The effects of adverse condition warning system characteristics on driver performance: an investigation of alarm signal type and threshold level

This study addresses the issues concerning the design of adverse condition warning systems (ACWS). ACWS are designed to sense adverse road and weather conditions as well as system states that can negatively impact driving performance leading to skids or accidents, and alert drivers to these conditions. In this case, an ACWS was designed to sense when a car was likely to skid. A virtual-driving environment was used to test two levels of alarm sensitivity (low and high) and two types of auditory alarm signal (Binary ON/OFF and Graded) along with a no-alarm control group. Dependent measures reflected driver performance, response to the alarm signal and trust in the alerting system. Results indicated that participants had fewer skids in the low sensitivity and graded alarm signal condition compared to some other alerting system configurations. Participants in the graded alarm signal condition also had a greater degree of lateral control over the vehicle. Additionally, trust was found to be lower for the high vs. low sensitivity alarm condition, indicating a reduction in trust when the alerting system activated more often, perhaps because participants did not feel the system was accurately reflecting a dangerous condition. This simulator-based research emphasizes the fact that while ACWS may provide an advantage in terms of vehicle control, characteristics of both the alerting signal and system configuration should be considered.     

ANN modeling of DNA sequences: new strategies using DNA shape code

Two new encoding strategies, namely, wedge and twist codes, which are based on the DNA helical parameters, are introduced to represent DNA sequences in artificial neural network (ANN)-based modeling of biological systems. The performance of the new coding strategies has been evaluated by conducting three case studies involving mapping (modeling) and classification applications of ANNs. The proposed coding schemes have been compared rigorously and shown to outperform the existing coding strategies especially in situations wherein limited data are available for building the ANN models.     

Neuro-fuzzy MIMO nonlinear control for ceramic roller kiln

Artificial neural networks (ANNs) and neuro-fuzzy systems (NFSs) have been widely used in modeling and control of many practical industrial nonlinear processes. However, most of them have concentrated on single-output systems only. In this paper, we present a comparative study using ANNs and co-active neuro-fuzzy inference system (CANFIS) in modeling a real, complicated multi-input–multi-output (MIMO) nonlinear temperature process of roller kiln used in ceramic tile manufacturing line. Using this study, we prove that CANFIS is better suited for modeling the temperature process in control phase. After that, a neural network (NN) controller has been developed to control the above mentioned temperature process due to a feedback control diagram. The designed controller performance is tested by a Visual C++ project and the resulting numerical data shows that this controller can work accurately and reliably when the roller kiln set-point temperature set changes.     

Text extraction and document image segmentation using matched wavelets and MRF model.

In this paper, we have proposed a novel scheme for the extraction of textual areas of an image using globally matched wavelet filters. A clustering-based technique has been devised for estim ating globally matched wavelet filters using a collection of groundtruth images. We have extended our text extraction scheme for the segmentation of document images into text, background, and picture components (which include graphics and continuous tone images). Multiple, two-class Fisher classifiers have been used for this purpose. We also exploit contextual information by using a Markov random field formulation-based pixel labeling scheme for refinement of the segmentation results. Experimental results have established effectiveness of our approach.     

Implementing agent teams in dynamic multiagent environments

Teamwork is becoming increasingly critical in multiagent environments ranging from virtual environments for training and education, to information integration on the internet, to potential multirobotic space missions. Teamwork in such complex, dynamic environments is more than a simple union of simultaneous individual activity, even if supplemented with preplanned coordination. Indeed, in these dynamic environments, unanticipated events can easily cause a breakdown in such preplanned coordination. The central hypothesis in this article is that for effective teamwork, agents should be provided explicit representation of team goals and plans, as well as an explicit representation of a model of teamwork to support the execution of team plans. In our work, this model of teamwork takes the form of a set of domain independent rules that clearly outline an agent's commitments and responsibilities as a participant in team activities, and thus guide the agent's social activities while executing team plans. This article describes two implementations of agent-teams based on the above principles, one for a realworld helicopter combat simulation, and one for the RoboCup soccer simulation. The article also provides a preliminary comparison of the two agent-teams to illustrate some of the strengths and weaknesses of RoboCup as a common test bed for multiagent systems.     

Development of spectral band cloning techniques for soil nutrients estimation

Satellite‐based monitoring is an indispensable tool to guide soil‐specific crop management. However, it has attained little success in the estimation of soil nutrients due to the limitations incurred from inherent spectral characteristics. In this study, spectral band cloning (SBC) is developed and proposed to augment the soil nutrient predictive capabilities of broadband satellite data. Fine‐spectral channels of spectrometers were synchronized with coarse resolution of IRS satellite data to generate nutrient‐sensitive cloned IRS bands. Soil samples, collected at the time of satellite image acquisition in Lop Buri, Thailand, were analyzed both spectrally and chemically, viz., soil organic matter (OM), phosphorus, potassium and iron. The resulting SBC‐based models showed acceptable correlations, which otherwise were unattainable from raw IRS bands through prevailing models. Accuracy and validation measures showed good agreements between the measured and estimated nutrient surfaces. It is concluded that the SBC is a promising method of quantitative soil nutrient mapping, and could further be used for identification and mapping of other indiscernible biophysical parameters.     

Improved Design of Beam Tunnel for 42 GHz Gyrotron

In gyrotron, there is the chance of generation and excitation of unwanted RF modes (parasite oscillations). These modes may interact with electron beam and consequently degrade the beam quality. This paper presents the improved design of the beam tunnel to reduce the parasite oscillations and the effect of beam tunnel geometry on the electron beam parameters. The design optimization of the beam tunnel has been done with the help of 3-D simulation software CST-Microwave Studio and the effect of beam tunnel geometry on the electron beam parameters has been analyzed by EGUN code.     

Linear-code multicast on parallel architectures

Parallel architectures involve communication with the aim of fast receiving of complete information at each node. Several architectures have been proposed to overcome the problem of high communicational and computational time complexity for transferring and receiving information. To reduce the complexity of such communication, we have implemented Linear Network Coding (LNC) in the parallel environment. For verification of our approach, we have considered some parallel architecture for implementing network coding approach and examined our results on these networks in a generic environment. We have formulated a standard approach for parallel networks, showing that by applying this approach effect of faulty nodes, information size and communication complexity exponentially decreases with code length.     

Recyclable Amberlyst-70 as a Catalyst for Biginelli Reaction: An Efficient One-Pot Green Protocol for the Synthesis of 3,4-Dihydropyrimidin-2(1H)-ones

An environmentally benign aqueous protocol for the synthesis of 3,4-dihydropyrimidin-2(1H)-ones using Amberlyst-70 as a recyclable catalyst has been developed. The use of resinous, nontoxic, thermally stable and inexpensive Amberlyst-70, as a recyclable heterogeneous catalyst, makes the process simple with negligible chemical waste. Thus renders this method an environmentally acceptable synthetic tool for Biginelli reaction.     

A Power Control MAC Protocol for Ad Hoc Networks

This paper presents a power control MAC protocol that allows nodes to vary transmit power level on a per-packet basis. Several researchers have proposed simple modifications of IEEE 802.11 to incorporate power control. The main idea of these power control schemes is to use different power levels for RTS–CTS and DATA–ACK. Specifically, maximum transmit power is used for RTS–CTS, and the minimum required transmit power is used for DATA–ACK transmissions in order to save energy. However, we show that these schemes can degrade network throughput and can result in higher energy consumption than when using IEEE 802.11 without power control. We propose a power control protocol which does not degrade throughput and yields energy saving.