scholarly journals Large-Scale Physical Modeling Synthesis, Parallel Computing, and Musical Experimentation: The NESS Project in Practice

2020 ◽  
Vol 43 (2-3) ◽  
pp. 31-47 ◽  
Author(s):  
Stefan Bilbao ◽  
James Perry ◽  
Paul Graham ◽  
Alan Gray ◽  
Kostas Kavoussanakis ◽  
...  
Keyword(s):  
Physical Modeling ◽  
Large Scale ◽  
Algorithm Design ◽  
Musical Instruments ◽  
Synthesis System ◽  
Professional Musicians ◽  
Musical Acoustics ◽  
Modeling Systems ◽  
Computational Resources ◽  
Theoretical Developments

Sound synthesis using physical modeling, emulating systems of a complexity approaching and even exceeding that of real-world acoustic musical instruments, is becoming possible, thanks to recent theoretical developments in musical acoustics and algorithm design. Severe practical difficulties remain, both at the level of the raw computational resources required, and at the level of user control. An approach to the first difficulty is through the use of large-scale parallelization, and results for a variety of physical modeling systems are presented here. Any progress with regard to the second difficulty requires, necessarily, the experience and advice of professional musicians. A basic interface to a parallelized large-scale physical modeling synthesis system is presented here, accompanied by first-hand descriptions of the working methods of five composers, each of whom generated complete multichannel pieces using the system.

scholarly journals Physical Modeling, Algorithms, and Sound Synthesis: The NESS Project

2020 ◽  
Vol 43 (2-3) ◽  
pp. 15-30 ◽  
Author(s):  
Stefan Bilbao ◽  
Charlotte Desvages ◽  
Michele Ducceschi ◽  
Brian Hamilton ◽  
Reginald Harrison-Harsley ◽  
...  
Keyword(s):  
Physical Modeling ◽  
Large Scale ◽  
Algorithm Design ◽  
Sound Synthesis ◽  
Synthesis Methods ◽  
String Instruments ◽  
Time Stepping ◽  
Modal Synthesis ◽  
Percussion Instruments ◽  
Simplifying Assumptions

Synthesis using physical modeling has a long history. As computational costs for physical modeling synthesis are often much greater than for conventional synthesis methods, most techniques currently rely on simplifying assumptions. These include digital waveguides, as well as modal synthesis methods. Although such methods are efficient, it can be difficult to approach some of the more detailed behavior of musical instruments in this way, including strongly nonlinear interactions. Mainstream time-stepping simulation methods, despite being computationally costly, allow for such detailed modeling. In this article, the results of a five-year research project, Next Generation Sound Synthesis, are presented, with regard to algorithm design for a variety of sound-producing systems, including brass and bowed-string instruments, guitars, and large-scale environments for physical modeling synthesis. In addition, 3-D wave-based modeling of large acoustic spaces is discussed, as well as the embedding of percussion instruments within such spaces for full spatialization. This article concludes with a discussion of some of the basics of such time-stepping methods, as well as their application in audio synthesis.

scholarly journals Nebula: ultra-efficient mapping-free structural variant genotyper

2021 ◽  
Author(s):  
Parsoa Khorsand ◽  
Fereydoun Hormozdiari
Keyword(s):  
Large Scale ◽  
Structural Variants ◽  
Sequencing Technologies ◽  
Generic Framework ◽  
Common Genetic Variants ◽  
Order Of Magnitude ◽  
Complex Events ◽  
Comparable Accuracy ◽  
Using Data ◽  
Computational Resources

Abstract Large scale catalogs of common genetic variants (including indels and structural variants) are being created using data from second and third generation whole-genome sequencing technologies. However, the genotyping of these variants in newly sequenced samples is a nontrivial task that requires extensive computational resources. Furthermore, current approaches are mostly limited to only specific types of variants and are generally prone to various errors and ambiguities when genotyping complex events. We are proposing an ultra-efficient approach for genotyping any type of structural variation that is not limited by the shortcomings and complexities of current mapping-based approaches. Our method Nebula utilizes the changes in the count of k-mers to predict the genotype of structural variants. We have shown that not only Nebula is an order of magnitude faster than mapping based approaches for genotyping structural variants, but also has comparable accuracy to state-of-the-art approaches. Furthermore, Nebula is a generic framework not limited to any specific type of event. Nebula is publicly available at https://github.com/Parsoa/Nebula.

scholarly journals Vehicular Crowdsourcing for Congestion Support in Smart Cities

Smart Cities ◽  
2021 ◽  
Vol 4 (2) ◽  
pp. 662-685
Author(s):  
Stephan Olariu
Keyword(s):  
Smart City ◽  
Large Scale ◽  
Smart Cities ◽  
Traffic Signal Control ◽  
Signal Timing ◽  
Adaptive Traffic Signal Control ◽  
Traffic Conditions ◽  
Direct Benefits ◽  
Traffic Signal Control Systems ◽  
Computational Resources

Under present-day practices, the vehicles on our roadways and city streets are mere spectators that witness traffic-related events without being able to participate in the mitigation of their effect. This paper lays the theoretical foundations of a framework for harnessing the on-board computational resources in vehicles stuck in urban congestion in order to assist transportation agencies with preventing or dissipating congestion through large-scale signal re-timing. Our framework is called VACCS: Vehicular Crowdsourcing for Congestion Support in Smart Cities. What makes this framework unique is that we suggest that in such situations the vehicles have the potential to cooperate with various transportation authorities to solve problems that otherwise would either take an inordinate amount of time to solve or cannot be solved for lack for adequate municipal resources. VACCS offers direct benefits to both the driving public and the Smart City. By developing timing plans that respond to current traffic conditions, overall traffic flow will improve, carbon emissions will be reduced, and economic impacts of congestion on citizens and businesses will be lessened. It is expected that drivers will be willing to donate under-utilized on-board computing resources in their vehicles to develop improved signal timing plans in return for the direct benefits of time savings and reduced fuel consumption costs. VACCS allows the Smart City to dynamically respond to traffic conditions while simultaneously reducing investments in the computational resources that would be required for traditional adaptive traffic signal control systems.

