Driving behavior analysis and classification by vehicle OBD data using machine learning.

The transportation industry's focus on improving performance and reducing costs has driven the integration of IoT and machine learning technologies. The correlation between driving style and behavior with fuel consumption and emissions has highlighted the need to classify different driver's driving patterns. In response, vehicles now come equipped with sensors that gather a wide range of operational data. The proposed technique collects critical vehicle performance data, including speed, motor RPM, paddle position, determined motor load, and over 50 other parameters through the OBD interface. The OBD-II diagnostics protocol, the primary diagnostic process used by technicians, can acquire this information via the car's communication port. OBD-II protocol is used to acquire real-time data linked to the vehicle's operation. This data are used to collect engine operation-related characteristics and assist with fault detection. The proposed method uses machine learning techniques, such as SVM, AdaBoost, and Random Forest, to classify driver's behavior based on ten categories that include fuel consumption, steering stability, velocity stability, and braking patterns. The solution offers an effective means to study driving behavior and recommend corrective actions for efficient and safe driving. The proposed model offers a classification of ten driver classes based on fuel consumption, steering stability, velocity stability, and braking patterns. This research work uses data extracted from the engine's internal sensors via the OBD-II protocol, eliminating the need for additional sensors. The collected data are used to build a model that classifies driver's behavior and can be used to provide feedback to improve driving habits. Key driving events, such as high-speed braking, rapid acceleration, deceleration, and turning, are used to characterize individual drivers. Visualization techniques, such as line plots and correlation matrices, are used to compare drivers' performance. Time-series values of the sensor data are considered in the model. The supervised learning methods are employed to compare all driver classes. SVM, AdaBoost, and Random Forest algorithms are implemented with 99%, 99%, and 100% accuracy, respectively. The suggested model offers a practical approach to examining driving behavior and suggesting necessary measures to enhance driving safety and efficiency.

Compact Back-End Electronics with Temperature Compensation and Efficient Data Management for In Situ SiPM-Based Radiation Detection.

A compact back-end interface for silicon photomultipliers (SiPMs) implementing Zener diode-based temperature compensation has been developed for the remote detection of beta and gamma radiation. Remote detection is facilitated by the development of an efficient data management system utilising MySQL database storage for recording periodic spectra data for wireless access over a private Wi-Fi network. A trapezoidal peak shaping algorithm has been implemented on an FPGA for the continuous conversation of pulses from the SiPM, signifying the detection of a radiological particle, into spectra. This system has been designed to fit within a 46 mm cylindrical diameter for in situ characterization, and can be attached to one or more SiPMs used in conjunction with a range of scintillators. LED blink tests have been used to optimise the trapezoidal shaper coefficients to maximise the resolution of the recorded spectra. Experiments with an array of SiPMs integrated with a NaI(Tl) scintillator exposed to sealed sources of Co-60, Cs-137, Na-22 and Am-241 have shown that the detector achieves a peak efficiency of 27.09 ± 0.13% for a gamma peak at 59.54 keV produced by Am-241, and a minimum energy resolution (Delta E/E) of 4.27 ± 1.16% for the 1332.5 keV gamma peak from Co-60.

Correction of Dynamical Properties of Data Acquisition Systems.

Accurate and fast measurements are important in many areas of everyday engineering and research activities. This paper proposes a method that gives such measurements, additionally shortening the time in which they are obtained. To achieve this, a supplementary discrete-time filter, estimating values of delayed samples of the measured signal, is attached to the output of the data acquisition system. This filter is identified with the use of classical estimation methods, based on a dynamical model of the data acquisition system. The definition of the cost function minimised during filter identification depends on the nature of the environment in which measurements are acquired. The considerations presented in this paper are illustrated with four corresponding simulated case study examples. They show how, in a very simple way, dynamical properties of data acquisition systems may be corrected, and thus improved, using the corresponding supplementary discrete-time filters. The improvement, measured by the correction quality index introduced in the paper, was from a few times up to more than 100. The paper also raises the issue of obtaining models for tuning of the supplementary discrete-time filter. The considerations presented may be applied to formulate the artificial intelligence of data acquisition systems as well as sensors. Finally, the paper proposes to implement this intelligence as a knowledge base of the expert system.

Advanced System-on-Chip Field-Programmable-Gate-Array-Powered Data Acquisition System for Pixel Detectors.

Particle detector systems require data acquisition systems (DAQs) as their back-end. This paper presents a new edge-computing DAQ that is capable of handling multiple pixel detectors simultaneously and was designed for particle-tracking experiments. The system was designed for the ROC4SENS readout chip, but its control logic can be adapted for other pixel detectors. The DAQ was based on a system-on-chip FPGA (SoC FPGA), which includes an embedded microprocessor running a fully functional Linux system. An application using a client-server architecture was developed to facilitate remote control and data visualization. The comprehensive DAQ is very compact, thus reducing the typical hardware load in particle tracking experiments, especially during the obligatory characterization of particle telescopes.

