Dynamic Voltage and Frequency Scaling for Wireless Network-on-Chip

Previously, research and design of Network-on-Chip (NoC) paradigms where mainly focused on improving the performance of the interconnection networks. With emerging wide range of low-power applications and energy constrained high-performance applications, it is highly desirable to have NoCs that are highly energy efficient without incurring performance penalty. In the design of high-performance massive multi-core chips, power and heat have become dominant constrains. Increased power consumption can raise chip temperature, which in turn can decrease chip reliability and performance and increase cooling costs. It was proven that Small-world Wireless Network-on-Chip (SWNoC) architecture which replaces multi-hop wire-line path in a NoC by high-bandwidth single hop long range wireless links, reduces the overall energy dissipation when compared to wire-line mesh-based NoC architecture. However, the overall energy dissipation of the wireless NoC is still dominated by wire-line links and switches (buffers). Dynamic Voltage Scaling is an efficient technique for significant power savings in microprocessors. It has been proposed and deployed in modern microprocessors by exploiting the variance in processor utilization. On a Network-on-Chip paradigm, it is more likely that the wire-line links and buffers are not always fully utilized even for different applications. Hence, by exploiting these characteristics of the links and buffers over different traffic, DVFS technique can be incorporated on these switches and wire-line links for huge power savings. In this thesis, a history based DVFS mechanism is proposed. This mechanism uses the past utilization of the wire-line links & buffers to predict the future traffic and accordingly tune the voltage and frequency for the links and buffers dynamically for each time window. This mechanism dynamically minimizes the power consumption while substantially maintaining a high performance over the system. Performance analysis on these DVFS enabled Wireless NoC shows that, the overall energy dissipation is improved by around 40% when compared Small-world Wireless NoCs

Bidirectional Encoder Representations Transformers for Improving CNN-LSTM Covid-19 Disease Detection Classifier

Early identification of COVID-19 diseased persons is crucial to avoid and prevent the transmission of the SARS-CoV-2 virus. To achieve this, lung Computed Tomography (CT) scan segmentation and categorization models have been broadly developed for COVID-19 diagnosis. Amongst, Multi-Scale function learning with an Attention-based UNet and Marginal Space Deep Ambiguity-attentive Transfer Learning (MS-AUNet-MSDATL) framework is developed to concurrently segment the COVID-19 infected regions and classify their risk levels from the CT/Chest X-Ray (CXR) scans. This model utilizes Convolutional Neural Network–Long Short Term Memory (CNN-LSTM) as a classifier for proper recognition. Although CNN-LSTM efficiently learns the spatial and temporal data, but it highly ignores the pixels and their adjacent information which results in lower classification rate. So, in this paper, Bidirectional Encoder Representations from Transformers (BERT) is introduced along with CNN-LSTM in MS-AUNet-MSDATL model to resolve the above mentioned issues for efficient COVID-19 risk level classification. Initially, the segmented CT and CXR images from MS-AUNet is given as input to the BERT model. BERT structure consist of stack of transformer encoder layers which extracts fixed features from a pre-trained models to obtain the numerical representation of the given image.  Then, the numerical expressions from the BERT are transformed to pre-learned CNN model for selecting the important features from the given representations.  The LSTM model receives the CNN output and generates a new representation based on the data order. In addition, Fully Connected Layer (FCL) maps the CNN-LSTM results into categorization classes to learn pixels and their adjacent information for COVID-19 risk level detection and diagnosis.  The complete work is termed as MS-AUNet-EMSDATL.  Finally, the test findings shows that the MS-AUNet-MS-B-DATL achieves accuracy of 98.67% and 98.55% on CT and CXR images compared to the other existing frameworks

A photovoltaic system using supercapacitor energy storage for power equilibrium and voltage stability

In a photovoltaic system, a stable voltage and of tolerable power equilibrium is needed. Hence, a dedicated analog charge controller for a storage system which controls energy flow to impose power equilibrium, and therefore, voltage stability on the load is required. We demonstrate here our successful design considerations employing supercapacitors as main energy storage as well as a buffer in a standalone photovoltaic system, incorporating a dedicated supercapacitor charge controller for the first time. Firstly, we demonstrated a photovoltaic system employing supercapacitors as main energy storage as well as a buffer in a standalone photovoltaic system. Secondly, we design a constant voltage maximum power point tracker (MPPT) for peak power extraction from the photovoltaic generator. Thirdly, we incorporated a supercapacitor charge controller for power equilibrium and voltage stability through a dedicated analog charge controller in our design, the first of its kind. Fourthly, we analyzed the use of supercapacitor storage to mitigate disequilibrium between power supply and demands, which, in turn, causes overvoltage or under voltage across the load. Lastly, we then went ahead to demonstrate the control of the energy flow in the system so as to maintain rated voltage across a variant demand load

