Search results for: signal detection theory
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 9127

Search results for: signal detection theory

8317 Image Processing Approach for Detection of Three-Dimensional Tree-Rings from X-Ray Computed Tomography

Authors: Jorge Martinez-Garcia, Ingrid Stelzner, Joerg Stelzner, Damian Gwerder, Philipp Schuetz

Abstract:

Tree-ring analysis is an important part of the quality assessment and the dating of (archaeological) wood samples. It provides quantitative data about the whole anatomical ring structure, which can be used, for example, to measure the impact of the fluctuating environment on the tree growth, for the dendrochronological analysis of archaeological wooden artefacts and to estimate the wood mechanical properties. Despite advances in computer vision and edge recognition algorithms, detection and counting of annual rings are still limited to 2D datasets and performed in most cases manually, which is a time consuming, tedious task and depends strongly on the operator’s experience. This work presents an image processing approach to detect the whole 3D tree-ring structure directly from X-ray computed tomography imaging data. The approach relies on a modified Canny edge detection algorithm, which captures fully connected tree-ring edges throughout the measured image stack and is validated on X-ray computed tomography data taken from six wood species.

Keywords: ring recognition, edge detection, X-ray computed tomography, dendrochronology

Procedia PDF Downloads 204
8316 Kernel Parallelization Equation for Identifying Structures under Unknown and Periodic Loads

Authors: Seyed Sadegh Naseralavi

Abstract:

This paper presents a Kernel parallelization equation for damage identification in structures under unknown periodic excitations. Herein, the dynamic differential equation of the motion of structure is viewed as a mapping from displacements to external forces. Utilizing this viewpoint, a new method for damage detection in structures under periodic loads is presented. The developed method requires only two periods of load. The method detects the damages without finding the input loads. The method is based on the fact that structural displacements under free and forced vibrations are associated with two parallel subspaces in the displacement space. Considering the concept, kernel parallelization equation (KPE) is derived for damage detection under unknown periodic loads. The method is verified for a case study under periodic loads.

Keywords: Kernel, unknown periodic load, damage detection, Kernel parallelization equation

Procedia PDF Downloads 267
8315 GPS Signal Correction to Improve Vehicle Location during Experimental Campaign

Authors: L. Della Ragione, G. Meccariello

Abstract:

In recent years the progress of the automobile industry in Italy in the field of reduction of emissions values is very remarkable. Nevertheless, their evaluation and reduction is a key problem, especially in the cities, which account for more than 50% of world population. In this paper we dealt with the problem of describing a quantitative approach for the reconstruction of GPS coordinates and altitude, in the context of correlation study between driving cycles / emission / geographical location, during an experimental campaign realized with some instrumented cars.

Keywords: air pollution, driving cycles, GPS signal, vehicle location

Procedia PDF Downloads 414
8314 Genetic Algorithm Based Node Fault Detection and Recovery in Distributed Sensor Networks

Authors: N. Nalini, Lokesh B. Bhajantri

Abstract:

In Distributed Sensor Networks, the sensor nodes are prone to failure due to energy depletion and some other reasons. In this regard, fault tolerance of network is essential in distributed sensor environment. Energy efficiency, network or topology control and fault-tolerance are the most important issues in the development of next-generation Distributed Sensor Networks (DSNs). This paper proposes a node fault detection and recovery using Genetic Algorithm (GA) in DSN when some of the sensor nodes are faulty. The main objective of this work is to provide fault tolerance mechanism which is energy efficient and responsive to network using GA, which is used to detect the faulty nodes in the network based on the energy depletion of node and link failure between nodes. The proposed fault detection model is used to detect faults at node level and network level faults (link failure and packet error). Finally, the performance parameters for the proposed scheme are evaluated.

Keywords: distributed sensor networks, genetic algorithm, fault detection and recovery, information technology

Procedia PDF Downloads 438
8313 Detection of Concrete Reinforcement Damage Using Piezoelectric Materials: Analytical and Experimental Study

Authors: C. P. Providakis, G. M. Angeli, M. J. Favvata, N. A. Papadopoulos, C. E. Chalioris, C. G. Karayannis

Abstract:

An effort for the detection of damages in the reinforcement bars of reinforced concrete members using PZTs is presented. The damage can be the result of excessive elongation of the steel bar due to steel yielding or due to local steel corrosion. In both cases the damage is simulated by considering reduced diameter of the rebar along the damaged part of its length. An integration approach based on both electromechanical admittance methodology and guided wave propagation technique is used to evaluate the artificial damage on the examined longitudinal steel bar. Two actuator PZTs and a sensor PZT are considered to be bonded on the examined steel bar. The admittance of the Sensor PZT is calculated using COMSOL 3.4a. Fast Furrier Transformation for a better evaluation of the results is employed. An effort for the quantification of the damage detection using the root mean square deviation (RMSD) between the healthy condition and damage state of the sensor PZT is attempted. The numerical value of the RSMD yields a level for the difference between the healthy and the damaged admittance computation indicating this way the presence of damage in the structure. Experimental measurements are also presented.

