Perturbation-Based Eigenvector Updates for On-Line Principal Components Analysis and Canonical Correlation Analysis

Principal components analysis is an important and well-studied subject in statistics and signal processing. Several algorithms for solving this problem exist, and could be mostly grouped into one of the following three approaches: adaptation based on Hebbian updates and deflation, optimization of a second order statistical criterion (like reconstruction error or output variance), and fixed point update rules with deflation. In this study, we propose an alternate approach that avoids deflation and gradient-search techniques. The proposed method is an on-line procedure based on recursively updating the eigenvector and eigenvalue matrices with every new sample such that the estimates approximately track their true values as would be calculated analytically from the current sample estimate of the data covariance matrix. The perturbation technique is theoretically shown to be applicable for recursive canonical correlation analysis, as well. The performance of this algorithm is compared with that of a structurally similar matrix perturbation-based method and also with a few other traditional methods like Sanger’s rule and APEX.

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Proposal for a novel and simple WDM NRZ-DPSK system

A novel and simple non-return-to-zero differential phase shift keying (NRZ-DPSK) wavelength division multiplexing (WDM) system, which can simultaneously demultiplex and demodulate multiple wavelengths, is proposed and investigated in this paper.The phase-to-intensity demodulation principle is based on detuned filtering, which is achieved by using a single commercial array waveguide grating (AWG) in our scheme.By properly choosing appropriate AWG channels at the transmitter, the AWG at the receiver can act as both the demultiplexer and the demodulator of the DPSK signals.Simulations at 10, 20, and 40 Gbit/s show good flexibility and performance for the proposed system.     

Ascertaining the importance of neurons to develop better brain-machine interfaces

In the design of brain-machine interface (BMI) algorithms, the activity of hundreds of chronically recorded neurons is used to reconstruct a variety of kinematic variables. A significant problem introduced with the use of neural ensemble inputs for model building is the explosion in the number of free parameters. Large models not only affect model generalization but also put a computational burden on computing an optimal solution especially when the goal is to implement the BMI in low-power, portable hardware. In this paper, three methods are presented to quantitatively rate the importance of neurons in neural to motor mapping, using single neuron correlation analysis, sensitivity analysis through a vector linear model, and a model-independent cellular directional tuning analysis for comparisons purpose. Although, the rankings are not identical, up to sixty percent of the top 10 ranking cells were in common. This set can then be used to determine a reduced-order model whose performance is similar to that of the ensemble. It is further shown that by pruning the initial ensemble neural input with the ranked importance of cells, a reduced sets of cells (between 40 and 80, depending upon the methods) can be found that exceed the BMI performance levels of the full ensemble.     

Hierarchical Self‐Assembly of Peptide‐Coated Carbon Nanotubes

Numerous applications, from molecular electronics to super‐strong composites, have been suggested for carbon nanotubes. Despite this promise, difficulty in assembling raw carbon nanotubes into functional structures is a deterrent for applications. In contrast, biological materials have evolved to self‐assemble, and the lessons of their self‐assembly can be applied to synthetic materials such as carbon nanotubes. Here we show that single‐walled carbon nanotubes, coated with a designed amphiphilic peptide, can be assembled into ordered hierarchical structures. This novel methodology offers a new route for controlling the physical properties of nanotube systems at all length scales from the nano‐ to the macroscale. Moreover, this technique is not limited to assembling carbon nanotubes, and could be modified to serve as a general procedure for controllably assembling other nanostructures into functional materials.     

An extended access control model for permissioned blockchain frameworks

Wireless Networks - In distributed environment, a digital transaction or operation requires transparency and trust among multiple stakeholders. Several approches address such...     

Lossless video compression based on backward adaptive pixel-based fast motion estimation

This paper presents a lossless video compression system based on a novel Backward Adaptive pixel-based fast Predictive Motion Estimation (BAPME). Unlike the widely used block-matching motion estimation techniques, this scheme predicts the motion on a pixel-by-pixel basis by comparing a group of past observed pixels between two adjacent frames, eliminating the need of transmitting side information. Combined with prediction and a fast search technique, the proposed algorithm achieves better entropy results and significant reduction in computation than pixel-based full search for a set of standard test sequences. Experimental results also show that BAPME outperforms block-based full search in terms of speed and entropy. We also provide the sub-pixel version of BAPME as well as integrate BAPME in a complete lossless video compression system. The experimental results are superior to the selected state-of-the-art schemes.     

A triple-mode hexa-standard reconfigurable TI cross-coupled ΣΔ modulator

Hardware reconfigurability is an attractive solution for modern multi-standard wireless systems. This paper analyses the performance and implementation of an efficient triple-mode hexa-standard reconfigurable sigma-delta (∑?) modulator designed for six different wireless communication standards. Enhanced noise-shaping characteristics and increased digitisation rate, obtained by time-interleaved cross-coupling of ∑? paths, have been utilised for the modulator design. Power/hardware efficiency and the capability to acclimate the requirements of wide hexa-standard specifications are achieved by introducing an advanced noise-shaping structure, the dual-extended architecture. Simulation results of the proposed architecture using Hspice shows that the proposed modulator obtains a peak signal-to-noise ratio of 83.4/80.2/67.8/61.5/60.8/51.03 dB for hexa-standards, i.e. GSM????????/Bluetooth/GPS/WCDMA/WLAN/WiMAX standards with significantly less hardware and low operating frequency. The proposed architecture is implemented in 45 nm CMOS process using a 1 V supply and 0.7 V input range with a power consumption of 1.93 mW. Both architectural- and transistor-level simulation results prove the effectiveness and feasibility of this architecture to accomplish multi-standard cellular communication characteristics.     

Design and implementation of a lightweight online certificate validation service

A PKI (public key infrastructure) provides for a digital certificate that can identify an individual or an organization. However, the existence of a certificate is a necessary but not sufficient evidence for its validity. The PKI needs to provide applications that use certificates with the ability to validate, at the time of usage, that a certificate is still valid (not revoked). One of the two standard protocols to check the revocation status of certificates is the Online Certificate Status Protocol (OCSP). In this article, we propose an OCSP-based implementation that enhances the performance of standard OCSP. In particular, we put special emphasis on those issues that affect security and performance when the validation service is deployed in a real scenario. Finally, we provide experimental results that show that our implementation outperforms standard OCSP.