Vector-extrapolated fast maximum likelihood estimation algorithms for emission tomography

A new class of fast maximum-likelihood estimation (MLE) algorithms for emission computed tomography (ECT) is developed. In these cyclic iterative algorithms, vector extrapolation techniques are integrated with the iterations in gradient-based MLE algorithms, with the objective of accelerating the convergence of the base iterations. This results in a substantial reduction in the effective number of base iterations required for obtaining an emission density estimate of specified quality. The mathematical theory behind the minimal polynomial and reduced rank vector extrapolation techniques, in the context of emission tomography, is presented. These extrapolation techniques are implemented in a positron emission tomography system. The new algorithms are evaluated using computer experiments, with measurements taken from simulated phantoms. It is shown that, with minimal additional computations, the proposed approach results in substantial improvement in reconstruction.     

A Comparison of Bit-Parallel and Bit-Serial Architectures for WDM Networks

Wavelength division multiplexing (WDM) is emerging as a viable solution to reduce the electronic processing bottleneck in very high-speed optical networks. A set of parallel and independent channels are created on a single fiber using this technique. Parallel communication utilizing the WDM channels may be accomplished in two ways: (i) bit serial, where each source-destination pair communicates using one wavelength and data are sent serially on this wavelength; and (ii) bit parallel, where each source-destination pair communicates using a subset of channels and data are sent in multiple-bit words. Three architectures are studied in the paper: single-hop bit-serial star, single-hop bit-parallel star, and multi-hop bit-parallel shufflenet. The objective of this paper is to evaluate these architectures with respect to average packet delay, network utilization, and link throughput. It is shown that the Shufflenet offers the lowest latency but suffers from high cost and low link throughput. The star topology with bit-parallel access offers lower latency than the bit-serial star, but is more expensive to implement.     

Analog and radio-frequency performance analysis of silicon-nanotube MOSFETs

This work presents a comparative study of the influence of various parameters on the analog and RF properties of silicon-nanotube MOSFETs and nanowire-based gate-all-around (GAA) MOSFETs. The important analog and RF performance parameters of SiNT FETs and GAA MOSFETs, namely drain current (I_d), transconductance to drain current ratio (g_m/I_d), I_on/I_off, the cut-off frequency (f_T) and the maximum frequency of oscillation (f_MAX) are evaluated with the help of Y- and H-parameters which are obtained from a 3-D device simulator, ATLAS^TM. It is found that the silicon-nanotube MOSFETs have far more superior analog and RF characteristics (g_m/I_d, f_T and f_MAX) compared to the nanowire-based gate-all-around GAA MOSFETs. The silicon-nanotube MOSFET shows an improvement of~2.5 and 3 times in the case of f_T and f_MAX values respectively compared with the nanowire-based gate-all-around (GAA) MOSFET.     

De-Duplication Complexity of Fingerprint Data in Large-Scale Applications

De-duplication using biometrics has gained much attention from research communities as it provides a unique identity for each and every individual among the large population.De-duplication is the process of removing the instances of multiple enrollments by the same person using the person’s biometric data.An important issue in the large-scale de-duplication applications is the speed of matching and the accuracy of the matching because the number of persons to be enrolled runs into millions.This paper presents an efficient method to improve the accuracy of fingerprint de-duplication in de-centralized manner.De-duplication accuracy decreases because of the noise present in the data,which would cause improper slap fingerprint segmentation.In this paper,an attempt is made to remove the noise present in the data by using binarization of slap fingerprint images and region labeling of desired regions with 8-adjacency neighborhood.The distinct feature of this technique is to remove the noise present in the data for an accurate slap fingerprint segmentation and improve the de-duplication accuracy.Experimental results demonstrate that the fingerprint segmentation rate and de-duplication accuracy are improved significantly.     

Autonomous Self-Sustained Liquid Crystal Actuators Enabling Active Photonic Applications

Advances in biomimicry have led to the rise of advanced robotics, posing promising revolutions across a variety of fields. Programmable self-sustained actuation in nature, such as human's heart beating, bird's wingbeats, and penguin's waddling, are intriguing and inspiring but challenging for device innovation, which hinders the emergence of autonomous self-feedback applications, especially in optics and photonics. Herein, the design, fabrication, and operation of crosslinked liquid crystal actuators are described that combine the programming of microstructures and the engineering of macroscopic shape morphing for active optics and photonics. The actuators consist of twisted nematic liquid crystal molecules with both elastic and optical anisotropies, resulting in large bending deformations in response to heat. Programmable bending motions and self-sustained waddling oscillations are demonstrated, further contributing to the achievements of dynamic 2D beam steering and self-sustained light field modulation. It is envisioned that these actuators with self-sustained performances without requiring turning the stimulus on-off will find applications in autonomous active optical systems, photonic applications, as well as self-governing robotics with the core feature of thermo-mechanical-optical transduction.     