Keywords: concrete reinforcement, damage detection, electromechanical admittance, experimental measurements, finite element method, guided waves, PZT

Procedia PDF Downloads 238
8312 Factors Drive Consumers to Purchase Digital Music: An Empirical Study

Authors: Chechen Liao, Yi-Jen Huang, Yu-Ting Lu

Abstract:

This study explores and complements digital aspects. In this study, we construct a research model based on the theory of reasoned action and extend it with the advantages and disadvantages of intangibility (convenience, perceived risk), some characteristics of digital products (price, variety, trialability), and factors related to entertainment (perceived playfulness) to predict what consumers really consider when they buy digital music. Eight hypotheses were tested and supported. Finally, we prove that the theory of reasoned action is still valid in the field of digital products.

Keywords: digital music, digital product, theory of reasoned action

Procedia PDF Downloads 420
8311 The Impact of Distributed Epistemologies on Software Engineering

Authors: Thomas Smith

Abstract:

Many hackers worldwide would agree that, had it not been for linear-time theory, the refinement of Byzantine fault tolerance might never have occurred. After years of significant research into extreme programming, we validate the refinement of simulated annealing. Maw, our new framework for unstable theory, is the solution to all of these issues.

Keywords: distributed, software engineering, DNS, DHCP

Procedia PDF Downloads 336
8310 Detection of Concrete Reinforcement Damage Using Piezoelectric Materials: Analytical and Experimental Study

Authors: C. P. Providakis, G. M. Angeli, M. J. Favvata, N. A. Papadopoulos, C. E. Chalioris, C. G. Karayannis

Abstract:

An effort for the detection of damages in the reinforcement bars of reinforced concrete members using PZTs is presented. The damage can be the result of excessive elongation of the steel bar due to steel yielding or due to local steel corrosion. In both cases the damage is simulated by considering reduced diameter of the rebar along the damaged part of its length. An integration approach based on both electro-mechanical admittance methodology and guided wave propagation technique is used to evaluate the artificial damage on the examined longitudinal steel bar. Two actuator PZTs and a sensor PZT are considered to be bonded on the examined steel bar. The admittance of the Sensor PZT is calculated using COMSOL 3.4a. Fast Furrier Transformation for a better evaluation of the results is employed. An effort for the quantification of the damage detection using the root mean square deviation (RMSD) between the healthy condition and damage state of the sensor PZT is attempted. The numerical value of the RSMD yields a level for the difference between the healthy and the damaged admittance computation indicating this way the presence of damage in the structure. Experimental measurements are also presented.

Keywords: concrete reinforcement, damage detection, electromechanical admittance, experimental measurements, finite element method, guided waves, PZT

Procedia PDF Downloads 276
8309 Global Migration and Endangered Majorities in Europe

Authors: Liav Orgad

Abstract:

This article challenges one of the most fundamental propositions in the democratic theory that the majority culture is protected merely by the forces of democracy and thus needs no special legal protection. By describing changes in the patterns of migration to Europe, in the face of the European society, and in the world as a whole, the Article demonstrates that the majority culture is no longer automatically protected by the forces of democracy. It claims that the changing reality is not adequately addressed by political theory and human rights law and advances the promotion of a new concept—'cultural majority rights'.

