scholarly journals Classification of Dataflow Actors with Satisfiability and Abstract Interpretation

2012 ◽  
Vol 3 (1) ◽  
pp. 49-69 ◽  
Author(s):  
Matthieu Wipliez ◽  
Mickaël Raulet
Keyword(s):  
Signal Processing ◽  
Abstract Interpretation ◽  
Expressive Power ◽  
Time Analysis ◽  
Synchronous Dataflow ◽  
Dataflow Programming ◽  
Execution Speed ◽  
Satisfiability Solving ◽  
Dataflow Models

Dataflow programming has been used to describe signal processing applications for many years, traditionally with cyclo-static dataflow (CSDF) or synchronous dataflow (SDF) models that restrict expressive power in favor of compile-time analysis and predictability. More recently, dynamic dataflow is being used for the description of multimedia video standards as promoted by the RVC standard (ISO/IEC 23001:4). Dynamic dataflow is not restricted with respect to expressive power, but it does require runtime scheduling in the general case, which may be costly to perform on software. The authors presented in a previous paper a method to automatically classify actors of a dynamic dataflow program within more restrictive dataflow models when possible, along with a method to transform the actors classified as static to improve execution speed by reducing the number of FIFO accesses (Wipliez & Raulet, 2010). This paper presents an extension of the classification method using satisfiability solving, and details the precise semantics used for the abstract interpretation of actors. The extended classification is able to classify more actors than what could previously be achieved.

scholarly journals Binding Time Analysis: Abstract Interpretation vs. Type Inference

1992 ◽  
Vol 21 (393) ◽  
Author(s):  
Jens Palsberg ◽  
Michael I. Schwartzbach
Keyword(s):  
Abstract Interpretation ◽  
Lambda Calculus ◽  
Type Inference ◽  
Time Analysis ◽  
Binding Time ◽  
Better Than ◽  
Binding Time Analysis

Binding time analysis is important in partial evaluators. Its task is to determine which parts of a program can be specialized if some of the expected input is known. Two approaches to do this are abstract interpretation and type inference. We compare two specific such analyses to see which one determines most program parts to be eliminable. The first is the abstract interpretation approach of Bondorf, and the second is the type inference approach o£ Gomard. Both apply to the untyped lambda calculus. We prove that Bondorf's analysis is better than Gomard's.

A Comparison of Machine Learning Algorithms for the Segmentation and Classification of Snow Micro Penetrometer Profiles on Arctic Sea Ice

2021 ◽  
Author(s):  
Julia Kaltenborn ◽  
Viviane Clay ◽  
Amy R. Macfarlane ◽  
Joshua Michael Lloyd King ◽  
Martin Schneebeli
Keyword(s):  
Machine Learning ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Support Vector ◽  
Snow Layer ◽  
Arctic Sea ◽  
Execution Speed

<p>Snow-layer classification is an essential diagnostic task for a wide variety of cryospheric science and climate research applications. Traditionally, these measurements are made in snow pits, requiring trained operators and a substantial time commitment. The SnowMicroPen (SMP), a portable high-resolution snow penetrometer, has been demonstrated as a capable tool for rapid snow grain classification and layer type segmentation through statistical inversion of its mechanical signal. The manual classification of the SMP profiles requires time and training and becomes infeasible for large datasets.</p><p>Here, we introduce a novel set of SMP measurements collected during the MOSAiC expedition and apply Machine Learning (ML) algorithms to automatically classify and segment SMP profiles of snow on Arctic sea ice. To this end, different supervised and unsupervised ML methods, including Random Forests, Support Vector Machines, Artificial Neural Networks, and k-means Clustering, are compared. A subsequent segmentation of the classified data results in distinct layers and snow grain markers for the SMP profiles. The models are trained with the dataset by King et al. (2020) and the MOSAiC SMP dataset. The MOSAiC dataset is a unique and extensive dataset characterizing seasonal and spatial variation of snow on the central Arctic sea-ice.</p><p>We will test and compare the different algorithms and evaluate the algorithms’ effectiveness based on the need for initial dataset labeling, execution speed, and ease of implementation. In particular, we will compare supervised to unsupervised methods, which are distinguished by their need for labeled training data.</p><p>The implementation of different ML algorithms for SMP profile classification could provide a fast and automatic grain type classification and snow layer segmentation. Based on the gained knowledge from the algorithms’ comparison, a tool can be built to provide scientists from different fields with an immediate SMP profile classification and segmentation. </p><p> </p><p>King, J., Howell, S., Brady, M., Toose, P., Derksen, C., Haas, C., & Beckers, J. (2020). Local-scale variability of snow density on Arctic sea ice. <em>The Cryosphere</em>, <em>14</em>(12), 4323-4339, https://doi.org/10.5194/tc-14-4323-2020.</p>

Instrumentation-Driven Model Detection and Actor Partitioning for Dataflow Graphs

