Ordinary Differential Equations (ODE) are used to model a wide range of physical processes. An ODE is an equation containing a function of one independent variable and its ordinary derivatives. This paper presents the development and application of a practical teaching module introducing java programming techniques to electronics, computer, and bioengineering students before they encounter digital signal processing and its applications in junior and senior level courses. This paper will focus primarily on how to solve ODEs using Java and Matlab programming tools. There are two basic types of boundary condition categories for ODEsinitial value problems and two-point boundary value problems. Initial value problems are simpler to solve because you only have to integrate the ODE one time. The solution of a two-point boundary value problem usually involves iterating between the values at the beginning and end of the range of integration. Runge-Kutta schemes are among the most commonly used techniques to solve initial-value problem ODEs. Matlab also presents several tools for modeling linear systems. These tools can be used to solve differential equations arising in such models, and to visualize the input-output relations. This paper attempts to describe how to use Java programming tool to solve initial value problems of ordinary differential equations (ODEs) using the Runge-Kutta scheme. It will also discuss how to represent initial value problems and demonstrate how to apply Matlab's ODE solvers to such problems. It will also explain how to select a solver and how to specify solver options for efficient, customized execution. This paper provides an introduction to the Ordinary Differential Equations(ODEs). After a quick overview of selected numerical methods for solving differential equations using Matlab, we will briefly give an account of Euler and modified Euler methods for solving first order differential equations. This will be followed by numerical method for systems specially Runge-Kutta schemes and applications of second order differential equations in mechanical vibrations and electric circuits by leveraging the power of Java and Matlab. This paper will explain how this learning and teaching module is instrumental for progressive learning of students; the paper will also demonstrate how the numerical and integral algorithms are derived and computed through leverage of the java data structures. As a result, there will be a discussion concerning the comparison of Java and Matlab programming as well as students' feedback. The result of this new approach is expected to strengthen the capacity and quality of our undergraduate degree programs and enhance overall student learning and satisfaction.
An Electroencephalogram (EEG) signal is the recording of the electrical activity (voltage fluctuations) along the scalp due to the currents that flow during synaptic excitations of the dendrites of many pyramidal neurons in the cerebral cortex. When neurons are activated, the synaptic currents are produced within the dendrites. This current produces the magnetic field giving rise to mapping of cortical activity measurable by magneto-encephalography (MEG) machines and a secondary electrical field over the scalp measurable by EEG systems. Mastery and expertise in clinical EEG interpretation is one of the most desirable diagnostic clinical skills in interpreting seizures, epilepsy, sleep disorders, and other neurocognitive studies.In most cases EEG activity is described in terms of frequency, amplitude, distribution or location, symmetry, synchrony, reactivity, morphology, rhythmicity and regulation. The dynamic nature of epileptic phenomena causes EEG signals to exhibit stochastic and non-stationary behavior. The time frequency distributions are potentially very useful for detecting and analyzing non-stationary epileptic EEGs. Although visual analysis of raw EEG traces is still the major clinical tool and the point of reference for other methods, we can relate visual analysis to mathematics with a time-frequency description. The EEG signal analysis is often complemented with MEG and functional magnetic resonance imaging (fMRI) to correlate specific EEG findings with pathology of the brain and selectively demonstrate the diagnosis of certain neuronal disease processes, and assessment parameters. This diagnostic tool is in no way to be taken as a final word to replace physician's clinical consultation and opinion, rather it is intended to be an early monitoring and warning tool which will aid in diagnosis of certain aspects of critical care and emergency medicine. This interactive teaching module will be highly beneficial since it will facilitate progressive learning of students by enhancing their understanding of clinical EEG parameters and their relationship with differential diagnosis of the patients.
Electrocardiogram (EKG, or ECG) is a transthoracic interpretation 12 of the electrical activity of the heart externally recorded by skin electrodes and captured over time. It can detect hypertrophy, heart block, fibrillation, electrolyte abnormalities, rhythm problems and other cardiac conditions. Heart murmurs are abnormal sounds during your heart beat cycle and can be heard with stethoscope. Mastery and expertise in clinical EKG (Electrocardiogram) interpretation is one of the most desirable clinical skills in bioengineering and medicine. It can probably only be achieved if one acquires a well rounded experience in understanding the pathophysiology, clinical status of the patient, and correlation with specific EKG findings. This paper presents the development and application of an innovative medical diagnostic tool for EKG monitoring, which can be used by engineering technology, health care, and medical students for quick health screening and cardiologic health assessment. Students progressively learn to monitor and interpret the conventional noninvasive electrocardiography by leveraging the power of java's graphical user interface and data structures.The paper explains the laboratory setup of a basic 3-lead EKG monitoring station using modern data acquisition tool and software for EKG feature extraction. Students will begin their analysis by looking at rate, rhythm, axis, hypertrophy, and infarction and correlate the characteristic appearance on the EKG with existing conditions, certain pathology, and drug or electrolyte effects. A diagnostic tool using Java and Objective-C programming is then developed. The graphical user interface will be used to correlate specific EKG findings with pathology of the heart and selectively demonstrate the diagnosis of certain cardiologic health screening and assessment parameters. This learning and teaching module 1 can be instrumental in progressive learning for BMET and EET students, by enhancing their understanding of clinical EKG instrumentation, parameters extraction and their relationship with differential diagnosis of the patients. It will give them a form of intellectual development, enhancing their skill-set, and challenging their creativity. This paper thereby serves as an interesting way to expose engineering technology, health care and medical students to this fascinating topic and gives them exposure to EKG instrumentation, and java programming while having fun learning the EKG interpretation, algorithms, early heart monitoring, diagnosis and intervention. Instrument SetupA typical experimental setup for EKG signal analysis used in our laboratory is described next. A high performance general-purpose biomedical instrumentation amplifier and a data acquisition system is required for pre and post processing of EKG signal. This amplifier with high input
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