scholarly journals Energy efficient convolutional neural networks for arrhythmia detection

2021 ◽  
Author(s):  
Nikoletta Katsaouni ◽  
Florian Aul ◽  
Lukas Krischker ◽  
Sascha Schmalhofer ◽  
Lars Hedrich ◽  
...  
Keyword(s):  
Cardiac Arrhythmias ◽  
Energy Efficient ◽  
Model Parameters ◽  
Portable Devices ◽  
Ecg Monitoring ◽  
Heart Activity ◽  
Small Energy ◽  
Arrhythmia Detection ◽  
Long Time ◽  
Increasing Demand

Electrocardiograms (ECG) record the heart activity and are the most common and reliable method to detect cardiac arrhythmias, such as atrial fibrillation (AFib). Lately, many commercially available devices such as smartwatches are offering ECG monitoring. Therefore, there is increasing demand for designing deep learning models with the perspective to be physically implemented on these small portable devices with limited energy supply. In this paper, a workflow for the design of small, energy-efficient recurrent convolutional neural network (RCNN) architecture for AFib detection is proposed. However, the approach can be well generalized to every type of long time series. In contrast to previous studies, that demand thousands of additional network neurons and millions of extra model parameters, the logical steps for the generation of a CNN with only 114 trainable parameters are described. The model consists of a small segmented CNN in combination with an optimal energy classifier. The architectural decisions are made by using the energy consumption as a metric in an equally important way as the accuracy. The optimisation steps are focused on the software which can be embedded afterwards on a physical chip. Finally, a comparison with some previous relevant studies suggests that the widely used huge CNNs for similar tasks are mostly redundant and unessentially computationally expensive.

scholarly journals Recent trends in smartphone-based detection for biomedical applications: a review

2021 ◽  
Vol 413 (9) ◽  
pp. 2389-2406 ◽  
Author(s):  
Soumyabrata Banik ◽  
Sindhoora Kaniyala Melanthota ◽  
Arbaaz ◽  
Joel Markus Vaz ◽  
Vishak Madhwaraj Kadambalithaya ◽  
...  
Keyword(s):  
Biomedical Applications ◽  
Histopathological Examination ◽  
Dark Field ◽  
Portable Devices ◽  
Food Technology ◽  
Wide Range ◽  
Technological Interventions ◽  
Recent Trends ◽  
Increasing Demand

AbstractSmartphone-based imaging devices (SIDs) have shown to be versatile and have a wide range of biomedical applications. With the increasing demand for high-quality medical services, technological interventions such as portable devices that can be used in remote and resource-less conditions and have an impact on quantity and quality of care. Additionally, smartphone-based devices have shown their application in the field of teleimaging, food technology, education, etc. Depending on the application and imaging capability required, the optical arrangement of the SID varies which enables them to be used in multiple setups like bright-field, fluorescence, dark-field, and multiple arrays with certain changes in their optics and illumination. This comprehensive review discusses the numerous applications and development of SIDs towards histopathological examination, detection of bacteria and viruses, food technology, and routine diagnosis. Smartphone-based devices are complemented with deep learning methods to further increase the efficiency of the devices.

scholarly journals Plant-Based Alternatives to Yogurt: State-of-the-Art and Perspectives of New Biotechnological Challenges

Foods ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 316
Author(s):  
Marco Montemurro ◽  
Erica Pontonio ◽  
Rossana Coda ◽  
Carlo Giuseppe Rizzello
Keyword(s):  
Lactic Acid ◽  
Lactic Acid Bacteria ◽  
State Of The Art ◽  
Protein Digestibility ◽  
The Novel ◽  
Sensory Features ◽  
Long Time ◽  
Biotechnological Processes ◽  
Nutritional Compounds ◽  
Increasing Demand

