Abstract

Monocular SLAM refers to using a single camera to estimate robot ego motion while building a map of the environment. While Monocular SLAM is a well studied problem, automating Monocular SLAM by integrating it with trajectory planning frameworks is particularly challenging. This paper presents a novel formulation based on Reinforcement Learning (RL) that generates fail safe trajectories wherein the SLAM generated outputs do not deviate largely from their true values. Quintessentially, the RL framework successfully learns the otherwise complex relation between perceptual inputs and motor actions and uses this knowledge to generate trajectories that do not cause failure of SLAM. We show systematically in simulations how the quality of the SLAM dramatically improves when trajectories are computed using RL. Our method scales effectively across Monocular SLAM frameworks in both simulation and in real world experiments with a mobile robot.

Abstract

We propose a novel motion segmentation formulation over spatio-temporal depth images obtained from stereo sequences that segments multiple motion models in the scene in an unsupervised manner . The motion segmentation is obtained at frame rates that compete with the speed of the stereo depth computation. This is possible due to a decoupling framework that first delineates spatial clusters and subsequently assigns motion labels to each of these cluster with analysis of a novel motion graph model. A principled computation of the weights of the motion graph that signifies the relative shear and stretch between possible clusters lends itself to a high fidelity segmentation of the motion models in the scene. Keywords: Motion

Abstract

Static power consumption is one of the most critical issues in CMOS digital circuits, and FinFET technology is being recognized as a valid solution for the problem. In this chapter, we utilize a logic-level leakage current estimation technique relying on an internal node voltage-based model. The model is implemented in the form of VHDL packages. By utilizing the capability of the model, the behavior of major leakage component has been analyzed separately for FinFET technology scaling over single- and multi-stage digital standard cells.

Abstract

The paper considers direction of arrival (DOA) estimation from long-term observations in a noisy environment. In such an environment the noise source might evolve, causing the stationary models to fail. Therefore a heteroscedastic Gaussian noise model is introduced where the variance can vary across observations and sensors. The source amplitudes are assumed independent zero-mean complex Gaussian distributed with unknown variances (i.e. the source powers), leading to stochastic maximum likelihood (ML) DOA estimation. The DOAs of plane waves are estimated from multi-snapshot sensor array data using sparse Bayesian learning (SBL) where the noise is estimated across both sensors and snapshots. Simulations demonstrate that taking the heteroscedastic noise into account improves DOA estimation.

Abstract

Post-capture control of the focus position of an image is a useful photographic tool. Changing the focus of a single image involves the complex task of simultaneously estimating the radiance and the defocus radius of all scene points. We introduce RefocusGAN, a deblurthen-reblur approach to single image refocusing. We train conditional adversarial networks for deblurring and refocusing using wide-aperture images created from light-fields. By appropriately conditioning our networks with a focus measure, an in-focus image and a refocus control parameter δ, we are able to achieve generic free-form refocusing over a single image.

Abstract

t. In addition to maximizing the revenue, retailers also aim at diversifying product offerings for facilitating sustainable revenue generation in the long run. Thus, it becomes a necessity for retailers to place appropriate itemsets in a limited k number of premium slots in retail stores for achieving the goals of revenue maximization and itemset diversification. In this regard, research efforts are being made to extract itemsets with high utility for maximizing the revenue, but they do not consider itemset diversification i.e., there could be duplicate (repetitive) items in the selected top-utility itemsets. Furthermore, given utility and support thresholds, the number of candidate itemsets of all sizes generated by existing utility mining approaches typically explodes. This leads to issues of memory and itemset retrieval times. In this paper, we present a framework and schemes for efficiently retrieving the top-utility itemsets of any given itemset size based on both revenue as well as the degree of diversification. Here, higher degree of diversification implies less duplicate items in the selected top-utility itemsets. The proposed schemes are based on efficiently determining and indexing the topλ high-utility and diversified itemsets. Experiments with a real dataset show the overall effectiveness and scalability of the proposed schemes in terms of execution time, revenue and degree of diversification w.r.t. a recent existing scheme.

Abstract

The present work is a multi-temporal satellite based spatial dynamic study of an important coastal habitat, the Pichavaram mangrove ecosystem, over a period of 15 years. The study discusses the importance and the status of mangroves on both global and regional scales. Maximum likelihood estimate method of supervised classification technique has been used to classify the land use-land cover changes in the Pichavaram Reserve Forest, Killai Reserve Forest and Pichavaram Extension. The status of the classes has been monitored using Landsat ETM+ of 2000, 2006, 2011, and OLI of 2016 satellite imageries. The total area of the Pichavaram mangrove showed a net increase of approximately 11.41% of the total study area within a span of 15 years (2000–2016)

