FPGA Architecture for 2D Discrete Fourier Transform Based on 2D Decomposition for Large-sized Data

Applications based on Discrete Fourier Transforms (DFT) are extensively used in several areas of signal and digital image processing. Of particular interest is the two-dimensional (2D) DFT which is more computation- and bandwidth-intensive than the one-dimensional (1D) DFT. Traditionally, a 2D DFT is computed using Row-Column (RC) decomposition, where 1D DFTs are computed along the rows followed by 1D DFTs along the columns. Both application specific and reconfigurable hardware have utilized this scheme for high-performance implementations of 2D DFT. However, architectures based on RC decomposition are not efficient for large input size data due to memory bandwidth constraints. In this paper, we propose an efficient architecture to implement 2D DFT for large-sized input data based on a novel 2D decomposition algorithm. This architecture achieves very high throughput by exploiting the inherent parallelism due to the algorithm decomposition and by utilizing the row-wise burst access pattern of the external memory. A high throughput memory interface has been designed to enable maximum utilization of the memory bandwidth. In addition, an automatic system generator is provided for mapping this architecture onto a reconfigurable platform of Xilinx Virtex-5 devices. For a 2K ×2K input size, the proposed architecture is 1.96 times faster than RC decomposition based implementation under the same memory constraints, and also outperforms other existing implementations.     

A Distortion-Sensitive Seam Carving Algorithm for Content-Aware Image Resizing

As displays become less expensive and are incorporated into more and more devices, there has been an increased focus on image resizing techniques to fill an image to an arbitrary screen size. Traditional methods such as cropping or resampling can introduce undesirable losses in information or distortion in perception. Recently, content-aware image retargeting methods have been proposed (Avidan and Shamir, ACM Trans Graphics 26(3), 2007; Guo et al., IEEE Trans Multimedia 11(5):856–867, 2009; Shamir and Avidan, Commun ACM 52(1), 2009; Simakov et al. 2008; Wolf et al. 2007), which produce exceptional results. In particular, seam carving, proposed by Avidan and Shamir, has gained attention as an effective solution. However, there are many cases where it can fail. In this paper we propose a distortion-sensitive seam carving algorithm for content-aware image resizing that improves edge preservation and decreases aliasing artifacts. In the proposed approach, we use local gradient information along with a thresholding technique to guide the seam selection process and provide a mechanism to halt seam carving when further processing would introduce unacceptable visual distortion in the resized image. Furthermore, anti-aliasing filter is used to reduce the aliasing artifacts caused by seam removal. Experiments have demonstrated superior performance over the current seam carving methods.     

Heavy metal pollution of river water and eco-friendly remediation using potent microalgal species

Pollution of rivers is mainly caused by anthropogenic activities such as discharge of effluent from industrial facilities, maintenance of sewage/effluent treatment plants, and dumping of solid waste on river banks. This study dealt with the pollution issues of the Cooum River in the well-known city of Chennai in South India. Water samples from 27 locations were collected and analyzed for 12 elements, including Ba, B, and Al, as well as heavy metals such as Pb, Cr, Mn, Fe, Co, Ni, Cu, Zn, and Cd. The samples showed levels of these elements that exceeded World Health Organization recommendations. Pearson correlation analysis revealed the inter-dependency among elements, and the contribution of each element based on factor loadings showed its percentage contribution compared to others. Water samples from six significant locations were chosen for remediation with three algae: Chlorella vulgaris, Scenedesmus dimorphus, and Phormedium sp. The uptake of pollutants led to the continuous growth of algae during the incubation period of 15 d, effectively removing heavy metals from the river water. The increasing levels of algal counts and the chlorophyll a content confirmed the algal growth during the incubation period, followed by a declining stage after the incubation period. The scanning electron microscopic images of algae before and after the remediation showed no remarkable modification of morphological patterns. This study showed that the uptake of heavy metals using algae is an effective water pollution remediation measure, making the process practicable in the field on a large scale in the near future.

     

Coriander leaf mediated biosynthesis of gold nanoparticles

Extracellular biological synthesis of gold nanoparticles was achieved by a simple biological procedure using coriander extract as the reducing agent. The aqueous gold ions when exposed to coriander leaf extract are reduced and resulted in the biosynthesis of gold nanoparticles in the size range from 6.75-57.91 nm. The gold nanoparticles were characterized by UV-Vis spectroscopy, X-ray diffraction (XRD), energy dispersive X-ray analysis (EDAX), fourier transform infra-red spectroscopy (FT-IR) and transmission electron microscopy (TEM). This eco-friendly approach for the synthesis of nanoparticles is simple, amenable for large scale commercial production and technical applications.     