Keywords: European migration, European demography, democratic theory, majority rights, integration

Procedia PDF Downloads 387
8308 Measurement System for Human Arm Muscle Magnetic Field and Grip Strength

Authors: Shuai Yuan, Minxia Shi, Xu Zhang, Jianzhi Yang, Kangqi Tian, Yuzheng Ma

Abstract:

The precise measurement of muscle activities is essential for understanding the function of various body movements. This work aims to develop a muscle magnetic field signal detection system based on mathematical analysis. Medical research has underscored that early detection of muscle atrophy, coupled with lifestyle adjustments such as dietary control and increased exercise, can significantly enhance muscle-related diseases. Currently, surface electromyography (sEMG) is widely employed in research as an early predictor of muscle atrophy. Nonetheless, the primary limitation of using sEMG to forecast muscle strength is its inability to directly measure the signals generated by muscles. Challenges arise from potential skin-electrode contact issues due to perspiration, leading to inaccurate signals or even signal loss. Additionally, resistance and phase are significantly impacted by adipose layers. The recent emergence of optically pumped magnetometers introduces a fresh avenue for bio-magnetic field measurement techniques. These magnetometers possess high sensitivity and obviate the need for a cryogenic environment unlike superconducting quantum interference devices (SQUIDs). They detect muscle magnetic field signals in the range of tens to thousands of femtoteslas (fT). The utilization of magnetometers for capturing muscle magnetic field signals remains unaffected by issues of perspiration and adipose layers. Since their introduction, optically pumped atomic magnetometers have found extensive application in exploring the magnetic fields of organs such as cardiac and brain magnetism. The optimal operation of these magnetometers necessitates an environment with an ultra-weak magnetic field. To achieve such an environment, researchers usually utilize a combination of active magnetic compensation technology with passive magnetic shielding technology. Passive magnetic shielding technology uses a magnetic shielding device built with high permeability materials to attenuate the external magnetic field to a few nT. Compared with more layers, the coils that can generate a reverse magnetic field to precisely compensate for the residual magnetic fields are cheaper and more flexible. To attain even lower magnetic fields, compensation coils designed by Biot-Savart law are involved to generate a counteractive magnetic field to eliminate residual magnetic fields. By solving the magnetic field expression of discrete points in the target region, the parameters that determine the current density distribution on the plane can be obtained through the conventional target field method. The current density is obtained from the partial derivative of the stream function, which can be represented by the combination of trigonometric functions. Optimization algorithms in mathematics are introduced into coil design to obtain the optimal current density distribution. A one-dimensional linear regression analysis was performed on the collected data, obtaining a coefficient of determination R2 of 0.9349 with a p-value of 0. This statistical result indicates a stable relationship between the peak-to-peak value (PPV) of the muscle magnetic field signal and the magnitude of grip strength. This system is expected to be a widely used tool for healthcare professionals to gain deeper insights into the muscle health of their patients.

Keywords: muscle magnetic signal, magnetic shielding, compensation coils, trigonometric functions.

Procedia PDF Downloads 49
8307 Challenging the Standard 24 Equal Quarter Tones Theory in Arab Music: A Case Study of Tetrachords Bayyātī and ḤIjāz

Authors: Nabil Shair

Abstract:

Arab music maqām (Arab modal framework) is founded, among other main characteristics, on microtonal intervals. Notwithstanding the importance and multifaceted nature of intonation in Arab music, there is a paucity of studies examining this subject based on scientific and quantitative approaches. The present-day theory concerning the Arab tone system is largely based on the pioneering treatise of Mīkhā’īl Mashāqah (1840), which proposes the theoretical division of the octave into 24 equal quarter tones. This kind of equal-tempered division is incompatible with the performance practice of Arab music, as many professional Arab musicians conceptualize additional pitches beyond the standard 24 notes per octave. In this paper, we refute the standard theory presenting the scale of well-tempered quarter tones by implementing a quantitative analysis of the performed intonation of two prominent tetrachords in Arab music, namely bayyātī and ḥijāz. This analysis was conducted with the help of advanced computer programs, such as Sonic Visualiser and Tony, by which we were able to obtain precise frequency data (Hz) of each tone every 0.01 second. As a result, the value (in cents) of all three intervals of each tetrachord was measured and accordingly compared to the theoretical intervals. As a result, a specific distribution of a range of deviation from the equal-tempered division of the octave was detected, especially the detection of a diminished first interval of bayyātí and diminished second interval of ḥijāz. These types of intonation entail a considerable amount of flexibility, mainly influenced by surrounding tones, direction and function of the measured tone, ornaments, text, personal style of the performer and interaction with the audience. This paper seeks to contribute to the existing literature dealing with intonation in Arab music, as it is a vital part of the performance practice of this musical tradition. In addition, the insights offered by this paper and its novel methodology might also contribute to the broadening of the existing pedagogic methods used to teach Arab music.

