Impact of human operators on communication network dependability

There is a scarcity of scientific literature about the characteristics of the control work and its demands in the critical domain of telecommunication. The motivation of the paper is to shed light on the context and content of the human operators’ work in maintaining the dependability of telecommunication networks. A case study was accomplished in a large telecommunication company. The research focused on clarifying the impact of human operators on the dependability of telecommunication network. Interviews confirmed earlier assumption among members of the communication network operations (CNO) community that the human operator has an important impact on the network dependability. Specific sources of errors in daily work were identified and strategies of avoiding erroneous action were described. By utilising core-task analysis and modelling approach, we defined generic control demands of the CNO work domain, and operators’ ways of coping with these demands. Regarding human dependability of communication network operations, a combination of resilient and proactive ways of acting is needed. Due to daily changes in the network and the continuous increase of its complexity, specifically change management on both organisational and work practice levels was required. The study is restricted to one Finnish telecommunication company, which limits possible empirical generalisation of the findings.     

MWCNT-Modified Gold Electrode Sensor for the Determination of Propyl Gallate in Vegetable Oils

A differential pulse voltammetric method for the determination of the synthetic food antioxidant, propyl gallate (PG) was developed. Determination of PG was carried out using multiwalled carbon nanotube (MWCNT)-modified gold electrode (GE) in a solution of 0.1 M acetate buffer (pH 7) as supporting electrolyte. The developed sensor showed an excellent catalytic activity and stability for PG oxidation. Effect of common interfering ions, amount of MWCNT–Nafion suspension, pH of the supporting electrolyte, and scan rate were studied. Under optimized conditions, PG concentration as low as 6.3?×?10^?7 M can be determined in food samples. The determination of PG in vegetable oils with the proposed sensor was in good agreement with those obtained by reported spectrophotometric method.     

Electrical behavior of conducting polymer based ‘polymeric–inorganic’ nanocomposite: Polyaniline and polypyrrole zirconium titanium phosphate

Composites are 21st century material used to meet the demand of improved materials and possess a combination of several desirable properties. Present study focussed on the conducting behavior of ‘polymeric–inorganic’ nanocomposite of conducting polymer polyaniline and polypyrrole. ‘Polymeric–inorganic’ nanocomposite cation-exchangers, i.e., polyaniline zirconium titanium phosphate (PANI-ZTP) and polypyrrole zirconium titanium phosphate (PPy-ZTP), were synthesized via sol–gel mixing of electrical conducting polymers into the matrices of inorganic precipitate of zirconium titanium phosphate (ZTP) having excellent ion exchange properties. The proposed nanocomposite possessed DC electrical conductivity in the semi-conducting range, i.e., 10^?5–10^?3 S cm^?1. The stability in terms of DC electrical conductivity retention was also studied in an oxidative environment by two slightly different techniques viz. isothermal and cyclic techniques. The DC electrical conductivity of composite material was found stable upto 110 °C under ambient conditions.     

Variational autoencoder for prosody-based speaker recognition

This paper describes a novel end-to-end deep generative model-based speaker recognition system using prosodic features. The usefulness of variational autoencoders (VAE) in learning the speaker-specific prosody representations for the speaker recognition task is examined herein for the first time. The speech signal is first automatically segmented into syllable-like units using vowel onset points (VOP) and energy valleys. Prosodic features, such as the dynamics of duration, energy, and fundamental frequency ( $F_0$), are then extracted at the syllable level and used to train/adapt a speaker-dependent VAE from a universal VAE. The initial comparative studies on VAEs and traditional autoencoders (AE) suggest that the former can efficiently learn speaker representations. Investigations on the impact of gender information in speaker recognition also point out that gender-dependent impostor banks lead to higher accuracies. Finally, the evaluation on the NIST SRE 2010 dataset demonstrates the usefulness of the proposed approach for speaker recognition.