Header Detection to Improve Multimedia Quality Over Wireless Networks

Wireless multimedia studies have revealed that forward error correction (FEC) on corrupted packets yields better bandwidth utilization and lower delay than retransmissions. To facilitate FEC-based recovery, corrupted packets should not be dropped so that maximum number of packets is relayed to a wireless receiver's FEC decoder. Previous studies proposed to mitigate wireless packet drops by a partial checksum that ignored payload errors. Such schemes require modifications to both transmitters and receivers, and incur packet-losses due to header errors. In this paper, we introduce a receiver-based scheme which uses the history of active multimedia sessions to detect transmitted values of corrupted packet headers, thereby improving wireless multimedia throughput. Header detection is posed as the decision-theoretic problem of multihypothesis detection of known parameters in noise. Performance of the proposed scheme is evaluated using trace-driven video simulations on an 802.11b local area network. We show that header detection with application layer FEC provides significant throughput and video quality improvements over the conventional UDP/IP/802.11 protocol stack     

An Antilock-Braking Algorithm for an Eddy-Current-Based Brake-By-Wire System

This paper presents a nonlinear sliding-mode-type controller for slip regulation in a braking event for an electromagnetic brake-by-wire (BBW)-system-equipped automobile. The electromagnetic BBW system under consideration consists of a set of eddy current brakes (ECBs) attached to the automobile wheels. The antilock-braking controller modifies the brake torque command generated by a supervisory controller which, in turn, is based on the driver's command sensed via brake pedal sensors. The modified brake torque command is then generated by a closed-loop actuator control algorithm to control the ECB system. It is shown in this paper that the proposed antilock-braking control system is stable in the sense of Lyapunov. Experimental results are presented for a test vehicle equipped with an eddy-current-based BBW system. Experimental results show that the proposed antilock brake control algorithm provides very good slip regulation in a braking event on low friction-coefficient surfaces (wet jennite) when compared with that of a braking event without the proposed antilock-braking control. Experimental results also indicate that the proposed antilock-braking control system provides a smooth stop for the vehicle     

Tuning of Dielectric Parameters of (NiFe<Subscript>2</Subscript>O<Subscript>4</Subscript>)<Subscript><Emphasis Type="Italic">x</Emphasis></Subscript>/CuTl-1223 Nano-superconductor Composites by Temperature and Frequency

Nickel ferrite (NiFe₂O₄) nanoparticles and Cu_0.5Tl_0.5Ba₂Ca₂Cu₃O_10?δ (CuTl-1223) superconductor were prepared separately and then mixed in an appropriate ratios at the final stage to obtain (NiFe₂O₄) _x /CuTl-1223 (x = 0, 0.5, and 1.0 wt%) nano-superconductor composites. There was no significant change observed in crystal structure of the host CuTl-1223 superconducting matrix after the addition of NiFe₂O₄ nanoparticles. The value of zero-resistivity critical temperature { T _{c(R = 0)} (K)} was decreased with increasing content of these nanoparticles in these composites. Maximum values of dielectric loss tangent (tanδ) at lowest possible frequency of 40 Hz were increased with the increase of operating temperature, while its values were decreased and become almost zero at higher frequencies for all these samples at all operating temperatures. A peak in dielectric loss tangent was shifted towards lower frequency values with the addition of these nanoparticles in CuTl-1223 superconducting matrix. The dielectric loss tangent peak was also shifted towards lower frequency values in all these samples with increasing operating temperature, which shows the relaxator-like behavior in these samples. The dielectric parameters of these composites can be tuned by frequency, operating temperatures, and nature and content of these nanoparticles.     

An Empirical Study of Factors Affecting e-Commerce Adoption among Small- and Medium-Sized Enterprises in a Developing Country: Evidence from Malaysia

The purpose of this paper is to empirically examine determinants of e-commerce adoption among Malaysian small- and medium-sized enterprises (SMEs). This study tested eight hypotheses on determinants affecting e-commerce adoption with empirical data from several survey instruments: an online questionnaire-based survey, mailed survey and questionnaire collected in-person from a sample of 307 SMEs in Malaysia. The findings show that e-commerce adoption within Malaysian SMEs is affected by perceived relative advantage, perceived compatibility, managers/owner's knowledge and expertise, management characteristics and external change agents. This study contributes to enhancing the understanding of the determinants of adopting e-commerce in SMEs and provides some interesting perspective from Malaysia. Those parties interested in promoting their business online may find these results helpful in guiding their efforts.     