Keywords: Arab music, intonation, performance practice, music theory, oral music, octave division, tetrachords, music of the middle east, music history, musical intervals

Procedia PDF Downloads 41
8306 Automated Feature Detection and Matching Algorithms for Breast IR Sequence Images

Authors: Chia-Yen Lee, Hao-Jen Wang, Jhih-Hao Lai

Abstract:

In recent years, infrared (IR) imaging has been considered as a potential tool to assess the efficacy of chemotherapy and early detection of breast cancer. Regions of tumor growth with high metabolic rate and angiogenesis phenomenon lead to the high temperatures. Observation of differences between the heat maps in long term is useful to help assess the growth of breast cancer cells and detect breast cancer earlier, wherein the multi-time infrared image alignment technology is a necessary step. Representative feature points detection and matching are essential steps toward the good performance of image registration and quantitative analysis. However, there is no clear boundary on the infrared images and the subject's posture are different for each shot. It cannot adhesive markers on a body surface for a very long period, and it is hard to find anatomic fiducial markers on a body surface. In other words, it’s difficult to detect and match features in an IR sequence images. In this study, automated feature detection and matching algorithms with two type of automatic feature points (i.e., vascular branch points and modified Harris corner) are developed respectively. The preliminary results show that the proposed method could identify the representative feature points on the IR breast images successfully of 98% accuracy and the matching results of 93% accuracy.

Keywords: Harris corner, infrared image, feature detection, registration, matching

Procedia PDF Downloads 292
8305 Numerical Simulation and Laboratory Tests for Rebar Detection in Reinforced Concrete Structures using Ground Penetrating Radar

Authors: Maha Al-Soudani, Gilles Klysz, Jean-Paul Balayssac

Abstract:

The aim of this paper is to use Ground Penetrating Radar (GPR) as a non-destructive testing (NDT) method to increase its accuracy in recognizing the geometric reinforced concrete structures and in particular, the position of steel bars. This definition will help the managers to assess the state of their structures on the one hand vis-a-vis security constraints and secondly to quantify the need for maintenance and repair. Several configurations of acquisition and processing of the simulated signal were tested to propose and develop an appropriate imaging algorithm in the propagation medium to locate accurately the rebar. A subsequent experimental validation was used by testing the imaging algorithm on real reinforced concrete structures. The results indicate that, this algorithm is capable of estimating the reinforcing steel bar position to within (0-1) mm.

Keywords: GPR, NDT, Reinforced concrete structures, Rebar location.

Procedia PDF Downloads 488
8304 Linking Theory to Practice: An Analysis of Papers Submitted by Participants in a Teacher Mentoring Course

Authors: Varda Gil, Ella Shoval, Tussia Mira

Abstract:

Teacher mentoring is a complex practical profession whose unique characteristic is the teacher-mentors' commitment to helping teachers link theory with teaching practice in the process of decision-making and in their reflections on teaching. The aim of this research is to examine the way practicing teacher-mentors participating in a teacher mentoring course made the connection between theory and practice. The researchers analyzed 20 final papers submitted by participants in a course to train teacher mentors. The participants were all veteran high-school teachers. The course comprised 112 in-class hours in addition to mentoring novices in the field. The course covered the following topics: The teacher-mentors' perception of their role; formative and summative evaluation of the novices; tutoring strategies and tools; types of learners; and ways of communicating and dealing with novice teachers' resistance to counseling. The course participants were required to write a 4-5 page reflective summary of their field mentoring practice. In addition, they were required to link theories explicitly learned in the course to their practice in the field. A qualitative analysis of the papers led to the creation of the taxonomy of the link between theory and practice relating to four topics: The kinds of links made between theory and practice, the quality of these links, the links made between private teaching theories and official teaching theory, and the qualities of these links. This taxonomy may prove to be a useful tool in the teacher-mentor training processes.

Keywords: taxonomy, teacher-mentors, theory, practice, teacher-mentor training

Procedia PDF Downloads 344
8303 Developing a Moodle Course for Translation Theory and Methodology: The Importance of Theory in Translation Studies and Its Application

Authors: Antonia Tsaknaki

Abstract:

There are many and divergent views on how the science of translation should be taught in academic institutions or colleges, meaning as an independent study area or as part of Linguistics, Literature or Foreign Languages Departments. A much more debated issue refers to the question of whether translation theory should be included in syllabuses and study programs or the focus should be solely on practicing the profession, that is translating texts. This dissertation examines prevailing views on the significance of translation theory in translation studies in order to design an open course on moodle. Taking into account that there is a remarkable percentage of translation professionals who are self-taught without having any specific studies, the course aims at helping either translation students or professional translators familiarize with concepts, methods and problem-solving strategies that are considered necessary during the process. It is organized in four modules where the learner is guided through a series of topics (register, equivalence, decision-making, level of naturalness, Skopos theory etc); after completing these topics, they are given assignments (further reading) and texts to work on in order to practice the skills obtained. The course does not focus on a specific language pair and therefore is suitable for every individual who needs a theoretical background to boost their performance or for institutions seeking to save classroom time but not at the expense of learners’ skills.

