Low Cost Device for Charging Mobile Phone using Another Smartphone

Mobile has been and will always remain one of the best companions for any human being. Mobile phones are considered as the live example of the advancement in technology on a daily basis. This era is marked by our complete dependence on this technology. The growing technology has introduced mobile phone, which plays an important role in communication. Since mobile phones have been made to be with the user all day and to carry out all the basis and high-performance task as per the demand of the user, the batteries need to be charged multiple times during a day. This imposes a burden on the user to carry a power bank while travelling; at times it becomes difficult if the power bank battery also drains out. This paper presents a small technique which may reduce this problem. The major components of the design are a capacitor of 2200 μF at 5.63 V and LED 1.5 V. The experimental data shows that the charging level of a mobile battery of 2100 mAh can be enhanced from 10-19 % in 35 minutes by consuming only 10% of the total energy of the other smart phone of battery 4000 mAh. Another experimental data shows that the charging level of a mobile battery of 2000 mAh can be enhanced from 14-37 % in 60 minutes by consuming only 20% of the total energy of the other smart phone of battery 4000 mAh. This low cost and simple designed USB On-the-Go (OTG) extension can now replace the necessity of carrying a power bank while travelling, which is expensive as compared to the above proposed technique as well.Citation: Gupta, V., Aggarwal, V., Sharma, K., and Sharma, N. (2018). Low Cost Device for Charging Mobile Phone using Another Smartphone. Trends in Renewable Energy, 4, 77-82. DOI: 10.17737/tre.2018.4.3.005

Phenotype-preserving metric design for high-content image reconstruction by generative inpainting

In the past decades, automated high-content microscopy demonstrated its ability to deliver large quantities of image-based data powering the versatility of phenotypic drug screening and systems biology applications. However, as the sizes of image-based datasets grew, it became infeasible for humans to control, avoid and overcome the presence of imaging and sample preparation artefacts in the images. While novel techniques like machine learning and deep learning may address these shortcomings through generative image inpainting, when applied to sensitive research data this may come at the cost of undesired image manipulation. Undesired manipulation may be caused by phenomena such as neural hallucinations, to which some artificial neural networks are prone. To address this, here we evaluate the state-of-the-art inpainting methods for image restoration in a high-content fluorescence microscopy dataset of cultured cells with labelled nuclei. We show that architectures like DeepFill V2 and Edge Connect can faithfully restore microscopy images upon fine-tuning with relatively little data. Our results demonstrate that the area of the region to be restored is of higher importance than shape. Furthermore, to control for the quality of restoration, we propose a novel phenotype-preserving metric design strategy. In this strategy, the size and count of the restored biological phenotypes like cell nuclei are quantified to penalise undesirable manipulation. We argue that the design principles of our approach may also generalise to other applications.Comment: 8 pages, 3 figures, conference proceeding

A Deep Learning Framework Using Data Augmentation for Accuracy Improvement to Analyze Users Posts on Social Media to Find Signs of Mental Illness

Through their posts on social media, users frequently express their feelings. A deep learning model was developed for this study to determine a user’s mental state based on the data they posted. We gathered articles for this purpose from Reddit forums dedicated to mental health. Our suggested model may disorder, anxiety, depression and Schizophrenia by examining  and learning posting information published by users. Based on their posts, we think our algorithm can help identify people who could be experiencing mental illness. The consequences of this model, which may be used in combination to other methods to track the mental health of individuals who use internet extensively, are also discussed in this paper

Solar Photovoltaic System

The history of solar cell development is briefly outlined, and the properties of the sun and solar radiation are reviewed. Properties of semiconductor materials that are important in the design and operation of solar cells are reviewed. The physical mechanisms involved in the generation and recombination of excess carriers are discussed and the basic equations of device physics are given. Both the dark and illuminated properties of p-n junctions are analyzed. Energy conversion efficiency limits are discussed for the photovoltaic process as well as the effects of various nonidealities on efficiency. Techniques for measuring the efficiency of photovoltaic devices are also described. The standard technology for making silicon solar cells is reviewed, and improved silicon cell technology is discussed. Considerations relevant to the detailed design of silicon cells are discussed. Several alternative device concepts are outlined and the structure and properties of solar cells made on some of the more developed alternatives to single-crystal silicon are discussed. Concentrating systems and photovoltaic systems components and applications are described. The design of stand-alone, residential, and centralized photovoltaic power systems is discussed. Solar photovoltaic modules are where the electricity gets generated but are only one of the many parts in a complete photovoltaic (PV) system. In order for the generated electricity to be useful in a home or business, a number of other technologies must be in place

