Skin Conductance Feedback Meditation: Evaluation of a Novel Physiology-Assisted Meditation Style

Background: Mindfulness meditation (MM) can be regarded as a practice for calming the mind, while the focus on their breathing can help meditators maintain a state of mindful presence. We have developed and evaluated an alternative method for reaching conscious states of pure being without the aid of a focus of attention. Methods: Skin conductance feedback was provided in real-time during meditation sessions (SCFM) and the practicability of this novel approach was evaluated. 30 participants - 15 meditation experts and 15 non-meditators - attended 6 sessions of meditation over the course of 3 days; 4 SCFM and 2 ordinary MM sessions were conducted. Each session was evaluated with physiological measures and a feedback questionnaire assessing subjective changes in body-related, emotional, and mental self-perception. Results: On average, 78.3% of the participants felt more integrated and connected, 84.2% perceived themselves as more calm and balanced, and 50% felt vitalized after SCFM sessions, while only 5% or less felt more agitated or depressed after the sessions. SCFM was not significantly different from MM. The positive effect of SCFM correlated positively with mindfulness self-ratings. Conclusion: The study demonstrates that SCFM is a viable method for performing a satisfying and consciousness-expanding meditation session.

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Affective Multimodal Control of Virtual Characters

International Journal of Virtual Reality ◽

10.20870/ijvr.2007.6.4.3864 ◽

2020 ◽

Vol 6 (4) ◽

pp. 43-54 ◽

Cited By ~ 2

Author(s):

Martin Klesen ◽

Patrick Gebhard

Keyword(s):

Real Time ◽

The Body ◽

Temporal Constraints ◽

Virtual Characters ◽

Fine Grained ◽

Novel Approach ◽

Control Body ◽

High Level ◽

The Mind ◽

Selection Of

In this paper we report about the use of computer generated affect to control body and mind of cognitively modeled virtual characters. We use the computational model of affect ALMA that is able to simulate three different affect types in real-time. The computation of affect is based on a novel approach of an appraisal language. Both the use of elements of the appraisal language and the simulation of different affect types has been evaluated. Affect is used to control facial expressions, facial complexions, affective animations, posture, and idle behavior on the body layer and the selection of dialogue strategies on the mind layer. To enable a fine-grained control of these aspects a Player Markup Language (PML) has been developed. The PML is player-independent and allows a sophisticated control of character actions coordinated by high-level temporal constraints. An Action Encoder module maps the output of ALMA to PML actions using affect display rules. These actions drive the real-time rendering of affect, gesture and speech parameters of virtual characters, which we call Virtual Humans.

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A novel approach to the real-time modelling of large urban drainage systems

Water Science & Technology ◽

10.2166/wst.1997.0638 ◽

1997 ◽

Vol 36 (8-9) ◽

pp. 19-24 ◽

Cited By ~ 6

Author(s):

Richard Norreys ◽

Ian Cluckie

Keyword(s):

Real Time ◽

Dynamic Models ◽

Drainage System ◽

Radar Data ◽

Urban Drainage ◽

Real Time Control ◽

Empirical Modelling ◽

Time Control ◽

Novel Approach ◽

Non Linear Systems

Conventional UDS models are mechanistic which though appropriate for design purposes are less well suited to real-time control because they are slow running, difficult to calibrate, difficult to re-calibrate in real time and have trouble handling noisy data. At Salford University a novel hybrid of dynamic and empirical modelling has been developed, to combine the speed of the empirical model with the ability to simulate complex and non-linear systems of the mechanistic/dynamic models. This paper details the ‘knowledge acquisition module’ software and how it has been applied to construct a model of a large urban drainage system. The paper goes on to detail how the model has been linked with real-time radar data inputs from the MARS c-band radar.

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A novel approach for phishing URLs detection using lexical based machine learning in a real-time environment

Computer Communications ◽

10.1016/j.comcom.2021.04.023 ◽

2021 ◽

Author(s):

Brij B. Gupta ◽

Krishna Yadav ◽

Imran Razzak ◽

Konstantinos Psannis ◽

Arcangelo Castiglione ◽

...