Keywords: MOOCs, moodle, online learning, open courses, translation, translation theory

Procedia PDF Downloads 127
8302 A Grounded Theory of Educational Leadership Development Using Generative Dialogue

Authors: Elizabeth Hartney, Keith Borkowsky, Jo Axe, Doug Hamilton

Abstract:

The aim of this research is to develop a grounded theory of educational leadership development, using an approach to initiating and maintaining professional growth in school principals and vice principals termed generative dialogue. The research was conducted in a relatively affluent, urban school district in Western Canada. Generative dialogue interviews were conducted by a team of consultants, and anonymous data in the form of handwritten notes were voluntarily submitted to the research team. The data were transcribed and analyzed using grounded theory. The results indicate that a key focus of educational leadership development is focused on navigating relationships within the school setting and that the generative dialogue process is helpful for principals and vice principals to explore how they might do this. Applicability and limitations of the study are addressed.

Keywords: generative dialogue, school principals, grounded theory, leadership development

Procedia PDF Downloads 338
8301 Introduce a New Model of Anomaly Detection in Computer Networks Using Artificial Immune Systems

Authors: Mehrshad Khosraviani, Faramarz Abbaspour Leyl Abadi

Abstract:

The fundamental component of the computer network of modern information society will be considered. These networks are connected to the network of the internet generally. Due to the fact that the primary purpose of the Internet is not designed for, in recent decades, none of these networks in many of the attacks has been very important. Today, for the provision of security, different security tools and systems, including intrusion detection systems are used in the network. A common diagnosis system based on artificial immunity, the designer, the Adhasaz Foundation has been evaluated. The idea of using artificial safety methods in the diagnosis of abnormalities in computer networks it has been stimulated in the direction of their specificity, there are safety systems are similar to the common needs of m, that is non-diagnostic. For example, such methods can be used to detect any abnormalities, a variety of attacks, being memory, learning ability, and Khodtnzimi method of artificial immune algorithm pointed out. Diagnosis of the common system of education offered in this paper using only the normal samples is required for network and any additional data about the type of attacks is not. In the proposed system of positive selection and negative selection processes, selection of samples to create a distinction between the colony of normal attack is used. Copa real data collection on the evaluation of ij indicates the proposed system in the false alarm rate is often low compared to other ir methods and the detection rate is in the variations.

Keywords: artificial immune system, abnormality detection, intrusion detection, computer networks

Procedia PDF Downloads 342
8300 Chaotic Dynamics of Cost Overruns in Oil and Gas Megaprojects: A Review

Authors: O. J. Olaniran, P. E. D. Love, D. J. Edwards, O. Olatunji, J. Matthews

Abstract:

Cost overruns are a persistent problem in oil and gas megaprojects. Whilst the extant literature is filled with studies on incidents and causes of cost overruns, underlying theories to explain their emergence in oil and gas megaprojects are few. Yet, a way to contain the syndrome of cost overruns is to understand the bases of ‘how and why’ they occur. Such knowledge will also help to develop pragmatic techniques for better overall management of oil and gas megaprojects. The aim of this paper is to explain the development of cost overruns in hydrocarbon megaprojects through the perspective of chaos theory. The underlying principles of chaos theory and its implications for cost overruns are examined and practical recommendations proposed. In addition, directions for future research in this fertile area provided.

