Terahertz (THz) Frequency Sources and Antennas - A Brief Review

In this paper we review THz radiation properties, generation methods, and antenna configurations. This paper suggests some new class of antennas that can be used at THz frequency, like optical antennas or Carbon nanotube antennas. THz technology has become attractive due to the low energy content and nonionizing nature of the signal. This property makes them suitable for imaging and sensing applications. But at the same time detection and generation of THz signals has been technologically challenging. This paper presents a comparative study of the generation techniques for THz frequency signals giving emphasis to the some new techniques like Quantum Cascade lasers which has created significant research interest. The main aim for this study is to find out the materials suitable for fabricating THz devices and antennas, a suitable method for generation of high power at THz frequency and an antenna that will make THz communication possible.     

Realization of very long wavelength infrared photovoltaic detector arrays on mercury cadmium telluride epitaxial layers grown on Si substrates

This paper proposes a development of n-on-p structures for realizing very long wavelength infrared (VLWIR) detector arrays on mercury cadmium telluride (HgCdTe) epitaxial layers grown on Si substrates. It is shown from a comparative study of zero-bias resistance-area product (R₀A) of diodes in n-on-p and p-on-n configurations that the n-on-p structure has promising potential to control contribution of dislocations, without actually reducing dislocation density below the current level (mid-10⁶ cm⁻²) of HgCdTe/Si material technology. The resulting gain will be in terms of both higher numerical magnitudes of R₀A and its reduced scatter.     

Effect of the fabrication method on the electrical properties of poly(acrylonitrile-co-butadiene-co-styrene)/carbon black composites

Poly(acrylonitrile-co-butadiene-co-styrene) (ABS), an engineering plastic, was combined with carbon black (CB) to increase its conductivity. The ABS/CB composites were prepared using two different methods: dissolution of ABS in Butan-2-one and manual mixing of the constituent materials. These fabrication methods led to different microstructures, which led to vastly different electrical properties. The microstructures were acquired using scanning electron microscopy (SEM) and optical microscopy, while the electrical conductivity was obtained using impedance spectroscopy. The percolation threshold of the composites fabricated using the manual mixing method was found to be much lower (0.0054 vol.% CB) than that of the composites fabricated using the dissolution method (2.7 vol.% CB).     

Algebraic immunity for cryptographically significant Boolean functions: analysis and construction

Recently, algebraic attacks have received a lot of attention in the cryptographic literature. It has been observed that a Boolean function f used as a cryptographic primitive, and interpreted as a multivariate polynomial over F/sub 2/, should not have low degree multiples obtained by multiplication with low degree nonzero functions. In this paper, we show that a Boolean function having low nonlinearity is (also) weak against algebraic attacks, and we extend this result to higher order nonlinearities. Next, we present enumeration results on linearly independent annihilators. We also study certain classes of highly nonlinear resilient Boolean functions for their algebraic immunity. We identify that functions having low-degree subfunctions are weak in terms of algebraic immunity, and we analyze some existing constructions from this viewpoint. Further, we present a construction method to generate Boolean functions on n variables with highest possible algebraic immunity /spl lceil/n/2/spl rceil/ (this construction, first presented at the 2005 Workshop on Fast Software Encryption (FSE 2005), has been the first one producing such functions). These functions are obtained through a doubly indexed recursive relation. We calculate their Hamming weights and deduce their nonlinearities; we show that they have very high algebraic degrees. We express them as the sums of two functions which can be obtained from simple symmetric functions by a transformation which can be implemented with an algorithm whose complexity is linear in the number of variables. We deduce a very fast way of computing the output to these functions, given their input.     

Benefit-Based Data Caching in Ad Hoc Networks

Data caching can significantly improve the efficiency of information access in a wireless ad hoc network by reducing the access latency and bandwidth usage. However, designing efficient distributed caching algorithms is nontrivial when network nodes have limited memory. In this article, we consider the cache placement problem of minimizing total data access cost in ad hoc networks with multiple data items and nodes with limited memory capacity. The above optimization problem is known to be NP-hard. Defining benefit as the reduction in total access cost, we present a polynomial-time centralized approximation algorithm that provably delivers a solution whose benefit is at least 1/4 (1/2 for uniform-size data items) of the optimal benefit. The approximation algorithm is amenable to localized distributed implementation, which is shown via simulations to perform close to the approximation algorithm. Our distributed algorithm naturally extends to networks with mobile nodes. We simulate our distributed algorithm using a network simulator (ns2) and demonstrate that it significantly outperforms another existing caching technique (by Yin and Cao [33]) in all important performance metrics. The performance differential is particularly large in more challenging scenarios such as higher access frequency and smaller memory.     

Automatic Test Generation for Combinational Threshold Logic Networks

We propose an automatic test pattern generation (ATPG) framework for combinational threshold networks. The motivation behind this work lies in the fact that many emerging nanotechnologies, such as resonant tunneling diodes (RTDs), single electron transistor (SET), and quantum cellular automata (QCA), implement threshold logic. Consequently, there is a need to develop an ATPG methodology for this type of logic. We have built the first automatic test pattern generator and fault simulator for threshold logic which has been integrated on top of an existing computer-aided design (CAD) tool. These exploit new fault collapsing techniques we have developed for threshold networks. We perform fault modeling, backed by HSPICE simulations, to show that many cuts and shorts in RTD-based threshold gates are equivalent to stuck-at faults at the inputs and output of the gate. Experimental results with the MCNC benchmarks indicate that test vectors were found for all testable stuck-at faults in their threshold network implementations.     