Keyword(s):

Machine Learning ◽

Real Time ◽

Novel Approach

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A novel approach to optimize the production cost of railway coaches of India using situational-based composite triangular and trapezoidal fuzzy LPP models

Complex & Intelligent Systems ◽

10.1007/s40747-021-00313-0 ◽

2021 ◽

Author(s):

Rakesh Kumar ◽

Gaurav Dhiman ◽

Neeraj Kumar ◽

Rajesh Kumar Chandrawat ◽

Varun Joshi ◽

...

Keyword(s):

Comparative Study ◽

Real Time ◽

Production Cost ◽

Upper Bounds ◽

Production Costs ◽

Cost Data ◽

Novel Approach ◽

Membership Grade ◽

Minimum Costs

AbstractThis article offers a comparative study of maximizing and modelling production costs by means of composite triangular fuzzy and trapezoidal FLPP. It also outlines five different scenarios of instability and has developed realistic models to minimize production costs. Herein, the first attempt is made to examine the credibility of optimized cost via two different composite FLP models, and the results were compared with its extension, i.e., the trapezoidal FLP model. To validate the models with real-time phenomena, the Production cost data of Rail Coach Factory (RCF) Kapurthala has been taken. The lower, static, and upper bounds have been computed for each situation, and then systems of optimized FLP are constructed. The credibility of each model of composite-triangular and trapezoidal FLP concerning all situations has been obtained, and using this membership grade, the minimum and the greatest minimum costs have been illustrated. The performance of each composite-triangular FLP model was compared to trapezoidal FLP models, and the intense effects of trapezoidal on composite fuzzy LPP models are investigated.

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Machine Learning Solutions for Bridge Scour Forecast Based on Monitoring Data

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/03611981211012693 ◽

2021 ◽

pp. 036119812110126

Author(s):

Negin Yousefpour ◽

Steve Downie ◽

Steve Walker ◽

Nathan Perkins ◽

Hristo Dikanski

Keyword(s):

Machine Learning ◽

Real Time ◽

Monitoring Data ◽

Sensor Data ◽

Monitoring Systems ◽

Bridge Scour ◽

Scour Monitoring ◽

Novel Approach ◽

Bayesian Inference Method ◽

Water Level Variations

Bridge scour is a challenge throughout the U.S.A. and other countries. Despite the scale of the issue, there is still a substantial lack of robust methods for scour prediction to support reliable, risk-based management and decision making. Throughout the past decade, the use of real-time scour monitoring systems has gained increasing interest among state departments of transportation across the U.S.A. This paper introduces three distinct methodologies for scour prediction using advanced artificial intelligence (AI)/machine learning (ML) techniques based on real-time scour monitoring data. Scour monitoring data included the riverbed and river stage elevation time series at bridge piers gathered from various sources. Deep learning algorithms showed promising in prediction of bed elevation and water level variations as early as a week in advance. Ensemble neural networks proved successful in the predicting the maximum upcoming scour depth, using the observed sensor data at the onset of a scour episode, and based on bridge pier, flow and riverbed characteristics. In addition, two of the common empirical scour models were calibrated based on the observed sensor data using the Bayesian inference method, showing significant improvement in prediction accuracy. Overall, this paper introduces a novel approach for scour risk management by integrating emerging AI/ML algorithms with real-time monitoring systems for early scour forecast.

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Deep Reinforcement Learning for End-to-End Local Motion Planning of Autonomous Aerial Robots in Unknown Outdoor Environments: Real-Time Flight Experiments

Sensors ◽

10.3390/s21072534 ◽

2021 ◽

Vol 21 (7) ◽

pp. 2534

Author(s):

Oualid Doukhi ◽

Deok-Jin Lee

Keyword(s):

