scholarly journals On the Minimum Density Interconnection Tree Problem

VLSI Design ◽  
1994 ◽  
Vol 2 (2) ◽  
pp. 157-169 ◽  
Author(s):  
C. J. Alpert ◽  
J. Cong ◽  
A. B. Kahng ◽  
G. Robins ◽  
M. Sarrafzadeh
Keyword(s):  
Steiner Tree ◽  
Spanning Trees ◽  
Problem Instance ◽  
Low Density ◽  
Minimum Area ◽  
Solution Quality ◽  
Extensive Simulation ◽  
Minimum Density ◽  
Proof Techniques ◽  
Simulation Results

We discuss a new minimum density objective for spanning and Steiner tree constructions. This formulation is motivated by the minimum-area layout objective, which is best achieved through balancing the usage of horizontal and vertical routing resources. We present two efficient heuristics for constructing low-density spanning trees and prove that their outputs are on average within small constants of optimal with respect to both tree cost and density. Our proof techniques suggest a non-uniform lower bound schema which can afford tighter estimates of solution quality for a given problem instance. Furthermore, the minimum density objective can be transparently combined with a number of previous interconnection objectives (e.g., minimizing tree radius or skew) without affecting solution quality with respect to these previous metrics. Extensive simulation results suggest that applications to VLSI global routing are promising.

scholarly journals Hybrid Chirp Signal Design for Improved Long-Range (LoRa) Communications

Signals ◽  
2022 ◽  
Vol 3 (1) ◽  
pp. 1-10
Author(s):  
Md. Noor-A-Rahim ◽  
M. Omar Khyam ◽  
Apel Mahmud ◽  
Xinde Li ◽  
Dirk Pesch ◽  
...  
Keyword(s):  
Long Range ◽  
Bit Error Rate ◽  
Modulation Scheme ◽  
Signal Design ◽  
Chirp Signal ◽  
Performance Gain ◽  
Extensive Simulation ◽  
Noise Interference ◽  
Chirp Signals ◽  
Simulation Results

Long-range (LoRa) communication has attracted much attention recently due to its utility for many Internet of Things applications. However, one of the key problems of LoRa technology is that it is vulnerable to noise/interference due to the use of only up-chirp signals during modulation. In this paper, to solve this problem, unlike the conventional LoRa modulation scheme, we propose a modulation scheme for LoRa communication based on joint up- and down-chirps. A fast Fourier transform (FFT)-based demodulation scheme is devised to detect modulated symbols. To further improve the demodulation performance, a hybrid demodulation scheme, comprised of FFT- and correlation-based demodulation, is also proposed. The performance of the proposed scheme is evaluated through extensive simulation results. Compared to the conventional LoRa modulation scheme, we show that the proposed scheme exhibits over 3 dB performance gain at a bit error rate of 10−4.

scholarly journals Impact of Foreshock Transients on the Flank Magnetopause and Magnetosphere and the Ionosphere

2021 ◽  
Vol 8 ◽  
Author(s):  
Chih-Ping Wang ◽  
Xueyi Wang ◽  
Terry Z. Liu ◽  
Yu Lin
Keyword(s):  
Magnetic Field ◽  
3D Structure ◽  
Hybrid Simulation ◽  
High Density ◽  
Low Density ◽  
Simulation Results ◽  
Ground Magnetic ◽  
Pressure Perturbations

