A 0.5 V bulk-input OTA with improved common-mode feedback for low-frequency filtering applications

This paper presents the design of a two-stage pseudo-differential operational transconductance amplifier (OTA) and its application in low-frequency continuous time filters. The OTA was designed in a 0.18 μm, 0.45 V V _T CMOS process. An improved bulk-mode common-mode feedback (CMFB) circuit has been designed which does not load the OTA compared to prior art. A self cascode load structure and partial positive feedback provide higher gain. The bulk terminals of all transistors have been biased to lower their threshold voltages (V_T) and maximize signal swing. The OTA operates at a supply voltage of 0.5 V and consumes only 28 μW of power. Rail-to-rail input is made possible by using the transistor’s bulk terminal as the input. For a load of 20 pF the OTA has a measured DC gain of 63 dB and a gain-bandwidth product of 570 kHz. To demonstrate the use of the OTA in practical circuits, three active RC filters were designed: a 10 kHz Butterworth filter, a 10 kHz Bessel filter, and a 2.5 kHz Tschebycheff filter.     

An overview of global climate changing in current scenario and mitigation action

Climate changing is a global threat to the world. There are so many reasons behind this problem. One of the major reasons is carbon emissions in atmosphere. The causes for this global threat are many, among them GHG (green house gas emission) is one of them. Also deforestation, land use change, sulfate aerosol and black carbon are the other major reason leading to the ozone layer depletion and changing climate.Due to the carbon emission atmosphere is being polluted and also so many disasters happen routinely. Atmosphere is getting hot day by day. Due to this unnatural and sudden temperature rise, glaciers are melting, so sudden flash floods occur. Agricultural sector is also suffering due to the global warming effects. This will also affect the productivity of grains world wide. Climate changing increases land and as well as sea temperature and alters precipitation quantity and patterns. As a result increasing the global average sea level, risk of coastal erosions, etc. climate change will be an added stress for the fisheries and aquaculture sectors. Effects will also be severe on coasts and marine ecosystems. Extreme events like drought, flood may also happen due to these impacts. This paper elaborately present the current situation of climate changing and the causes of its vulnerable effects, also the mitigation action of climate changing are also discussed.     

An evaluation of contrast enhancement techniques for mammographic breast masses.

The main aim of this paper is to propose a novel set of metrics that measure the quality of the image enhancement of mammographic images in a computer-aided detection framework aimed at automatically finding masses using machine learning techniques. Our methodology includes a novel mechanism for the combination of the metrics proposed into a single quantitative measure. We have evaluated our methodology on 200 images from the publicly available digital database for screening mammograms. We show that the quantitative measures help us select the best suited image enhancement on a per mammogram basis, which improves the quality of subsequent image segmentation much better than using the same enhancement method for all mammograms.     

Reduction of Hamilton's variational principle

This paper builds on the initial work of Marsden and Scheurle on nonabelian Routh reduction. The main objective is to carry out the reduction of variational principles in further detail. In particular, we obtain reduced variational principles which are the symplectic analogue of the well-known reduced variational principles for the Euler-Poincare equations and the Lagrange-Poincare equations. On the Lagrangian side, the symplectic analogue is obtained by suitably imposing the constraints of preservation of the momentum map.     

A Characterisation of Optimal Channel Assignments for Cellular and Square Grid Wireless Networks

In this paper we first present a uniformity property that characterises optimal channel assignments for networks arranged as cellular or square grids. Then, we present optimal channel assignments for cellular and square grids; these assignments exhibit a high value for δ₁ – the separation between channels assigned to adjacent stations. We prove an upper bound on δ₁ for such optimal channel assignments. This upper bound is greater than the value of δ₁ exhibited by our assignments. Based on empirical evidence, we conjecture that the value our assignments exhibit is a tight upper bound on δ₁.     

Interactive publication: The document as a research tool

The increasing prevalence of multimedia and research data generated by scientific work affords an opportunity to reformulate the idea of a scientific article from the traditional static document, or even one with links to supplemental material in remote databases, to a self-contained, multimedia-rich interactive publication. This paper describes our concept of such a document, and the design of tools for authoring (Forge) and visualization/analysis (Panorama). They are platform-independent applications written in Java, and developed in Eclipse¹ using its Rich Client Platform (RCP) framework. Both applications operate on PDF files with links to XML files that define the media type, location, and action to be performed. We also briefly cite the challenges posed by the potentially large size of interactive publications, the need for evaluating their value to improved comprehension and learning, and the need for their long-term preservation by the National Library of Medicine and other libraries.     

