Loop-based Approach Research Articles

Loop-based Markov Chain for Next Cell Prediction

With the development in the 5G, Software Defined Networking(SDN), Network Function Virtualization(NFV), which are the cornerstones of the 5G, are being developed as well. One of the biggest development of 5G is, dense of Machine-to-Machine (M2M) communication is excessively increased compared to older generations. Therefore, monitoring of these machines (or user equipment) will also become critical. Various Network Functions (NFs) have been given specific tasks so that this monitoring and other critical tasks can be done with the NFV innovation in 5G. Network Data Analytics Function (NWDAF), one of these functions, is responsible for creating analytics (i.e., statistics or predictions) by collecting data from other Network Functions. In particular, in order to generate predictions, it will be possible to use Machine Learning (ML) Models and Artificial Intelligence (AI) for the operations specified within the NWDAF. In this study, next possible cell for a User Equipment is predicted by using Markov Chain with novel loop-based approach.

Energy Efficient Novel Design of Static Random Access Memory Memory Cell in Quantum-dot Cellular Automata Approach

This paper introduces a peculiar approach of designing Static Random Access Memory (SRAM) memory cell in Quantum-dot Cellular Automata (QCA) technique. The proposed design consists of one 3-input MG, one 5-input MG in addition to a (2×1) Multiplexer block utilizing the loop-based approach. The simulation results reveals the excellence of the proposed design. The proposed SRAM cell achieves 16% and 15% improvement in terms of total number of Cell counts and Area. Similarly, the proposed design structure realizes the overall power dissipation savings up to 35.3% at maximum energy dissipation of circuit, 38.6% at average energy dissipation of circuit, 36.1% at minimum energy dissipation of circuit, 36.4% at average energy dissipation of circuit and 40.1% at average switching energy dissipation compared to the latest reported designs. The power analysis and structural analysis of the proposed design is compared with its state-of-the-art counterpart designs, using QCAPro and QCADesigner 2.0.3 tools. The proposed QCA based SRAM cell design can be taken as a base design in building an ultra-low power information generating systems like Microprocessors.

Modeling of various biological networks via LCMARS

In system biology, the interactions between components such as genes, proteins, can be represented by a network. To understand the molecular mechanism of complex biological systems, construction of their networks plays a crucial role. However, estimation of these biological networks is a challenging problem because of their high dimensional and sparse structures. Several statistical methods are proposed to overcome this issue. The Conic Multivariate Adaptive Regression Splines (CMARS) is one of the recent nonparametric methods developed for high dimensional and correlated data. This model is suggested to improve the performance of the Multivariate Adaptive Regression Spline (MARS) approach which is a complex model under the generalized additive models. From previous studies, it has been shown that MARS can be a promising model for the description of steady-state activations of biological networks if it is modified as a lasso-type regression via the main effects. In this study, we convert the full description of CMARS as a loop-based approach, so-called LCMARS, by including both main and second-order interaction effects since this description has performed better in benchmark real datasets. Here, we generate various scenarios based on distinct distributions and dimensions to compare the performance of LCMARS with MARS and Gaussian Graphical Model (GGM) in terms of accuracy measures via Monte Carlo runs. Additionally, different real biological datasets are used to observe the performance of underlying methods.

An approach to task-based parallel programming for undergraduate students

This paper presents the description of a compulsory parallel programming course in the bachelor degree in Informatics Engineering at the Barcelona School of Informatics, Universitat Politècnica de Catalunya UPC-BarcelonaTech. The main focus of the course is on the shared-memory programming paradigm, which facilitates the presentation of fundamental aspects and notions of parallel computing. Unlike the “traditional” loop-based approach, which is the focus of parallel programming courses in other universities, this course presents the parallel programming concepts using a task-based approach. Tasking allows students to explore a broader set of parallel decomposition strategies, including linear, iterative and recursive strategies, and their implementation using the current version of OpenMP (OpenMP 4.5), which offers mechanisms (pragmas and intrinsic functions) to easily map these strategies into parallel programs. Simple models to understand the benefits of a task decomposition and the trade-offs introduced by different kinds of overheads are included in the course, together with the use of tools that allow an easy exploration of different task decomposition strategies and their potential parallelism (Tareador) and instrumentation and analysis of task parallel executions on real machines (Extrae and Paraver).

Solving the double-banana rigidity problem: a loop-based approach

Abstract. Rigidity detection is an important tool for structural synthesis of mechanisms, as it helps to unveil possible sources of inconsistency in Grübler's count of degrees of freedom (DOFs) and thus to generate consistent kinematical models of complex mechanisms. One case that has puzzled researchers over many decades is the famous "double-banana" problem, which is a representative counter-example of Laman's rigidity condition formula for which existing standard DOF counting formulas fail. The reason for this is the body-by-body and joint-by-joint decomposition of the interconnection structure in classical algorithms, which does not unveil structural isotropy groups for example when whole substructures rotate about an "implied hinge" according to Streinu. In this paper, a completely new approach for rigidity detection for cases as the "double-banana" counterexample in which bars are connected by spherical joints is presented. The novelty of the approach consists in regarding the structure not as a set of joint-connected bodies but as a set of interconnected loops. By tracking isolated DOFs such as those arising between pairs of spherical joints, rigidity/mobility subspaces can be easily identified and thus the "double-banana" paradox can be resolved. Although the paper focuses on the solution of the double-banana mechanism as a special case of paradox bar-and-joint frameworks, the procedure is valid for general body-and-joint mechanisms, as is shown by the decomposition of spherical joints into a series of revolute joints and their rigid-link interconnections.