scholarly journals Anisotropy of the Microwave Background and Biased Galaxy Formation

1988 ◽  
Vol 130 ◽  
pp. 43-50
Author(s):  
Nick Kaiser
Keyword(s):  
Galaxy Formation ◽  
Large Scale ◽  
Microwave Background ◽  
Galaxy Clustering ◽  
Clear View ◽  
Hot Gas ◽  
Density Perturbations ◽  
The Universe ◽  
Theoretical Developments

Fluctuations in the microwave background will have been imprinted at z ≃ 1000, when the photons and the plasma decoupled. On angular scales greater than a few degrees these fluctuations provide a clear view of any primordial density perturbations, and therefore a clean test of theories which invoke such fluctuations from which to form the structure we see in the universe. On smaller angular scales the predictions are less certain: reionization of the gas may modify the spectrum of the primordial fluctuations, and secondary fluctuations may be generated.Here I shall review some recent theoretical developments. A brief survey is made of the currently popular theories for the primordial perturbations, with emphasis on the predictions for large scale anisotropy. One major uncetainty in the predictions arises from the normalisation of the fluctuations to e.g. galaxy clustering, and much attention is given to the question of ‘biased’ galaxy formation. The effect of reionization on the primordial fluctuations is discussed, as is the anisotropy generated from scattering off hot gas in clusters, groups and galaxies.

scholarly journals Secure Large-Scale Airport Simulations Using Distributed Computational Resources

2001 ◽  
Author(s):  
William J. Mcdermott ◽  
David A. Maluf ◽  
Yuri Gawdiak ◽  
Peter Tran
Keyword(s):  
Large Scale ◽  
Computational Resources

scholarly journals Reaction Diffusion and Chemotaxis for Decentralized Gathering on FPGAs

2009 ◽  
Vol 2009 ◽  
pp. 1-15 ◽  
Author(s):  
Bernard Girau ◽  
César Torres-Huitzil ◽  
Nikolaos Vlassopoulos ◽  
José Hugo Barrón-Zambrano
Keyword(s):  
Large Scale ◽  
Complex Dynamics ◽  
Diffusion Mechanism ◽  
Reaction Diffusion ◽  
Fpga Implementation ◽  
Cellular Slime Mold ◽  
Reaction Diffusion Model ◽  
Cellular Slime ◽  
Computational Resources ◽  
Embedded Implementation

We consider here the feasibility of gathering multiple computational resources by means of decentralized and simple local rules. We study such decentralized gathering by means of a stochastic model inspired from biology: the aggregation of theDictyostelium discoideumcellular slime mold. The environment transmits information according to a reaction-diffusion mechanism and the agents move by following excitation fronts. Despite its simplicity this model exhibits interesting properties of self-organization and robustness to obstacles. We first describe the FPGA implementation of the environment alone, to perform large scale and rapid simulations of the complex dynamics of this reaction-diffusion model. Then we describe the FPGA implementation of the environment together with the agents, to study the major challenges that must be solved when designing a fast embedded implementation of the decentralized gathering model. We analyze the results according to the different goals of these hardware implementations.

scholarly journals Sounderfeit: cloning a physical model using a conditional adversarial autoencoder

2018 ◽  
Vol 18 (1) ◽  
pp. 44-60 ◽  
Author(s):  
Stephen Sinclair
Keyword(s):  
Neural Network ◽  
Parameter Estimation ◽  
Real Time ◽  
Physical Model ◽  
Physical Modeling ◽  
Synthesis System ◽  
Bidirectional Relationship ◽  
Adversarial Training ◽  
Recorded Data

An adversarial autoencoder conditioned on known parameters of a physical modeling bowed string syn- thesizer is evaluated for use in parameter estimation and resynthesis tasks. Latent dimensions are provided to cap- ture variance not explained by the conditional parameters. Results are compared with and without the adversarial training, and a system capable of “copying” a given parameter-signal bidirectional relationship is examined. A real- -time synthesis system built on a generative, conditioned and regularized neural network is presented, allowing to construct engaging sound synthesizers based purely on recorded data. 

scholarly journals Algorithm for Counting Cars in Large-scale Video Surveillance Systems

2020 ◽  
pp. 100-108
Author(s):  
Arsenii Shirokov ◽  
Denis Kuplyakov ◽  
Anton Konushin
Keyword(s):  
Video Surveillance ◽  
Large Scale ◽  
Surveillance Systems ◽  
Motion Model ◽  
People Tracking ◽  
Detection Frequency ◽  
Computational Resources ◽  
Sparse Set ◽  
The Given ◽  
Distributed Tracking

The article deals with the problem of counting cars in large-scale video surveillance systems. The proposed method is based on car tracking and counting the number of tracks intersecting the given signal line. We use a distributed tracking algorithm. It reduces the amount of necessary computational resources and increases performance up to realtime by detecting vehicles in a sparse set of frames. We adapted and modified the approach previously proposed for people tracking. Proposed improvement of the speed estimation module and refinement of the motion model reduced the detection frequency by 3 times. The experimental evaluation shows that the proposed algorithm allows reaching an acceptable counting quality with a detection frequency of 3 Hz.

Sign in / Sign up

Export Citation Format

Share Document