Optimal cycle time and inventory decisions in coordinated and non-coordinated two-echelon inventory system under inflation and time value of money

In this paper, a mathematical model is developed for a coordinated and non-coordinated two-echelon inventory system comprising of a single manufacturer and a single retailer. The objective of the model is to demonstrate the optimality of cycle time and inventory decisions under the phenomena of different inflation rates at the manufacturer and retailer points. Also, it is aimed at determining the annual net revenue of the supply chain (SC). In the proposed model, the present value of the inflated ordering/set-up costs, purchase/unit costs, carrying costs and the gross revenue is computed for the retailer, manufacturer and the SC. A numerical example is devised to illustrate the optimality of decision variables and the objective function. Also, the sensitivity analysis is carried out. Results show that the present value of the annual net revenue of the retailer, manufacturer and SC decreases with increased inflation rate at the retailer and decreased inflation rate at the manufacturer simultaneously.     

Total Power Optimization for Combinational Logic Using Genetic Algorithms

Power consumption is a top priority in high performance circuit design today. Many low power techniques have been proposed to tackle the ever serious, highly pressing power consumption problem, which is composed of both dynamic and static power in the nanometer era. The static power consumption nowadays receives even more attention than that of dynamic power consumption when technology scales below 100 nm. In order to mitigate the aggressive power consumption, various existing low power techniques are often used; however, they are often applied independently or combined with two or at most three different techniques together, and that is not sufficient to address the escalating power issue. In this paper, we present a power optimization framework for the minimization of total power consumption in combinational logic through multiple V _dd assignment, multiple V _th assignment, device sizing, and stack forcing, while maintaining performance requirements. These four power reduction techniques are properly encoded into the genetic algorithm and evaluated simultaneously. The overhead imposed by the insertion of level converters is also taken into account. The effectiveness of each power reduction mechanism is verified, as are the combinations of different approaches. Experimental results are presented for a number of 65 nm benchmark circuits that span typical circuit topologies, including inverter chains, SRAM decoders, multiplier, and a 32 bit carry adder. Our experiments show that the combination of four low power techniques is the effective way to achieve low power budget. The framework is general and can be easily extended to include other design-time low power techniques, such as multiple gate length or multiple gate oxide thickness.     

The Role of the Prefrontal Cortex in the Maintenance of Verbal Working Memory: An Event-Related fMRI Analysis.

Neuroimaging studies have been inconclusive in characterizing the role of the prefrontal cortex (PFC) for maintaining increasingly larger amounts of information in working memory (WM). To address this question, the authors collected event-related functional MRI data while participants performed an item-recognition task in which the number of to-be-remembered letters was parametrically modulated. During maintenance of information in WM, the dorsolateral and the ventrolateral PFC exhibited linearly increasing activation in response to increasing WM load. Prefrontal regions could not be distinguished from one another on the basis of load sensitivity, but the dorsolateral PFC had stronger functional connectivity with the parietal and motor cortex than the ventrolateral PFC. These results suggest an increasingly important role for the PFC in actively maintaining information as the amount of that information increases. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

New approach for prediction of influence of vehicle dynamics parameters on instability of unmanned track vehicle using robotic approach

Journal of Mechanical Science and Technology - Achieving stability of unmanned, heavy tracked vehicles is challenging, especially under conditions of teleoperation because the remote operator...     

Thermal Stability, Phase Evolution, and Crystallization in Si-B-C-N Ceramics Derived from a Polyborosilazane Precursor

Amorphous Si-B-C-N ceramic powder samples obtained by thermolysis of boron-modified polysilazane, {B[C₂H₄Si(H)NH]₃} _n , were isothermally annealed at different temperatures (1400–1800°C) and hold times (3, 10, 30, and 100 h). A qualitative and semiquantitative analysis of the crystallization behavior of the materials was performed using X-ray diffraction (XRD). The phase evolution was additionally followed by ¹¹B and ²⁹Si MAS NMR as well as by FT-IR spectroscopy in transmission and diffuse reflection (DRIFTS) modes. Bulk chemical analyses of selected samples were performed to determine changes in the chemistry/phase composition of the materials. It was observed that silicon carbide is the first phase to nucleate around 1400–1500°C, whereas silicon nitride nucleates at and above 1700°C. Crystallization accelerates with increasing annealing temperature and proceeds with increasing annealing time. Furthermore, the surface area of the powders strongly influences the thermal stability of silicon nitride and thus controls overall chemical and phase composition of the materials on thermal treatment.