Due to the increasing demand for milk alternatives, related to both health and ethical needs, plant-based yogurt-like products have been widely explored in recent years. With the main goal to obtain snacks similar to the conventional yogurt in terms of textural and sensory properties and ability to host viable lactic acid bacteria for a long-time storage, several plant-derived ingredients (e.g., cereals, pseudocereals, legumes, and fruits) as well as technological solutions (e.g., enzymatic and thermal treatments) have been investigated. The central role of fermentation in yogurt-like production led to specific selections of lactic acid bacteria strains to be used as starters to guarantee optimal textural (e.g., through the synthesis of exo-polysaccharydes), nutritional (high protein digestibility and low content of anti-nutritional compounds), and functional (synthesis of bioactive compounds) features of the products. This review provides an overview of the novel insights on fermented yogurt-like products. The state-of-the-art on the use of unconventional ingredients, traditional and innovative biotechnological processes, and the effects of fermentation on the textural, nutritional, functional, and sensory features, and the shelf life are described. The supplementation of prebiotics and probiotics and the related health effects are also reviewed.

Selective Linear Segmentation for Detecting Relevant Parameter Changes*

2020 ◽  
Author(s):  
Arnaud Dufays ◽  
Elysee Aristide Houndetoungan ◽  
Alain Coën
Keyword(s):  
Time Series ◽  
Hedge Fund ◽  
Expectation Maximization Algorithm ◽  
Model Parameters ◽  
Relevant Parameter ◽  
Time Varying ◽  
Long Time ◽  
Model Selection Uncertainty ◽  
Penalized Likelihood Approach ◽  
Changes Over Time

Abstract Change-point (CP) processes are one flexible approach to model long time series. We propose a method to uncover which model parameters truly vary when a CP is detected. Given a set of breakpoints, we use a penalized likelihood approach to select the best set of parameters that changes over time and we prove that the penalty function leads to a consistent selection of the true model. Estimation is carried out via the deterministic annealing expectation-maximization algorithm. Our method accounts for model selection uncertainty and associates a probability to all the possible time-varying parameter specifications. Monte Carlo simulations highlight that the method works well for many time series models including heteroskedastic processes. For a sample of fourteen hedge fund (HF) strategies, using an asset-based style pricing model, we shed light on the promising ability of our method to detect the time-varying dynamics of risk exposures as well as to forecast HF returns.

scholarly journals Diagnostic calibration of a hydrological model in an alpine area

2014 ◽  
Vol 11 (1) ◽  
pp. 1253-1300 ◽  
Author(s):  
Z. He ◽  
F. Tian ◽  
H. C. Hu ◽  
H. V. Gupta ◽  
H. P. Hu
Keyword(s):  
Hydrological Modeling ◽  
Hydrological Model ◽  
Generation Mechanism ◽  
Spatiotemporal Variability ◽  
Model Parameters ◽  
Distributed Hydrological Model ◽  
Runoff Generation ◽  
Alpine Area ◽  
Long Time ◽  
Tailan River Basin

Abstract. Hydrological modeling depends on single- or multiple-objective strategies for parameter calibration using long time sequences of observed streamflow. Here, we demonstrate a diagnostic approach to the calibration of a hydrological model of an alpine area in which we partition the hydrograph based on the dominant runoff generation mechanism (groundwater baseflow, glacier melt, snowmelt, and direct runoff). The partitioning reflects the spatiotemporal variability in snowpack, glaciers, and temperature. Model parameters are grouped by runoff generation mechanism, and each group is calibrated separately via a stepwise approach. This strategy helps to reduce the problem of equifinality and, hence, model uncertainty. We demonstrate the method for the Tailan River basin (1324 km2) in the Tianshan Mountains of China with the help of a semi-distributed hydrological model (THREW).

scholarly journals Monitor-Watcher Use, Nurses’ Knowledge of Electrocardiographic Monitoring, and Arrhythmia Detection

2021 ◽  
Vol 30 (1) ◽  
pp. 38-44
Author(s):  
Marjorie Funk ◽  
Kristopher P. Fennie ◽  
Krista A. Knudson ◽  
Halley Ruppel
Keyword(s):  
Intensive Care Unit ◽  
Closed Loop ◽  
Test Accuracy ◽  
Ecg Monitoring ◽  
Knowledge Test ◽  
Arrhythmia Detection ◽  
Electrocardiographic Monitoring ◽  
Online Test ◽  
The Impact ◽  
Nurses Knowledge

