Real time digital filtering performance with a 486D-66 MHz processing engine

1995 ◽  
Author(s):  
A.A. Chanerley
Keyword(s):  
Real Time ◽  
Digital Filtering ◽  
Processing Engine

Hidden grids: paths of expressive gesture between instruments, music and dance

2000 ◽  
Vol 5 (1) ◽  
pp. 17-26 ◽  
Author(s):  
SILVIA LANZALONE
Keyword(s):  
Real Time ◽  
Direct Interaction ◽  
Digital Filtering ◽  
Live Electronics ◽  
Computer Mouse

In his work Contropasso (1998–9) Michelangelo Lupone collaborates with Massimo Moricone in the dance showcase piegapiaga achieving direct interaction between dancers and live electronics performance. The choreography takes advantage of acoustic events as generated by three dancers and further elaborated on via computer by the composer through use of granular algorithms and digital filtering, allowing the construction of the musical events to occur in real time. The live electronics performer changes sound parameters in relation to the dancers' movements by use of the program SDP – Sonorous Drawing Plane (S. Lanzalone) – created specifically for the control of different synthesis algorithms allowing them to be processed on systems such as Fly30 (CRM) and Mars (IRIS). SDP reads and converts computer mouse data as the operator creates lines corresponding to performance gestures, thus creating both visible and audible output. This software allows a single gesture to control more than one parameter, thus creating complex changes in the audio programme output. The article deals with different compositions, performances and didactic situations the author has experienced using SDP.

scholarly journals A Flexible IoT Stream Processing Architecture Based on Microservices

Information ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 565
Author(s):  
Luca Bixio ◽  
Giorgio Delzanno ◽  
Stefano Rebora ◽  
Matteo Rulli
Keyword(s):  
Real Time ◽  
Query Language ◽  
Stream Processing ◽  
Heterogeneous Data ◽  
Core Level ◽  
Processing Unit ◽  
Reference Architecture ◽  
Real Time Processing ◽  
Stream Processing Engines ◽  
Processing Engine

The Internet of Things (IoT) has created new and challenging opportunities for data analytics. The IoT represents an infinitive source of massive and heterogeneous data, whose real-time processing is an increasingly important issue. IoT applications usually consist of multiple technological layers connecting ‘things’ to a remote cloud core. These layers are generally grouped into two macro levels: the edge level (consisting of the devices at the boundary of the network near the devices that produce the data) and the core level (consisting of the remote cloud components of the application). The aim of this work is to propose an adaptive microservices architecture for IoT platforms which provides real-time stream processing functionalities that can seamlessly both at the edge-level and cloud-level. More in detail, we introduce the notion of μ-service, a stream processing unit that can be indifferently allocated on the edge and core level, and a Reference Architecture that provides all necessary services (namely Proxy, Adapter and Data Processing μ-services) for dealing with real-time stream processing in a very flexible way. Furthermore, in order to abstract away from the underlying stream processing engine and IoT layers (edge/cloud), we propose: (1) a service definition language consisting of a configuration language based on JSON objects (interoperability), (2) a rule-based query language with basic filter operations that can be compiled to most of the existing stream processing engines (portability), and (3) a combinator language to build pipelines of filter definitions (compositionality). Although our proposal has been designed to extend the Senseioty platform, a proprietary IoT platform developed by FlairBit, it could be adapted to every platform based on similar technologies. As a proof of concept, we provide details of a preliminary prototype based on the Java OSGi framework.

scholarly journals A Distributed Stream Processing Middleware Framework for Real-Time Analysis of Heterogeneous Data on Big Data Platform: Case of Environmental Monitoring

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3166
Author(s):  
Adeyinka Akanbi ◽  
Muthoni Masinde
Keyword(s):  
Big Data ◽  
Environmental Monitoring ◽  
Real Time ◽  
Stream Processing ◽  
Heterogeneous Data ◽  
Legacy Systems ◽  
Time Analysis ◽  
Real Time Analysis ◽  
Distributed Stream Processing ◽  
Processing Engine

