9755080
A 1.2V buck converter with a novel on-chip low-voltage current-sensing scheme
A low-voltage on-chip current-sensing circuit for buck power converter is proposed. The minimum supply voltage of the proposed current-sensing circuit is 1.2V (where V/sub th/ = 0.85V at room temperature), and the sensing accuracy is higher than 94%. The proposed buck converter is able to operate at a 1.2-V supply with higher than 98% power-conversion efficiency and with a maximum output current of 120mA.
18463934
Design of a 1.5-V high-order curvature-compensated CMOS bandgap reference
A low-voltage low-noise high-order curvature-compensated CMOS bandgap reference is proposed in this paper. Two resistor strings are added to the bandgap circuit to reduce the supply voltage. In addition, a temperature-dependent resistor ratio generated by a high-resistive poly resistor is used to provide the high-order compensation. The proposed bandgap voltage reference can operate down to a 1.5V supply (where V/sub th/ = 0.9V at 0/spl deg/C) with a temperature coefficient of less than 15.2ppm//spl deg/C and an average line regulation of 5.5mV/V.
61028395
An energy minimisation approach to stereo-temporal dense reconstruction
We propose a novel energy minimisation framework for the dense reconstruction of stereo image sequences that incorporates data fidelity as well as spatial and temporal regularity. An iterated dynamic programming scheme is proposed to minimise the energy function. We also present an efficient implementation of the minimisation scheme by introducing morphological decomposition techniques to solve the dynamic programming subproblem. Our proposed method is capable of reconstructing dynamic scenes with complex motion. Results are presented demonstrating the strength of our proposed algorithm.
7407086
Planning under uncertainty using model predictive control for information gathering
Abstract This paper considers trajectory planning problems for autonomous robots in information gathering tasks. The objective of the planning is to maximize the information gathered within a finite time horizon. It is assumed that either the Extended Kalman Filter (EKF) or the Extended Information Filter (EIF) is applied to estimate the features of interest and the information gathered is expressed by the covariance matrix or information matrix. It is shown that the planning process can be formulated as an optimal control problem for a nonlinear control system with a gradually identified model. This naturally leads to the Model Predictive Control (MPC) planning strategy, which uses the updated knowledge about the model to solve a finite horizon optimal control problem at each time step and only executes the first control action. The proposed MPC framework is demonstrated through solutions to two challenging information gathering tasks: (1) Simultaneous planning, localization, and map building (SPLAM) and (2) Multi-robot Geolocation. It is shown that MPC can effectively deal with dynamic constraints, multiple robots/features and a range of objective functions.
32776525
Maximum Segmented Image Information Thresholding
Abstract Utilizing information theory and considering image segmentation from a communication perspective, the image segmentation process is interpreted as a data processing step that operates on a gray-scale image and produces a segmented image. It is shown that the segmented image contains a certain amount of information about the scene, which is defined as segmented image information (SII). It is proposed that the SII should be maximized when an image is thresholded, and this is known as the maximum segmented image information (MSII) thresholding criterion. The MSII thresholding criterion possesses better properties as compared with the minimum error (MINE) and the uniform error (UNFE) thresholding criteria. Based on the MSII thresholding criterion, an MSII thresholding algorithm is proposed for the thresholding of real images. The MSII thresholding algorithm is evaluated against several well-known thresholding algorithms. The good thresholding results of both synthetic and real images confirm the capabilities of the proposed MSII thresholding algorithm.
6383602
Efficient dynamic mining of constrained frequent sets
Data mining is supposed to be an iterative and exploratory process. In this context, we are working on a project with the overall objective of developing a practical computing environment for the human-centered exploratory mining of frequent sets. One critical component of such an environment is the support for the dynamic mining of constrained frequent sets of items. Constraints enable users to impose a certain focus on the mining process; dynamic means that, in the middle of the computation, users are able to (i) change (such as tighten or relax) the constraints and/or (ii) change the minimum support threshold, thus having a decisive influence on subsequent computations. In a real-life situation, the available buffer space may be limited, thus adding another complication to the problem.In this article, we develop an algorithm, called DCF, for Dynamic Constrained Frequent-set computation. This algorithm is enhanced with a few optimizations, exploiting a lightweight structure called a segment support map. It enables DCF to (i) obtain sharper bounds on the support of sets of items, and to (ii) better exploit properties of constraints. Furthermore, when handling dynamic changes to constraints, DCF relies on the concept of a delta member generating function, which generates precisely the sets of items that satisfy the new but not the old constraints. Our experimental results show the effectiveness of these enhancements.
2108143
CanTree: a tree structure for efficient incremental mining of frequent patterns
Since its introduction, frequent-pattern mining has been the subject of numerous studies, including incremental updating. Many existing incremental mining algorithms are Apriori-based, which are not easily adoptable to FP-tree based frequent-pattern mining. In this paper, we propose a novel tree structure, called CanTree (canonical-order tree), that captures the content of the transaction database and orders tree nodes according to some canonical order. By exploiting its nice properties, the CanTree can be easily maintained when database transactions are inserted, deleted, and/or modified. For example, the CanTree does not require adjustment, merging, and/or splitting of tree nodes during maintenance. No rescan of the entire updated database or reconstruction of a new tree is needed for incremental updating. Experimental results show the effectiveness of our CanTree.