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We analyse the results from a longitudinal natural field experiment that provides an environmental nudge by reinforcing positive environmental norms. Posters asking individuals to use less paper are pasted on printers (Times) and in office cubicles (Umendall) of two information technology firms around New Delhi, India. A third firm (India Mart) does not get the nudge and serves as our control. During the nudge period, employees of the firms that get the conservation message on average expend a significantly lower number of sheets per day as compared to the pre-nudge period, measured both in terms of paper used and paper wasted. Furthermore, after the nudge is removed, the per capita paper wastage for one of the firms that received it (Times) does not increase significantly over the next month and a half, whereas for the other firm (Umendall) it does but remains significantly lower than the per capita wastage in the pre-nudge period. In terms of total paper used, the nudged firms show a reduction in the number of sheets used several weeks after the nudge is removed. For the control firm, the wastage and usage of printer paper does not change significantly over the duration of the experiment. A robustness check with no nudge intervention done by computing per capita wastage and usage for the same dates exactly one year later does not show a reduction over the same time period and offers support for our conclusions. [ABSTRACT FROM AUTHOR]

For a long time, Polarimetric Synthetic Aperture Radar (PolSAR) data was not available free of cost, so the applications were limited. With the recent increase in availability of PolSAR data due to missions like ALOS-2, UAVSAR and Sentinel, the data can be acquired through these sensors periodically. Since the volume of data is large, applying traditional classifiers and conventional machine learning algorithms becomes confounding, as in that case, manual feature extraction must be done by the researcher for training the model. To automate this feature extraction step and accelerate the process, many researchers have used various deep learning algorithms. However, most studies fail to tap into the potential of PolSAR data by not utilizing the complete range of complex data of float-32 bit-depth. The work in this paper suggests the development of a generalized deep learning model for nine elements of coherency matrix [ T 3 ] in the float-32 data-space from ALOS/PALSAR-2 L-Band sensor. Semantic segmentation is used to classify land-covers into various classes like water, settlement, forest, open land, wetlands etc. This is done through modification on the UNet backbone. The deep learning model is trained using data on San Francisco and New Delhi regions and tested on data from Mumbai region. It is observed that the classification accuracy for Mumbai region is " 93.82 % ". This kind of system can assist the decision makers like urban planners to take informed decisions. [ABSTRACT FROM AUTHOR]

An automatic detection method for disease diagnosis plays an important role in the medical field. A computer-aided diagnosis (CAD) system for seizure detection using electroencephalogram (EEG) data is significant in protecting patients' lives. This paper presents machine learning algorithm-based seizure detection approach implemented in both software and hardware using EEG recordings. The input EEG signal is subjected to pre-processing, decomposition using wavelet transform and classification by a support vector machine (SVM) classifier. The maximum and variance statistical values of the decomposed EEG sub-bands are computed to generate a feature vector, while classification accuracy and computational complexity are taken into account. Gaussian Radial Basis Function (RBF) kernel based Non-Linear SVM (NLSVM) is used in this proposed approach to solve both two-class and three-class classification problems. In three-class classification, the one-against-one (OAO) technique is used. The competency of the proposed technique is evaluated using two distinct datasets provided by Bonn University, Germany & Neurology and Sleep Centre, New Delhi. The adequate parameters of the NLSVM classifier model are determined by using MATLAB software in the training phase and the best performing NLSVM model is selected for implementation on the Field-Programmable Gate Array (FPGA). The proposed seizure detection method is implemented with Verilog code and examined on a Xilinx Zynq-7000 XC7Z020 FPGA board to evaluate its performance. The experimental results show that the proposed method with a minimal number of features achieved a good performance metric for seizure detection and is comparable with that of the state-of-the-art methods. [ABSTRACT FROM AUTHOR]