Optical computing: introduction by the guest editors to the feature in the 1 May 1988 issue

The feature in the 1 May 1988 issue of Applied Optics includes a collection of papers originally presented at the 1987 Lake Tahoe Topical Meeting on Optical Computing. These papers emphasize digital optical computing systems, optical interconnects, and devices for optical computing, but analog optical processing is considered as well

SciTech News Volume 71, No. 1 (2017)

Columns and Reports From the Editor 3 Division News Science-Technology Division 5 Chemistry Division 8 Engineering Division Aerospace Section of the Engineering Division 9 Architecture, Building Engineering, Construction and Design Section of the Engineering Division 11 Reviews Sci-Tech Book News Reviews 12 Advertisements IEEE

Submicron Deformation Field Measurements II: Improved Digital Image Correlation

This is the second paper in a series of three devoted to the applicaiton of Scanning Tunneling Microscopy to mechanics problems. In this paper improvements to the Digital Image Correlation method are outlined, a technique that compares digital images of a specimen surface before and after deformation to deduce its (2-D) surface displacement field and strains. The necessity of using the framework of large deformation theory for accurately addressing rigid body rotations to reduce associated errors in the strain components is pointed out. In addition, the algorithm is extended to compute the three-dimensional surface displacement field from Scanning Tunneling Microscope data; also, significant improvements are achieved in the rate as well as the robustness of the convergence. For Scanning Tunneling Microscopy topographs the resolution yields 4.8 nm for the in-plane and 1.5 nm for the out-of-plane displacement components spanning an area of 10 μm x 10 μm

Why Noise and Dispersion may Seriously Hamper Nonlinear Frequency-Division Multiplexing

The performance of optical fiber systems based on nonlinear frequency-division multiplexing (NFDM) or on more conventional transmission techniques is compared through numerical simulations. Some critical issues affecting NFDM systems-namely, the strict requirements needed to avoid burst interaction due to signal dispersion and the unfavorable dependence of performance on burst length-are investigated, highlighting their potentially disruptive effect in terms of spectral efficiency. Two digital processing techniques are finally proposed to halve the guard time between NFDM symbol bursts and reduce the size of the processing window at the receiver, increasing spectral efficiency and reducing computational complexity.Comment: The manuscript has been submitted to Photonics Technology Letters for publicatio

A Simple Flood Forecasting Scheme Using Wireless Sensor Networks

This paper presents a forecasting model designed using WSNs (Wireless Sensor Networks) to predict flood in rivers using simple and fast calculations to provide real-time results and save the lives of people who may be affected by the flood. Our prediction model uses multiple variable robust linear regression which is easy to understand and simple and cost effective in implementation, is speed efficient, but has low resource utilization and yet provides real time predictions with reliable accuracy, thus having features which are desirable in any real world algorithm. Our prediction model is independent of the number of parameters, i.e. any number of parameters may be added or removed based on the on-site requirements. When the water level rises, we represent it using a polynomial whose nature is used to determine if the water level may exceed the flood line in the near future. We compare our work with a contemporary algorithm to demonstrate our improvements over it. Then we present our simulation results for the predicted water level compared to the actual water level.Comment: 16 pages, 4 figures, published in International Journal Of Ad-Hoc, Sensor And Ubiquitous Computing, February 2012; V. seal et al, 'A Simple Flood Forecasting Scheme Using Wireless Sensor Networks', IJASUC, Feb.201

Wavelet transform - artificial neural network receiver with adaptive equalisation for a diffuse indoor optical wireless OOK link

This paper presents an alternative approach for signal detection and equalization using the continuous wavelet transform (CWT) and the artificial neural network (ANN) in diffuse indoor optical wireless links (OWL). The wavelet analysis is used for signal preprocessing (feature extraction) and the ANN for signal detection. Traditional receiver architectures based on matched filter (MF) experience significant performance degradation in the presence of artificial light interference (ALI) and multipath induced intersymbol interference (ISI). The proposed receiver structure reduces the effect of ALI and ISI by selecting a particular scale of CWT that corresponds to the desired signal and classifying the signal into binary 1 and 0 based on an observation vector. By selecting particular scales corresponding to the signal, the effect of ALI is reduced. We show that there is little variation when using 30 and 5 neurons in the first layer, with one layer ANN model showing a consistently worse BER performance than other models, whilst the 15 neuron model show some behaviour anomalies from a BER of approximately 10-3. The simulation results show that the Wavelet-ANN architecture outperforms the traditional MF based receiver even with the filter is matched to the ISI affected pulse shape. The Wavelet-ANN receiver is also capable of providing a bit error rate (BER) performance comparable to the equalized forms of traditional receiver structure

Principles of Neuromorphic Photonics

In an age overrun with information, the ability to process reams of data has become crucial. The demand for data will continue to grow as smart gadgets multiply and become increasingly integrated into our daily lives. Next-generation industries in artificial intelligence services and high-performance computing are so far supported by microelectronic platforms. These data-intensive enterprises rely on continual improvements in hardware. Their prospects are running up against a stark reality: conventional one-size-fits-all solutions offered by digital electronics can no longer satisfy this need, as Moore's law (exponential hardware scaling), interconnection density, and the von Neumann architecture reach their limits. With its superior speed and reconfigurability, analog photonics can provide some relief to these problems; however, complex applications of analog photonics have remained largely unexplored due to the absence of a robust photonic integration industry. Recently, the landscape for commercially-manufacturable photonic chips has been changing rapidly and now promises to achieve economies of scale previously enjoyed solely by microelectronics. The scientific community has set out to build bridges between the domains of photonic device physics and neural networks, giving rise to the field of \emph{neuromorphic photonics}. This article reviews the recent progress in integrated neuromorphic photonics. We provide an overview of neuromorphic computing, discuss the associated technology (microelectronic and photonic) platforms and compare their metric performance. We discuss photonic neural network approaches and challenges for integrated neuromorphic photonic processors while providing an in-depth description of photonic neurons and a candidate interconnection architecture. We conclude with a future outlook of neuro-inspired photonic processing.Comment: 28 pages, 19 figure