Design of a multivariate control system is a challenging task owing to inherent process nonlinearities, multivariable interactions, and unstable zeros in the process dynamics. Prioritization of multiple control objectives is conventionally achieved using scalarization approach by appropriate weighting factors in the augmented objective function. However, the optimality of these parameters does not hold in presence of unmeasured disturbance and different setpoints. Hence, prioritization in the objectives is not achieved in such circumstances. On the other hand, priority in various objectives can effectively be realized in lexicographic optimization approach. We use the lexicographic optimization approach to address setpoint tracking control problem in linear MPC. To validate the efficacy of the lexicographic ordering approach, multivariate quadruple tank process is considered. Tuning issues with conventional MPC and its remedies using lexicographic approach are discussed in the present work.

This work reports the efficacy of self-learning in extremely rural settings in India through the methodology of Spoken Tutorial. Demographic factors like age or gender did not have any impact on learning. Beginners to IT were found to perform in LibreOffice, as competently as experienced users of MS Office. Teachers of these schools, who were trained using the methodology of Spoken Tutorial, were able to train their students easily using the same method.

Textbook Companion is a useful documentation approach that can be produced effectively through crowdsourcing of students and faculty members. Although extrinsic motivation is the driving factor, we cannot rule out the existence of intrinsic motivation. Scilab and Python textbook companions are found to be useful to their creators and to the community. To achieve success in more challenging projects, such as DWSIM flowsheeting, eSim circuit simulation and OpenFOAM case study, increasing the levels of motivation is proposed. Enhancing the access to laptops will increase the size and quality of the crowd, which is indispensable for the crowdsourcing to succeed.

We have attempted to create affordable programmable logical controllers (PLC) that can be used to train millions of students in developing countries. Version 1 of the PLC is based on AtMega16 and costs 600 ($10). Using the free and open source software (FOSS) LD Micro, one can learn ladder logic. To enhance the capabilities, using Raspberry PI, Version 2 of the PLC, costing 1,500 ($25) is made. We can use this device with the FOSS OpenPLC to learn ladder logic. We can also learn function block based programming using the FOSS 4DIAC. As ladder logic and function block based programming are industrial standards, students trained using our approach will be useful to industry.

This paper presents a case study of 278 first year students enrolled in a CS101 course of the department of Computer Science at the Indian Institute of Technology Bombay. The sufficiency of a low cost (Rs.7,500 or $110) laptop for this course is assessed. From the feedback questionnaires, the students' perception on the likability of this device is assessed. It is found that the students like this device. Users of this device used it as much others who had access to more expensive computers, and also performed as well as others in the course. This device is useful for several other applications also, and hence is a candidate for mass deployment in developing countries, such as India.

Computer programming is a challenging subject to teach and learn both for students as well as teachers. Cognitive skills such as programming comprehension and debugging are the most important skills necessary for a programmer. Students have a difficulty to build these cognitive skills either due to lack of resources, pedagogy and feedback mechanisms. In order to address some of these challenges spoken tutorial puzzle based approach has been discussed focusing on programming comprehension and debugging skills of the learners. A study has been conducted to analyze the cognitive difficulties of students in programming education through spoken tutorial based workshops. This study comprised of a group of non-CS engineering under graduates. It is noted that majority of students showed cognitive difficulties related to programming, debugging and program comprehension, irrespective of the programming language used. This paper discusses how puzzles help in building the programming skills of the learners. We also discuss the challenges and benefits of using puzzles to teach programming skills.

Computer scientists and educators have argued that teaching programming skills helps enhance thinking skills and good problem solving aptitude. Learning to program is difficult for many students. Although several factors that affect learning have been identified over the years, we are still far f

We evaluate the perceptions and the intensity of students who study online courses using video tutorials. Using the data of the past viewers, and the concept of similarity of interest, we recommend videos for further learning. As the total number of videos in the repertoire is large, this helps reduce the information overload. Although spoken tutorials are used for this study, the results are generic. This work is based on an empirical model obtained through a data mining approach.