Keywords: chaos theory, oil and gas, cost overruns, megaprojects

Procedia PDF Downloads 546
8299 A Supervised Approach for Detection of Singleton Spam Reviews

Authors: Atefeh Heydari, Mohammadali Tavakoli, Naomie Salim

Abstract:

In recent years, we have witnessed that online reviews are the most important source of customers’ opinion. They are progressively more used by individuals and organisations to make purchase and business decisions. Unfortunately, for the reason of profit or fame, frauds produce deceptive reviews to hoodwink potential customers. Their activities mislead not only potential customers to make appropriate purchasing decisions and organisations to reshape their business, but also opinion mining techniques by preventing them from reaching accurate results. Spam reviews could be divided into two main groups, i.e. multiple and singleton spam reviews. Detecting a singleton spam review that is the only review written by a user ID is extremely challenging due to lack of clue for detection purposes. Singleton spam reviews are very harmful and various features and proofs used in multiple spam reviews detection are not applicable in this case. Current research aims to propose a novel supervised technique to detect singleton spam reviews. To achieve this, various features are proposed in this study and are to be combined with the most appropriate features extracted from literature and employed in a classifier. In order to compare the performance of different classifiers, SVM and naive Bayes classification algorithms were used for model building. The results revealed that SVM was more accurate than naive Bayes and our proposed technique is capable to detect singleton spam reviews effectively.

Keywords: classification algorithms, Naïve Bayes, opinion review spam detection, singleton review spam detection, support vector machine

Procedia PDF Downloads 288
8298 Literature Review and Evaluation of the Internal Marketing Theory

Authors: Hsiao Hsun Yuan

Abstract:

Internal marketing was proposed in 1970s. The theory of the concept has continually changed over the past forty years. This study discussed the following themes: the definition and implication of internal marketing, the progress of its development, and the evolution of its theoretical model. Moreover, the study systematically organized the strategies of the internal marketing theory adopted on enterprise and how they were put into practice. It also compared the empirical studies focusing on how the existent theories influenced the important variables of internal marketing. The results of this study are expected to serve as references for future exploration of the boundary and studies aiming at how internal marketing is applied to different types of enterprises.

Keywords: corporate responsibility, employee organizational performance, internal marketing, internal customer

Procedia PDF Downloads 337
8297 Design and Development of Ssvep-Based Brain-Computer Interface for Limb Disabled Patients

Authors: Zerihun Ketema Tadesse, Dabbu Suman Reddy

Abstract:

Brain-Computer Interfaces (BCIs) give the possibility for disabled people to communicate and control devices. This work aims at developing steady-state visual evoked potential (SSVEP)-based BCI for patients with limb disabilities. In hospitals, devices like nurse emergency call devices, lights, and TV sets are what patients use most frequently, but these devices are operated manually or using the remote control. Thus, disabled patients are not able to operate these devices by themselves. Hence, SSVEP-based BCI system that can allow disabled patients to control nurse calling device and other devices is proposed in this work. Portable LED visual stimulator that flickers at specific frequencies of 7Hz, 8Hz, 9Hz and 10Hz were developed as part of this project. Disabled patients can stare at specific flickering LED of visual stimulator and Emotiv EPOC used to acquire EEG signal in a non-invasive way. The acquired EEG signal can be processed to generate various control signals depending upon the amplitude and duration of signal components. MATLAB software is used for signal processing and analysis and also for command generation. Arduino is used as a hardware interface device to receive and transmit command signals to the experimental setup. Therefore, this study is focused on the design and development of Steady-state visually evoked potential (SSVEP)-based BCI for limb disabled patients, which helps them to operate and control devices in the hospital room/wards.

Keywords: SSVEP-BCI, Limb Disabled Patients, LED Visual Stimulator, EEG signal, control devices, hospital room/wards

Procedia PDF Downloads 211
8296 Automating 2D CAD to 3D Model Generation Process: Wall pop-ups

Authors: Mohit Gupta, Chialing Wei, Thomas Czerniawski

Abstract:

In this paper, we have built a neural network that can detect walls on 2D sheets and subsequently create a 3D model in Revit using Dynamo. The training set includes 3500 labeled images, and the detection algorithm used is YOLO. Typically, engineers/designers make concentrated efforts to convert 2D cad drawings to 3D models. This costs a considerable amount of time and human effort. This paper makes a contribution in automating the task of 3D walls modeling. 1. Detecting Walls in 2D cad and generating 3D pop-ups in Revit. 2. Saving designer his/her modeling time in drafting elements like walls from 2D cad to 3D representation. An object detection algorithm YOLO is used for wall detection and localization. The neural network is trained over 3500 labeled images of size 256x256x3. Then, Dynamo is interfaced with the output of the neural network to pop-up 3D walls in Revit. The research uses modern technological tools like deep learning and artificial intelligence to automate the process of generating 3D walls without needing humans to manually model them. Thus, contributes to saving time, human effort, and money.