SMU Open-Source Platform for Synchronized Measurements.

The ramping trend of cheap and performant single board computers (SBC) is growingly offering unprecedented opportunities in various domains, taking advantage of the widespread support and flexibility offered by an operating system (OS) environment. Unfortunately, data acquisition systems implemented in an OS environment are traditionally considered not to be suitable for reliable industrial applications. Such a position is supported by the lack of hardware interrupt handling and deterministic control of timed operations. In this study, the authors fill this gap by proposing an innovative and versatile SBC-based open-source platform for CPU-independent data acquisition. The synchronized measurement unit (SMU) is a high-accuracy device able to perform multichannel simultaneous sampling up to 200 kS/s with sub-microsecond synchronization precision to a GPS time reference. It exhibits very low offset and gain errors, with a minimum bandwidth beyond 20 kHz, SNR levels above 90 dB and THD as low as -110 dB. These features make the SMU particularly attractive for the power system domain, where synchronized measurements are increasingly required for the geographically distributed monitoring of grid operating conditions and power quality phenomena. We present the characterization of the SMU in terms of measurement and time synchronization accuracy, proving that this device, while low-cost, guarantees performance compliant with the requirements for synchrophasor-based applications in power systems.

Physical Implementation of Reservoir Computing through Electrochemical Reaction.

Nonlinear dynamical systems serving reservoir computing enrich the physical implementation of computing systems. A method for building physical reservoirs from electrochemical reactions is provided, and the potential of chemical dynamics as computing resources is shown. The essence of signal processing in such systems includes various degrees of ionic currents which pass through the solution as well as the electrochemical current detected based on a multiway data acquisition system to achieve switchable and parallel testing. The results show that they have respective advantages in periodic signals and temporal dynamic signals. Polyoxometalate molecule in the solution increases the diversity of the response current and thus improves their abilities to predict periodic signals. Conversely, distilled water exhibits great computing power in solving a second-order nonlinear problem. It is expected that these results will lead to further exploration of ionic conductance as a nonlinear dynamical system and provide more support for novel devices as computing resources.

A Low-Cost IoT System for Real-Time Monitoring of Climatic Variables and Photovoltaic Generation for Smart Grid Application.

Monitoring and data acquisition are essential to recognize the renewable resources available on-site, evaluate electrical conversion efficiency, detect failures, and optimize electrical production. Commercial monitoring systems for the photovoltaic system are generally expensive and closed for modifications. This work proposes a low-cost real-time internet of things system for micro and mini photovoltaic generation systems that can monitor continuous voltage, continuous current, alternating power, and seven meteorological variables. The proposed system measures all relevant meteorological variables and directly acquires photovoltaic generation data from the plant (not from the inverter). The system is implemented using open software, connects to the internet without cables, stores data locally and in the cloud, and uses the network time protocol to synchronize the devices' clocks. To the best of our knowledge, no work reported in the literature presents these features altogether. Furthermore, experiments carried out with the proposed system showed good effectiveness and reliability. This system enables fog and cloud computing in a photovoltaic system, creating a time series measurements data set, enabling the future use of machine learning to create smart photovoltaic systems.

Near Theoretical Gigabit Link Efficiency for Distributed Data Acquisition Systems.

Link efficiency, data integrity, and continuity for high-throughput and real-time systems is crucial. Most of these applications require specialized hardware and operating systems as well as extensive tuning in order to achieve high efficiency. Here, we present an implementation of gigabit Ethernet data streaming which can achieve 99.26% link efficiency while maintaining no packet losses. The design and implementation are built on OpenPET, an opensource data acquisition platform for nuclear medical imaging, where (a) a crate hosting multiple OpenPET detector boards uses a User Datagram Protocol over Internet Protocol (UDP/IP) Ethernet soft-core, that is capable of understanding PAUSE frames, to stream data out to a computer workstation; (b) the receiving computer uses Netmap to allow the processing software (i.e., user space), which is written in Python, to directly receive and manage the network card's ring buffers, bypassing the operating system kernel's networking stack; and

Data systems for the Linac coherent light source.

The data systems for X-ray free-electron laser (FEL) experiments at the Linac coherent light source (LCLS) are described. These systems are designed to acquire and to reliably transport shot-by-shot data at a peak throughput of 5 GB/s to the offline data storage where experimental data and the relevant metadata are archived and made available for user analysis. The analysis and monitoring implementation (AMI) and Photon Science ANAlysis (psana) software packages are described. Psana is open source and freely available.

Creating a driving profile for older adults using GPS devices and naturalistic driving methodology.