Engineering and implementing software architectural patterns based on feedback loops

A highly decentralized system of autonomous service components consists of multiple and interacting feedback loops which can be organized into a variety of architectural patterns. The highly complex nature of these loops make engineering and implementation of these patterns a very challenging task. In this paper, we present SimSOTA - an integrated Eclipse plug-in to architect, engineer and implement self-adaptive systems based on our feedback loop-based approach. SimSOTA adopts model-driven development to model and simulate complex self-adaptive architectural patterns, and to automate the generation of Java-based implementation code. The approach is validated using a case study in cooperative electric vehicles.

A Phase Locked Loop-Based Approach to Real-Time Modal Analysis on Synchrophasor Measurements

This paper presents a phase locked loop-based approach for real-time estimation of electro-mechanical modal properties of slow-frequency oscillations in a power grid. The approach utilizes a closed-loop feedback control system to automatically adjust a locally generated signal to optimally fit the targeted signal from real-time measurements in order to obtain modal properties directly from the local signal. The paper tests the approach on a variety of datasets including simulated and real synchrophasor data. Compared to a traditional spectral analysis based approach, this new approach has better adaptability and accuracy especially when the length of a measured signal is limited, or the signal contains modes with close frequencies or has floating modal properties, which is not unusual in a power grid due to its nonlinearity. Applied to wide-area synchrophasor measurements, the new approach may help system operators monitor slow-frequency oscillations, especially inter-area modes, which is important for a smart grid to maintain angular stability under disturbances.

Reducing branch predictor leakage energy by exploiting loops

With the scaling of technology, leakage energy will become the dominant source of energy consumption. Besides cache memories, branch predictors are among the largest on-chip array structures and consume nontrivial leakage energy. This paper proposes two cost-effective loop-based strategies to reduce the branch predictor leakage without impacting prediction accuracy or performance. The loop-based approaches exploit the fact that loops usually only contain a small number of instructions and, hence, even fewer branch instructions while taking a significant fraction of the execution time. Consequently, all the nonactive entries of branch predictors can be placed into the low leakage mode during the loop execution in order to reduce leakage energy. Compiler and circuit supports are discussed to implement the proposed leakage-reduction strategies. Compared to the recently proposed decay-based approach, our experimental results show that the loop-based approach can extract 16.2% more dead time of the branch predictor, on average, leading to more leakage energy savings without impacting the branch prediction accuracy and performance.

A loop-based approach in clustering and routing in Mobile Ad hoc NETworks

The effects of UV intensity and turbidity on selected microbial indicator inactivation were investigated. Results showed that UV disinfection was effective in killing all the selected microbial indicators, the resistance order of the microorganisms was as follows: MS-2 coliphage > Bacillus subtilis > E. coli > Staphylococcus aureus and Candida albicans. UV intensity had influence on the inactivation of all the microorganisms, high UV disinfection efficency was obtained with higher UV intensity. Turbidity had impact on the bacteria inactivation rate, but there was no evidence that turbidity had any negative contribution to MS-2 coliphage. Under the same UV dosage, higher UV intensity could overcome the negative influence of turbidity on UV performance, enhanced microorganism inactivation effect in turbidity water.

Loop-based interconnect modeling and optimization approach for multigigahertz clock network design

A highly efficient loop-based interconnect modeling methodology is proposed for multigigahertz clock network design and optimization. Closed-form loop resistance and inductance models are proposed for fully shielded global clock interconnect structures, which capture high-frequency effects including inductance and proximity effects. The models are validated through comparisons with electromagnetic simulations and measured data taken from a Power4 chip. This modeling methodology greatly improves the clock interconnect simulation efficiency and enables fast physical design exploration. Examples of interconnect performance optimization are demonstrated and design guidelines are proposed.

Inductance model and analysis methodology for high-speed on-chip interconnect

With operating frequencies entering the multi-gigahertz range, inductance has become an important consideration in the design and analysis of on-chip interconnects. In this paper, we present an accurate and efficient inductance modeling and analysis methodology for high-performance interconnect. We determine the critical elements for a PEEC based model by analyzing the current flow in the power grid and signal interconnect. The proposed model includes distributed interconnect resistance, inductance and capacitance, device decoupling capacitances, quiescent switching currents in the grid, pad connections, and pad/package inductance. We propose an efficient methodology for extracting these elements, using statistical models for on-chip decoupling capacitance and switching currents. Simulation results show the importance of various elements for accurate inductance analysis. We also demonstrate the accuracy of the proposed model compared to the traditional loop-based inductance approach. Since the proposed model can consist of hundreds of thousands of RLC elements, and a fully dense mutual inductance matrix, we propose a number of acceleration techniques that enable efficient analysis of large interconnect structures.