Iterative Fuzzy Clustering Algorithm With Supervision to Construct Probabilistic Fuzzy Rule Base From Numerical Data

To deal with data patterns with linguistic ambiguity and with probabilistic uncertainty in a single framework, we construct an interpretable probabilistic fuzzy rule-based system that requires less human intervention and less prior knowledge than other state of the art methods. Specifically, we present a new iterative fuzzy clustering algorithm that incorporates a supervisory scheme into an unsupervised fuzzy clustering process. The learning process starts in a fully unsupervised manner using fuzzy c-means (FCM) clustering algorithm and a cluster validity criterion, and then gradually constructs meaningful fuzzy partitions over the input space. The corresponding fuzzy rules with probabilities are obtained through an iterative learning process of selecting clusters with supervisory guidance based on the notions of cluster-pureness and class-separability. The proposed algorithm is tested first with synthetic data sets and benchmark data sets from the UCI Repository of Machine Learning Database and then, with real facial expression data and TV viewing data.     

Fault‐tolerant display image‐data‐manipulation unit for system‐on‐panel

Abstract— Bit‐partitioned and conventional shifts, as well as type transformations of multimedia data, are frequently used for display image‐processing systems. A data manipulation unit with fault‐recovery capability based on redundancies is proposed for system‐on‐panel with low processing technology yield. Utilizing data manipulations that are similar to normal shift operation, a proposed data‐manipulation unit is designed with a few additional paths added to the existing barrel shifter. The design methodologies are verified with FPGA and the performance is evaluated in terms of the advantages.     

Stacked graphs--geometry & aesthetics

In February 2008, the New York Times published an unusual chart of box office revenues for 7500 movies over 21 years. The chart was based on a similar visualization, developed by the first author, that displayed trends in music listening. This paper describes the design decisions and algorithms behind these graphics, and discusses the reaction on the Web. We suggest that this type of complex layered graph is effective for displaying large data sets to a mass audience. We provide a mathematical analysis of how this layered graph relates to traditional stacked graphs and to techniques such as ThemeRiver, showing how each method is optimizing a different “energy function”. Finally, we discuss techniques for coloring and ordering the layers of such graphs. Throughout the paper, we emphasize the interplay between considerations of aesthetics and legibility.      

Reconstruction of cellular biological structures from optical microscopy data

Developments in optical microscopy imaging have generated large high-resolution data sets that have spurred medical researchers to conduct investigations into mechanisms of disease, including cancer at cellular and subcellular levels. The work reported here demonstrates that a suitable methodology can be conceived that isolates modality-dependent effects from the larger segmentation task and that 3D reconstructions can be cognizant of shapes as evident in the available 2D planar images. In the current realization, a method based on active geodesic contours is first deployed to counter the ambiguity that exists in separating overlapping cells on the image plane. Later, another segmentation effort based on a variant of Voronoi tessellations improves the delineation of the cell boundaries using a Bayesian formulation. In the next stage, the cells are interpolated across the third dimension thereby mitigating the poor structural correlation that exists in that dimension. We deploy our methods on three separate data sets obtained from light, confocal, and phase-contrast microscopy and validate the results appropriately.     

Effective on-line algorithms for reliable due date quotation and large-scale scheduling

We consider the sequencing of a series of jobs that arrive at a single processor over time. At each job’s arrival time, a due date must be quoted for the job, and the job must complete processing before its quoted due date. The objective is to minimize the sum (or average) of quoted due dates, or equivalently, the average quoted lead time. In this paper, we propose on-line heuristics for this problem and characterize the conditions under which these heuristics are asymptotically optimal. Computational testing further demonstrates the relative effectiveness of these heuristics under various conditions. Both authors made equal contributions to this paper and are listed in alphabetical order.     

HERMES: embedded file system design for A/V application

Embedded systems such as PVR, set-top box, HDTV put unique demand on I/O subsystem design. Underlying software, particularly file system, needs to be elaborately designed so that it can meet tight constraints of consumer electronics platform: performance, price, reliability, and etc. In this work, we develop state-of-art file system elaborately tailored for A/V workload. There are two design objectives in our file system: performance and support for logical level abstraction. For performance, we develop a number of novel features: extent based allocation, single level file structure with block index augmentation scheme, aggressive free block allocation to minimize disk fragmentation, elaborate file system meta data layout, separation of name space data and file data and etc. HERMES enables the user to view file as a collection of semantic units (frame or audio samples). HERMES file system encompasses most of state-of-the-art file system technologies published in preceding works. Via extensive physical experiment, we verify that HERMES file system successfully addresses the original issues: good scalability, predictable I/O latency (minimizing variability in I/O latency), efficient disk head movement and etc. This is the result of harmonious effort of large I/O size, aggressive free block allocation algorithm, data block placement strategy, file organization, layout of HERMES file system and etc. The result of performance experiments indicate that HERMES file system prototype successfully meets the file system constraints for high volume and high bandwidth multimedia application. HERMES file system exhibits superior performance to EXT2 file system (Linux) and XFS file system (SGI).

3D registration of human face using evolutionary computation and Kriging interpolation

This paper proposes a fast and robust 3D human face geometric data registration strategy dedicated for image-guided medical applications. The registration scheme is composed of a coarse transformation stage and a fine-tuning stage. In the first stage, fuzzy c-mean is used to reduce the data amount of template 3D image, and evolutionary computation is implemented to find optimal initial pose for the Iterative Closest Point plus k-dimensional (KD) tree scheme. In the second stage, the huge reference image data are replaced by a Kriging model. The time-consuming search for corresponding points in evaluating the degree of misalignment is substituted by projecting the points in the template image onto the model. To illustrate the validity and applicability of the proposed approach, a problem composed of 174 635 points reference image and an 11 280 points template image is demonstrated. Computational results show that our approach accelerates the registration process from 1361.28 seconds to 432.85 seconds when compared with the conventional ICP plus K-D tree scheme, while the average misalignment reduces from 11.35 mm to 2.33 mm. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Learning type PID control system using input dependence reinforcement scheme

PID control has widely used in the field of process control and a lot of methods have been used to design PID parameters. When the characteristic values of a controlled object are changed due to a change over the years or disturbance, the skilled operators observe the feature of the controlled responses and adjust the PID parameters using their knowledge and know-how, and a lot of labors are required to do it. In this research, we design a learning type PID control system using the stochastic automaton with learning function, namely learning automaton, which can autonomously adjust the control parameters updating the state transition probability using relative amount of controlled error. We show the effectiveness of the proposed learning type PID control system by simulations. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008