2013 ◽  
Vol 4 (1) ◽  
pp. 1-21 ◽  
Author(s):  
Ilya Chukhman ◽  
Shuoxin Lin ◽  
William Plishker ◽  
Chung-Ching Shen ◽  
Shuvra S. Bhattacharyya
Keyword(s):  
Signal Processing ◽  
Legacy Systems ◽  
Dataflow Graphs ◽  
Analysis Techniques ◽  
Legacy Code ◽  
The Core ◽  
Systems On Chip ◽  
Dataflow Model ◽  
On Chip ◽  
Dataflow Models

Dataflow modeling offers a myriad of tools to improve optimization and analysis of signal processing applications, and is often used by designers to help design, implement, and maintain systems on chip for signal processing. However, maintaining and upgrading legacy systems that were not originally designed using dataflow methods can be challenging. Designers often convert legacy code to dataflow graphs by hand, a process that can be difficult and time consuming. In this paper, the authors developed a method to facilitate this conversion process by automatically detecting the dataflow models of the core functions from bodies of legacy code. They focus first on detecting static dataflow models, such as homogeneous and synchronous dataflow, and then present an extension that can also detect dynamic dataflow models. Building on the authors’ algorithms for dataflow model detection, they present an iterative actor partitioning process that can be used to partition complex actors into simpler sub-functions that are more prone to analysis techniques.

scholarly journals Capillary Sensor with Disposable Optrode for Diesel Fuel Quality Testing

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 1980 ◽  
Author(s):  
Michal Borecki ◽  
Przemyslaw Prus ◽  
Michael L. Korwin-Pawlowski
Keyword(s):  
Signal Processing ◽  
Diesel Fuel ◽  
Optoelectronic Device ◽  
Data Series ◽  
Fuel Quality ◽  
Engine Operation ◽  
Quality Classification ◽  
Artificial Neural Network Classifier ◽  
Quality Type

Diesel fuel quality can be considered from many different points of view. Fuel producers, fuel consumers, and ecologists have their own ideas. In this paper, a sensor of diesel fuel quality type, and fuel condition that is oriented to the fuel’s consumers, is presented. The fuel quality types include premium, standard, and full bio-diesel classes. The fuel conditions include fuel fit for use and fuel degraded classes. The classes of fuel are connected with characteristics of engine operation. The presented sensor uses signal processing of an optoelectronic device monitoring fuel samples that are locally heated to the first step of boiling. Compared to previous works which consider diesel fuel quality sensing with disposable optrodes which use a more complex construction, the sensor now consists only of a capillary probe and advanced signal processing. The signal processing addresses automatic conversion of the data series to form a data pattern, estimates the measurement uncertainty, eliminates outlier data, and determines the fuel quality with an intelligent artificial neural network classifier. The sensor allows the quality classification of different unknown diesel fuel samples in less than a few minutes with the measurement costs of a single disposable capillary probe and two plugs.

scholarly journals Role of EEG as Biomarker in the Early Detection and Classification of Dementia

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Noor Kamal Al-Qazzaz ◽  
Sawal Hamid Bin MD. Ali ◽  
Siti Anom Ahmad ◽  
Kalaivani Chellappan ◽  
Md. Shabiul Islam ◽  
...  
Keyword(s):  
Signal Processing ◽  
Early Detection ◽  
Patient Treatment ◽  
Treatment Programs ◽  
Treatment Benefit ◽  
Medical Practitioners ◽  
Early Stages ◽  
Clinical Support

The early detection and classification of dementia are important clinical support tasks for medical practitioners in customizing patient treatment programs to better manage the development and progression of these diseases. Efforts are being made to diagnose these neurodegenerative disorders in the early stages. Indeed, early diagnosis helps patients to obtain the maximum treatment benefit before significant mental decline occurs. The use of electroencephalogram as a tool for the detection of changes in brain activities and clinical diagnosis is becoming increasingly popular for its capabilities in quantifying changes in brain degeneration in dementia. This paper reviews the role of electroencephalogram as a biomarker based on signal processing to detect dementia in early stages and classify its severity. The review starts with a discussion of dementia types and cognitive spectrum followed by the presentation of the effective preprocessing denoising to eliminate possible artifacts. It continues with a description of feature extraction by using linear and nonlinear techniques, and it ends with a brief explanation of vast variety of separation techniques to classify EEG signals. This paper also provides an idea from the most popular studies that may help in diagnosing dementia in early stages and classifying through electroencephalogram signal processing and analysis.

Classification of the emotional states based on the EEG signal processing

2009 ◽  
Author(s):  
Martin Macas ◽  
Michal Vavrecka ◽  
Vaclav Gerla ◽  
Lenka Lhotska
Keyword(s):  
Signal Processing ◽  
Emotional States ◽  
Eeg Signal ◽  
Eeg Signal Processing
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