There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

We explore in this chapter questions of existence and uniqueness for solutions to stochastic differential equations and offer a study of their properties. This endeavor is really a study of diffusion processes. Loosely speaking, the term diffusion is attributed to a Markov process which has continuous sample paths and can be characterized in terms of its infinitesimal generator.

Stochastic Differential Equations

System Identification I

Computer and Robot Vision Vol. 1, by R.M. Haralick and Linda G. Shapiro, Addison-Wesley, 1992, ISBN 0-201-10887-1.

Computer and Robot Vision

The Biometry of Plant Growth

Consider one special closed loop system structure with one unknown plant and two unknown controllers,i.e. feed forward controller and feedback controllers, our missions are to identify this plant and design these two controllers without any physical principles. Based on the measured input-output data sequence, direct data driven scheme is proposed to achieve these dual goals. For the case of parameterized plant and two parameterized controllers, two kinds of unknown parameter vectors are estimated from the data sequence, and the iterative idea is applied to identify them recursively until to be convergence. In addition, controller validation modular is added to testify whether the designed controllers are satisfied, being from the idea of model validation in system identification. Then theoretical analysis on direct data driven scheme can be implemented to guarantee UAV flight control structure work well, bringing the detailed UAV flight control performance.

https://ieeexplore.ieee.org/ielx7/6287639/6514899/09915567.pdf

Direct Data Driven Scheme for UAV Flight Control

This document presents the methodology to implement a project of innovation in the classroom for the course of analysis and synthesis of mechanisms using 3D prototypes that can assist in understanding theoretic concepts that require spatial abilities This project was conducted with faculty and students that generated different types of models that were improved through iterative exercises of design and experimentation The application of this project was based on the experiential learning methodology and principles from the Montessori philosophy

Design Of Interactive Learning Cyber-Physical Tools for Mechanical Design Engineering Courses

This work presents a method and an algorithm for implementing the control of two micro-pumps with tolerance to sensors faults. These micro-pumps are used in a micro-volume deposit system, which is intended to help with biomedical and laboratory tests that involve the use of medical samples. This deposit system performs the suction or deposit of the required volumes of fluid. A requirement for this system is to protect the samples and reagents used in the process for the cost associated with gathering the samples. In this way, the idea is to have a fault tolerant system, which can ensure the integrity of the samples. The method and algorithm are implemented using a technique called analytic redundancy Muenchhof et al. (Eur J Control 15, 2009 [1]), which allows reducing the number of physical redundant sensors in a system. For this implementation, we propose the use of a physical sensor and an analytic sensor, using the model of suction-expulsion of the micro-pump from a previous work.

Fault Tolerance Methodology for Micro-volume Deposit System

A control system of a Magneto-Rheological (MR) damper force based on the Internal Model Control (IMC) strategy is proposed. Dynamic models of the MR damper were built exploiting Artificial Neural Networks (ANN). The control system was implemented with a controller based on an inverse ANN model of the MR damper and a forward model as internal nominal model. The feasibility of the control system was compared versus a direct-inverse control scheme. Simulated results demonstrated a practical and feasible solution.

IMC-based control system on a MR damper

According to the World Health Organization (WHO), stress can be defined as any type of alteration that causes physical, emotional, or psychological tension. A very important concept that is sometimes confused with stress is anxiety. The difference between stress and anxiety is that stress usually has an existing cause. Once that activator has passed, stress typically eases. In this respect, according to the American Psychiatric Association, anxiety is a normal response to stress and can even be advantageous in some circumstances. By contrast, anxiety disorders differ from temporary feelings of anxiousness or nervousness with more intense feelings of fear or anxiety. The Diagnostic and Statistical Manual (DSM-5) explicitly describes anxiety as exorbitant concern and fearful expectations, occurring on most days for at least 6 months, about a series of events. Stress can be measured by some standardized questionnaires; however, these resources are characterized by some major disadvantages, the main one being the time consumed to interpret them; i.e., qualitative information must be transformed to quantitative data. Conversely, a physiological recourse has the advantage that it provides quantitative spatiotemporal information directly from brain areas and it processes data faster than qualitative supplies. A typical option for this is an electroencephalographic record (EEG). We propose, as a novelty, the application of time series (TS) entropies developed by us to inspect collections of EEGs obtained during stress situations. We investigated this database related to 23 persons, with 1920 samples (15 s) captured in 14 channels for 12 stressful events. Our parameters reflected that out of 12 events, event 2 (Family/financial instability/maltreatment) and 10 (Fear of disease and missing an important event) created more tension than the others. In addition, the most active lobes reflected by the EEG channels were frontal and temporal. The former is in charge of performing higher functions, self-control, self monitoring, and the latter is in charge of auditory processing, but also emotional handling. Thus, events E2 and E10 triggering frontal and temporal channels revealed the actual state of participants under stressful situations. The coefficient of variation revealed that E7 (Fear of getting cheated/losing someone) and E11 (Fear of suffering a serious illness) were the events with more changes among participants. In the same sense, AF4, FC5, and F7 (mainly frontal lobe channels) were the most irregular on average for all participants. In summary, by means of dynamic entropy analysis, the goal is to process the EEG dataset in order to elucidate which event and brain regions are key for all participants. The latter will allow us to easily determine which was the most stressful and on which brain zone. This study can be applied to other caregivers datasets. All this is a novelty.

https://www.mdpi.com/1660-4601/20/10/5913/pdf?version=1684801771

Ricardo A. Ramirez-Mendoza

Papers

Direct Data Driven Scheme for UAV Flight Control

Design Of Interactive Learning Cyber-Physical Tools for Mechanical Design Engineering Courses

Fault Tolerance Methodology for Micro-volume Deposit System

IMC-based control system on a MR damper

Stress Response Analysis via Dynamic Entropy in EEG: Caregivers in View