The system capacity available to a multi-tier Web based application is often a dynamic quantity. Most static threshold-based overload control mechanisms are best suited to situations where the system's capacity is constant or the bottleneck resource is known. However, with varying capacity, the admission control mechanism needs to adapt dynamically. We propose and implement an adaptive admission control mechanism that adjusts the admitted load to compensate for changes in system capacity. The proposed solution is implemented as a proxy server between clients and front-end Web servers. The proxy monitors dasiablack-boxpsila performance metrics-response time and rate of successfully completed requests (goodput). With these measurements as indicators of system state, we employ a control theory based feedback loop to dynamically determine the rate of admitted requests. The objective is to balance changes in response time and changes in goodput, while preventing overloads due to reduction in available system capacity. We evaluate our mechanism with experiments on a test-bed and find that it is able to maintain higher productivity than a static admission control scheme.

A distributed connection admission control (CAC) scheme where stations independently admit or reject flows based on channel utilization threshold is easy to implement for WLANs. However, owing to variable protocol capacity, the advisable threshold also is variable, limiting the efficacy of fixed threshold based CAC. If the admission threshold is tuned to reflect the current protocol capacity, better throughput can be obtained while avoiding WLAN overload. In this paper, we propose a feedback based scheme to tune the utilization threshold for admission. We select the performance metric used for feedback, derive the system model from empirical data, and use control theory to design a feedback controller. Further, we present an analytical model and heuristics using which the controller can be adapted to work under diverse operating scenarios. Simulation results of proportional and proportional-integral controllers in OPNET suggest that the feedback based CAC is able to avoid WLAN overload and achieve high throughput despite large changes in protocol capacity.

Often, data used in online decision making (for example, in determining how to react to changes in process behavior, traffic flow control, etc.) is dynamic in nature and hence the timeliness of the data delivered to the decision making process becomes very important. The delivered data must conform to certain time or value based application specific consistency requirements. The design of mechanisms for such data delivery is challenging given that dynamic data changes rapidly and unpredictably, the latter making it very hard to use simple prediction techniques. To address these challenges we develop mechanisms to obtain timely and consistency-preserving updates for dynamic data by pulling data from the source at strategically chosen points in time, providing quality of service (QoS) guarantees. Motivated by the need for practical system design, but using formal analytical techniques, we offer a systematic approach based on control-theoretic principles. We present a stochastic controller based on the linear quadratic Gaussian (LQG) technique as a means for deciding when to next refresh data from a source. A simple enhancement of the LQG algorithm allows us to provide QoS guarantees. Using real-world traces of real-time data we show the superior performance of our feedback-driven control-theoretic approach by comparing with a previously proposed adaptive refresh technique, a pattern matching technique, and a proportional controller with dynamically changing tuning criteria.

Often, data used in online decision making (for example, in determining how to react to changes in process behavior, traffic flow control, etc.) is dynamic in nature and hence the timeliness of the data delivered to the decision making process becomes very important. The delivered data must conform to certain time or value based application specific inconsistency bounds. A system designed to disseminate dynamic data can exploit user-specified coherency requirements by fetching and disseminating only those changes that are of interest to users and ignoring immediate changes. But, the design of mechanisms for such data delivery is challenging given that dynamic data changes rapidly and unpredictably, the latter making it very hard to use simple prediction techniques. In this paper, we address these challenges. Specifically, we develop mechanisms to obtain timely and consistency-preserving updates for dynamic data by pulling data from the source at strategically chosen points in time. Motivated by the need for practical system design, but using formal analytical techniques, we offer a systematic approach based on control-theoretic principles. Our solution is also unique from a control-theoretic perspective due to the presence of inherent nonlinear system components and the dependence between the sampled time and sampled value. A proportional controller with dynamically changing tuning criteria is used in this work as a means of deciding when to next refresh data from the source. Using real world traces of real-time data we show the superior performance of our feedback-driven control-theoretic approach by comparing with (i) a previously proposed adaptive refresh technique and (ii) a new pattern matching technique.