A resource allocation policy for delay minimization in fetching capacitated feeds

As social media services such as Twitter and Facebook are gaining popularity, the amount of information published from those services is explosively growing. Most of them use feeds to facilitate distribution of a huge volume of content they publish. In this context, many users subscribe to feeds to acquire up-to-date information through feed aggregation services, and recent real-time search engines also increasingly utilize feeds to promptly find recent web content when it is produced. Accordingly, it is necessary for such services to effectively fetch feeds for minimizing fetching delay, while at the same time maximizing the number of fetched entries. Fetching delay is a time lag between entry publication and retrieval, which is primarily incurred by finiteness of fetching resources. In this paper, we consider a polling-based approach among the methods applicable to fetching feeds, which bases on a specific schedule for visiting feeds. While the existing polling-based approaches have focused on the allocation of fetching resources to feeds in order to either reduce the fetching delay or increase the number of fetched entries, we propose a resource allocation policy that can optimize both objectives. Extensive experiments have been carried out to evaluate the proposed model, in comparison with the existing alternative methods.     

Microscopic image restoration based on tensor factorization of rotated patches

In microscopic image processing for analyzing biological objects, structural characters of objects such as symmetry and orientation can be used as a prior knowledge to improve the results. In this study, we incorporated filamentous local structures of neurons into a statistical model of image patches and then devised an image processing method based on tensor factorization with image patch rotation. Tensor factorization enabled us to incorporate correlation structure between neighboring pixels, and patch rotation helped us obtain image bases that well reproduce filamentous structures of neurons. We applied the proposed model to a microscopic image and found significant improvement in image restoration performance over existing methods, even with smaller number of bases.     

Pedestrian Detection and Tracking Using Deformable Part Models and Kalman Filtering

Abstract： Pedestrian detection techniques are important and challenging especially for complex real world scenes. They can be used for ensuring pedestrian safety, ADASs （advance driver assistance systems） and safety surveillance systems. In this paper, we propose a novel approach for multi-person tracking-by-detection using deformable part models in Kalman filtering framework. The Kalman filter is used to keep track of each person and a unique label is assigned to each tracked individual. Based on this approach, people can enter and leave the scene randomly. We test and demonstrate our results on Caltech Pedestrian benchmark, which is two orders of magnitude larger than any other existing datasets and consists of pedestrians varying widely in appearance, pose and scale. Complex situations such as people occluded by each other are handled gracefully and individual persons can be tracked correctly after a group of people split. Experiments confirm the real-time performance and robustness of our system, working in complex scenes. Our tracking model gives a tracking accuracy of 72.8% and a tracking precision of 82.3%. We can further reduce false positives by 2.8%, using Kalman filtering.     

A robust fault diagnosis and accommodation scheme for robot manipulators

This paper investigates an algorithm for robust fault diagnosis (FD) in uncertain robotic systems by using a neural sliding mode (NSM) based observer strategy. A step by step design procedure will be discussed to determine the accuracy of fault estimation. First, an uncertainty observer is designed to estimate the uncertainties based on a first neural network (NN1). Then, based on the estimated uncertainties, a fault diagnosis scheme will be designed by using a NSM observer which consists of both a second neural network (NN2) and a second order sliding mode (SOSM), connected serially. This type of observer scheme can reduce the chattering of sliding mode (SM) and guarantee finite time convergence of the neural network (NN). The obtained fault estimations are used for fault isolation as well as fault accommodation to self-correct the failure systems. The computer simulation results for a PUMA560 robot are shown to verify the effectiveness of the proposed strategy.     

EvoOligo: Oligonucleotide Probe Design With Multiobjective Evolutionary Algorithms

Probe design is one of the most important tasks in successful deoxyribonucleic acid microarray experiments. We propose a multiobjective evolutionary optimization method for oligonucleotide probe design based on the multiobjective nature of the probe design problem. The proposed multiobjective evolutionary approach has several distinguished features, compared with previous methods. First, the evolutionary approach can find better probe sets than existing simple filtering methods with fixed threshold values. Second, the multiobjective approach can easily incorporate the user's custom criteria or change the existing criteria. Third, our approach tries to optimize the combination of probes for the given set of genes, in contrast to other tools that independently search each gene for qualifying probes. Lastly, the multiobjective optimization method provides various sets of probe combinations, among which the user can choose, depending on the target application. The proposed method is implemented as a platform called EvoOligo and is available for service on the Web. We test the performance of EvoOligo by designing probe sets for 19 types of Human Papillomavirus and 52 genes in the Arabidopsis Calmodulin multigene family. The design results from EvoOligo are proven to be superior to those from well-known existing probe design tools, such as OligoArray and OligoWiz.     

