scholarly journals Textural Composition: Aesthetics, Techniques, and Spatialization for High-Density Loudspeaker Arrays

2017 ◽  
Vol 41 (1) ◽  
pp. 34-45 ◽  
Author(s):  
Kerry L. Hagan
Keyword(s):  
Point Source ◽  
Real Time ◽  
Computer Music ◽  
High Density ◽  
Personal Journey ◽  
Compositional Practice ◽  
Sound Objects ◽  
The Aesthetic

This article documents a personal journey of compositional practice that led to the necessity for working with high-density loudspeaker arrays (HDLAs). I work with textural composition, an approach to composing real-time computer music arising from acousmatic and stochastic principles in the form of a sound metaobject. Textural composition depends upon highly mobile sounds without the need for trajectory-based spatialization procedures. In this regard, textural composition is an intermediary aesthetic—between “tape music” and real-time computer music, between sound objects and soundscape, and between point-source and trajectory-based, mimetic spatialization. I begin with the aesthetics of textural composition, including the musical and sonic spaces it needs to inhabit. I then detail the techniques I use to create textures for this purpose. I follow with the spatialization technique I devised that supports the aesthetic requirements. Finally, I finish with an example of an exception to my techniques, one where computational requirements and the HDLA required me to create a textural composition without my real-time strategies.

scholarly journals Real-Time Lossless Compression for Ultra-High-Density Synchrophasor and Point on Wave Data

2021 ◽  
pp. 1-1
Author(s):  
Chang Chen ◽  
Weikang Wang ◽  
Yin He ◽  
Lingwei Zhan ◽  
Yilu Liu
Keyword(s):  
Real Time ◽  
Lossless Compression ◽  
High Density ◽  
Wave Data

scholarly journals Intuitive real-time control strategy for high-density myoelectric hand prosthesis using deep and transfer learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Simon Tam ◽  
Mounir Boukadoum ◽  
Alexandre Campeau-Lecours ◽  
Benoit Gosselin
Keyword(s):  
Real Time ◽  
Transfer Learning ◽  
Control Strategy ◽  
High Density ◽  
Fine Tuning ◽  
Mode Switching ◽  
Correct Prediction ◽  
Learning Approach ◽  
Predictive Values ◽  
Hand Prosthesis

AbstractMyoelectric hand prostheses offer a way for upper-limb amputees to recover gesture and prehensile abilities to ease rehabilitation and daily life activities. However, studies with prosthesis users found that a lack of intuitiveness and ease-of-use in the human-machine control interface are among the main driving factors in the low user acceptance of these devices. This paper proposes a highly intuitive, responsive and reliable real-time myoelectric hand prosthesis control strategy with an emphasis on the demonstration and report of real-time evaluation metrics. The presented solution leverages surface high-density electromyography (HD-EMG) and a convolutional neural network (CNN) to adapt itself to each unique user and his/her specific voluntary muscle contraction patterns. Furthermore, a transfer learning approach is presented to drastically reduce the training time and allow for easy installation and calibration processes. The CNN-based gesture recognition system was evaluated in real-time with a group of 12 able-bodied users. A real-time test for 6 classes/grip modes resulted in mean and median positive predictive values (PPV) of 93.43% and 100%, respectively. Each gesture state is instantly accessible from any other state, with no mode switching required for increased responsiveness and natural seamless control. The system is able to output a correct prediction within less than 116 ms latency. 100% PPV has been attained in many trials and is realistically achievable consistently with user practice and/or employing a thresholded majority vote inference. Using transfer learning, these results are achievable after a sensor installation, data recording and network training/fine-tuning routine taking less than 10 min to complete, a reduction of 89.4% in the setup time of the traditional, non-transfer learning approach.

The Unbearable Lightness of the Image

2017 ◽  
Vol 10 (2-3) ◽  
pp. 109-132 ◽  
Author(s):  
Donatella Della Ratta
Keyword(s):  
Web 2.0 ◽  
Real Time ◽  
New Technologies ◽  
Political Commitment ◽  
Life Activity ◽  
Bearing Witness ◽  
Ethical Discourse ◽  
The Aesthetic ◽  
The Moment ◽  
The Web

In this essay, I reflect on the aesthetic, political and material implications of filming as a continuous life activity since the beginning of the 2011 uprising in Syria. I argue that the blurry, shaky and pixelated aesthetics of Syrian user-generated videos serve to construct an ethical discourse (Ranciére 2009a; 2013) to address the genesis and the goal of the images produced, and to shape a political commitment to the evidence-image (Didi-Huberman 2008). However, while the unstable visuals of the handheld camera powerfully reconnect, both at a symbolic and aesthetic level, to the truthfulness of the moment of crisis in which they are generated, they fail to produce a clearer understanding of the situation and a counter-hegemonic narrative. In this article, I explore how new technologies have impacted this process of bearing witness and documenting events in real time, and how they have shaped a new understanding of the image as a networked, multiple object connected with the living archive of history, in a permanent dialogue with the seemingly endless flow of data nurtured by the web 2.0.