Challenges and opportunities for next-generation intracortically based neural prostheses

Neural prosthetic systems aim to help disabled patients by translating neural signals from the brain into control signals for guiding computer cursors, prosthetic arms, and other assistive devices. Intracortical electrode arrays measure action potentials and local field potentials from individual neurons, or small populations of neurons, in the motor cortices and can provide considerable information for controlling prostheses. Despite several compelling proof-of-concept laboratory animal experiments and an initial human clinical trial, at least three key challenges remain which, if left unaddressed, may hamper the translation of these systems into widespread clinical use. We review these challenges: achieving able-bodied levels of performance across tasks and across environments, achieving robustness across multiple decades, and restoring able-bodied quality proprioception and somatosensation. We also describe some emerging opportunities for meeting these challenges. If these challenges can be largely or fully met, intracortically based neural prostheses may achieve true clinical viability and help increasing numbers of disabled patients.     

An Adaptive Learning Scheme for Medium Access with Channel Reservation in Wireless Networks

Providing effective medium access for wireless networks is a challenging task. Most of the existing protocols of IEEE 802.11 Medium Access Control (MAC) work towards the goal of achieving effective channel access by developing various backoff procedures. In this paper, we make an attempt to develop a new medium access protocol named Learning Automata (LA) based Wireless Channel Reservation (LAWCR). We use an LA approach to implement reservation for channel access for single hop wireless networks. Also, sequence procedure is used to improve the reservation mechanism. The performance of the proposed LAWCR scheme show significant improvements over the legacy DCF protocol with respect to important criteria such as the average time spent in the buffer and the throughput.     

Study of Shubnikov-de Haas oscillations and measurement of hole effective mass in compressively strained InXGa1−XSb quantum wells

In_XGa_1−XSb has the highest hole mobility amongst all III-V semiconductors which can be enhanced further with the use of strain. The use of confinement and strain in In_XGa_1−XSb quantum wells lifts the degeneracy between the light and heavy hole bands which leads to reduction in the hole effective mass in the lowest occupied band and an increase in the mobility. We present magnetotransport measurements on compressively strained In_XGa_1−XSb and GaSb quantum wells. Hall-bar and Van de Pauw structures were fabricated and Shubnikov-de Haas oscillations in the temperature range of T = 2-10 K for magnetic fields of B = 0-9 T were measured. The reduction of effective hole mass with strain was quantified. These results are in excellent agreement with modeling results from band structure calculations of the effective hole mass in the presence of strain and confinement.     

Data analytics in wireless systems and IoT issues and challenges

The emergence of data engineering along with the support of Online Social Networks is growing by millions every day due to the introduction of wireless systems and Internet of Things. The rapid growth of usage of smart devices helps to create new generation knowledge sharing platforms. Data Analytics has a major role to play in the growth and success of wireless and IoT applications. The growth of data has become exponential and is difficult to analyze. Many researchers depend on the data available on Wireless Systems and IoT for developing new generation services and applications. With the opportunity of information and communication technologies like heterogeneous networking, cloud computing, web services, crowd sensing and data mining, ubiquitous and asynchronous information sharing is feasible. But this also brings out a lot of provocations with respect to conflicting standards, data portability, data aggregation, data distribution, differential context and communication overhead. The smart information and communication technology has changed many features of human lifestyle: personal and work places.     

On reducing delay in mobile data collection based wireless sensor networks

In a wireless sensor network, battery power is a limited resource on the sensor nodes. Hence, the amount of power consumption by the nodes determines the node and network lifetime. This in turn has an impact on the connectivity and coverage of the network. One way to reduce power consumed is to use a special mobile data collector (MDC) for data gathering, instead of multi-hop data transmission to the sink. The MDC collects the data from the nodes and transfers it to the sink. Various kinds of MDC approaches have been explored for different assumptions and constraints. But in all the models proposed, the data latency is usually high, due to the slow speed of the mobile nodes. In this paper, we propose a new model of mobile data collection that reduces the data latency significantly. Using a combination of a new touring strategy based on clustering and a data collection mechanism based on wireless communication, we show that the delay can be reduced significantly without compromising on the advantages of MDC based approach. Using extensive simulation studies, we analyze the performance of the proposed approach and show that the packet delay reduces by more than half when compared to other existing approaches.