Numerical Simulation and Design of Low PAPR FBMC Communication System for 5G Applications

Unlike SC-FDMA (Single-Carrier Frequency Division Multiple Access), merging only DFT (Discrete Fourier Transform) addition with FBMC-OQAM (filter group multi-carrier with offset quadrature amplitude modulation) only cuts the marginal PAPR. (Peak-to-average power ratio). To take advantage of the single carrier effect of DFT extension, special conditions for the coefficients of the IQ (in-phase and quadrature phase) channels of every single subcarrier ought to be met. As a beginning point, we first originate this form, which we call the ITSM (Identical Time-Shifted Multi-Carrier) condition. Then, depending on this condition, we put forward a new FBMC for low PAPR. The foremost features of the offered way out are summarized as: First, to additionally raise the PAPR reduction, we created four candidate versions of the FBMC waveform for DFT spreading out and ITSM conditions and carefully chosen one with the least peak power. Even with various candidate generations, unlike the traditional SI (Side information) based PAPR reduction scheme, the focal computational fragments (such as DFT and IDFT) are shared and need only be executed one time. Therefore, matched to the prior DFT-expanded FBMC, the overhead in complexity is small, and the recommended pattern can realize a PAPR reduction comparable to SC-FDMA. Second, in the projected pattern each one pass on only two bits of SI from a block of FBMC-OQAM symbols. And so, the SI overhead is meaningfully lesser than a conventional SI-based scheme such as SLM (Selective Mapping) or PTS (Partial Transmission Sequence).The whole work is executed using MATLAB software. The PAPR of FBMC system has been significantly reduced after the application of proposed algorithm. PAPR was reduced by 25 % after the use of DFT spreading and ITSM conditioning

Input Prioritization for Testing Neural Networks

Deep neural networks (DNNs) are increasingly being adopted for sensing and control functions in a variety of safety and mission-critical systems such as self-driving cars, autonomous air vehicles, medical diagnostics, and industrial robotics. Failures of such systems can lead to loss of life or property, which necessitates stringent verification and validation for providing high assurance. Though formal verification approaches are being investigated, testing remains the primary technique for assessing the dependability of such systems. Due to the nature of the tasks handled by DNNs, the cost of obtaining test oracle data---the expected output, a.k.a. label, for a given input---is high, which significantly impacts the amount and quality of testing that can be performed. Thus, prioritizing input data for testing DNNs in meaningful ways to reduce the cost of labeling can go a long way in increasing testing efficacy. This paper proposes using gauges of the DNN's sentiment derived from the computation performed by the model, as a means to identify inputs that are likely to reveal weaknesses. We empirically assessed the efficacy of three such sentiment measures for prioritization---confidence, uncertainty, and surprise---and compare their effectiveness in terms of their fault-revealing capability and retraining effectiveness. The results indicate that sentiment measures can effectively flag inputs that expose unacceptable DNN behavior. For MNIST models, the average percentage of inputs correctly flagged ranged from 88% to 94.8%

A Classification and Assessment of Research Streams on Low Cost Modeling in Civil Aviation Transportation Industry

This article attempts to identify key research streams in Civil Aviation Transportation Industry during the past decade and highlights the evolution of the literature. Progress in six established research thrusts and a new research stream is discussed. Using content analysis, the existing research is also examined from a methodological point of view. The review provides evidence for an increasingly sophisticated and rich body of knowledge in global Civil Aviation Transportation Industry. Keywords: Civil Aviation Transportation Industry (CATI), Low Cost Strategies (LCS), Low Cost Carriers (LCCs), Classification, Assessment