Background/Objectives: Road tests and driving simulators are most commonly used in research studies and clinical evaluations of older drivers. Our objective was to describe the process and associated challenges in adapting an existing, commercial, off-the-shelf (COTS), in-vehicle device for naturalistic, longitudinal research to better understand daily driving behavior in older drivers. Design: The Azuga G2 Tracking Device TM was installed in each participant's vehicle, and we collected data over 5 months (speed, latitude/longitude) every 30-seconds when the vehicle was driven.  Setting: The Knight Alzheimer's Disease Research Center at Washington University School of Medicine. Participants: Five individuals enrolled in a larger, longitudinal study assessing preclinical Alzheimer disease and driving performance.  Participants were aged 65+ years and had normal cognition. Measurements:  Spatial components included Primary Location(s), Driving Areas, Mean Centers and Unique Destinations.  Temporal components included number of trips taken during different times of the day.  Behavioral components included number of hard braking, speeding and sudden acceleration events. Methods:  Individual 30-second observations, each comprising one breadcrumb, and trip-level data were collected and analyzed in R and ArcGIS.  Results: Primary locations were confirmed to be 100% accurate when compared to known addresses.  Based on the locations of the breadcrumbs, we were able to successfully identify frequently visited locations and general travel patterns.  Based on the reported time from the breadcrumbs, we could assess number of trips driven in daylight vs. night.  Data on additional events while driving allowed us to compute the number of adverse driving alerts over the course of the 5-month period. Conclusions: Compared to cameras and highly instrumented vehicle in other naturalistic studies, the compact COTS device was quickly installed and transmitted high volumes of data. Driving Profiles for older adults can be created and compared month-to-month or year-to-year, allowing researchers to identify changes in driving patterns that are unavailable in controlled conditions.

Is naturalistic driving research possible with highly instrumented cars? Lessons learnt in three research centres.

This paper provides an overview of the experiences using Highly Instrumented Cars (HICs) in three research Centres across Europe; Spain, the UK and Greece. The data collection capability of each car is described and an overview presented relating to the relationship between the level of instrumentation and the research possible. A discussion then follows which considers the advantages and disadvantages of using HICs for ND research. This includes the obtrusive nature of the data collection equipment, the cost of equipping the vehicles with sophisticated Data Acquisition Systems (DAS) and the challenges for data storage and analysis particularly with respect to video data. It is concluded that the use of HICs substantially increases the depth of knowledge relating to the driver's behaviour and their interaction with the vehicle and surroundings. With careful study design and integration into larger studies with Low(ly) instrumented Cars (LICs), HICs can contribute significantly and in a relatively naturalistic manner to the driver behaviour research.

A low cost concept for data acquisition systems applied to decentralized renewable energy plants.

The present paper describes experiences of the use of monitoring and data acquisition systems (DAS) and proposes a new concept of a low cost DAS applied to decentralized renewable energy (RE) plants with an USB interface. The use of such systems contributes to disseminate these plants, recognizing in real time local energy resources, monitoring energy conversion efficiency and sending information concerning failures. These aspects are important, mainly for developing countries, where decentralized power plants based on renewable sources are in some cases the best option for supplying electricity to rural areas. Nevertheless, the cost of commercial DAS is still a barrier for a greater dissemination of such systems in developing countries. The proposed USB based DAS presents a new dual clock operation philosophy, in which the acquisition system contains two clock sources for parallel information processing from different communication protocols. To ensure the low cost of the DAS and to promote the dissemination of this technology in developing countries, the proposed data acquisition firmware and the software for USB microcontrollers programming is a free and open source software, executable in the Linux and Windows® operating systems.

Nuevo dosímetro de zona programable para la medición y monitoreo operacional de la radiación gamma externa / A new programmable zone dosimeter for operational monitoring and measurement of external gamma radiation

RESUMEN La detección y la medición de las radiaciones nucleares se han convertido en un importantísimo renglón en la aplicación de los detectores nucleares y específicamente los Geiger-Muller. Los tubos Geiger con una alta sensibilidad de detección, su robusta construcción y un simple circuito adjunto continúan siendo uno de los detectores más usados en todas las áreas de aplicación en la física e investigaciones nucleares. Se presenta un nuevo diseño de instrumento para medir la tasa de dosis externa ambiental de radiación gamma desde 0,05 µSv/h hasta 10 mSv/h. Consta de tres elementos fundamentales: el detector Geiger-Muller, una tarjeta electrónica de adquisición y control y el software de aplicación. El instrumento se comunica a través de una interfase USB con la computadora solo para variar y ajustar los parámetros de la calibración. El software sedesarrolló en lenguaje C utilizando el compilador PICC4.08.

ABSTRACT The detection and measurement of nuclear radiation have turn into an important aspect in the application of nuclear detectors and specifically, the Geiger Muller tubes. Endowed with high detection sensibility, robust construction and a relative simplicity of the associated circuit, the Geiger Tubes are still one of the most widely used detectors in all areas of application in physics and nuclear research. A new design of the instrument enables measurements of the environmental external dose rate of g (gamma) radiation from 0.05 µSv/h to 10 mSv/h. It consists of three elements: Geiger-Muller detector, a data acquisition and control card and the application software. The instrument is connected to the computer through a USB interface only to vary and adjust the calibration parameters. The software was developed in C programming language using the PICC4.08 compiler.