Mesoscale (on the scales of a few minutes and a few RE) magnetosheath and magnetopause perturbations driven by foreshock transients have been observed in the flank magnetotail. In this paper, we present the 3D global hybrid simulation results to show qualitatively the 3D structure of the flank magnetopause distortion caused by foreshock transients and its impacts on the tail magnetosphere and the ionosphere. Foreshock transient perturbations consist of a low-density core and high-density edge(s), thus, after they propagate into the magnetosheath, they result in magnetosheath pressure perturbations that distort magnetopause. The magnetopause is distorted locally outward (inward) in response to the dip (peak) of the magnetosheath pressure perturbations. As the magnetosheath perturbations propagate tailward, they continue to distort the flank magnetopause. This qualitative explains the transient appearance of the magnetosphere observed in the flank magnetosheath associated with foreshock transients. The 3D structure of the magnetosheath perturbations and the shape of the distorted magnetopause keep evolving as they propagate tailward. The transient distortion of the magnetopause generates compressional magnetic field perturbations within the magnetosphere. The magnetopause distortion also alters currents around the magnetopause, generating field-aligned currents (FACs) flowing in and out of the ionosphere. As the magnetopause distortion propagates tailward, it results in localized enhancements of FACs in the ionosphere that propagate anti-sunward. This qualitatively explains the observed anti-sunward propagation of the ground magnetic field perturbations associated with foreshock transients.

Fuzzy Logic Controller for Force Feedback Control of Quadcopter via Tether

2020 ◽  
Author(s):  
Bennett Breese ◽  
Drew Scott ◽  
Shraddha Barawkar ◽  
Manish Kumar
Keyword(s):  
Fuzzy Logic ◽  
Control Strategy ◽  
Fuzzy Logic Controller ◽  
Force Feedback ◽  
Power Transmission ◽  
Extensive Simulation ◽  
Control Scheme ◽  
Area Surveillance ◽  
Simulation Results ◽  
Long Endurance

Abstract Tethered drone systems can be used to perform long-endurance tasks such as area surveillance and relay stations for wireless communication. However, all the existing systems use tethers only for data and power transmission from a stationary point on the ground. This work presents a control strategy that enables a quadcopter to follow a moving tether anchor. A force feedback controller is implemented using Fuzzy Logic. Using force-based strategy provides effective compliance between the tether’s anchor and the drone. The drone can thus be controlled by mere physical movement/manipulation of tether. This enhances the safety of current tethered drone systems and simplifies the flying of drones. Fuzzy Logic provides an intuitive edge to the control of such systems and allows handling noise in force sensors. Extensive simulation results are presented in this paper showing the effectiveness of the proposed control scheme.

Analyzing the Effect of Node Density on the Performance of the LAR-1P Algorithm

2012 ◽  
Vol 7 (2) ◽  
pp. 16-29
Author(s):  
Hussein Al-Bahadili ◽  
Ali Maqousi ◽  
Reyadh S. Naoum
Keyword(s):  
Network Performance ◽  
Ad Hoc ◽  
High Density ◽  
Low Density ◽  
Probabilistic Algorithms ◽  
Excellent Performance ◽  
Node Density ◽  
Simulation Results ◽  
Mobile Ad Hoc ◽  
Hoc Networks

The location-aided routing scheme 1 (LAR-1) and probabilistic algorithms are combined together into a new algorithm for route discovery in mobile ad hoc networks (MANETs) called LAR-1P. Simulation results demonstrated that the LAR-1P algorithm reduces the number of retransmissions as compared to LAR-1 without sacrificing network reachability. Furthermore, on a sub-network (zone) scale, the algorithm provides an excellent performance in high-density zones, while in low-density zones; it preserves the performance of LAR-1. This paper provides a detailed analysis of the performance of the LAR-1P algorithm through various simulations, where the actual numerical values for the number of retransmissions and reachability in high- and low-density zones were computed to demonstrate the effectiveness and significance of the algorithm and how it provides better performance than LAR-1 in high-density zones. In addition, the effect of the total number of nodes on the average network performance is also investigated.

