Natural convection from isothermal flat surfaces

Local heat-transfer coefficients along a flat plate in natural convection in air were measured using Boelter-Schmidt type heat flux meters. Experiments were carried out for different temperature differences in heating and cooling, and with inclinations varying from the horizontal “facing upwards” position, through the vertical position, to the horizontal “facing downwards” position.

The results are presented in terms of local Nusselt number as a function of the local Grashof number “tangential component”. All runs were in the range accepted as that of laminar boundary layer flow. However, under certain conditions when the normal velocity component of the air is directed away from the surface, separated flow is indicated along the trailing part of the surface, well before turbulence sets in in the boundary layer. Separation starts at a certain point along the surface. This point is nearer to the leading edge the higher the temperature difference, and the larger the inclination of the surface to the vertical.

In a separation region, the flux density is uniform. In all other regions the results agreed closely with established theories of laminar boundary layer flow.

A leading adiabatic section, used in some of the experiments, did not affect the results. An appendix gives relations recommended for engineering calculations.     

Discrete-time implementation of high-gain observers for numerical differentiation

High-gain observers have been used in non-linear control to estimate derivatives of the output. In this paper, we study discrete-time implementation of high-gain observers and their use as numerical differentiators, in noise-free as well as noisy measurements. We show that discretization using the bilinear transformation method gives better results than other discretization methods. We also show that many of the available numerical differentiators are special cases of the bilinear discrete-time equivalents of full-order or reduced-order high-gain observers.     

Crypt analysis of two time pads in case of compressed speech

Keystream reuse, also known as the two time pad problem, is a well known weakness in stream ciphers. The implementers of the cryptographic algorithms are still underestimating this threat. The keystream reuse exploitation techniques presented so far assume the underlying plaintext to be textual data and all the heuristics presented previously are based on the language characteristics of the underlying text based data, which fail when compression is applied on the plaintext before encryption. This paper presents exploitation techniques for two time pads in case of stream ciphered digitized and compressed speech signals. In this paper we show that how an adversary can automatically recover the digitized speech signals encrypted under the same keystream provided the language (e.g. English) and digital encoding/compression scheme details of the underlying speech signals are known. Our technique of cryptanalysis is based on the modeling of the speech parameters by exploiting the inter frame correlations between each components of the speech vectors in different frames and then using these models to decode the two speech signals in the keystream reuse scenario. The technique is flexible enough to incorporate all modern speech coding schemes based on parameter or hybrid encoding and compression techniques. The simulation experiments have showed promising results for most of the present day speech digitization and compression techniques.     

Activation process-dependent characteristics of novel thermionic oxide cathodes for CRT application

The emission and conductivity characteristics of oxide cathodes depend largely on the activation process. In this paper, the electrical properties of new type of oxide cathodes for cathode ray tube (CRT) application, supplied by LG Philips Displays, have been investigated in relation to different cathode activation regimes. The influence of the activation process over different durations has been investigated. A temperature of T=1425 K was chosen to be higher than the optimum cathode activation temperature (T=1200 K), and the other temperature of T=1125 K was lower than that. The electron activation energy (E) was found to vary in the range from 0.58 to 2.28 eV for cathodes activated at the higher temperature regime, and from 1.08 to 1.9 eV for those activated at the lower temperature regime. Scanning electron microscopy (SEM) and electron diffraction X-ray (EDX) analyses show a structural phase transformation in the oxide material that was activated at 1125 K for a period of 1-12 hours. The SEM mapping shows a large contamination of Ba in the top layer of oxide material. The activator agents tungsten and aluminum are found to penetrate into the BaO/spl bsol/SrO layer in two different ways.     

A Full Connectable and High Scalable Key Pre-distribution Scheme Based on Combinatorial Designs for Resource-Constrained Devices in IoT Network

Considering the internet of things (IoT), end nodes such as wireless sensor network, RFID and embedded systems are used in many applications. These end nodes are known as resource-constrained devices in the IoT network. These devices have limitations such as computing and communication power, memory capacity and power. Key pre-distribution schemes (KPSs) have been introduced as a lightweight solution to key distribution in these devices. Key pre-distribution is a special type of key agreement that aims to select keys called session keys in order to establish secure communication between devices. One of these design types is the using of combinatorial designs in key pre-distribution, which is a deterministic scheme in key pre-distribution and has been considered in recent years. In this paper, by introducing a key pre-distribution scheme of this type, we stated that the model introduced in the two benchmarks of KPSs comparability had full connectivity and scalability among the designs introduced in recent years. Also, in recent years, among the combinatorial design-based key pre-distribution schemes, in order to increase resiliency as another criterion for comparing KPSs, attempts were made to include changes in combinatorial designs or they combine them with random key pre-distribution schemes and hybrid schemes were introduced that would significantly reduce the design connectivity. In this paper, using theoretical analysis and maintaining full connectivity, we showed that the strength of the proposed design was better than the similar designs while maintaining higher scalability.

