17392876
High-level software energy macro-modeling
This paper presents an efficient and accurate high-level software energy estimation methodology using the concept of characterization-based macro-modeling. In characterization-based macro-modeling, a function or sub-routine is characterized using an accurate lower-level energy model of the target processor, to construct a macro-model that relates the energy consumed in the function under consideration to various parameters that can be easily observed or calculated from a high-level programming language description. The constructed macro-models eliminate the need for significantly slower instruction-level interpretation or hardware simulation that is required in conventional approaches to software energy estimation. We present two different approaches to macro-modeling for embedded software that offer distinct efficiency-accuracy characteristics: (i) complexity-based macro-modeling, where the variables that determine the algorithmic complexity of the function under consideration are used as macro-modeling parameters, and (ii) profiling-based macro-modeling, where internal profiling statistics for the functions are used as parameters in the energy macro-models. We have experimentally validated our software energy macro-modeling techniques on a wide range of embedded software routines and two different target processor architectures. Our experiments demonstrate that high-level macro-models constructed using the proposed techniques are able to estimate the energy consumption to within 95% accuracy on the average, while commanding speedups of one to five orders-of-magnitude over current instruction-level and architectural energy estimation techniques.
625121
A comprehensive high-level synthesis system for control-flow intensive behaviors
In this paper, we describe a comprehensive high-level synthesis system for control-flow intensive as well as data-dominated behaviors. We propose a new control-data flow graph model to preserve the parallelism inherent in the application, as well as to facilitate high-level synthesis. Our algorithm, which is based on an iterative improvement strategy, performs clock selection, scheduling, module selection, resource allocation and assignment simultaneously to fully derive the benefits of design space exploration at the behavior level. The system can be used to optimize area, power or energy, by selecting the cost function accordingly. Experimental results show that for energy-optimized designs, energy is reduced by up to 79.4% (an average of 42.2%), with an average of 24.8% area overhead, compared to area-optimized designs. For power-optimized designs, power is reduced by up to 70.8% (an average of 56.7%), with an average of 25.2% area overhead, compared to area-optimized designs. No Vdd scaling is performed to obtain the above results.
11321791
A new face detection method based on shape information
Automatic detection of human faces is one of the most difficult problems in pattern recognition. In many practical applications (e.g. personal identification using photo-IDs), face images often have a simple background. In this paper, we propose a new method based on shape information. Our system can be very efficient for images with simple background. Input image is first enhanced by means of histogram equalization, followed by edge detection based on a multiple-scale filter. The extracted edges are then linked using a method based on an energy function. The face contour is finally extracted using the direction information of the linked edges. Experimental results are included to verify the effectiveness of this method. (C) 2000 Elsevier Science B.V. All rights reserved.
14857874
Anti-aliasing and Continuity with Trapezoidal Shadow Maps
This paper proposes a new shadow map technique termed trapezoidal shadow maps to calculate high quality shadows in real-time applications. To address the resolution problem of the standard shadow map approach, our technique approximates the eye's frustum as seen from the light with a trapezoid to warp it onto a shadow map. Such a trapezoidal approximation, which may first seem straightforward, is carefully designed to achieve the goal of good shadow quality for objects from near to far, and to address the continuity problem that is found in all existing shadow map approaches. The continuity problem occurs mainly when the shadow map quality changes significantly from frame to frame due to the motion of the eye or the light. This results in flickering of shadows. On the whole, our proposed approach is simple to implement without using complex data structures and it maps well to graphics hardware as shown in our experiments with large virtual scenes of hundreds of thousands to over a million of triangles.
37912790
Asia Pacific Bioinformatics Network
1231228
Font Recognition Based on Global Texture Analysis
We describe a novel texture analysis-based approach toward font recognition. Existing methods are typically based on local typographical features that often require connected components analysis. In our method, we take the document as an image containing some specific textures and regard font recognition as texture identification. The method is content-independent and involves no detailed local feature analysis. Experiments are carried out by using 14000 samples of 24 frequently used Chinese fonts (six typefaces combined with four styles), as well as 32 frequently used English fonts (eight typefaces combined with four styles). An average recognition rate of 99.1 percent is achieved. Experimental results are also included on the robustness of the method against image degradation (e.g., pepper and salt noise) and on the comparison with existing methods.
2166869
Font recognition based on global texture analysis
In this paper, we describe a new texture analysis based approach towards font recognition. Existing methods are typically based on local features that often require connected components analysis. In our new method, we take the document as an image containing some special textures, and font recognition as texture identification. The method is content independent and involves no local feature analysis. Global features are extracted by texture analysis. We apply the well-established 2D Gabor filtering technique to extract such features and a weighted Euclidean distance classifier to fulfil the recognition task. Experiments are made using 6,000 samples of 24 frequently used Chinese fonts (6 typefaces combined with 4 styles) and very promising results are achieved.