Diana Costin

Bio: Diana Costin is an academic researcher from Grigore T. Popa University of Medicine and Pharmacy. The author has contributed to research in topics: Population & Heart rate variability. The author has an hindex of 4, co-authored 15 publications receiving 33 citations.

Electronic System for Real-Time Indoor Air Quality Monitoring

Abstract: Since the general population spends approximately 90% of their lifetime indoors, indoor air quality has a direct impact on human health, comfort, and performance. In this paper we propose an indoor air quality monitoring system (IAQMS) capable of performing real-time measurements of a wide range of ambient air parameters like CO, CO$_{\mathbf{2}}$equivalent (CO$_{\mathbf{2}}$eq), EtOH, H$_{\mathbf{2}} {, \mathbf{H}} _{\mathbf{2}}$S, NO$_{\mathbf{2}} {, \mathbf{O}} _{\mathbf{3}} {, \mathbf{PM}} _{\mathbf{1.0}} {, \mathbf{PM}} _{\mathbf{2.5}} {, \mathbf{PM}} _{\mathbf{4.0}} {, \mathbf{PM}} _{\mathbf{10}}$, RH/T, Rn, SO$_{\mathbf{2}}$, typical PM size, (TPS) and total VOCs (tVOCs). The data generated by the 8 detectors of the system is processed by an Arduino Mega 2560 REV3 microcontroller, sent via the ESP-01S Wi-Fi module to the Blynk server, and then displayed within the iOS/Android Blynk mobile app using three visualization techniques: line charts, gauges, and numerical values. Based on the data, stakeholders could make informed decisions regarding the necessary actions to undertake to improve their indoor air quality.

Drivers’ Drowsiness Detection and Warning Systems for Critical Infrastructures

Abstract: Road traffic accidents, due to driver fatigue, tend to inflict high mortality rates comparing with accidents involving rested drivers. Currently there is an emerging automotive industry trend towards equipping vehicles with various driver-assistance technologies. Third parties also started producing complementary systems, including ones that can detect the driver's degree of fatigue, but this growing field requires further research and development.The main purpose of this paper is the development and implementation of a system capable to detecting and alert, in real-time, the driver's level of fatigue. A system like this is expected to make the driver aware of the assumed danger when his level of driving and taking decisions are reduced and is indicating a sleep break as the necessary approach. By monitoring the state of the human eyes, it is assumed that the signs of driver fatigue can be detected early enough to prevent a possible road accident, which could result in severe injuries or ultimately, in fatalities. Hence, in this work the authors are focused on the video monitoring of the driver face, especially on his eyes position in time, when open or closed, using a machine learning object detection algorithm, the Haar Cascade. Two pretrained Haar classifiers, a face cascade, and an eye cascade were imported from the OpenCV GitHub repository. The OpenCV library, as well as other required packages, were installed on a BeagleBone Black Wireless development board. The software implementation, in order to achieve the driver's drowsiness detection, was made through the Python software program. The proposed system manages to alert if the eyes of the driver are being kept closed for more than a certain amount of time by triggering a set of warning lights and sounds. The large-scale implementation of this type of system will drop the number of road accidents caused by the drivers’ fatigue, thus saving countless lives and bringing a reduction of the socio-economic costs associated with these tragic events.

Short-term Heart Rate Variability using wrist-worn pulse wave monitor compared to a Holter ECG

Abstract: The physiological variation of heart rate, controlled by autonomic nervous system, is called Heart Rate Variability (HRV). Measuring HRV reveals a wide range of useful information about humans' body and health status, including prognostic issues.

Image enhancement by multiobjective optimization and bio-inspired heuristics

Abstract: In image analysis and computer vision applications the results precision and correctness depend on the quality of processed images. The most common parameter which subjectively defines the image quality is its contrast. In this paper an image contrast enhancement procedure based on multiobjective optimization is proposed. The contrast gain which has to be maximized and tone distortion which has to be minimized are used as optimization criteria. Because the histogram optimization is a high-dimensional problem, as optimization algorithm the usage of nature-inspired heuristics is proposed. Particularly, in the experiments presented in this paper, the Particle Swarming Optimization algorithm is used. Our contrast enhancement method proves to be superior to traditional techniques like histogram equalization in terms of contrast gain and tone distortion, both criteria being optimized.

Combinatorial versus Priority Based Optimization in Resource Constrained Project Scheduling Problems by Nature Inspired Metaheuristics

Abstract: This paper explores the behavior of the Flower Pollination Algorithm (FPA) and Particle Swarm Optimization (PSO) metaheuristic algorithm in resolving Resource Constrained Project Sched ...

Applications of low-cost sensing technologies for air quality monitoring and exposure assessment: How far have they gone?