Neural Network-Based Metaheuristic Parameterization with Application to the Vehicle Matching Problem in Ride-Hailing Services

2019 ◽  
Vol 2673 (10) ◽  
pp. 311-320
Author(s):  
Arslan Ali Syed ◽  
Irina Gaponova ◽  
Klaus Bogenberger
Keyword(s):  
Neural Network ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Computation Time ◽  
Problem Instance ◽  
Neighborhood Search ◽  
Matching Problem ◽  
Solution Quality ◽  
Actual Problem ◽  
Parameter Values

The majority of transportation problems include optimizing some sort of cost function. These optimization problems are often NP-hard and have an exponential increase in computation time with the increase in the model size. The problem of matching vehicles to passenger requests in ride hailing (RH) contexts typically falls into this category.Metaheuristics are often utilized for such problems with the aim of finding a global optimal solution. However, such algorithms usually include lots of parameters that need to be tuned to obtain a good performance. Typically multiple simulations are run on diverse small size problems and the parameters values that perform the best on average are chosen for subsequent larger simulations.In contrast to the above approach, we propose training a neural network to predict the parameter values that work the best for an instance of the given problem. We show that various features, based on the problem instance and shareability graph statistics, can be used to predict the solution quality of a matching problem in RH services. Consequently, the values corresponding to the best predicted solution can be selected for the actual problem. We study the effectiveness of above described approach for the static assignment of vehicles to passengers in RH services. We utilized the DriveNow data from Bavarian Motor Works (BMW) for generating passenger requests inside Munich, and for the metaheuristic, we used a large neighborhood search (LNS) algorithm combined with a shareability graph.

scholarly journals Unified Modeling of Familial Mediterranean Fever and Cryopyrin Associated Periodic Syndromes

2015 ◽  
Vol 2015 ◽  
pp. 1-18 ◽  
Author(s):  
Yasemin Bozkurt ◽  
Alper Demir ◽  
Burak Erman ◽  
Ahmet Gül
Keyword(s):  
Familial Mediterranean Fever ◽  
Nonlinear Ordinary Differential Equations ◽  
Extensive Simulation ◽  
Unified Modeling ◽  
Caspase 1 ◽  
Hereditary Autoinflammatory Diseases ◽  
Clinical Observations ◽  
Genes Encoding ◽  
Mediterranean Fever ◽  
Simulation Results

Familial mediterranean fever (FMF) and Cryopyrin associated periodic syndromes (CAPS) are two prototypical hereditary autoinflammatory diseases, characterized by recurrent episodes of fever and inflammation as a result of mutations inMEFVandNLRP3genes encoding Pyrin and Cryopyrin proteins, respectively. Pyrin and Cryopyrin play key roles in the multiprotein inflammasome complex assembly, which regulates activity of an enzyme, Caspase 1, and its target cytokine, IL-1β. Overproduction of IL-1βby Caspase 1 is the main cause of episodic fever and inflammatory findings in FMF and CAPS. We present a unifying dynamical model for FMF and CAPS in the form of coupled nonlinear ordinary differential equations. The model is composed of two subsystems, which capture the interactions and dynamics of the key molecular players and the insults on the immune system. One of the subsystems, which contains a coupled positive-negative feedback motif, captures the dynamics of inflammation formation and regulation. We perform a comprehensive bifurcation analysis of the model and show that it exhibits three modes, capturing the Healthy, FMF, and CAPS cases. The mutations in Pyrin and Cryopyrin are reflected in the values of three parameters in the model. We present extensive simulation results for the model that match clinical observations.

Cellular Differential Evolution for Optimization

2011 ◽  
Vol 474-476 ◽  
pp. 1770-1775
Author(s):  
Gui Wu Hu ◽  
Xiao Yong Du
Keyword(s):  
Cellular Automata ◽  
Differential Evolution ◽  
Information Exchange ◽  
Cellular Structure ◽  
Mutation Operator ◽  
Solution Quality ◽  
Base Vector ◽  
Quality Stability ◽  
Simulation Results ◽  
And Diffusion