Selective potentiometric determination of uric acid with uricase immobilized on ZnO nanowires

In this study, a potentiometric uric acid biosensor was fabricated by immobilization of uricase onto zinc oxide (ZnO) nanowires. Zinc oxide nanowires with 80-150 nm in diameter and 900 nm to 1.5 μm in lengths were grown on the surface of a gold coated flexible plastic substrate. Uricase was electrostatically immobilized on the surface of well aligned ZnO nanowires resulting in a sensitive, selective, stable and reproducible uric acid biosensor. The potentiometric response of the ZnO sensor vs Ag/AgCl reference electrode was found to be linear over a relatively wide logarithmic concentration range (1-650 μM) suitable for human blood serum. By applying a Nafion^® membrane on the sensor the linear range could be extended to 1-1000 μM at the expense of an increased response time from 6.25 s to less than 9 s. On the other hand the membrane increased the sensor durability considerably. The sensor response was unaffected by normal concentrations of common interferents such as ascorbic acid, glucose, and urea.     

Asynchronous algorithms for poisson's equation with nonlinear boundary conditions

Poisson's equation with nonlinear boundary conditions is discretized with the method of lines to obtain a system of second order differential equations with multi-point boundary conditions. This differential system is converted, using invariant imbedding for each one-dimensional problem, into a fixed point problem and then the asynchronous algorithms are applied.     

Ameliorating Effects of Exogenously Applied Proline on Seed Composition,Seed Oil Quality and Oil Antioxidant Activity of Maize (Zea mays L.) under Drought Stress

This study was carried out to appraise whether or not the exogenous application of a potential osmoprotectant, proline, could ameliorate the adverse effects of drought stress on maize seed and seed oil composition, as well as oil antioxidant activity. Water stress reduced the kernel sugar, oil, protein and moisture contents and most of the seed macro- and micro-elements analyzed in both maize cultivars but it increased the contents of seed fiber and ash. Water stress increased the oil oleic acid content with a subsequent decrease in the amount of linoleic acid, resulting in an increased oil oleic/linoleic ratio for both maize cultivars. However, no variation was observed in oil stearic and palmitic acids content due to water stress. A considerable drought induced an increase in seed oil α-, γ-, δ- and total tocopherols and flavonoids were observed in both maize cultivars. However, oil phenolic and carotenoid content as well as 1,1-diphenyl-2-picryl-hydrazyl (DPPH) free radical scavenging activity decreased. Foliar-applied proline significantly increased the content of seed sugar, oil, protein, moisture, fiber and ash in both maize cultivars under well irrigated and water deficit conditions. Furthermore, exogenous application of proline increased the oil oleic and linoleic acid contents. The concentrations of antioxidant compounds namely phenolics, carotenoids, flavonoids and tocopherols estimated in the seed oil increased due to foliar-applied proline under water deficit conditions that was positively correlated with the enhanced oil DPPH free radical scavenging activity. Moreover, the increase in the contents of these antioxidant compounds and oil antioxidant activity due to the foliar application of proline was noted to be more pronounced under water deficit conditions.     

Development of a semi-quantitative food frequency questionnaire for use in United Arab Emirates and Kuwait based on local foods

Background  

The Food Frequency Questionnaire (FFQ) is one of the most commonly used tools in epidemiologic studies to assess long-term nutritional exposure. The purpose of this study is to describe the development of a culture specific FFQ for Arab populations in the United Arab Emirates (UAE) and Kuwait.     

A Framework of Deep Learning and Selection-Based Breast Cancer Detection from Histopathology Images

Breast cancer (BC) is a most spreading and deadly cancerous malady which is mostly diagnosed in middle-aged women worldwide and effecting beyond a half-million people every year. The BC positive newly diagnosed cases in 2018 reached 2.1 million around the world with a death rate of 11.6% of total cases. Early diagnosis and detection of breast cancer disease with proper treatment may reduce the number of deaths. The gold standard for BC detection is biopsy analysis which needs an expert for correct diagnosis. Manual diagnosis of BC is a complex and challenging task. This work proposed a deep learning-based (DL) solution for the early detection of this deadly disease from histopathology images. To evaluate the robustness of the proposed method a large publically available breast histopathology image database containing a total of 277524 histopathology images is utilized. The proposed automatic diagnosis of BC detection and classification mainly involves three steps. Initially, a DL model is proposed for feature extraction. Secondly, the extracted feature vector (FV) is passed to the proposed novel feature selection (FS) framework for the best FS. Finally, for the classification of BC into invasive ductal carcinoma (IDC) and normal class different machine learning (ML) algorithms are used. Experimental outcomes of the proposed methodology achieved the highest accuracy of 92.7% which shows that the proposed technique can successfully be implemented for BC detection to aid the pathologists in the early and accurate diagnosis of BC.     

Application of linearized power system model to selection of PST controller input signal

This paper presents a selection method for phase shift transformer PST controller input signal. Simple conditions of input signal selection are obtained using frequency response analysis. The proposed method requires a suitable modeling of power system including PST device. The two types of power system models described in this paper are the nonlinear equations system model and the linearized model. The linearized model is represented as a block diagram transfer function model and as a state space representation model. The block diagram model presented in this paper has been used for the PST feedback input signal selection. Five locally available measurements at the switching node of the PST are considered. The results of frequency domain tests have confirmed that the block diagram transfer function model is a useful tool for power system analysis. The LQR method is used to achieve the final verification and the choice of input control signal.