     

Scientific Workflow Makespan Minimization in Edge Multiple Service Providers Environment

The edge computing model offers an ultimate platform to support scientific and real-time workflow-based applications over the edge of the network. However, scientific workflow scheduling and execution still facing challenges such as response time management and latency time. This leads to deal with the acquisition delay of servers, deployed at the edge of a network and reduces the overall completion time of workflow. Previous studies show that existing scheduling methods consider the static performance of the server and ignore the impact of resource acquisition delay when scheduling workflow tasks. Our proposed method presented a meta-heuristic algorithm to schedule the scientific workflow and minimize the overall completion time by properly managing the acquisition and transmission delays. We carry out extensive experiments and evaluations based on commercial clouds and various scientific workflow templates. The proposed method has approximately 7.7% better performance than the baseline algorithms, particularly in overall deadline constraint that gives a success rate.

     

Hybrid Fractal-Wavelet Method for Multi-Channel EEG Signal Compression

In this paper, a hybrid method is proposed for multi-channel electroencephalograms (EEG) signal compression. This new method takes advantage of two different compression techniques: fractal and wavelet-based coding. First, an effective decorrelation is performed through the principal component analysis of different channels to efficiently compress the multi-channel EEG data. Then, the decorrelated EEG signal is decomposed using wavelet packet transform (WPT). Finally, fractal encoding is applied to the low frequency coefficients of WPT, and a modified wavelet-based coding is used for coding the remaining high frequency coefficients. This new method provides improved compression results as compared to the wavelet and fractal compression methods.     

Improved Turbo Decoding Using Soft Combining Principle

One great challenge in wireless communication systems is to ensure reliable communications. Turbo codes are known by their interesting capabilities to deal with transmission errors. In this paper, we present a novel turbo decoding scheme based on soft combining principle. Our method improves decoding performance using soft combining technique inside the turbo decoder. Working on Max-Log-Maximum a Posteriori (Max-Log-MAP) turbo decoding algorithm and using an Additive White Gaussian Noise (AWGN) channel model and 16 Quadrature Amplitude Modulation (16QAM), simulation results show that the suggested solution is efficient and outperforms the conventional Max-Log-MAP algorithm in terms of Bit Error Rate (BER). The performance analysis is carried out in terms of BER by varying parameters such as the Energy per bit to Noise power spectral density ratio ( \(\text {E}_{\text {b}}/\text {N}_{\text {o}}\) ), and decoding iterations number. We call our proposed solution Soft Combined Turbo Codes.     

Application of LS-SVM strategy to estimate H2S loading capacity in different ionic liquids and alkanolamines

Favorable properties of aqueous solutions are improved with the addition of different materials for separation of hydrogen sulfide (H₂S). Also, equilibrium data and available equations for solubility estimation of this gas are only valid for specific solutions and limited ranges of temperature and pressure. In this regard, a machine learning model based on Support vector machine (SVM) algorithm is proposed and developed with mixtures containing different amines and ionic liquids to predict H₂S solubility over wide ranges of temperature (298–434.5 K), pressure (13–9319 kPa), overall mass concentration (3.82–100%) and mixture's apparent molecular weight (18.39–556.17 g/mol). The accuracy of the performance of this network was evaluated by regression analysis on calculated and experimental data, which had not been used in network training.     

Parallelisation of suboptimal spectrum search

The authors suggest that effective algorithms for spectrum search, such as those used for detecting spread spectrum signals, may be derived by selecting suboptimal algorithms and then recovering some of the lost efficacy through parallelisation methods. This thesis is motivated by considering a simple yet meaningful example of a spectrum search technique that exhibits what at first may seem to be counterintuitive behaviour.<>