Background Electrocardiographic telemetry monitors are ubiquitous in hospitals. Dedicated monitor watchers, either on the unit or in a centralized location, are often responsible for observing telemetry monitors and responding to their alarms. The impact of use of monitor watchers is not known. Objectives To evaluate the association of monitor-watcher use with (1) nurses’ knowledge of electrocardiographic (ECG) monitoring and (2) accuracy of arrhythmia detection. Methods Baseline data from 37 non–intensive care unit cardiac patient care areas in 17 hospitals in the Practical Use of the Latest Standards for Electrocardiography trial were analyzed. Nurses’ knowledge (n = 1136 nurses) was measured using a validated, 20-item online test. Accuracy of arrhythmia detection (n = 1189 patients) was assessed for 5 consecutive days by comparing arrhythmias stored in the monitor with nurses’ documentation. Multiple regression was used to evaluate the association of use of monitor watchers with scores on the ECG-monitoring knowledge test. The association of monitor-watcher use with accuracy of arrhythmia detection was examined by χ2 analysis. Results Of the 37 units, 13 (35%) had monitor watchers. Use of monitor watchers was not independently associated with ECG-monitoring knowledge (P = .08). The presence of monitor watchers also was not significantly associated with the accuracy of arrhythmia detection (P = .94). Conclusion Although the use of monitor watchers was not associated with diminished nurses’ knowledge of ECG monitoring, it also was not associated with more accurate arrhythmia detection. If implementing a monitor-watcher program, critical safety points, such as ensuring closed-loop communication, must be considered.

scholarly journals Prediction of peak and termination of novel coronavirus COVID-19 epidemic in Iran

2020 ◽  
Vol 31 (11) ◽  
pp. 2050152
Author(s):  
Sepehr Rafieenasab ◽  
Amir-Pouyan Zahiri ◽  
Ehsan Roohi
Keyword(s):  
Statistical Data ◽  
Sir Model ◽  
Model Parameters ◽  
Peak Time ◽  
Infected People ◽  
Epidemic Curve ◽  
Immunization Rates ◽  
Long Time ◽  
Novel Coronavirus ◽  
Official Data

The growth and development of COVID-19 transmission have significantly attracted the attention of many societies, particularly Iran, that have been struggling with this contagious, infectious disease since late February 2020. In this study, the known “Susceptible-Infectious-Recovered (SIR)” and some other mathematical approaches were used to investigate the dynamics of the COVID-19 epidemic to provide a suitable assessment of the COVID-19 virus epidemic in Iran. The epidemic curve and SIR model parameters were obtained with the use of Iran’s official data. The recovered people were considered alongside the official number of confirmed victims as the reliable long-time statistical data. The results offer important predictions of the COVID-19 virus epidemic such as the realistic number of victims, infection rate, peak time and other characteristics. Besides, the effectiveness of infection and immunization rates to the number of infected people and epidemic end time are reported. Finally, different suggestions for decreasing victims are offered.

scholarly journals Predicting residual service life of concrete infrastructure: a considerably controversial subject

2019 ◽  
Vol 289 ◽  
pp. 08002
Author(s):  
Joost Gulikers
Keyword(s):  
Service Life ◽  
Life Prediction ◽  
Wide Spectrum ◽  
Probabilistic Approach ◽  
Service Life Prediction ◽  
Model Parameters ◽  
Residual Service Life ◽  
Site Investigations ◽  
Increasing Demand ◽  
Life Predictions

There is an increasing demand from asset owners for service life prediction of existing reinforced concrete structures. This requires assessment of the current condition and modelling to allow for a prediction. This paper critically discusses a number of subjects relevant for service life prediction with respect to durability related to chloride-induced reinforcement corrosion. The subjects include the physical meaning and variability of the end-of-service-life criterion, the validity of the deterioration models, the availability, variability and reality level of input values for some model parameters, as well as some practical issues concerning site investigations. The findings are exemplified by calculation examples using both a deterministic as well as a full probabilistic approach. It is anticipated that in the future a full probabilistic approach will be adopted which makes service life predictions more prone to manipulation of input values, as literature provides a wide spectrum of values to choose from. Although a probabilistic approach seems very impressive to most asset owners it usually disguises the lack of knowledge, responsibility and liability of the consultant involved. It is concluded that asset owners will be easy prey for consultants to play a lucrative numbers game eventually providing a desirable and realistic outcome, mostly already known beforehand.