Point torque representations of ciliary flows

Ciliary flows are generated by a vast array of eukaryotic organisms, from unicellular algae to mammals, and occur in a range of different geometrical configurations. We employ a point torque -- or `rotlet' -- model to capture the time-averaged ciliary flow above a planar rigid wall. We demonstrate the advantages (i.e. accuracy and computational efficiency) of using this, arguably simpler approach compared to other singularity-based models in Stokes flows. Then, in order to model ciliary flows in confined spaces, we extend the point torque solution to a bounded domain between two plane parallel no-slip walls. The flow field is resolved using the method of images and Fourier transforms, and we analyze the role of confinement by comparing the resultant fluid velocity to that of a rotlet near a single wall. Our results suggest that the flow field of a single cilium is not changed significantly by the confinement, even when the distance between the walls is commensurate with the cilium's length.Comment: 20 pages, 13 figure

Adsorption of Hexavalent Chromium using Levigated Alumina GRM-1909

Use of commercially available levigated alumina GRM-1909 for adsorption of Hexavalent chromium is investigated. In this study, the adsorption of Cr(VI) was carried out in a batch mode and rate of adsorption was evaluated at varying conditions viz. pH, adsorbent dose, initial concentration and contact time. The maximum removal of Cr(VI) (98%) was observed at pH 4 with an adsorbent dose 1 gm/100 ml and chromium concentration of 10 mg/l. The adsorption study revealed that pH and adsorbent dose play significant roles towards Cr(VI) adsorption. Langmuir and Freundlich adsorption isotherm models were applied. The Langmuir isotherm fitted the equilibrium adsorption data better with a correlation coefficient of R2 = 0.971. The present study reveals that the commercially available levigated alumina can be an ideal candidate for the removal of chromium (VI) metal ions from waste water

CorE from Myxococcus xanthus Is a Copper-Dependent RNA Polymerase Sigma Factor

The dual toxicity/essentiality of copper forces cells to maintain a tightly regulated homeostasis for this metal in all living organisms, from bacteria to humans. Consequently, many genes have previously been reported to participate in copper detoxification in bacteria. Myxococcus xanthus, a prokaryote, encodes many proteins involved in copper homeostasis that are differentially regulated by this metal. A σ factor of the ECF (extracytoplasmic function) family, CorE, has been found to regulate the expression of the multicopper oxidase cuoB, the P1B-type ATPases copA and copB, and a gene encoding a protein with a heavy-metal-associated domain. Characterization of CorE has revealed that it requires copper to bind DNA in vitro. Genes regulated by CorE exhibit a characteristic expression profile, with a peak at 2 h after copper addition. Expression rapidly decreases thereafter to basal levels, although the metal is still present in the medium, indicating that the activity of CorE is modulated by a process of activation and inactivation. The use of monovalent and divalent metals to mimic Cu(I) and Cu(II), respectively, and of additives that favor the formation of the two redox states of this metal, has revealed that CorE is activated by Cu(II) and inactivated by Cu(I). The activation/inactivation properties of CorE reside in a Cys-rich domain located at the C terminus of the protein. Point mutations at these residues have allowed the identification of several Cys involved in the activation and inactivation of CorE. Based on these data, along with comparative genomic studies, a new group of ECF σ factors is proposed, which not only clearly differs mechanistically from the other σ factors so far characterized, but also from other metal regulators

An insight into spray pulsed reactor through mathematical modeling of catalytic dehydrogenation of cyclohexane

A mathematical model has been developed to study the impact of nozzle-catalyst distance and bulk gas temperature on the conversion and hydrogen evolution rate in a spray pulse reactor. The effects of reactor configuration and operating parameters on conversion and evolution rate were predicted with more than 90% accuracy. Reactor optimization and sensitivity analysis were carried out and an optimal design of nozzle-catalyst distance 5 cm and bulk gas temperature of 50 �C were proposed. The optimized design was predicted to increase the conversion from approximately 32e74%. The model could be in general used for designing any endothermic heterogeneous catalytic reaction in a spray pulse reactor

An insight into spray pulse reactor through mathematical modelling of dehydrogenation of cyclohexane

A mathematical model has been developed to study the impact of nozzle-catalyst distance and bulk gas temperature on the conversion and hydrogen evolution rate in a spray pulse reactor. The effects of reactor configuration and operating parameters on conversion and evolution rate were predicted with more than 90% accuracy. Reactor optimization and sensitivity analysis were carried out and an optimal design of nozzle-catalyst distance 5 cm and bulk gas temperature of 50 �C were proposed. The optimized design was predicted to increase the conversion from approximately 32e74%. The model could be in general used for designing any endothermic heterogeneous catalytic reaction in a spray pulse reactor