13451430
Non-negative matrix factorization framework for face recognition
Non-negative Matrix Factorization (NMF) is a part-based image representation method which adds a non-negativity constraint to matrix factorization. NMF is compatible with the intuitive notion of combining parts to form a whole face. In this paper, we propose a framework of face recognition by adding NMF constraint and classifier constraints to matrix factorization to get both intuitive features and good recognition results. Based on the framework, we present two novel subspace methods: Fisher Non-negative Matrix Factorization (FNMF) and PCA Non-negative Matrix Factorization (PNMF). FNMF adds both the non-negative constraint and the Fisher constraint to matrix factorization. The Fisher constraint maximizes the between-class scatter and minimizes the within-class scatter of face samples. Subsequently, FNMF improves the capability of face recognition. PNMF adds the non-negative constraint and characteristics of PCA, such as maximizing the variance of output coordinates, orthogonal bases, etc. to matrix factorization. Therefore, we can get intuitive features and desirable PCA characteristics. Our experiments show that FNMF and PNMF achieve better face recognition performance than NMF and Local NMF.
8192681
Image Retrieval Using Schwarz Representation of One-Dimensional Feature
Retrieval efficiency and accuracy are two important issues in designing a content based database retrieval system. In order to retrieve efficiently, we must extract feature to build index. Recently intensive research focused on how to extract one-dimensional features and calculate the distance between them, such as color histogram, Fourier descriptor, image shape spectrum (ISS). We develop a new method to match one-dimensional feature function in multiscale space using Schwarz representation. It can obtain closed form match function and similarity measure instead of traditional optimization. Thus we can calculate the global distance when the local information of feature function is matched. In this paper, we use the center distance function of shape as the feature functions. We calculate their Schwarz representation as indices, and calculate the optimal distance as similarity measure to sort the images. Experimental results show its efficiency and accuracy.
8145594
Effects of global interconnect optimizations on performance estimation of deep submicron design
In this paper, we quantify the impact of global interconnect optimization techniques that address such design objectives as delay, peak noise, delay uncertainty due to noise, power, and cost. In doing so, we develop a new system-performance simulation model as a set of studies within the MARCO GSRC Technology Extrapolation (GTX) system. We model a typical point-to-point global interconnect and focus on accurate assessment of both circuit and design technology with respect to such issues as inductance, signal line shielding, dynamic delay, buffer placement uncertainty and repeater staggering. We demonstrate, for example, that optimal wire sizing models need to consider inductive effects-and that use of more accurate (-1,3) worst-case capacitive coupling noise switch factors substantially increases peak noise estimates compared to traditional (0,2) bounds. We also find that optimal repeater sizes are significantly smaller than conventional models would suggest, especially when considering energy-delay issues.
14957395
A statistical performance simulation methodology for VLSI circuits
A statistical performance simulation (SPS) methodology for VLSI circuits is presented. Traditional methods of worst-case corner analysis lack accuracy and Monte-Carlo simulations cannot be applied to VLSI circuits because of their complexity. SPS methodology is accurate because no statistical information about the device parameter variation is lost. It achieves efficiency by analyzing the smaller circuit blocks and generating the performance distribution for the entire circuit. Circuit evaluation at any specified performance level is possible.
9059175
On thermal effects in deep sub-micron VLSI interconnects
This paper presents a comprehensive analysis of the thermal effects in advanced high performance interconnect systems arising due to self-heating under various circuit conditions, including electrostatic discharge. Technology (Cu, low-k etc.) and scaling effects on the thermal characteristics of the interconnects, and on their electromigration reliability has been analyzed simultaneously, which will have important implications for providing robust and aggressive deep sub-micron interconnect design guidelines. Furthermore, the impact of these thermal effects on the design (driver sizing) and optimization of the interconnect length between repeaters at the upper-level signal lines are investigated.
14906808
Dynamic data broadcasting with traffic awareness
Data dissemination has significantly served as a scalable data delivery mechanism in wireless networks. However, even though the broadcast traffic has the nature of dynamic changes, most previous research efforts were elaborated upon the premise of static workloads and access patterns without having proper traffic awareness. In this paper, we address the existence of client impatience and accordingly devise an online traffic awareness mechanism based on a novel selective deferment and reflection (SDR) technique to estimate the dynamic workloads and access patterns in a granularity of a broadcast cycle. In comparison with prior probing and feedback approaches, our design is of practical usefulness in that it has low complexity and is light-weight without performance degradation. With various dynamic traffic scenarios, the experimental results show that with an increasing/decreasing workload, the real access frequency distribution is bounded by two specific estimated distributions. This fact in turn suggests us to employ a trigonometric tuning method to further enhance the estimation. In addition, we examine that the mean difference between the estimated access frequency distribution and the real one is very small, consequently indicating the feasibility and reliability of our proposed data broadcast mechanism with traffic awareness.
863906
Per-Flow Queueing by Dynamic Queue Sharing
Per-flow queuing is believed to be able to guarantee advanced Quality of Service (QoS) for each flow. With the dramatic increase of link speed and number of traffic flows, per-flow queuing faces a great challenge since millions of queues need to be maintained for implementation in a traditional sense. In this paper, by setting only a small number of physical queues, we propose a Dynamic Queue Sharing (DQS) mechanism to achieve an equal performance to the pure per-flow queuing with a lower cost. The proposed mechanism is based on an interesting fact that the number of simultaneous active flows in the router buffer is far less than that of in-progress flows. In DQS, a physical queue is dynamically created on-demand when a new flow comes and then dynamically released when the flow temporarily pauses. Hashing and binary sorting tree (or linked list) are combined to manage the mapping between flows and queues, so as to isolate flows in different queues. Theoretical analysis and traces experiments are conducted to evaluate DQS. The results demonstrate that when the parameters are well set, the operation delay is less than two time cycles in average with an extra memory of 16k bits.