Keywords: neural networks, Yolo, 2D to 3D transformation, CAD object detection

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8295 The Convergence of IoT and Machine Learning: A Survey of Real-time Stress Detection System

Authors: Shreyas Gambhirrao, Aditya Vichare, Aniket Tembhurne, Shahuraj Bhosale

Abstract:

In today's rapidly evolving environment, stress has emerged as a significant health concern across different age groups. Stress that isn't controlled, whether it comes from job responsibilities, health issues, or the never-ending news cycle, can have a negative effect on our well-being. The problem is further aggravated by the ongoing connection to technology. In this high-tech age, identifying and controlling stress is vital. In order to solve this health issue, the study focuses on three key metrics for stress detection: body temperature, heart rate, and galvanic skin response (GSR). These parameters along with the Support Vector Machine classifier assist the system to categorize stress into three groups: 1) Stressed, 2) Not stressed, and 3) Moderate stress. Proposed training model, a NodeMCU combined with particular sensors collects data in real-time and rapidly categorizes individuals based on their stress levels. Real-time stress detection is made possible by this creative combination of hardware and software.

Keywords: real time stress detection, NodeMCU, sensors, heart-rate, body temperature, galvanic skin response (GSR), support vector machine

Procedia PDF Downloads 58
8294 Epileptic Seizure Prediction Focusing on Relative Change in Consecutive Segments of EEG Signal

Authors: Mohammad Zavid Parvez, Manoranjan Paul

Abstract:

Epilepsy is a common neurological disorders characterized by sudden recurrent seizures. Electroencephalogram (EEG) is widely used to diagnose possible epileptic seizure. Many research works have been devoted to predict epileptic seizure by analyzing EEG signal. Seizure prediction by analyzing EEG signals are challenging task due to variations of brain signals of different patients. In this paper, we propose a new approach for feature extraction based on phase correlation in EEG signals. In phase correlation, we calculate relative change between two consecutive segments of an EEG signal and then combine the changes with neighboring signals to extract features. These features are then used to classify preictal/ictal and interictal EEG signals for seizure prediction. Experiment results show that the proposed method carries good prediction rate with greater consistence for the benchmark data set in different brain locations compared to the existing state-of-the-art methods.

Keywords: EEG, epilepsy, phase correlation, seizure

Procedia PDF Downloads 297
8293 Implications of Stakeholder Theory as a Critical Theory

Authors: Louis Hickman

Abstract:

Stakeholder theory is a powerful conception of the firm based on the notion that a primary focus on shareholders is inadequate and, in fact, detrimental to the long-term health of the firm. As such it represents a departure from prevalent business school teachings with their focus on accounting and cost controls. Herein, it is argued that stakeholder theory can be better conceptualized as a critical theory, or one which represents a fundamental change in business behavior and can transform the behavior of businesses if accepted. By arguing that financial interests underdetermine the success of the firm, stakeholder theory further democratizes business by endorsing an increased awareness of the importance of non-shareholder stakeholders. Stakeholder theory requires new, non-financial, measures of success that provide a new consciousness for management and businesses when conceiving their actions and place in society. Thereby, stakeholder theory can show individuals through self-reflection that the capitalist impulses to generate wealth cannot act as primary drivers of business behavior, but rather, that we would choose to support interests outside ourselves if we made the decision in free discussion. This is due to the false consciousness embedded in our capitalism that the firm’s finances are the foremost concern of modern organizations at the expense of other goals. A focus on non-shareholder stakeholders in addition to shareholders generates greater benefits for society by improving the state of customers, employees, suppliers, the community, and shareholders alike. These positive effects generate further positive gains in well-being for stakeholders and translate into increased health for the future firm. Additionally, shareholders are the only stakeholder group that does not provide long-term firm value since there are not always communities with qualified employees, suppliers capable of providing the quality of product needed, or persons with purchasing power for all conceivable products. Therefore, the firm’s long-term health is benefited most greatly by improving the greatest possible parts of the society in which it inhabits, rather than solely the shareholder.

Keywords: capitalism, critical theory, self-reflection, stakeholder theory

Procedia PDF Downloads 326
8292 A Comparison of YOLO Family for Apple Detection and Counting in Orchards

Authors: Yuanqing Li, Changyi Lei, Zhaopeng Xue, Zhuo Zheng, Yanbo Long

Abstract:

In agricultural production and breeding, implementing automatic picking robot in orchard farming to reduce human labour and error is challenging. The core function of it is automatic identification based on machine vision. This paper focuses on apple detection and counting in orchards and implements several deep learning methods. Extensive datasets are used and a semi-automatic annotation method is proposed. The proposed deep learning models are in state-of-the-art YOLO family. In view of the essence of the models with various backbones, a multi-dimensional comparison in details is made in terms of counting accuracy, mAP and model memory, laying the foundation for realising automatic precision agriculture.