Tissue-Engineered Vascular Grafts: Emerging Trends and Technologies

Vascular tissue engineering has made prodigious progress in recent years by converging multidisciplinary approaches. Latest technological advancements foster the development of next-generation tissue-engineered vascular grafts (TEVGs) for treating various vasculopathies. While traditional therapeutic methods rely on bypassing the severely damaged vessels with synthetic counterparts with no growth potential, contemporary perspectives focus on biodegradable conduits bestowing an inherent remodeling capability. This review highlights emerging innovative trends and technologies adopted to pragmatically fulfill current scientific needs while improving overall TEVG performance in pre-clinical and clinical settings. A comprehensive overview of various milestones achieved in the past few decades is first summarized, followed by an appraisal of the significant hurdles for clinical translation. The latest techniques to rationally address critical challenges, viz., intimal hyperplasia, thrombosis, constructive graft remodeling, and adequate neo-tissue formation are discussed. Finally, an update on ongoing clinical trials is provided and future perspectives required to persuade TEVGs to become a clinical reality are delineated.     

Machine Learning Based Resource Utilization and Pre-estimation for Network on Chip (NoC) Communication

Network on chip (NoC) is the solution to solve the problem of larger system on chip and bus based communication system. NoC provides scalable, highly reliable and modular approach for on chip communication and related problems. The wireless communication technologies such as IEEE 802.15.4 Zigbee technology follow mesh, star and cluster tree topology. The paper focuses on the development of machine learning model for design and FPGA synthesis of mesh, ring and fat tree NoC for different cluster size (N = 2, 4, 8, 16, 32, 64, 128 and 256). The fat-tree based topologies incorporate more links near the root of the tree, in order to fulfill the requirement for higher communication demand closer to the root of the tree, as compared to its leafs. It is an indirect topology in which not all routers are identical in terms of number of ports connecting to other routers or elements in the network. The research article presents the use of machine learning techniques to predict the FPGA resource utilization for NoC in advance. The present study helps in NoC chip planning before designing the chip itself by taking into account known hardware design parameters, memory utilization and timing parameters such as minimum and maximum period, frequency support etc. The machine learning is carried out based on multiple linear regression, decision tree regression and random forest regression which estimate the accuracy of the design and good performance. The interprocess communication among nodes is verified using Virtex-5 FPGA, in which data flows in packets and can vary up to ‘n’ bit. The designs are developed in Xilinx ISE 14.2 and simulated in Modelsim 10.1b with the help of VHDL programming language. The developed model has been validated and has performed well on independent test data.     

Secondary Electron Intensity Contrast Imaging and Friction Properties of Micromechanically Cleaved Graphene Layers on Insulating Substrates

We report on the surface properties (friction and work function) of micromechanically cleaved graphene layers placed on thermally gown thick insulating (～295 nm of SiO₂) films on commercial Si (001) substrates. By employing atomic force microscopy (AFM) and scanning electron microscopy with varying primary-electron acceleration voltage (V _acc) in secondary electron imaging (SEI) mode, we determined the coefficient of friction (μ) and electronic work function (Φ), respectively, as functions of the number of graphene layers (n). The friction coefficient was deduced from line scans of friction maps obtained simultaneously while measuring AFM topography. The findings show that supported mono-, bi-, and trilayer graphene all yield similar results (～0.03), in contrast to multilayer (～0.027) and thicker graphite (～0.015) flakes. From the SEI contrast variation, we obtained a reproducible discrete distribution of SE intensity stemming from atomically thick graphene layers on a thick insulating substrate. We were able to determine the number of graphene layers (i.e., n) from the SE intensity contrast or the SE intensity itself. Moreover, we found a distinct linear relationship between the relative SE intensity from the graphene layers and their number, provided a relatively lower V _acc was used. The different contrast in SEI micrographs at lower V _acc is attributed to the fact that the generation of secondary electrons emitted from the graphene was affected by the different work functions corresponding to different n values (or thickness contrast, C). This simple and facile method is superior to the conventional optical method in its capability to characterize graphene over sub-1-μm² areas on various insulating substrates. These results are supplemented by optical microscopy, high-resolution transmission electron microscopy, and Raman spectroscopy and Raman mapping that yield the structural quality (or disorder) of the graphene layers, albeit semiquantitatively.     

Scalability and performance of an agent‐based network management middleware

Rapid growth of computer network sizes and uses necessitate analysis of network application middleware in terms of its scalability as well as performance. In this paper we analyze a distributed network management middleware based on agents that can be dispatched to locations where they can execute close to the managed nodes. The described middleware operates between the network protocol layer and the application layer and uses standard TCP protocol and SNMP probes to interface the network. By aggregating requests from many users into a single agent, our system allows multiple managers to probe problem areas with minimal management traffic overhead. We discuss and quantify the benefits of the described middleware by implementing real‐time network managers using our system. The main result of this paper is a comparison of scalability and efficiency of our agent‐based management middleware and traditional SNMP‐based data collection. To this end, we measured traffic in both real and simulated networks. In the latter case, we designed, used and described here a method of separating simulated application flow into separate subflows to simplify design of simulations. Copyright © 2004 John Wiley & Sons, Ltd.