Study of fuel insolubles: Formation conditions and characterization of copper compounds

The solids formed in the systems containing copper in different oxidation states, in the presence and absence of hexanoic acid and dodecanethiol, together and separately, at room temperature, were studied. The experiments were performed in a mineral oil matrix, free of sulfur compounds and metals; a diesel oil matrix; and the reagents alone, without any matrix. The deposits formed were analyzed by elemental analysis (EA), infrared spectroscopy (IR), thermogravimetric analysis (TGA) and total copper by inductively coupled plasma-optical emission spectrometry (ICP-OES), for their identification and determination of the probable structures. In samples containing both hexanoic acid and dodecanethiol, when there is copper(I), anhydrous copper(II) hexanoate is preferentially formed, but in samples of copper(II), copper(II) mercaptate is formed first. In samples of metallic copper in mineral oil matrix, no deposit formations occur. In all cases in which deposits were formed, they were the same as in diesel oil matrix.     

Design of Online Vitals Monitor by Integrating Big Data and IoT

In this work, we design a multisensory IoT-based online vitals monitor (hereinafter referred to as the VITALS) to sense four bedside physiological parameters including pulse (heart) rate, body temperature, blood pressure, and peripheral oxygen saturation. Then, the proposed system constantly transfers these signals to the analytics system which aids in enhancing diagnostics at an earlier stage as well as monitoring after recovery. The core hardware of the VITALS includes commercial off-the-shelf sensing devices/medical equipment, a powerful microcontroller, a reliable wireless communication module, and a big data analytics system. It extracts human vital signs in a pre-programmed interval of 30 min and sends them to big data analytics system through the WiFi module for further analysis. We use Apache Kafka (to gather live data streams from connected sensors), Apache Spark (to categorize the patient vitals and notify the medical professionals while identifying abnormalities in physiological parameters), Hadoop Distributed File System (HDFS) (to archive data streams for further analysis and long-term storage), Spark SQL, Hive and Matplotlib (to support caregivers to access/visualize appropriate information from collected data streams and to explore/understand the health status of the individuals). In addition, we develop a mobile application to send statistical graphs to doctors and patients to enable them to monitor health conditions remotely. Our proposed system is implemented on three patients for 7 days to check the effectiveness of sensing, data processing, and data transmission mechanisms. To validate the system accuracy, we compare the data values collected from established sensors with the measured readouts using a commercial healthcare monitor, the Welch Allyn® Spot Check. Our proposed system provides improved care solutions, especially for those whose access to care services is limited.     

Mathematical modeling of a box-type solar cooker employing an asymmetric compound parabolic concentrator

A novel design of solar cooker is introduced. The cooker is of box-type equipped with an asymmetric compound parabolic concentrator (CPC) as booster-reflector. It consists of an insulated box equipped with a vertical double glazing cover on a side, and a vertical absorber plate laid out just behind the transparent cover. The booster-reflector is fixed on the glazed side of the box. The absorber plate and the glazing form a vertical channel, open at the top and bottom, and enclosed at the sides. The two openings allow the inside air circulation. A mathematical model of the heat transfer processes involved with this solar cooker, containing a cooking pot loaded with water and deposited on the box floor; was developed and the effects of various parameters, such as solar radiation, load of water and clouds on the dynamic behavior of the cooker are studied.     

Generation of green hydrogen using self-sustained regenerative fuel cells: Opportunities and challenges

The ever-increasing energy demand, depleting fossil fuel reserves, and rising temperatures due to greenhouse gas emissions have necessitated the transition towards the generation of green and clean energy through renewable energy sources. Solar energy is one such renewable energy source that has received significant attention owing to its abundance and inexhaustibility. However, solar energy alone cannot replace fossil fuels in the energy portfolio. There exists a need to develop another clean energy source that can potentially act as an alternative to conventional fuels. Hydrogen proves to be an ideal candidate in this domain and can be sustainably generated by water electrolysis by powering the electrolyzer using solar energy. The hydrogen thus synthesized has net zero carbon emissions and is a suitable asset for decarbonizing the environment. This review encompasses the generation of hydrogen using PV-Electrolyzer systems and addresses the challenges associated with the same. Overcoming these drawbacks can ensure a strong position for hydrogen as an alternative fuel in the energy infrastructure. By employing electrolyzers that are fueled by renewable energy and then using that hydrogen to feed a fuel cell, this study aims to clarify the potential and constraints of producing green hydrogen. Since this area of research has not yet been fully investigated, a review article that enables and encourages academics to develop original solutions is urgently needed.     

