IRON-MAN: An Approach To Perform Temporal Motionless Analysis of Video using CNN in MPSoC

This paper proposes a novel human-inspired methodology called IRON-MAN ( Integrated RatiONal prediction and Motionless ANalysis ) for mobile multi-processor systems-on-chips (MPSoCs). The methodology integrates analysis of the previous image frames of the video to represent the analysis of the current frame in order to perform Temporal Motionless Analysis of the Video ( TMAV ). This is the first work on TMAV using Convolutional Neural Network (CNN) for scene prediction in MPSoCs. Experimental results show that our methodology outperforms state-of-the-art. We also introduce a metric named, Energy Consumption per Training Image ( ECTI ) to assess the suitability of using a CNN model in mobile MPSoCs with a focus on energy consumption and lifespan reliability of the device

TMAV: Temporal Motionless Analysis of Video using CNN in MPSoC

Analyzing video for traffic categorization is an important pillar of Intelligent Transport Systems. However, it is difficult to analyze and predict traffic based on image frames because the representation of each frame may vary significantly within a short time period. This also would inaccurately represent the traffic over a longer period of time such as the case of video. We propose a novel bio-inspired methodology that integrates analysis of the previous image frames of the video to represent the analysis of the current image frame, the same way a human being analyzes the current situation based on past experience. In our proposed methodology, called IRON-MAN (Integrated Rational prediction and Motionless ANalysis), we utilize Bayesian update on top of the individual image frame analysis in the videos and this has resulted in highly accurate prediction of Temporal Motionless Analysis of the Videos (TMAV) for most of the chosen test cases. The proposed approach could be used for TMAV using Convolutional Neural Network (CNN) for applications where the number of objects in an image is the deciding factor for prediction and results also show that our proposed approach outperforms the state-of-the-art for the chosen test case. We also introduce a new metric named, Energy Consumption per Training Image (ECTI). Since, different CNN based models have different training capability and computing resource utilization, some of the models are more suitable for embedded device implementation than the others, and ECTI metric is useful to assess the suitability of using a CNN model in multi-processor systems-on-chips (MPSoCs) with a focus on energy consumption and reliability in terms of lifespan of the embedded device using these MPSoCs

Synthesis, Characterisation, Structure, and Magnetic Properties of [Ni{H 2 NCH(CH 3 )CH 2 NH 2 } 3 ][Fe(CN) 5 NO]·H 2 O

The complex [Ni(pn

SoCodeCNN: Program Source Code for Visual CNN Classification Using Computer Vision Methodology

Automated feature extraction from program source-code such that proper computing resources could beallocated to the program is very difficult given the current state of technology. Therefore, conventionalmethods call for skilled human intervention in order to achieve the task of feature extraction from programs.This research is the first to propose a novel human-inspired approach to automatically convert programsource-codes to visual images. The images could be then utilized for automated classification by visualconvolutional neural network (CNN) based algorithm. Experimental results show high prediction accuracyin classifying the types of program in a completely automated manner using this approach

Sauromatum horsfieldii (Araceae – Areae): an addition to the Flora of India

Sauromatum horsfieldii (Araceae – Areae) is reported here as a new record for India. A detailed description and photographic illustration are provided, along with an account and revised key of the Indian species of Sauromatum

Synthesis and characterization of diorganotin(IV) dichloride adducts of Schiff bases

1177-1181Four diorganotin(IV) dichloride adduct complexes, Me2SnCl2(HL)2 and Ph2SnCl2(HL)2, derived from bidentate N-substituted salicylideneimine ligands, HL [o-HOC6H4CH= NC6H4R-4; R = Me, Cl, OMe] have been synthesized by the reaction of dimethyl- or diphenyltin(IV) dichloride with the respective Schiff bases in refluxing ethanol and characterized by IR, NMR (1H, 13C, 119Sn) spectroscopy and elemental analysis. The molecular structure of Me2SnCl2(HOC6H4CH=NC6H4Me-4)2 1a is determined by single crystal X-ray diffraction. The structural analysis reveal that two crystallographically independent molecules reside on centres of symmetry. The tin atoms display all-trans octahedral coordination, the two Schiff base ligands being O-bonded as iminium phenolato zwitterions. Solution NMR studies indicate similar coordination for all the four compounds

India’s photovoltaic potential amidst air pollution and land constraints

Summary: India aims for ambitious solar energy goal to fulfill its climate commitment but there are limited studies on solar resource assessment considering both environmental and land availability constraints. The present work attempts to address this issue using satellite-derived air pollution, radiation, and land use data over the Indian region. Surface insolation over India has been decreasing at a rate of −0.29 ± 0.19 Wm-2 y−1 between 2001 and 2018. Solar resources over nearly 98%, 40%, and 39% of the Indian landmass are significantly impacted by aerosols, clouds, and both aerosols and clouds respectively. Only 29.3% of the Indian landmass is presently suitable for effective solar photovoltaic harnessing, but this is further declining by −0.21% annually, causing a presumptive loss of 50 GW solar potential, translating 75 TWh power generation. Lowering two decades of aerosol burden can make 8% additional landmass apt for photovoltaic use. Alleviating aerosol-induced dimming can fast-track India’s solar energy expansion