T c enhancement in superconductor-semiconductor arrays

Subsequent to our observation that the Tl- and Bi-based cuprate high-T _c superconductors are built of superconductor-semiconductor arrays (P. C. W. Fung and W. Y. Kwok,J. Superconduct., this issue), we investigate in this paper the possibility ofT _c enhancement arising from the effect of change of phonon spectrum and the effect of size quantization when one or more semiconductor blocks is attached to the basic superconductor in the unit cell.     

Phase I IASC-ASCE Structural Health Monitoring Benchmark Problem Using Simulated Data

Structural health monitoring (SHM) is a promising field with widespread application in civil engineering. Structural health monitoring has the potential to make structures safer by observing both long-term structural changes and immediate postdisaster damage. However, the many SHM studies in the literature apply different monitoring methods to different structures, making side-by-side comparison of the methods difficult. This paper details the first phase in a benchmark SHM problem organized under the auspices of the IASC–ASCE Structural Health Monitoring Task Group. The scale-model structure adopted for use in this benchmark problem is described. Then, two analytical models based on the structure—one a 12 degree of freedom (DOF) shear-building model, the other a 120-DOF model, both finite element based—are given. The damage patterns to be identified are listed as well as the types and number of sensors, magnitude of sensor noise, and so forth. MATLAB computer codes to generate the response data for the various cases are explained. The codes, as well as details of the ongoing Task Group activities, are available on the Task Group web site at 〈http://wusceel.cive.wustl.edu/asce.shm/〉.     

Dynamic construction site layout planning using max-min ant system

Construction site layout planning (CSLP) is a dynamic multi-objective optimization (MOO) problem as there are different facilities employed in the different construction phases of a construction project. In this study, a new method using continuous dynamic searching scheme to guide the max-min ant system (MMAS) algorithm, which is one of the ant colony optimization (ACO) algorithms, to solve the dynamic CSLP problem under the two congruent objective functions of minimizing safety concerns and reducing construction cost is proposed. Using weighted sum method the MOO problem can be solved by the proposed MMAS method. An office building case was used to verify the capability of the proposed method to solve dynamic CSLP problem and the results are promising. The approach could be benchmarked by researchers using other advanced optimization algorithms to solve the same problem or expand the applications to other fields.     

Calculation of Mueller matrices and polarization signatures for aslab of random medium using vector radiative transfer

The Mueller matrix which characterizes a slab of random medium containing spherical particles is calculated by using the vector radiative transfer theory. The vector radiative transfer equation is solved for arbitrarily polarized incident waves. The background refractive index of the slab is allowed to be different from the surrounding media. The scattering specific intensities for four independent polarized incident waves are calculated and used to construct the Mueller matrix, which contains multiple scattering due to the randomly distributed particles governed by the vector radiative transfer theory. The calculated are found to be symmetrical, and there are eight nonvanishing matrix elements. Polarization signatures are obtained in the backscattering direction from the Mueller matrix of the reflection side     

Accelerating Machine-Learning Algorithms on FPGAs using Pattern-Based Decomposition

Machine-learning algorithms are employed in a wide variety of applications to extract useful information from data sets, and many are known to suffer from super-linear increases in computational time with increasing data size and number of signals being processed (data dimension). Certain principal machine-learning algorithms are commonly found embedded in larger detection, estimation, or classification operations. Three such principal algorithms are the Parzen window-based, non-parametric estimation of Probability Density Functions (PDFs), K-means clustering and correlation. Because they form an integral part of numerous machine-learning applications, fast and efficient execution of these algorithms is extremely desirable. FPGA-based reconfigurable computing (RC) has been successfully used to accelerate computationally intensive problems in a wide variety of scientific domains to achieve speedup over traditional software implementations. However, this potential benefit is quite often not fully realized because creating efficient FPGA designs is generally carried out in a laborious, case-specific manner requiring a great amount of redundant time and effort. In this paper, an approach using pattern-based decomposition for algorithm acceleration on FPGAs is proposed that offers significant increases in productivity via design reusability. Using this approach, we design, analyze, and implement a multi-dimensional PDF estimation algorithm using Gaussian kernels on FPGAs. First, the algorithm’s amenability to a hardware paradigm and expected speedups are predicted. After implementation, actual speedup and performance metrics are compared to the predictions, showing speedup on the order of 20× over a 3.2 GHz processor. Multi-core architectures are developed to further improve performance by scaling the design. Portability of the hardware design across multiple FPGA platforms is also analyzed. After implementing the PDF algorithm, the value of pattern-based decomposition to support reuse is demonstrated by rapid development of the K-means and correlation algorithms.     

Time-hopping and frequency-hopping multiple-access packetcommunications

Time-hopping and frequency-hopping multiple-access (TH/FHMA) packet communication systems are proposed and investigated. In TH/FHMA communication systems, a message packet is encoded into several subpackets via a Reed-Solomon error correcting code. The subpackets are transmitted over the channel using time-hopping and frequency-hopping patterns. It is assumed that the channel is noiseless and the side information is perfect so that all subpacket collisions can be correctly detected. Slot-synchronous and totally asynchronous TH/FHMA systems are analyzed in detail, and they are shown to have excellent throughputs at small packet erasure rates. Various time-hopping techniques which significantly reduce the multiple-access interference are developed     

Locating the eye in human face images using fractal dimensions

Facial feature extraction is an important step in many applications such as human face recognition, video conferencing, surveillance systems, human computer interfacing etc. The eye is the most important facial feature. A reliable and fast method for locating the eye pairs in an image is vital to many practical applications. A new method for locating eye pairs based on valley field detection and measurement of fractal dimensions is proposed. Possible eye candidates in an image with a complex background are identified by valley field detection. The eye candidates are then grouped to form eye pairs if their local properties for eyes are satisfied. Two eyes are matched if they have similar roughness and orientation as represented by fractal dimensions. A modified approach to estimating fractal dimensions that is less sensitive to lighting conditions and provides information about the orientation of an image under consideration is proposed. Possible eye pairs are further verified by comparing the fractal dimensions of the eye-pair window and the corresponding face region with the respective means of the fractal dimensions of the eye-pair windows and the face regions. The means of the fractal dimensions are obtained based on a number of facial images in a database. Experiments have shown that this approach is fast and reliable     

Smart Skin-Adhesive Patches: From Design to Biomedical Applications

With the advancement of medical and digital technologies, smart skin adhesive patches have emerged as a key player for complex medical purposes. In particular, skin adhesive patches with integrated electronics have created an excellent platform for monitoring health conditions and intelligent medication. However, the efficient design of the adhesive patches is still challenging as it requires a strong combination of network structure, adhesion, physical properties, and biocompatibility. To design an assimilated device, one must have a deep knowledge of various skin adhesive patches. This article provides a comprehensive review of the recent advances in skin-adhesive patches, including hydrogel-based adhesive patches, transdermal patches, and electronic skin (E-skin) patches, for various biomedical applications such as wound healing, drug delivery, biosensing, and health monitoring. Furthermore, the key challenges, implementable strategies, and future designs that can potentially provide researchers in designing innovative multipurpose smart skin patches are discussed. These advanced approaches are promising for managing the health and fitness of patients who require regular medical care.