scholarly journals Aggressive, Tense or Shy? Identifying Personality Traits from Crowd Videos

2017 ◽  
Author(s):  
Aniket Bera ◽  
Tanmay Randhavane ◽  
Dinesh Manocha
Keyword(s):  
Personality Traits ◽  
Real Time ◽  
Personality Trait ◽  
User Study ◽  
Movement Patterns ◽  
Time Algorithm ◽  
High Density ◽  
Motion Model ◽  
Classification Criterion ◽  
Trait Theory

We present a real-time algorithm to automatically classify the behavior or personality of a pedestrian based on his or her movements in a crowd video. Our classification criterion is based on Personality Trait theory. We present a statistical scheme that dynamically learns the behavior of every pedestrian and computes its motion model. This model is combined with global crowd characteristics to compute the movement patterns and motion dynamics and use them for crowd prediction. Our learning scheme is general and we highlight its performance in identifying the personality of different pedestrians in low and high density crowd videos. We also evaluate the accuracy by comparing the results with a user study.

scholarly journals Apophatic Inquiry: Living the Questions Themselves

2020 ◽  
Vol 19 ◽  
pp. 160940692095897
Author(s):  
Merel Visse ◽  
Finn Thorbjørn Hansen ◽  
Carlo J. W. Leget
Keyword(s):  
Qualitative Research ◽  
Real Time ◽  
Knowledge Production ◽  
The Body ◽  
Life Itself ◽  
Research Praxis ◽  
The Aesthetic ◽  
Life Phenomena ◽  
Significant Attention ◽  
Connected Spaces

In qualitative research, the importance of knowledge production is illustrated by the confidence in logos, that still flags. Although there is significant attention for approaches that are inclusive to the body, affect and non-rational dimensions, these approaches still aim to generate understandings by the appropriation of knowledge. This paper critiques that view and proposes another view of inquiry that centers the praxis of living the questions instead. Here, research is seen as a gradual unfolding of a process. The quest that belongs with this view of research is concerned with how to make space for life phenomena to emerge. We frame this as apophatic inquiry, a non-methodology, as it is not a matter of applying activities in a set of steps. For apophatic inquiry, a process of unknowing and wonder is imperative. The paper discusses how to foster a triadic inter-beingness in a research praxis that fosters the calling forth of and reflection on phenomena. For that, the researcher nurtures awareness and reflection on a triadic sphere of three closely connected spaces: the Inner Space, the Aesthetic Space, and the Wondrous Space. By being receptive to the impressions that unfold within and between these spaces, the research becomes part of a process of living a question in real-time. Thus, living and life itself become the heart of the research.

Accelerando: A Real-Time, General Purpose Computer Music System

1989 ◽  
Vol 13 (4) ◽  
pp. 54 ◽  
Author(s):  
Keith Lent ◽  
Russell Pinkston ◽  
Peter Silsbee
Keyword(s):  
Real Time ◽  
Computer Music ◽  
General Purpose ◽  
General Purpose Computer ◽  
Purpose Computer

scholarly journals Real-Time Detection Of Gravitational Microlensing

1996 ◽  
Vol 173 ◽  
pp. 221-226 ◽  
Author(s):  
M.R. Pratt ◽  
C. Alcock ◽  
R.A. Allsman ◽  
D. Alves ◽  
T.S. Axelrod ◽  
...  
Keyword(s):  
Point Source ◽  
Real Time ◽  
Temporal Resolution ◽  
Proof Of Concept ◽  
Inertial Motion ◽  
Source Point ◽  
Gravitational Microlensing ◽  
Real Time Detection ◽  
Mass Position

Real-time detection of microlensing has moved from proof of concept in 1994 (Udalski et al. 1994a, Alcock et al. 1994) to a steady stream of events this year. Global dissemination of these events by the MACHO and OGLE collaborations has made possible intensive photometric and spectroscopic follow up from widely dispersed sites confirming the microlensing hypothesis (Benetti 1995). Improved photometry and increased temporal resolution from follow up observations greatly increases the possibility of detecting deviations from the standard point-source, point-lens, inertial motion microlensing model. These deviations are crucial in understanding individual lensing systems by breaking the degeneracy between lens mass, position and velocity. We report here on GMAN (Global Microlensing Alert Network), the coordinated follow up of MACHO alerts.

Sign in / Sign up

Export Citation Format

Share Document