A multi-functional platform for implementing intelligent and ubiquitous functions of smart substations under SCADA

For more efficient and economical management of substations under SCADA (Supervisory Control and Data Acquisition) systems, the concept of a smart substation has been introduced using intelligent and ubiquitous IT (Information Technology) techniques. A multi-functional platform needs to perform more intelligent and ubiquitous functions of smart substation effectively. In this paper, we propose a multi-functional platform to implement smart substations effectively. A prototype hardware, functions and communication interfaces on an embedded platform are introduced. Also, we suggest operating system architecture for smart substations.     

On-line tuning PID parameters in an idling engine based on a modified BP neural network by particle swarm optimization

PID control systems are widely used in many fields, and many methods to tune the parameters of PID controllers are known. When the characteristics of the object are changed, the traditional PID control should be adjusted by empirical knowledge. This may result in a worse performance by the system. In this article, a new method to tune PID parameters, called the back-propagation network modified by particle swarm optimization, is proposed. This algorithm combines conventional PID control with a back propagation neural network (BPNN) and particle swarm optimization (PSO). This method is demonstrated in the engine idling-speed control problem. The proposed method provides considerable performance benefits compared with a traditional controller in this simulation.     

Applications, techniques, and microfluidic interfacing for nanoscale biosensing

Biosensors based on nanotechnology are rapidly developing and are becoming widespread in the biomedical field and analytical chemistry. For these nanobiosensors to reach their potential, they must be integrated with appropriate packaging techniques, which are usually based on nano/microfluidics. In this review we provide a summary of the latest developments in nanobiosensors with a focus on label-based (fluorescence and nanoparticle) and label-free methods (surface plasmon resonance, micro/nanocantilever, nanowires, and nanopores). An overview on how these sensors interface with nano/microfluidics is then presented and the latest papers in the area summarized.     

Effect of matrix liquid phase on interphase formation in SiC fibre-reinforced Si2N2O-Al2O3-CaO composites

Matrix compositions based on Si₂N₂O, with Al₂O₃ and CaO additions, were used to hot press Nicalon SiC fibre-reinforced composites at 1600 °C. With both CaO and Al₂O₃ additions, eutectic melting formed an appreciable volume of liquid phase during hot pressing, which remained as a stable glassy phase in the cooled composites. This liquid phase fostered formation of 240 nm thick carbon-rich interphases between the fibres and the matrix. These interphases showed relatively low interfacial shear strength and resulted in composites which showed non-catastrophic, notch-independent fracture. Matrices using either Al₂O₃ or CaO did not form adequate liquid phase to form coarse interphases, and fracture was catastrophic in nature. Post-heat treatment of the composites at 1000 °C showed peripheral oxidation (removal of the carbon content of the interphase) indicating limited protection afforded when glassy phase was present in the matrix. Controlled cooling in the hot press did not cause the liquid regions to devitrify.     

Fractal and topological characterization of branching patterns on the fracture surface of cross-linked dimethacrylate resins

The branching patterns formed as a result of crack growth in dimethacrylate resins below their glass transition temperatures looked similar to fractal trees. The skeletons of the patterns were analysed numerically for their topological and geometrical properties. The number of branches, N _i , mean branch lengths, N _i , and branch angles of a particular order, defined according to the Strahler and inverted Weibel schemes, followed exponential scaling behaviour: N _i (R _b )^–i and L _i (R _l ) ⁱ . Using the relationship for the fractal dimension D=In R _B /In R _L , a value of D=1.4 was obtained for the fracture pattern. Fractal behaviour was also examined by the box-counting method which indicated a power-law dependence of the mass on the box size with fractal dimension exponent D=1.4 in the case of the fracture pattern. However, the mass-shell method for both the fracture pattern and the fractal trees gave an exponential increase of mass with distance from the origin, rather than the power-law behaviour expected for fractals. This was attributed to the fact that branches of different sizes were distributed in restricted regions of space closer to the periphery, rather than uniformly over the whole pattern.