In recent years, the application and wide adoption of Internet of Things (IoT)-based technologies have increased the proliferation of monitoring systems, which has consequently exponentially increased the amounts of heterogeneous data generated. Processing and analysing the massive amount of data produced is cumbersome and gradually moving from classical ‘batch’ processing—extract, transform, load (ETL) technique to real-time processing. For instance, in environmental monitoring and management domain, time-series data and historical dataset are crucial for prediction models. However, the environmental monitoring domain still utilises legacy systems, which complicates the real-time analysis of the essential data, integration with big data platforms and reliance on batch processing. Herein, as a solution, a distributed stream processing middleware framework for real-time analysis of heterogeneous environmental monitoring and management data is presented and tested on a cluster using open source technologies in a big data environment. The system ingests datasets from legacy systems and sensor data from heterogeneous automated weather systems irrespective of the data types to Apache Kafka topics using Kafka Connect APIs for processing by the Kafka streaming processing engine. The stream processing engine executes the predictive numerical models and algorithms represented in event processing (EP) languages for real-time analysis of the data streams. To prove the feasibility of the proposed framework, we implemented the system using a case study scenario of drought prediction and forecasting based on the Effective Drought Index (EDI) model. Firstly, we transform the predictive model into a form that could be executed by the streaming engine for real-time computing. Secondly, the model is applied to the ingested data streams and datasets to predict drought through persistent querying of the infinite streams to detect anomalies. As a conclusion of this study, a performance evaluation of the distributed stream processing middleware infrastructure is calculated to determine the real-time effectiveness of the framework.

Real-Time Monitoring of Brainstem Auditory Evoked Response (BAER) during Cerebellopontine Angle (CPA) Surgery

1986 ◽  
Vol 95 (5) ◽  
pp. 538-542 ◽  
Author(s):  
Paul E. Hammerschlag ◽  
H.M. Berg ◽  
L.S. Prichep ◽  
E.R. John ◽  
N.L. Cohen ◽  
...  
Keyword(s):  
Real Time ◽  
Signal To Noise Ratio ◽  
Digital Filtering ◽  
Real Time Monitoring ◽  
Auditory Evoked Responses ◽  
Auditory Evoked Response ◽  
The Face ◽  
Functional Consequences ◽  
Brainstem Auditory Evoked Response ◽  
Early Indication

The signal-to-noise ratio of brainstem auditory evoked responses (BAER) can be greatly enhanced by use of optimal digital filtering before averaging. This permits accurate assessment of auditory nerve status every 5 to 10 seconds, making real-time intraoperative monitoring possible. The major advantages yielded by real-time monitoring—in our experience thus far—have been (1) identification of potentially adverse functional consequences of apparently uneventful surgical maneuvers, reducing postoperative dysfunction, (2) early indication of potential for improved clinical function, and (3) potential identification and localization of neural tissue in the face of absent surgical landmarks. Examples of these advantages will be provided from case studies, and the possibility that real-time monitoring may improve ability to preserve hearing will be discussed.

scholarly journals Streaming Big Data Analysis for Real-Time Sentiment based Targeted Advertising

2017 ◽  
Vol 7 (1) ◽  
pp. 402 ◽  
Author(s):  
Lekha R. Nair ◽  
Sujala D. Shetty ◽  
Siddhant Deepak Shetty
Keyword(s):  
Big Data ◽  
Social Network ◽  
Real Time ◽  
Social Network Sites ◽  
Big Data Analysis ◽  
Customer Support ◽  
Targeted Advertising ◽  
Great Relevance ◽  
Streaming Big Data ◽  
Processing Engine

Big Data constituting from the information shared in the various social network sites have great relevance for research to be applied in diverse fields like marketing, politics, health or disaster management. Social network sites like Facebook and Twitter are now extensively used for conducting business, marketing products and services and collecting opinions and feedbacks regarding the same. Since data gathered from these sites regarding a product/brand are up-to-date and are mostly supplied voluntarily, it tends to be more realistic, massive and reflects the general public opinion. Its analysis on real time can lead to accurate insights and responding to the results sooner is undoubtedly advantageous than responding later.  In this paper, a cloud based system for real time targeted advertising based on tweet sentiment analysis is designed and implemented using the big data processing engine Apache Spark, utilizing its streaming library. Application is meant to promote cross selling and provide better customer support.

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