Enhanced photocatalytic activity of graphene oxide/titania nanosheets composites for methylene blue degradation

In the present work, we report enhanced photocatalytic degradation of methylene blue dye in aqueous solution by using ultra-thin anatase TiO₂ nanosheets (NSs) combined with graphene oxide (GO) as a photocatalyst. The two-dimensional ultra-thin anatase TiO₂ NSs are fabricated via chemical exfoliation. By completely delaminating a lepidocrocite-type layered protonic titanate H_xTi_2−x/4□_x/4O₄·H₂O (x=0.7, □: vacancy) into individual layers through ion exchange with tetrabutylammonium (TBA⁺) cations, well-dispersed ultra-thin colloidal Ti_0.91O₂ NSs with a lateral size up to a few micrometers are obtained. Subsequent acid treatment induces colloidal Ti_0.91O₂ to reassemble and precipitate into a gelation form, followed by thermal annealing to convert the Ti_0.91O₂ gelation into anatase TiO₂ nanosheets as photocatalyst for methylene blue degradation. TiO₂ NSs show a high photocatalytic degradation efficiency of 53.2% due to the ultra-thin thickness for facile electron transfering and large surface area for methylene blue absorption. Moreover, photocatalytic effect can be further improved by simply adding GO suspension to achieve colloidal self-assembly of GO and TiO₂ NSs. An optimal GO content of 3 wt% further increases the photocatalytic degradation efficiency to 91.2% due to faster electron–hole seperation and improved surface area provided by GO. This work provides a simple but effective approach by combing graphene oxide with TiO₂ nanosheets synthesized via the exfoliation method for methylene blue degradation.     

Architectural implementation of modified K-best algorithm for detection in MIMO systems

We present novel architectures for the modified K-best algorithm and its very-large-scale integration implementation for spatially multiplexed wireless multiple-input multiple-output systems. The objective was to propose a simplified architecture based on the algorithm and to significantly improve the suitability for hardware implementation. Two different architecture designs were proposed: a distributed arithmetic- based tree-search detector and a breadth-first search sphere detector. The implementations were performed to obtain a configurable architectural solution for different antenna configurations and constellations. The synthesis analysis shows that the proposed architectures achieve a throughput of > 500 Mbps with reduced hardware complexity compared to previously reported architectures.     

Quantization of deep neural networks for accumulator-constrained processors

We introduce an Artificial Neural Network (ANN) quantization methodology for platforms without wide accumulation registers. This enables fixed-point model deployment on embedded compute platforms that are not specifically designed for large kernel computations (i.e. accumulator-constrained processors). We formulate the quantization problem as a function of accumulator size, and aim to maximize the model accuracy by maximizing bit width of input data and weights. To reduce the number of configurations to consider, only solutions that fully utilize the available accumulator bits are being tested. We demonstrate that 16 bit accumulators are able to obtain a classification accuracy within 1% of the floating-point baselines on the CIFAR-10 and ILSVRC2012 image classification benchmarks. Additionally, a near-optimal 2 × speedup is obtained on an ARM processor, by exploiting 16 bit accumulators for image classification on the All-CNN-C and AlexNet networks.     

Modeling of an efficient high power wind energy conversion system using self-excited multi-phase machines

Self-Excited Multi-phase Induction Generators (SEMPIG) producing power from wind turbines infar-flung fields are receiving the interest of recent researchers. Small and medium energy wind generators involve 3-phase induction generators. SEMPIG with significant reliability, better fault tolerance, reduced harmonics, and high power-handling capability using reduced current per phase and maintaining the same per phase voltage makes it the promising contender in high-power WECS. This investigation analyzes the proficiency of energy efficient stand-alone self-excited seven-phase induction generator (SE7PIG) in wind energy schemes. This paper develops a cogent model from the two-phase equivalent circuit of SE7PIG using Matlab/Simulink. The study examines the modeling aspects of the SE7PIG with incorporated significant 7-rectifier and 7-phase inverter. The machine operational parameters speed, torque, voltage and current are presented. The 2MW WECS with 7-phase induction generator is analyzed with and without DC link converter. The results are compared with the 3-phase counterpart to illustrate the efficacy of SE7PIG for megawatt wind-power generation. The paper confirms the competence of SE7PIG with better fault tolerance and significant energy output with a decrease in phase current.     

LLVM-based automation of memory decoupling for OpenCL applications on FPGAs

The availability of OpenCL High-Level Synthesis (OpenCL-HLS) has made FPGAs an attractive platform for power-efficient high-performance execution of massively parallel applications. At the same time, new design challenges emerge for massive thread-level parallelism on FPGAs. One major execution bottleneck is the high number of memory stalls exposed to data-path which overshadows the benefits of data-path customization.This article presents a novel LLVM-based tool for decoupling memory access from computation when synthesizing massively parallel OpenCL kernels on FPGAs. To enable systematic decoupling, we use the idea of kernel parallelism and implement a new parallelism granularity that breaks down kernels to separate data-path and memory-path (memory read/write) which work concurrently to overlap the computation of current threads^[1] with the memory access of future threads (memory pre-fetching at large scale). At the same time, this paper proposes an LLVM-based static analysis to detect the decouplable data for resolving the data dependency and maximize concurrency across the kernels.The experimental results on eight Rodinia benchmarks on Intel Stratix V FPGA demonstrate significant performance and energy improvement over the baseline implementation using Intel OpenCL SDK. The proposed sub-kernel parallelism achieves more than 2x speedup, with only 3% increase in resource utilization, and 7% increase in power consumption which reduces the overall energy consumption more than 40%.