Cluster-Based Saliency-Guided Content-Aware Image Retargeting

Straightforward image resizing operators without considering image contents (e.g., uniform scaling) cannot usually produce satisfactory results, while content-aware image retargeting aims to arbitrarily change image size while preserving visually prominent features. In this paper, a cluster-based saliency-guided seam carving algorithm for content-aware image retargeting is proposed. To cope with the main drawback of the original seam carving algorithm relying on only gradient-based image importance map, we integrate a gradient-based map and a cluster-based saliency map to generate a more reliable importance map, resulting in better single image retargeting results. Experimental results have demonstrated the efficacy of the proposed algorithm.     

A comparison of methods for detecting hot topics

In scientometrics for trend analysis, parameter choices for observing trends are often made ad hoc in past studies. For examples, different year spans might be used to create the time sequence and different indices were chosen for trend observation. However, the effectiveness of these choices was hardly known, quantitatively and comparatively. This work provides clues to better interpret the results when a certain choice was made. Specifically, by sorting research topics in decreasing order of interest predicted by a trend index and then by evaluating this ordering based on information retrieval measures, we compare a number of trend indices (percentage of increase vs. regression slope), trend formulations (simple trend vs. eigen-trend), and options (various year spans and durations for prediction) in different domains (safety agriculture and information retrieval) with different collection scales (72500 papers vs. 853 papers) to know which one leads to better trend observation. Our results show that the slope of linear regression on the time series performs constantly better than the others. More interestingly, this index is robust under different conditions and is hardly affected even when the collection was split into arbitrary (e.g., only two) periods. Implications of these results are discussed. Our work does not only provide a method to evaluate trend prediction performance for scientometrics, but also provides insights and reflections for past and future trend observation studies.     

Simpler Throughput Analysis of CDMA/Unslotted ALOHA Radio Networks with Variable Message Length Based on M/M/inf Queueing Model

We propose a new and exact method to analyze the throughput of the DS CDMA unslotted ALOHA system with variable number of fixed-length packets. The proposed scheme requires two-dimensional summation while the previous requires four. Thus the proposed analysis scheme is of less computational complexity. The proposed analysis result is also slightly closer to the simulation result than the previous analysis result is, especially in heavier offered load.     

Parallel Domain-decomposed Taiwan Multi-scale Community Ocean Model (PD-TIMCOM)

The Parallel Domain-decomposed Taiwan Multi-scale Community Ocean Model (PD-TIMCOM) was developed to provide a flexible and efficient community ocean model for simulating a variety of idealized and real ocean flows over a wide range of scales and boundary conditions. The model is particularly targeted at resolving multi-scale dynamics in the ocean environment, ranging from small scale turbulence to the global circulation gyres. The novel parallel algorithm improves the efficiency of the Error Vector Propagating (EVP) method, a simple direct solver for the typical pressure Poisson equations in the PD-TIMCOM. The new approach is ideal for multiple processes and takes advantage of parallel domain-decomposition, which can significantly reduce the operational counts and computational costs simultaneously. The speed-up is proportional to the number of domains, thus making the PD-TIMCOM a practical eddy-resolving global ocean model for climate projection. We illustrate the parallel performance based on the 1/4° global adaptation of PD-TIMCOM. Our results show accurate meso-scale variability, the reasonable separation of several western boundary currents from the coast, and the appropriate watermass distribution in the global ocean. Consistent with satellite altimetry, the results also show clear mean fronts in the Kuroshio Extension and extensive Kuroshio–Oyashio interaction. This leads to a quasi-equilibrium eddy field associated with three meandering jets in the Kuroshio Extension and Gulf Stream.     

A rough set based approach to patent development with the consideration of resource allocation

Patent strategy is the overriding mechanism that helps direct investment, resource allocation, expectations, and policy development within an organization. Much studies of patent, for example, patent classification, patent analysis, patent management, patent strategy planning have been made. Due to the high cost of devoting to the research and development for a new patent application, it is essential for a company to develop the patent portfolio based on analyzing related information for fitting with cost constraint and maximizing the benefit. However, only few research attempts to develop new patents with the consideration of resource allocation, for example, optimizing budget utilization. In addition, the previous studies did not derive significant technologies and induct rules for resource allocation through patent analysis. In some cases, the patent analysis may process qualitative information that is difficult to analyze by standard statistical techniques. The rough set approach, which is suitable for processing qualitative information, is required to induct decision rules to derive critical technologies of patents. In this paper, a systematic approach to analyze existing patent information based on rough set theory with the consideration of resource allocation is developed. A case study is presented to demonstrate the contribution of the proposed approach which assists on decision-making in patent reform or invention with constraint resource.     