Phenomenological Modeling of Carpeted Surface for Drop Simulation of Portable Electronics

2019 ◽  
Vol 141 (2) ◽  
Author(s):  
Abhi Sirimamilla ◽  
Hua Ye ◽  
Yinan Wu
Keyword(s):  
Mechanical Response ◽  
Viscoelastic Model ◽  
Single Layer ◽  
Material Model ◽  
Impact Testing ◽  
Consumer Electronics ◽  
Model Parameters ◽  
Portable Devices ◽  
Impact Surface ◽  
The Impact

Using finite element (FE) analysis to simulate drop impact is widely adopted by the consumer electronics industry in the design process of portable devices. Most of such simulations model impact surface as a rigid or simple elastic surface. While this approach is valid for many common hard surfaces such as wood, tile, or concrete, it often does not provide a realistic risk assessment if the impact surface is a soft surface such as carpet. This paper describes a methodology to create a material model for carpeted impact surface that is suited for FE drop simulation. A multilayer hyperelastic–viscoelastic material model is used to model the mechanical response of the carpet under mechanical impact. Quasi-static and impact testing on the industrial carpet were performed to calibrate the model parameters with the help of optimization. Validation of the model was done by comparing the simulation predictions with measurements from the impact tests performed at different heights. Much better correlation between experimental measurements and simulation predictions were observed when using the multilayer hyper-viscoelastic model for carpet than using a single layer homogenous model. This approach can provide a better estimate and a more accurate representation for device drop risk on carpeted surfaces for design and development of portable products. The methodology can also be used to derive material models for other similar impact surfaces.

Cosinor Analysis for Temperature Time Series Data of Long Duration

2007 ◽  
Vol 9 (1) ◽  
pp. 30-41 ◽  
Author(s):  
Nikhil S. Padhye ◽  
Sandra K. Hanneman
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Model Fit ◽  
Series Data ◽  
Model Parameters ◽  
Cosinor Analysis ◽  
Long Time ◽  
Improved Model ◽  
Data Length ◽  
Cosinor Models

The application of cosinor models to long time series requires special attention. With increasing length of the time series, the presence of noise and drifts in rhythm parameters from cycle to cycle lead to rapid deterioration of cosinor models. The sensitivity of amplitude and model-fit to the data length is demonstrated for body temperature data from ambulatory menstrual cycling and menopausal women and from ambulatory male swine. It follows that amplitude comparisons between studies cannot be made independent of consideration of the data length. Cosinor analysis may be carried out on serial-sections of the series for improved model-fit and for tracking changes in rhythm parameters. Noise and drift reduction can also be achieved by folding the series onto a single cycle, which leads to substantial gains in the model-fit but lowers the amplitude. Central values of model parameters are negligibly changed by consideration of the autoregressive nature of residuals.

scholarly journals AOCR: adaptive on chip router algorithm for multicore domain controller platform

2018 ◽  
Vol 7 (4) ◽  
pp. 2246
Author(s):  
T Shanmuganathan ◽  
U Ramachandraiah
Keyword(s):  
Energy Efficient ◽  
Cost Reduction ◽  
Network Performance ◽  
Rapid Development ◽  
Routing Algorithm ◽  
Routing Algorithms ◽  
Core Domain ◽  
Latency Reduction ◽  
Increasing Demand ◽  
On Chip

In the recent years, with the rapid development of semiconductor technologies and increasing demand for more effective multi-Core Domain Controller platforms, there is a clear demand for effective routing algorithms that can be used to route the packets between these platforms, while enhancing an on chip network performance, achieving a better latency and throughput. This paper proposes an adaptive on Chip Router algorithm with a simple adaptive routing algorithm based on runtime weighted arbitration and resource allocation methodology, where the routing decisions are minimized for applications-specific MDCU platforms. The proposed scheme is evaluated by simulations and its performance in terms of latency, area, power consumption and cost reduction per vehicle are presented. The results show that, 24.5% of latency reduction, 62.25% area utilization optimization and 63.76% of energy efficient compare with existing methods.  

Sign in / Sign up

Export Citation Format

Share Document