Finally we discuss an on-line implementation for patient-specific adaptability.
Uncovering the dynamics of community structures in complex networks helps us to explore how such community structures change over time. But, understanding these structures is very challenging, especifically in dynamic...
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ISBN:
(数字)9781728157542
ISBN:
(纸本)9781728157559
Uncovering the dynamics of community structures in complex networks helps us to explore how such community structures change over time. But, understanding these structures is very challenging, especifically in dynamic complex networks where network structure changes frequently and interaction between the individuals changes over time. Recently, many dynamic community detection algorithms have been introduced to capture the dynamics of network community structures. In this paper, we present a detailed analysis of the dynamic community detection algorithms in terms of computation time and accuracy. To provide detailed and extensive analysis, we tested dynamic algorithms on small, medium and large real-world network dataset. Based on the analysis results and network properties, we provide some guidelines that may help to choose the best dynamic community detection algorithms for the given dynamic complex networks.
The presented strategy of automatic baseline detection in chromatograms combines fuzzy logic and neural network approaches. It is based on a verbal description of a baseline referring to a 2D image of a chromatogram i...
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The presented strategy of automatic baseline detection in chromatograms combines fuzzy logic and neural network approaches. It is based on a verbal description of a baseline referring to a 2D image of a chromatogram instead of a data vector. Baselines are expected to touch data points on the lower border of the chromatogram forming a mainly horizontal and straight line. That description has been translated into a couple of algorithms forming a two-stage approach first proceeding on a local, and second, on a global level. The first stage assigns a value regarded as the degree of baseline membership or significance to each data point; the second uses a global optimization strategy for coordinating these significances and for producing the final curve, simultaneously. The statistical stability of the proposed approach is superior to known approaches, while keeping the computational effort low.
Lane detection plays a crucial role for Advanced Driver Assitance System (ADAS) or autonomous driving applications. Literature shows a lot of lane detection algorithms can work in real time with good results. However,...
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ISBN:
(纸本)9781479983407
Lane detection plays a crucial role for Advanced Driver Assitance System (ADAS) or autonomous driving applications. Literature shows a lot of lane detection algorithms can work in real time with good results. However, they require much computer processing and cannot be embedded in a vehicle ECU without deep software optimizations. In this paper, we discuss the embeddability of lane detection algorithms by comparing state-of-the-art algorithms in terms of functional performance and computational timing. We identify what essential parts of lane detection are time consuming, and show these parts can be computed in real time on embedded systems.
Pre-coding is a general understanding of beamforming to achieve the transmission diversity of the signal. Basically transmitter sends the information which is already coded according to the known knowledge about the c...
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ISBN:
(纸本)9781538632444
Pre-coding is a general understanding of beamforming to achieve the transmission diversity of the signal. Basically transmitter sends the information which is already coded according to the known knowledge about the channel conditions to the receiver. The pre-coding technique leads to achieve a good performance by eliminating the degradation effects even in the correlation environment upon the channel in the communication system. In this paper, the different detection algorithms: Zero forcing (ZF), Minimum mean square error (MMSE) and Block diagonalization (BD) are used in pre-coding. Many solutions have been proposed for the Co-Channel Interference problem. The main aim of this paper is to select the most suitable Coding techniques to adopt. The analysis is carried by assuming the channel as Rayleigh and modulation scheme is QPSK with 4×4 MIMO system.
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