This paper presents an implementation of a pulse mode multilayer neural network with on chip learning. Taking advantage of the compactness of the multiplierless solutions proposed in the literature, we apply a multipl...
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This paper presents an implementation of a pulse mode multilayer neural network with on chip learning. Taking advantage of the compactness of the multiplierless solutions proposed in the literature, we apply a multiplierless architecture, in which the synapse is made up with a DDFS and the neuron uses a nonlinear adder. A programmable activation function is proposed by means of an adjustable pulse multiplier so that the activation function slope can be adjusted without any added hardware cost. The proposed architecture was tested in a signature recognition system. It shows good learning capability. The corresponding design was implemented into a Virtex II PRO XC2VP7 Xilinx FPGA
This paper presents an implementation of a signature recognition system based on pulse mode multilayer neural networks with on chip learning. Taking advantage of the compactness of the multiplierless solutions of puls...
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This paper presents an implementation of a signature recognition system based on pulse mode multilayer neural networks with on chip learning. Taking advantage of the compactness of the multiplierless solutions of pulse mode operations, we apply an architecture, in which the synapse is made up with a DDFS and the neuron uses a nonlinear adder. A programmable activation function is proposed by means of an adjustable pulse multiplier so that the activation function slope can be adjusted without any added hardware cost. Good learning capability is obtained. As illustration, we consider a signature learning application. The corresponding design was implemented into an FPGA platform ( virtex II PRO XC2VP7).
This paper proposes the use of differential electromyography (EMG) signals between muscles for phoneme classification, with which a Japanese speech synthesiser system can be constructed using fewer electrodes. In dist...
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This paper proposes the use of differential electromyography (EMG) signals between muscles for phoneme classification, with which a Japanese speech synthesiser system can be constructed using fewer electrodes. In distinction from traditional methods using differential EMG signals between bipolar electrodes on the same muscle, an EMG signal is derived as differential between monopolar signals on two different muscles in the proposed method. Then, frequency-based feature patterns are extracted with filter banks, and classification of phonemes is realized by using a probabilistic neural network, which combines feature reduction and pattern classification processes in a single network structure. Experimental results show that the proposed method can achieve considerably high classification performance with fewer electrodes
Batteries and ultracapacitors have significantly different energy storage and power delivery capabilities. Electrical traction motors in hybrid electric vehicles have characteristic power and energy demands, and a sin...
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Batteries and ultracapacitors have significantly different energy storage and power delivery capabilities. Electrical traction motors in hybrid electric vehicles have characteristic power and energy demands, and a single energy storage technology may not be optimized to meet both the minimum power and energy demands. In this paper, we investigate the effect of combining batteries and ultracapacitors, both actively and passively, to produce a more versatile electrical energy storage system for hybrid electric vehicles. Hybridized energy storage systems result in increased component life cycles, decreased internal resistance losses, and reduced cost and mass when compared to either battery-only or ultracapacitor-only configurations
Although region-of-interest (ROI) based video coding has been well studied for some other video coding standards, its application for H.264 is still of significant interest because there exists a dilemma in the detect...
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Although region-of-interest (ROI) based video coding has been well studied for some other video coding standards, its application for H.264 is still of significant interest because there exists a dilemma in the detection of the ROI due to the rate distortion optimization. In this paper, the ROI is first detected by using the direct MAD that is determined without motion information. In this way, the ROI-motion dilemma of H.264/AVC is solved. The rate control scheme with ROI concern can then adjust the quantization parameter (QP) to allocate more bits to the ROI, so the overall subjective visual quality is improved.
This paper presents an improved rate control scheme for the H.264/AVC video coding scheme. By analyzing the relationship between direct mean absolute difference (MAD) and actual MAD, a new MAD prediction scheme is int...
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This paper presents an improved rate control scheme for the H.264/AVC video coding scheme. By analyzing the relationship between direct mean absolute difference (MAD) and actual MAD, a new MAD prediction scheme is introduced to enhance traditional linear MAD prediction model, which is unable to predict abrupt MAD fluctuations. Our proposed adaptive model could reduce MAD prediction error by up to 34%. One simple sum bit quadratic R-Q model is also presented to solve the problem caused by inaccurate texture bits estimation of H.264/AVC. With the new MAD prediction model and R-Q model, our proposed scheme could reduce the mismatch between actual frame bits and target frame bits by up to 32%, and the buffer occupancy is much closer to the ideal status. Meanwhile, reconstructed video quality is also improved by up to 0.21 dB at low bitrate
The rapid control of sonar-guided vehicles through obstacle fields has been a goal of robotics for decades. How sensory data is represented strongly affects how obstacles and goal information can be combined to select...
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The rapid control of sonar-guided vehicles through obstacle fields has been a goal of robotics for decades. How sensory data is represented strongly affects how obstacles and goal information can be combined to select a direction of travel. While typical approaches combine attractive and repulsive effects to directly determine steering, we are investigating an algorithm that evaluates multiple directions simultaneously followed by a winner-take-all (WTA) function which then guides steering. In this paper we describe a neuromorphic VLSI implementation of this algorithm using the inherent echo delay to create a range-dependent gain in a 'race-to-first-spike' neural WTA circuit. The chip was fabricated in a commercially-available 0.5 mum CMOS process and in this paper we present preliminary test results
This paper presents the summary of the image processing of a vision-based three-axis attitude determination sensor, which observes a solar system oblate body such as a planet or a planet's moon. From the observed ...
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ISBN:
(纸本)0877035288
This paper presents the summary of the image processing of a vision-based three-axis attitude determination sensor, which observes a solar system oblate body such as a planet or a planet's moon. From the observed image it is possible to extract two independent directions and, consequently, to estimate the three-axis spacecraft attitude. By measuring two independent directions, the sensor can operate in a stand-alone configuration. In this paper, algorithms to determine the required parameters using the observed image are briefly described. Sensitivity is investigated by giving correlation results associated with image translation and rotation, two required process to derive the sensor attitude data.
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