Abstract: Over the past decade, a range of sensor technologies became available on the market, enabling a revolutionary shift in air pollution monitoring and assessment. With their cost of up to three orders of magnitude lower than standard/reference instruments, many avenues for applications have opened up. In particular, broader participation in air quality discussion and utilisation of information on air pollution by communities has become possible. However, many questions have been also asked about the actual benefits of these technologies. To address this issue, we conducted a comprehensive literature search including both the scientific and grey literature. We focused upon two questions: (1) Are these technologies fit for the various purposes envisaged? and (2) How far have these technologies and their applications progressed to provide answers and solutions? Regarding the former, we concluded that there is no clear answer to the question, due to a lack of: sensor/monitor manufacturers' quantitative specifications of performance, consensus regarding recommended end-use and associated minimal performance targets of these technologies, and the ability of the prospective users to formulate the requirements for their applications, or conditions of the intended use. Numerous studies have assessed and reported sensor/monitor performance under a range of specific conditions, and in many cases the performance was concluded to be satisfactory. The specific use cases for sensors/monitors included outdoor in a stationary mode, outdoor in a mobile mode, indoor environments and personal monitoring. Under certain conditions of application, project goals, and monitoring environments, some sensors/monitors were fit for a specific purpose. Based on analysis of 17 large projects, which reached applied outcome stage, and typically conducted by consortia of organizations, we observed that a sizable fraction of them (~ 30%) were commercial and/or crowd-funded. This fact by itself signals a paradigm change in air quality monitoring, which previously had been primarily implemented by government organizations. An additional paradigm-shift indicator is the growing use of machine learning or other advanced data processing approaches to improve sensor/monitor agreement with reference monitors. There is still some way to go in enhancing application of the technologies for source apportionment, which is of particular necessity and urgency in developing countries. Also, there has been somewhat less progress in wide-scale monitoring of personal exposures. However, it can be argued that with a significant future expansion of monitoring networks, including indoor environments, there may be less need for wearable or portable sensors/monitors to assess personal exposure. Traditional personal monitoring would still be valuable where spatial variability of pollutants of interest is at a finer resolution than the monitoring network can resolve.

Data analysis and reporting

Abstract: Selection and compression of flight data for real time monitoring and data reduction for Gemini flights

Grasshopper optimization algorithm tuned maximum power point tracking for solar photovoltaic systems

Abstract: Solar Photovoltaic (PV) system is an excellent renewable energy solution in today’s scenario. Harvesting maximum power from the solar PV system under dynamic meteorological conditions is a challenging task. Numerous bio-inspired Maximum Power Point Tracking (MPPT) strategies have been proposed in the literature. The conventional methods of MPPT control are easy and simple to implement, but has drawbacks such as steady state oscillations and inability to track the maximum power under swiftly varying irradiances and partial shading conditions. This paper proposes a Grasshopper Optimization Algorithm (GOA) tuned MPPT technique with the objective of obtaining optimal duty cycle, D, to control a DC–DC boost converter. The efficacy of the proposed system under start up transients, line disturbances, load disturbances, servo conditions and partial shading conditions are evaluated and compared with the conventional Perturb and Observe (P&O) based MPPT and the familiar Particle Swarm Optimization (PSO) based MPPT algorithm using MATLAB Simulink platform. It is observed that the proposed GOA tuned MPPT technique gives good steady state and dynamic response compared to P&O and PSO based MPPT algorithms, verified in terms of rise time, settling time, percentage maximum overshoot, Integral Squared Error and Integral Absolute Error.

Atmega328P-based X-ray Machine Exposure Time Measurement Device with an Android Interface

Abstract: The purpose of this study was to design an X-ray microcontroller-based ATmega328P microcontroller exposure time measurement device. That can be done by integrating an X-ray detection circuit, analog signal conditioner, ATmega328P microcontroller and Bluetooth module HC-05 to display and control the measurement results on mobile phones Android. The benefits of this research are expected to be able to increase knowledge and expertise in the field of radiology instruments through X-ray machine parameter measurement techniques and assist technicians to calibrate the X-ray exposure time parameters.

An Overview on the Latest Nature-Inspired and Metaheuristics-Based Image Registration Algorithms

Abstract: The development of automated image registration (IR) methods is a well-known issue within the computer vision (CV) field and it has been largely addressed from multiple viewpoints. IR has been applied to a high number of real-world scenarios ranging from remote sensing to medical imaging, artificial vision, and computer-aided design. In the last two decades, there has been an outstanding interest in the application of new optimization approaches for dealing with the main drawbacks present in the early IR methods, e.g., the Iterative Closest Point (ICP) algorithm. In particular, nature-inspired computation, e.g., evolutionary computation (EC), provides computational models that have their origin in evolution theories of nature. Moreover, other general purpose algorithms known as metaheuristics are also considered in this category of methods. Both nature-inspired and metaheuristic algorithms have been extensively adopted for tackling the IR problem, thus becoming a reliable alternative for optimization purposes. In this contribution, we aim to perform a comprehensive overview of the last decade (2009–2019) regarding the successful usage of this family of optimization approaches when facing the IR problem. Specifically, twenty-four methods (around 16 percent) of more than one hundred and fifty different contributions in the state-of-the-art have been selected. Several enhancements have been accordingly provided based on the promising outcomes shown by specific algorithmic designs. Finally, our research has shown that the field of nature-inspired and metaheuristic algorithms has increased its interest in the last decade to address the IR problem, and it has been highlighted that there is still room for improvement.