A Pervasive Computing Curriculum for Engineering and Science Students

Today, there's an urgent need for well-trained pervasive computing engineers and scientists. Pervasive computing has evolved into a critical field, influencing a broad range of industries from healthcare to manufacturing to defense. However, most computer engineering and computer science programs pay marginal attention, at best, to the field. In this issue, the authors of the Rochester Institute of Technology describes a set of courses on pervasive computing topics and a supporting laboratory designed for both computer engineering and computer science students     

A rule-based fuzzy-logic approach for the measurement of manufacturing flexibility

Manufacturing flexibility is a difficult to quantify concept that defies universal definition. This paper presents a novel fuzzy-logic approach for measuring manufacturing flexibility that exploits linguistic variables for quantifying pertinent factors affecting commonly utilized flexibility types. Towards this end, we identify and measure the contribution of specified state variables towards the assumed flexibility types in order to compute an overall flexibility index for a generic manufacturing system. The suggested framework provides a convenient end user approach amenable to software implementation that is exemplified through the development of a prototypical software tool called “Flexibility Evaluator”.     

Electrical conductivity in iodine-doped ethyl cellulose

The electrical conductivity of solution-grown ethyl cellulose (EC) films, 5–30 μm thick, has been studied in the sandwich configuration (metal–EC–metal) as a function of iodine concentration from 0.5 to 5.0 wt% ratio. The studies were conducted in the temperature range 333–383 K, while the field was varied over the range (3.0–5.5) × 10⁴V/cm. Aluminium was used as the lower electrode, while the upper electrode was of Al, Ag, Cu, Au or Sn. Certain transient effects such as a large burst of current immediately after the application of field were observed. An attempt was made to identify the nature of the current by comparing the observed dependence on electric field, electrode material and temperature with the respective characteristic features of the existing theories of electrical conduction. The results show that the electrical conduction follows Ohm's law at lower fields, while at higher fields, space-charge limited current (SCLC) was observed. It was also found that Richardson–Schottky emission was responsible, to some extent, for the transport of charge carriers in the polymer. The conductivity of the films increased on doping with iodine. The dopant molecules are considered to act as additional trapping centes and provide links between the polymer molecules in the amorphous region, thus resulting in the formation of charge transfer complexes.     

Multi-Model Approach to Assess the Dynamics of Hydrologic Components in a Tropical Ecosystem

Estimation of terrestrial water budget at global and regional scales are essential for efficient agricultural water management, flood predictions, and, hydrological modeling. In hydrological modeling, it is a challenging task to quantify the major hydrological components like runoff, evapotranspiration (ET), and total water storage (TWS) due to improper and limited availability of detailed meteorological datasets. Furthermore, there has been no consensus to answer a-decade-long critical question that a less data-intensive models can be an alternate to robust data-intensive models in data scarce conditions. This study aims at multi-model approach over the single models usage for representing the hydrological behaviour in the Kangsabati River Basin (KRB), India. It is done by applying the standard model selection criteria over various hydrological models. Two hydrological models are selected, a semi- distributed model, Variable Infiltration Capacity (VIC-3 L), and a conceptually lumped model, Identification of unit Hydrograph and Component flows from Rainfall, Evapotranspiration and Streamflow (IHACRES). Both models were calibrated against the observed daily discharge at the KRB outlet for the period of 2001–2006 and validated for 2008–2010. The results show that both VIC-3 L and IHACRES produce reasonable runoff estimates at daily and monthly time scale in the KRB. The ET estimates show that VIC-3 L and IHACRES captured the seasonal variations with the percent change of 0.4% and 6.6% respectively. As IHACRES is simpler, parsimonious, fewer parameters, and better performances, it can be useful for hydrological modeling in data-scarce regions.

