Mechanism design for time critical and cost critical task execution via crowdsourcing

An exciting application of crowdsourcing is to use social networks in complex task execution. In this paper, we address the problem of a planner who needs to incentivize agents within a network in order to seek their help in executing an atomic task as well as in recruiting other agents to execute the task. We study this mechanism design problem under two natural resource optimization settings: (1) cost critical tasks, where the planner's goal is to minimize the total cost, and (2) time critical tasks, where the goal is to minimize the total time elapsed before the task is executed. We identify a set of desirable properties that should ideally be satisfied by a crowdsourcing mechanism. In particular, sybil-proofness and collapse-proofness are two complementary properties in our desiderata. We prove that no mechanism can satisfy all the desirable properties simultaneously. This leads us naturally to explore approximate versions of the critical properties. We focus our attention on approximate sybil-proofness and our exploration leads to a parametrized family of payment mechanisms which satisfy collapse-proofness. We characterize the approximate versions of the desirable properties in cost critical and time critical domain.

Speed control of brushed DC motor for low cost electric cars

As petrol prices are going up in developing countries in upcoming decades low cost electric cars will become more and more popular in developing world. One of the main deciding factors for success of electric cars specially in developing world in upcoming decades will be its cost. This paper shows a cost effective method to control the speed of low cost brushed D.C. motor by combining a IC 555 Timer with a High Boost Converter. The main purpose of using High Boost Converter since electric cars needs high voltage and current which a High Boost Converter can provide even with low battery supply.

A Low-Cost System for Measurement and Spectral Analysis of Motor Acoustic Noise

Workplace noise has become one of the major issues in industry not only because of workers’ health but also due to safety. Electric motors, particularly, inverter fed induction motors emit objectionably high levels of noise. This has led to the emergence of a research area, concerned with measurement and mitigation of the acoustic noise. This paper presents a lowcost option for measurement and spectral analysis of acoustic noise emitted by electric motors. The system consists of an electret microphone, amplifier and filter. It makes use of the windows sound card and associated software for data acquisition and analysis. The measurement system is calibrated using a professional sound level meter. Acoustic noise measurements are made on an induction motor drive using the proposed system as per relevant international standards. These measurements are seen to match closely with those of a professional meter.

Experimental results on low cost microcontroller based DC drive implementation

This paper presents the experimental results for an attractive control scheme implementation using an 8 bit microcontroller. The power converter involved is a 3 phase full controlled bridge rectifier. A single quadrant DC drive has been realized and results have been presented for both open and closed loop implementations.

An Alternating l(p) - l(2) Projections Algorithm (ALPA) for Speech Modeling using Sparsity Constraints

We address the problem of separating a speech signal into its excitation and vocal-tract filter components, which falls within the framework of blind deconvolution. Typically, the excitation in case of voiced speech is assumed to be sparse and the vocal-tract filter stable. We develop an alternating l(p) - l(2) projections algorithm (ALPA) to perform deconvolution taking into account these constraints. The algorithm is iterative, and alternates between two solution spaces. The initialization is based on the standard linear prediction decomposition of a speech signal into an autoregressive filter and prediction residue. In every iteration, a sparse excitation is estimated by optimizing an l(p)-norm-based cost and the vocal-tract filter is derived as a solution to a standard least-squares minimization problem. We validate the algorithm on voiced segments of natural speech signals and show applications to epoch estimation. We also present comparisons with state-of-the-art techniques and show that ALPA gives a sparser impulse-like excitation, where the impulses directly denote the epochs or instants of significant excitation.

COST AND PERFORMANCE TRADEOFFS OF ALTERNATIVE SOLAR-DRIVEN S-CO2 BRAYTON CYCLE CONFIGURATIONS

This paper evaluates cost and performance tradeoffs of alternative supercritical carbon dioxide (s-CO2) closed-loop Brayton cycle configurations with a concentrated solar heat source. Alternative s-CO2 power cycle configurations include simple, recompression, cascaded, and partial cooling cycles. Results show that the simple closed-loop Brayton cycle yielded the lowest power-block component costs while allowing variable temperature differentials across the s-CO2 heating source, depending on the level of recuperation. Lower temperature differentials led to higher sensible storage costs, but cycle configurations with lower temperature differentials (higher recuperation) yielded higher cycle efficiencies and lower solar collector and receiver costs. The cycles with higher efficiencies (simple recuperated, recompression, and partial cooling) yielded the lowest overall solar and power-block component costs for a prescribed power output.