This paper is to illustrate the Cellular Differential Evolution with the cellular structure originated from Cellular automata. Cellular neighbor local search has been designed; base vector or global best in mutation operator is substituted by neighborhood-best, which overcomes the weakness of single selection relating to global best, and balances the contradiction of local and global search, and improves the diversity of population. In addition, cellular structure ensures information exchange, inheritance and diffusion. Finally, a specific algorithm has been implemented: compared with similar variants of DE, the simulation results on 9 benchmark functions demonstrate that cellular differential evolutions are provided with obvious advantages in the solution-quality, stability and speed. <b></b>

scholarly journals Hybrid Chirp Signal Design for Improved Long-Range (LoRa) Communications

2021 ◽  
Author(s):  
Md. Noor-A-Rahim ◽  
Mohammad Omar Khyam ◽  
Apel Mahmud ◽  
Xinde Li ◽  
Dirk Pesch ◽  
...  
Keyword(s):  
Long Range ◽  
Bit Error Rate ◽  
Modulation Scheme ◽  
Signal Design ◽  
Chirp Signal ◽  
Performance Gain ◽  
Extensive Simulation ◽  
Noise Interference ◽  
Chirp Signals ◽  
Simulation Results

Long-range (LoRa) communication has attracted much attention recently due to its application for many Internet of Things applications. However, one of the key problems of the LoRa technology is it is vulnerable to noise/interference due to the use of only up-chirp signals during modulation. In this paper, to solve this problem, unlike the conventional LoRa modulation scheme, we propose a modulation scheme for LoRa communication based on joint up- and down-chirps. A fast Fourier transform (FFT) based demodulation scheme is devised to detect modulated symbols. To further improve demodulation performance, a hybrid demodulation scheme, comprised of FFT and correlation-based demodulation is also proposed. The performance of the proposed scheme is evaluated through extensive simulation results. Compared to the conventional LoRa modulation scheme, we show that the proposed scheme exhibits over 3 dB performance gain at bit error rate of 10^-4.

scholarly journals A STOCHASTIC DEVELOPMENT OF CLOUD COMPUTING BASED TASK SCHEDULING ALGORITHM

2019 ◽  
Vol 2019 (1) ◽  
pp. 41-48 ◽  
Author(s):  
Karunakaran V
Keyword(s):  
Virtual Machines ◽  
Cost Minimization ◽  
Scheduling Algorithm ◽  
Cloud Infrastructure ◽  
Solution Quality ◽  
Task Scheduling Algorithm ◽  
Simulation Results ◽  
Selection For ◽  
Stochastic Development ◽  
Data Centres

Due to diversity of services with respect to technology and resources, it is challenging to choose virtual machines (VM) from various data centres with varied features like cost minimization, reduced energy consumption, optimal response time and so on in cloud Infrastructure as a Service (IaaS) environment. The solutions available in the market are exhaustive computationally and aggregates multiple objectives to procure single trade-off that affects the solution quality inversely. This paper describes a hybrid algorithm that facilitates VM selection for scheduling applications based on Gravitational Search and Non-dominated Sorting Genetic Algorithm (GSA and NSGA). The efficiency of the proposed algorithm is verified by the simulation results.

scholarly journals High-Efficient Syndrome-Based LDPC Reconciliation for Quantum Key Distribution

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1440
Author(s):  
Hao-Kun Mao ◽  
Yu-Cheng Qiao ◽  
Qiong Li
Keyword(s):  
Quantum Key Distribution ◽  
Information Leakage ◽  
Key Distribution ◽  
Channel Noise ◽  
Low Density ◽  
Parity Check ◽  
Promising Technique ◽  
Low Density Parity Check ◽  
High Efficient ◽  
Simulation Results

Quantum key distribution (QKD) is a promising technique to share unconditionally secure keys between remote parties. As an essential part of a practical QKD system, reconciliation is responsible for correcting the errors due to the quantum channel noise by exchanging information through a public classical channel. In the present work, we propose a novel syndrome-based low-density parity-check (LDPC) reconciliation protocol to reduce the information leakage of reconciliation by fully utilizing the syndrome information that was previously wasted. Both theoretical analysis and simulation results show that our protocol can evidently reduce the information leakage as well as the number of communication rounds.

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