Sorptive storage of natural gas onto dry and wet phillipsite: Study of dynamics,storage and delivery

In this work, storage of methane on phillipsite, a naturally occurring zeolite, was studied on wet and dry basis. The temperature and pressure dynamics were investigated. The temperature gradient due to the adsorption of methane on phillipsite was less than 1 °C indicating higher thermal conductivity of this material. A decreased in temperature resulted in higher adsorption capacity. Storage of methane onto phillipsite can be enhanced by wetting the bed and thus hydrate formation. The time needed to reach equilibrium depends strongly on the water content of the adsorbent. The results revealed that the total delivery capacities for dry and wet phillipsite were 32.5 V/V and 74.13 V/V, respectively. A maximum delivery capacity of 138 V/V was reached using wet phillipsite with 350 g water content. The volumetric delivery capacity results indicated that the steady state delivery is more realistic for adsorptive natural gas (ANG) systems.     

Design and Prototype of a Performance Tool Interface for OpenMP

This paper proposes a performance tools interface for OpenMP, similar in spirit to the MPI profiling interface in its intent to define a clear and portable API that makes OpenMP execution events visible to runtime performance tools. We present our design using a source-level instrumentation approach based on OpenMP directive rewriting. Rules to instrument each directive and their combination are applied to generate calls to the interface consistent with directive semantics and to pass context information (e.g., source code locations) in a portable and efficient way. Our proposed OpenMP performance API further allows user functions and arbitrary code regions to be marked and performance measurement to be controlled using new OpenMP directives. To prototype the proposed OpenMP performance interface, we have developed compatible performance libraries for the Expert automatic event trace analyzer [17, 18] and the TAU performance analysis framework [13]. The directive instrumentation transformations we define are implemented in a source-to-source translation tool called OPARI. Application examples are presented for both Expert and TAU to show the OpenMP performance interface and OPARI instrumentation tool in operation. When used together with the MPI profiling interface (as the examples also demonstrate), our proposed approach provides a portable and robust solution to performance analysis of OpenMP and mixed-mode (OpenMP+MPI) applications.     

Optimization of Electrocardiogram Classification Using Dipper Throated Algorithm and Differential Evolution

Electrocardiogram (ECG) signal is a measure of the heart’s electrical activity. Recently, ECG detection and classification have benefited from the use of computer-aided systems by cardiologists. The goal of this paper is to improve the accuracy of ECG classification by combining the Dipper Throated Optimization (DTO) and Differential Evolution Algorithm (DEA) into a unified algorithm to optimize the hyperparameters of neural network (NN) for boosting the ECG classification accuracy. In addition, we proposed a new feature selection method for selecting the significant feature that can improve the overall performance. To prove the superiority of the proposed approach, several experiments were conducted to compare the results achieved by the proposed approach and other competing approaches. Moreover, statistical analysis is performed to study the significance and stability of the proposed approach using Wilcoxon and ANOVA tests. Experimental results confirmed the superiority and effectiveness of the proposed approach. The classification accuracy achieved by the proposed approach is (99.98%).     

Brain Tumor Auto-Segmentation on Multimodal Imaging Modalities Using Deep Neural Network

Due to the difficulties of brain tumor segmentation, this paper proposes a strategy for extracting brain tumors from three-dimensional Magnetic Resonance Image (MRI) and Computed Tomography (CT) scans utilizing 3D U-Net Design and ResNet50, taken after by conventional classification strategies. In this inquire, the ResNet50 picked up accuracy with 98.96%, and the 3D U-Net scored 97.99% among the different methods of deep learning. It is to be mentioned that traditional Convolutional Neural Network (CNN) gives 97.90% accuracy on top of the 3D MRI. In expansion, the image fusion approach combines the multimodal images and makes a fused image to extricate more highlights from the medical images. Other than that, we have identified the loss function by utilizing several dice measurements approach and received Dice Result on top of a specific test case. The average mean score of dice coefficient and soft dice loss for three test cases was 0.0980. At the same time, for two test cases, the sensitivity and specification were recorded to be 0.0211 and 0.5867 using patch level predictions. On the other hand, a software integration pipeline was integrated to deploy the concentrated model into the webserver for accessing it from the software system using the Representational state transfer (REST) API. Eventually, the suggested models were validated through the Area Under the Curve–Receiver Characteristic Operator (AUC–ROC) curve and Confusion Matrix and compared with the existing research articles to understand the underlying problem. Through Comparative Analysis, we have extracted meaningful insights regarding brain tumour segmentation and figured out potential gaps. Nevertheless, the proposed model can be adjustable in daily life and the healthcare domain to identify the infected regions and cancer of the brain through various imaging modalities.