Keywords: agricultural object detection, deep learning, machine vision, YOLO family

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8291 Modeling Intention to Use 3PL Services: An Application of the Theory of Planned Behavior

Authors: Nasrin Akter, Prem Chhetri, Shams Rahman

Abstract:

The present study tested Ajzen’s Theory of Planned Behavior (TPB) model to explain the formation of business customers’ intention to use 3PL services in Bangladesh. The findings show that the TPB model has a good fit to the data. Based on theoretical support and suggested modification indices, a refined TPB model was developed afterwards which provides a better predictive power for intention. Consistent with the theory, the results of a structural equation analysis revealed that the intention to use 3PL services is predicted by attitude and subjective norms but not by perceived behavioral control. Further investigation indicated that the paths between (attitude and intention) and (subjective norms and intention) did not statistically differ between 3PL user and non-user. Findings of this research provide an evidence base to formulate business strategies to increase the use of 3PL services in Bangladesh to enhance productivity and to gain economic efficiency.

Keywords: Bangladesh, intention, third-party logistics, Theory of Planned Behavior

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8290 YOLO-IR: Infrared Small Object Detection in High Noise Images

Authors: Yufeng Li, Yinan Ma, Jing Wu, Chengnian Long

Abstract:

Infrared object detection aims at separating small and dim target from clutter background and its capabilities extend beyond the limits of visible light, making it invaluable in a wide range of applications such as improving safety, security, efficiency, and functionality. However, existing methods are usually sensitive to the noise of the input infrared image, leading to a decrease in target detection accuracy and an increase in the false alarm rate in high-noise environments. To address this issue, an infrared small target detection algorithm called YOLO-IR is proposed in this paper to improve the robustness to high infrared noise. To address the problem that high noise significantly reduces the clarity and reliability of target features in infrared images, we design a soft-threshold coordinate attention mechanism to improve the model’s ability to extract target features and its robustness to noise. Since the noise may overwhelm the local details of the target, resulting in the loss of small target features during depth down-sampling, we propose a deep and shallow feature fusion neck to improve the detection accuracy. In addition, because the generalized Intersection over Union (IoU)-based loss functions may be sensitive to noise and lead to unstable training in high-noise environments, we introduce a Wasserstein-distance based loss function to improve the training of the model. The experimental results show that YOLO-IR achieves a 5.0% improvement in recall and a 6.6% improvement in F1-score over existing state-of-art model.

Keywords: infrared small target detection, high noise, robustness, soft-threshold coordinate attention, feature fusion

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8289 Gravitational Wave Solutions in Modified Gravity Theories

Authors: Hafiza Rizwana Kausar

Abstract:

In this paper, we formulate the wave equation in modified theories, particularly in f(R) theory, scalar-tensor theory, and metric palatine f(X) theory. We solve the wave equation in each case and try to find maximum possible solutions in the form polarization modes. It is found that modified theories present at most six modes however the mentioned metric theories allow four polarization modes, two of which are tensor in nature and other two are scalars.

Keywords: gravitational waves, modified theories, polariozation modes, scalar tensor theories

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8288 Comparative Analysis of Dissimilarity Detection between Binary Images Based on Equivalency and Non-Equivalency of Image Inversion

Authors: Adnan A. Y. Mustafa

Abstract:

Image matching is a fundamental problem that arises frequently in many aspects of robot and computer vision. It can become a time-consuming process when matching images to a database consisting of hundreds of images, especially if the images are big. One approach to reducing the time complexity of the matching process is to reduce the search space in a pre-matching stage, by simply removing dissimilar images quickly. The Probabilistic Matching Model for Binary Images (PMMBI) showed that dissimilarity detection between binary images can be accomplished quickly by random pixel mapping and is size invariant. The model is based on the gamma binary similarity distance that recognizes an image and its inverse as containing the same scene and hence considers them to be the same image. However, in many applications, an image and its inverse are not treated as being the same but rather dissimilar. In this paper, we present a comparative analysis of dissimilarity detection between PMMBI based on the gamma binary similarity distance and a modified PMMBI model based on a similarity distance that does distinguish between an image and its inverse as being dissimilar.

Keywords: binary image, dissimilarity detection, probabilistic matching model for binary images, image mapping

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