     

Hydrological Response to Agricultural Land Use Heterogeneity Using Variable Infiltration Capacity Model

Hydrological responses corresponding to the agricultural land use alterations are critical for planning crop management strategies, water resources management, and environmental evaluations. However, accurate estimation and evaluation of these hydrological responses are restricted by the limited availability of detailed crop classification in land use and land cover. An innovative approach using state-of-the-art Variable Infiltration Capacity (VIC) model is utilized by setting up the crop-specific vegetation parameterization and analyse the effect of uniform and heterogeneous agricultural land use over the hydrological responses of the basin, in the Kangsabati River Basin (KRB). Thirteen year simulations (1998–2010) based on two different scenarios i.e., single-crop in agricultural land use (SC-ALU) and multi-crop in agricultural land use (MC-ALU) patterns are incorporated in the model and calibrated (1998–2006) and validated (2007–2010) for the streamflow at Reservoir and Mohanpur in the KRB. The results demonstrated that the VIC model improved the estimates of hydrological components, especially surface runoff and evapotranspiration (ET) at daily and monthly timescales corresponding to MC-ALU than SC-ALU (NSC?>?0.7). Grid-scale ET estimates are improved after incorporating heterogeneous agricultural land use (NSC?>?0.55 and R²?>?0.55) throughout the period of 1998–2010. This study improves our understanding on how the change in agricultural land use in the model settings alters the basin hydrological characteristics, and to provide model-based approaches for best management practices in irrigation scheduling, crop water requirement, and management strategies in the absence of flux towers, eddy covariance, and lysimeters in the basin.

     

Privacy preserving Back-propagation neural network learning over arbitrarily partitioned data

Neural networks have been an active research area for decades. However, privacy bothers many when the training dataset for the neural networks is distributed between two parties, which is quite common nowadays. Existing cryptographic approaches such as secure scalar product protocol provide a secure way for neural network learning when the training dataset is vertically partitioned. In this paper, we present a privacy preserving algorithm for the neural network learning when the dataset is arbitrarily partitioned between the two parties. We show that our algorithm is very secure and leaks no knowledge (except the final weights learned by both parties) about other party’s data. We demonstrate the efficiency of our algorithm by experiments on real world data.     

SVM based robust watermarking for enhanced medical image security

Medical images are more typical than any other ordinary images, since it stores patient’s information for diagnosis purpose. Such images need more security and confidentiality as total diagnosis depends on it. In telemedicine applications, transmission of medical image via open channel, demands strong security and copyright protection. In our proposed robust watermarking model, a double layer security is introduced to ensure the robustness of embedded data. The embedded data is scrambled using a unique key and then a transform domain based hybrid watermarking technique is used to embed the scrambled data into the transform coefficients of the host image. The data embedding in medical images involves more attention, so that the diagnosis part must not be affected by any modification. Therefore, Support Vector Machine (SVM) is used as a classifier, which classify a medical image into two regions i.e. Non Region of Interest (NROI) and Region of Interest (ROI) to embed watermark data into the NROI part of the medical image, using the proposed embedding algorithm. The objective of the proposed model is to avoid any quality degradation to the medical image. The simulation is performed to measure the Peak Signal to Noise Ratio (PSNR) for imperceptibility and Structural Similarity Index (SSIM) to test the robustness. The experimented result shows, robustness and imperceptibility with SSIM of more than 0.50 and PSNR of more than 35 dB for proposed watermarking model.

     

Linearized motion estimation for articulated planes

In this paper, we describe the explicit application of articulation constraints for estimating the motion of a system of articulated planes. We relate articulations to the relative homography between planes and show that these articulations translate into linearized equality constraints on a linear least-squares system, which can be solved efficiently using a Karush-Kuhn-Tucker system. The articulation constraints can be applied for both gradient-based and feature-based motion estimation algorithms and to illustrate this, we describe a gradient-based motion estimation algorithm for an affine camera and a feature-based motion estimation algorithm for a projective camera that explicitly enforces articulation constraints. We show that explicit application of articulation constraints leads to numerically stable estimates of motion. The simultaneous computation of motion estimates for all of the articulated planes in a scene allows us to handle scene areas where there is limited texture information and areas that leave the field of view. Our results demonstrate the wide applicability of the algorithm in a variety of challenging real-world cases such as human body tracking, motion estimation of rigid, piecewise planar scenes, and motion estimation of triangulated meshes.