Adaptive nonuniform polar quantization application to high quality speech compression

The logarithmic companding technique has shown to be extremely useful in speech quantization with rate of 8 bits/sample. However, for lower bit rates it is not the ideal solution for high quality speech coding. Because of that, in this paper we establish source coding scheme which enables better spectrum efficiency for input that has a large dynamic range. Since our wish is also to improve signal quality in comparison with quality defined with standards G.711 and G.712, we opt for adaptive technique application to the speech coding. Our research shows that proper design of forward gain-adaptive polar quantization can enable compression of about 1 bit/sample as well as significantly better quality than in case of using coder designed according to standard G.711. Furthermore, performances can be sustained over the whole speech dynamic range. Also, if the requisite speech quality is not supposed to be lower than G.712 standard quality, the achieved compression can be almost 1.5 bits/sample. Besides, we propose knew simple encoding rule which can additionally reduce bit rate for 0.1 bit/sample.     

Negative bias temperature instability in n-channel power VDMOSFETs

Negative gate bias is used in some applications for faster switching off the n-channel MOS devices. It is shown in this study that NBT stress-related instability in commercial n-channel power VDMOSFETs could be actually more serious than in corresponding p-channel devices. NBT stress is found to create equal V_T shifts in both device types, whereas the subsequent positive bias annealing results in more serious overall V_T instability in n-channel devices. The changes in the densities of stress-induced interface traps in two device types are equal as well, but significant amounts of NBT stress-induced border traps are only found in n-channel devices. All the results are discussed in terms of hydrogen reaction and diffusion model.     

Modeling of dibenzothiophene oxidative desulfurization using response surface methodology

The effect of reaction temperature, mixing speed and oxidant to catalyst volume ratio, including their interactions on the oxidative desulfurization of dibenzothiophene by using response surface methodology was studied. Hydrogen peroxide was used as oxidant and acetic acid as catalyst. The obtained model accurately predicts conversion of dibenzothiophene and the best conversion of 98.7% was observed at temperature 70°C, mixing speed of 1250 rpm and oxidant to catalyst volume ratio of 1:1. At high temperatures, a major limitation of the desulfurization process is the mass transfer and the high mixing speed is needed to achieve an efficient process.     

High Definition Digital Fabrication of Active Organic Devices by Molecular Jet Printing

We introduce a high resolution molecular jet (MoJet) printing technique for vacuum deposition of evaporated thin films and apply it to fabrication of 30 μm pixelated (800 ppi) molecular organic light emitting devices (OLEDs) based on aluminum tris(8‐hydroxyquinoline) (Alq₃) and fabrication of narrow channel (15 μm) organic field effect transistors (OFETs) with pentacene channel and silver contacts. Patterned printing of both organic and metal films is demonstrated, with the operating properties of MoJet‐printed OLEDs and OFETs shown to be comparable to the performance of devices fabricated by conventional evaporative deposition through a metal stencil. We show that the MoJet printing technique is reconfigurable for digital fabrication of arbitrary patterns with multiple material sets and high print accuracy (of better than 5 μm), and scalable to fabrication on large area substrates. Analogous to the concept of “drop‐on‐demand” in Inkjet printing technology, MoJet printing is a “flux‐on‐demand” process and we show it capable of fabricating multi‐layer stacked film structures, as needed for engineered organic devices.     

Algorithm for tissue ischemia estimation based on electrical impedance spectroscopy

The purpose of this paper is to present an algorithm developed for real-time estimation of skeletal muscle ischemia, based on parameters extracted from in vivo obtained electrical impedance spectra. A custom impedance spectrometer was used to acquire data sets: complex impedance spectra measured at 27 frequencies in the range of 100 Hz-1 MHz, and tissue pH. Twenty-nine in vivo animal studies on rabbit anterior tibialis muscle were performed to gather data on the behavior of tissue impedance during ischemia. An artificial neural network (ANN) was used to quantitatively describe the relationship between the parameters of complex tissue impedance spectra and tissue ischemia via pH. The ANN was trained on 1249, and tested on 946 ischemic tissue impedance data sets. A correlation of 94.5% and a standard deviation of 0.15 pH units was achieved between the ANN estimated pH and measured tissue pH values.     

An Example of Human-Centric Sensor Network: Be Responsible Platform

The paper addresses the Be Responsible platform, a representative example of human-centric sensing network which, relying on the Info-Communication and Internet of Things concepts, aims to improve the communication between citizens, on one side, and government authorities, on the other side, with the purpose of resolving various problems encountered in a modern society. A widespread network of human-centric smartphone devices and PCs has been used as a valuable source of data collected and processed in order to trigger adequate actions upon problem resolving. In this way, humans acting as sensor operators and data sources become a powerful factor in improving their society. The Be Responsible platform enables the people to report the irregularities in various aspects of their social life (grey economy, misuse of government vehicles, ecology, traffic), and hence to take active part in resolving them. It also promotes positive and responsible social and institutional behavior, which has to be nurtured in any society seeking progress. The number of submitted reports as well as the money collected from the fine revenues and subsequently invested in projects of social good, undoubtedly prove that what could not be achieved through years of traditional government-led policy approach, was achieved in months of citizen engagement.     

E-business technologies for xRM: Exploring the readiness of public broadcasters

This paper examines the possibilities of enhancing relationship management between public service broadcasters and their stakeholders by harnessing the potential of newest e-business technologies and concepts, such as: social media, digital and interactive TV, and mobile computing. In order to facilitate adoption of relationship management strategy, a framework for leveraging relationship management is proposed. Evaluation was performed within the e-business system of a public broadcaster. This empirical study explored the readiness and expected benefits of relationship management implementation. The research results have indicated that a public broadcaster could achieve a higher level of social responsibility, improve business sustainability, and empower communication and collaboration with all the stakeholders by using emerging technologies for relationship management activities.     

Detection of Structural Features in Biological Signals

In this article structures in biological signals are treated. The simpler—directly visible in the signals, which still demand serious methods and algorithms in the feature detection, similarity investigation and classification. The major actions in this domain are of geometric, thus simpler sort, though there are still hard problems related to simple situations. The other large class of less simple signals unsuitable for direct geometric or statistic approach, are signals with interesting frequency components and behavior, those suitable for spectroscopic analysis. Semantics of spectroscopy, spectroscopic structures and research demanded operations and transformations on spectra and time spectra are presented. The both classes of structures and related analysis methods and tools share a large common set of algorithms, all of which aiming to the full automatization. Some of the signal features present in the brain signal patterns are demonstrated, with the contexts relevant in BCI, brain computer interfaces. Mathematical representations, invariants and complete characterization of structures in broad variety of biological signals are in the central focus.