12,647 research outputs found
Understanding and Tuning Mass-Interaction Networks Through Their Modal Representation
International audienceSound is all about vibration, and the GENESIS environment provides an efficient way for modeling and simulating complex vibrating structures, enabling to produce rich sounds. In this paper, we propose an overview of tools recently developed and available within the GENESIS environment, allowing a better understanding on how mass-interaction networks behave and introducing some enhanced tuning of their vibrating properties. All these tools try to address an inherent need of any creative process either in the physical world or in GENESIS, which is to create bidirectional connections between properties of a phenomenon, in our case, audible sounds, and properties of what produced it, here, mass-interaction networks. For this purpose, we will introduce the topological and modal representations of such mass-interaction networks and appreciate how relevant it can be to switch between these different representations to really apprehend its inner properties and those of the sounds it produces
16th Sound and Music Computing Conference SMC 2019 (28–31 May 2019, Malaga, Spain)
The 16th Sound and Music Computing Conference (SMC 2019) took place in Malaga, Spain, 28-31 May 2019 and it was organized by the Application of Information and Communication Technologies Research group (ATIC) of the University of Malaga (UMA). The SMC 2019 associated Summer School took place 25-28 May 2019. The First International Day of Women in Inclusive Engineering, Sound and Music Computing Research (WiSMC 2019) took place on 28 May 2019. The SMC 2019 TOPICS OF INTEREST included a wide selection of topics related to acoustics, psychoacoustics, music, technology for music, audio analysis, musicology, sonification, music games, machine learning, serious games, immersive audio, sound synthesis, etc
Machine Learning and Integrative Analysis of Biomedical Big Data.
Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues
Vibration control in plates by uniformly distributed PZT actuators interconnected via electric networks
In this paper a novel device aimed at controlling the mechanical vibrations
of plates by means of a set of electrically-interconnected piezoelectric
actuators is described. The actuators are embedded uniformly in the plate
wherein they connect every node of an electric network to ground, thus playing
the two-fold role of capacitive element in the electric network and of couple
suppliers. A mathematical model is introduced to describe the propagation of
electro-mechanical waves in the device; its validity is restricted to the case
of wave-forms with wave-length greater than the dimension of the piezoelectric
actuators used. A self-resonance criterion is established which assures the
possibility of electro-mechanical energy exchange. Finally the problem of
vibration control in simply supported and clamped plates is addressed; the
optimal net-impedance is determined. The results indicate that the proposed
device can improve the performances of piezoelectric actuationComment: 22 page
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
mi-gen~: An Efficient and Accessible Mass-Interaction Sound Synthesis Toolbox
International audienc
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