Classification of coffee using artificial neural network

The paper presents a method for classifying coffees according to their scents using artificial neural network (ANN). The proposed method of uses genetic algorithm (GA) to determine the optimal parameters and topology of ANN. It uses adaptive backpropagation to accelerate the training process so that the entire optimization process can be achieved in an accelerated time. The optimized ANN has successfully classified the coffees using a relatively small set of training data. The performance of the optimized ANN compare significantly better than the methods proposed by other researchers.published_or_final_versio

Performance evaluation of FPGA implementations of high-speed addition algorithms

Driven by the excellent properties of FPGAs and the need for high-performance and flexible computing machines, interest in FPGA-based computing machines has increased dramatically. Fixed-point adders are essential building blocks of any computing systems. In this work, various high-speed addition algorithms are implemented in FPGAs devices, and their performance is evaluated with the objective of finding and developing the most appropriate addition algorithms for implementing in FPGAs, and laying the ground-work for evaluating and constructing FPGA-based computing machines. The results demonstrate that the performance of adders built with the FPGAs dedicated carry logic combined with some other addition algorithms will be greatly improved, especially for larger adders.published_or_final_versio

Single-image random-dot stereogram by crosstalk

A fast, simple and memory saving algorithm for stereogram generation is presented in this paper. It is a ray-tracing like algorithm making use of the cross-talk effect in stereoscopic computer graphics for generating single-image random-dot stereogram. It actively looks for the smallest equivalent class of points with the same color so that it gives the greatest freedom of coloring for artistic design with stereogram.published_or_final_versio

FPGA adders: performance evaluation and optimal design

Delay models and cost analyses developed for ASIC technology are not useful in designing and implementing FPGA devices. The authors discuss costs and operational delays of fixed-point adders on Xilinx 4000 series devices and propose timing models and optimization schemes for carry-skip and carry-select adders.published_or_final_versio

FPGA-based floating-point datapath design for geometry processing

Geometry processing comprises of a great many computationally intensive floating-point operations. Real- time graphics systems generally use application-specific custom designed parallel hardware to provide the high performance computation power. When designing a graphics engine on a FPGA-based configurable computing system, cost- effectiveness is important. This paper investigates and proposes a cost-effective FPGA-based floating-point datapath for geometry process. It is designed to be a basic building block for FPGA-based geometry processors. The implemented datapath operates at a frequency of 6.25 Mhz and has an average floating-point operation time of 10.2 microseconds.published_or_final_versio

Resource reservation in wireless networks based on pattern recognition

Resource reservation is very important for handoff control in wireless networks. Many researches have aimed to predict the user's destination cell based on its movement pattern for efficient resource reservation. In the future networks with small size cells, handoffs will occur more frequently and the user's movement will be more like a random process, so it is not practical to predict the accurate destination of a user. We propose a statistical strategy for resource reservation through the estimation of a user's transfer probabilities, which represent the possibilities of the user leaving the current cell and entering the neighboring cells. The resources reserved for a user in each base station are proportional to the user's transfer probabilities. A mathematical model is proposed to obtain the transfer probabilities of a user from the initial states (position, velocity and direction) through simulation of the user's movement. Neural networks are developed to predict the transfer probabilities of a user from the initial states and facilitate efficient resource reservation.published_or_final_versio

Application of artificial neural networks in sales forecasting

The aim of the work presented in this paper is to forecast sales volumes as accurately as possible and as far into the future as possible. The choice of network topology was Silva's adaptive backpropagation algorithm and the network architectures were selected by genetic algorithms (GAs). The networks were trained to forecast from 1 month to 6 months in advance and the performance of the network was tested after training. The test results of artificial neural networks (ANNs) are compared with the time series smoothing methods of forecasting using several measures of accuracy. The outcome of the comparison proved that the ANNs generally perform better than the time series smoothing methods of forecasting. Further recommendations resulting from this paper are presentedpublished_or_final_versio

<title>Novel design of neural networks for handwritten Chinese character recognition</title>

Handwritten Chinese character recognition system invariably sue different image processing techniques to preprocess the input image before the main classification and recognition techniques are used. The authors proposed a different approach to the system philosophy of solving the handwritten Chinese character recognition problem for no preprocessing is necessary. The Chinese characters are treat as ideographs. The proposed system comprise of a Rough Classifier which control the different Fine Classifiers. Each classifier is an optimized artificial neural network using genetic algorithms. A reduced system has been implemented. The result shows that the proposed system has higher recognition rate than the similar systems reported and is more efficiency.published_or_final_versio

Long-term effects of psychosocial interventions on internet-related disorders : a meta-analysis

202305 bcwwNot applicableRGCPublished24 month