Abstract

Many domain specific decisions of our day-to-day lives are dependent on weather. Weather and its variables have a significant impact on crop gro wth stages and crop producti vity.Weather forecast service has been started by In dia Meteorological Department (IMD) from the year 1945. To help the farming community, IMD has started pro viding district level agro-meteorological advisory based on the district-level Medium Range Forecast (MRF) f rom the year2008 fo r all districts through 130 agro-meteorological field units (AMFUs). Based on the MRF for five days, each AMFU is providing crop-s pecific agromet advice for the districts covered by that AMFU. To impro ve the effectiveness, IMD has started block level MRF on an experimental basis from the year 2015. Based on block level MRF, IMD is planning to provide block-level agromet advisory based on the block-level MRF. In such a situation, agromet scientisst haveto prepare agromet advisory for several blocks and crops at the earliest after receiving the block level MRF which will be disseminated to the farmers and stakeholders. In this context, research efforts have to be made to develop efficient system to enable agromet scientists to prepare the block-level agromet advice based on the block-level MRF. Normally, it can be observed that the weather and cro ps of nearby blocks may not vary significantly. S o, there is an opportunity to reuse the agromet advice prepared for one block to the nearby blocks. In this paper, we have pro posed an IT- based framewo rk to enable the agromet scientists to prepare the block level agromet advisoryby exploitingthe fact that the agromet advice which has been prepared for one block can be reused for another block if the weather condition and the crop is the same. The proposed framework pro vides the scope to exploit the reuse of agromet advisory prepared for one block toother blocks and could improve the efficiency of agromet scientists for the preparation of block-level agromet advisory based on the MRF

Abstract

Weather-based decision support systems (DSSs) are being built to improve the efficiency of the production systems in the domains of healthcare, agriculture, transport, governance and so on. Normally, a weather con-dition (WC) is represented by the statistical values of weather variables for a given duration (e.g. a day or a week). In a weather-based DSS, given a WC, the domain experts prepare the appropriate suggestions to improve the efficiency of the stakeholders. Normally, once the domain experts prepare a suggestion for a given WC that belongs to certain period (e.g. year or season), there is a scope to reuse the same suggestion for the similar WCs of other period(s). As a result, the performance of the DSS could be improved. In this paper, to improve reuse, we have proposed a notion of category-based WC (CWC) which is formed by using the categories of weather variables in the respective domain. By considering the context of agromet advisory service operated by India Meteorological Department (IMD) and the corresponding weather categories provided by IMD, we have ana-lyzed the extent of reuse among CWCs by conducting the experiments on 30 years of weather data collected at Rajendranagar, Hyderabad, Telangana state. The experiments are conducted by considering two types of CWCs with the duration equal to one day and five days. By varying the number of weather variables in CWC from one to five, we have computed the extent of reuse among CWCs of different periods of the following period types: year, season, and phenophases (i.e., growth stages) of the Rice crop. The results show that there is a significant similarity among the CWCs of the given period and the CWCs of the preceding periods of each period type. For any domain including agriculture, the results provide an opportunity to improve the efficiency of weather-based agricultural DSSs by improving the reuse of the weather-based suggestions

Abstract

t. An attempt has been made to retrieve columnar density of methane (XCH4) over a suburban area of the Shadnagar in India from the high spatial and moderate spectral resolution (5 nm
0.5 nm) airborne visible/infrared imaging spectrometer next generation (AVIRIS-NG) data. The data collected were part of the joint airborne campaign by National Aeronautics Space Administration/Jet Propulsion Laboratory and Indian Space Research Organisation over several Indian sites during December 2015 to January 2016. The XCH4 was retrieved following the Moderate Resolution Imaging Spectroradiometer (MODIS) water vapor retrieval algorithm and was compared with the ground-based Fourier transform infrared (FTIR) observations. FTIR measured CH4 transmittance was fitted against Fast Atmospheric Signature Code3 (FASCODE3) simulated transmittance, and found their residual lies within
1%, which shows a very good retrieval sensitivity of FTIR. Hence FTIR observations are used for the validation of AVIRIS-NG derived XCH4 and found in good agreement. In the present study, we utilized airborne imaging radiance data in the CH4 absorbing channel at 2.365
0.006 μm and nonabsorbing channels at 2.325
0.006 μm and 2.365
0.006 μm to compute atmospheric transmittance spectrum. Using a line-by-line radiative transfer algorithm, the XCH4 was estimated from AVIRIS-NG transmittance data. Prior to the application of the MODIS water vapor algorithm on AVIRIS-NG transmittance, it was also compared with the FASCODE3 simulated transmittance and found comparable. Study revealed that within the Shadnagar region, retrieved XCH4 varies little to moderate with an average of 9 × 1027 molecules cm−2 with the highest value of 12 × 1027 molecules cm−2 near the source region of the agriculture field. The study demonstrates the capability of AVIRIS-NG to quantify the column density of atmospheric species such as CH4 over a region