Reinforcement Learning ◽

Real Time ◽

Autonomous Navigation ◽

Control Technique ◽

Local Motion ◽

Aerial Robots ◽

Novel Approach ◽

Outdoor Environments ◽

Aerial Vehicle ◽

High Level

Autonomous navigation and collision avoidance missions represent a significant challenge for robotics systems as they generally operate in dynamic environments that require a high level of autonomy and flexible decision-making capabilities. This challenge becomes more applicable in micro aerial vehicles (MAVs) due to their limited size and computational power. This paper presents a novel approach for enabling a micro aerial vehicle system equipped with a laser range finder to autonomously navigate among obstacles and achieve a user-specified goal location in a GPS-denied environment, without the need for mapping or path planning. The proposed system uses an actor–critic-based reinforcement learning technique to train the aerial robot in a Gazebo simulator to perform a point-goal navigation task by directly mapping the noisy MAV’s state and laser scan measurements to continuous motion control. The obtained policy can perform collision-free flight in the real world while being trained entirely on a 3D simulator. Intensive simulations and real-time experiments were conducted and compared with a nonlinear model predictive control technique to show the generalization capabilities to new unseen environments, and robustness against localization noise. The obtained results demonstrate our system’s effectiveness in flying safely and reaching the desired points by planning smooth forward linear velocity and heading rates.

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Design of an Intelligent Data Cache with Replacement Policy

International Journal of Embedded and Real-Time Communication Systems ◽

10.4018/ijertcs.2019040106 ◽

2019 ◽

Vol 10 (2) ◽

pp. 87-107

Author(s):

B. Shameedha Begum ◽

N. Ramasubramanian

Keyword(s):

Real Time ◽

Replacement Policy ◽

Data Cache ◽

Cache Performance ◽

Performance Requirements ◽

Novel Approach ◽

Reconfigurable Caches ◽

Real Time Applications ◽

Hard Real Time ◽

Replacement Algorithms

Embedded systems are designed for a variety of applications ranging from Hard Real Time applications to mobile computing, which demands various types of cache designs for better performance. Since real-time applications place stringent requirements on performance, the role of the cache subsystem assumes significance. Reconfigurable caches meet performance requirements under this context. Existing reconfigurable caches tend to use associativity and size for maximizing cache performance. This article proposes a novel approach of a reconfigurable and intelligent data cache (L1) based on replacement algorithms. An intelligent embedded data cache and a dynamic reconfigurable intelligent embedded data cache have been implemented using Verilog 2001 and tested for cache performance. Data collected by enabling the cache with two different replacement strategies have shown that the hit rate improves by 40% when compared to LRU and 21% when compared to MRU for sequential applications which will significantly improve performance of embedded real time application.

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The Use of Infrared Thermography as a Novel Approach for Real-Time Validation of PCR Thermocyclers

Food Analytical Methods ◽

10.1007/s12161-009-9103-2 ◽

2009 ◽

Vol 3 (2) ◽

pp. 116-119 ◽

Cited By ~ 1

Author(s):

Hugo Ahlm Grønlund ◽

Charlotta Löfström ◽

Jens Bue Helleskov ◽

Jeffrey Hoorfar

Keyword(s):

Real Time ◽

Infrared Thermography ◽

Novel Approach

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A Successful Adaptation of Systematic Desensitization in the Treatment of a Phobia of Babies

Behaviour Change ◽

10.1017/s0813483900008810 ◽

1985 ◽

Vol 2 (1) ◽

pp. 59-64

Author(s):

Michael Free ◽

Margaret Beekhuis

Keyword(s):

Young Woman ◽

Skin Conductance ◽

Treatment Strategy ◽

Physiological Measures ◽

Systematic Desensitization ◽

Physiological Measure ◽

One Year

A case study is presented of a young woman with an unusual phobia, a fear of babies. Barabasz's (1977) technique of systematic desensitization using psycho-physiological measures was chosen as the main treatment strategy. Difficulties arose as the client was unable to visualise scenes involving babies. Nor could she look at photographs of babies long enough for the hierarchy to be ordered using a psycho-physiological measure (skin conductance). A set of photographs was eventually used for the hierarchy, but it was ordered in terms of the length of time the client could look at the various photographs. Systematic desensitization was carried out using the set of photographs instead of imaginary scenes, together with some in vivo exposure in the latter stages of treatment. At termination the client could approach babies without discomfort. Improvement was maintained at one year follow-up.

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