THERMAL STRESSES OF ROTATING ROLLS IN ROLLING PROCESSING

In a modern rolling mill, proper roll cooling has been identified as a critical factor in the problems of excessive roll wear and poor surface finish of rolled product. In this paper, an analytical solution to determine the cyclic stresses produced in the roll is developed. An understanding of the cyclic stresses can be utilized to evaluate roll wear and leads to proper cooling practices for increasing roll life and better product surface quality. In the present analysis, a rotating roll subjected to surface heat fluxes and convective cooling is considered under the assumption that it rotates at high speeds. The methods of the thermoelastic displacement potential and the general stress function are used to solve the governing partial differential equations in order to calculate the thermal stresses. Examples of typical rolling conditions are given to demonstrate the feasibility and applicability of the model developed     

Detection of viable antibiotic‐resistant/sensitive Acinetobacter baumannii in indoor air by propidium monoazide quantitative polymerase chain reaction

Acinetobacter baumannii represents a significant cause of nosocomial infections. Therefore, we combined real‐time quantitative polymerase chain reaction (PCR) with the propidium monoazide (PMA‐qPCR) to assess the feasibility of detecting viable, airborne A. baumannii. The biological collection efficiencies of three samplers for collecting airborne A. baumannii were evaluated by PMA‐qPCR in a chamber study. After sampling, the effects of storage in collection fluid on A. baumannii were evaluated. The results showed that the culturable ratio of A. baumannii measured using the culture method was significantly correlated with the viable ratio measured using PMA‐qPCR, but was not significantly correlated with the qPCR results. It was indicated that the AGI‐30 impinger and the BioSampler were much more effective than the Nuclepore filter sampler for collecting airborne A. baumannii. The storage temperature was critical for aerosol samples, as the loss of viable A. baumannii was minimized when the PMA‐bound DNA was stored at ?20°C or if the collected cells were stored at 4°C and subsequently processed by PMA‐qPCR within 1 month. The PMA‐qPCR method was also to distinguish between colistin‐sensitive and colistin‐resistant A. baumannii, and no colistin‐sensitive A. baumannii was detected by PMA‐qPCR upon treatment of the BioSampler collection medium with 2 μg/ml colistin for 5 min.     

Localised heating of tumours utilising injectable magnetic nanoparticles for hyperthermia cancer therapy

This study reports an investigation of hyperthermia cancer therapy utilising an alternating magnetic field to induce a localised temperature increase on tumours by using injectable magnetic nanoparticles. In-vitro and in-vivo experiments represent the feasibility of hyperthermia cancer therapy. A feedback temperature control system was first developed to keep the nanoparticles at a constant temperature to prevent overheating in the tumours such that a safer and more precise cancer therapy becomes feasible. By using the feedback temperature control system, magnetic nanoparticles can be heated up to the specific constant temperatures, 37, 40, 42, 45, 46 and 47degC, respectively, with a variation less than 0.2degC. With this approach, the in-vitro survival rate of tumour cells at different temperatures can be systematically explored. It was experimentally found that the survival rate of cancer cells can be greatly reduced while CT-26 cancer cells were heated above 45degC. Besides, localised temperatures increase as high as 59.5degC can be successfully generated in rat livers by using the proposed method. Finally, complete regression of tumour was achieved. The developed method used injectable magnetic nanoparticles and may provide a promising approach for hyperthermia cancer therapy.     

Numerical simulation of protein stamping process driven by capillary force

Numerical simulations based on first-principle conservation laws of mass and momentum are performed to observe flow characteristics during the stamping process. The protein solution is transferred by a new design of microstamps with microchannels and printed on a bottom substrate. Furthermore, key physics of the stamping process and the control factors achieving uniform spot size can be identified and optimized after these simulations.     

A Genetic Algorithm for Assessing Flatness in Automated Manufacturing Systems

The measurement of flatness for manufactured parts is one of the most frequently used procedures in automated manufacturing systems. Measuring instruments are commonly utilized in taking measurement data from manufactured surfaces for inspection purposes. The measurement data is then used to evaluate the geometric information, from errors associated with its surface. The study proposes the computational approaches for flatness with respect to ASME Y14.5M-1994 standard. The proposed methods consider the trade-off between the accuracy of flatness and the efficiency of inspection. Two approaches of computational metrology based on genetic algorithms are proposed to explore the optimality of flatness measurement and the flatness feasibility analysis. The results show that the optimization algorithms provide exact flatness errors and adequate tolerance size. Received: February 2004 / Accepted: September 2005