Two thermocouples low power wireless sensors network

This paper presents technologies and experiments of a wireless sensors using two thermocouples network. It was established that the energy consumption during sensor measurements is usually up to 10 times lower compared to the energy consumption at the time of establishing wireless connection for most protocols. For this reason, new simplified wireless connection protocol was created. Extremely low energy wireless sensor hardware and software equipment was designed. The newly created universal measurement module allows the use not only thermocouples, but also various types of analogue sensors, thermocouples, pressure bridges, Resistance Temperature Detectors (RTD) and digital sensors communicating through SPI or I2C interface. The newly designed specific power supply scheme allows to supply the sensor and radio module with the voltage from 1.2 V to 3.6 V batteries. When conducting periodic measurements every second, the use of newly designed hardware and software equipment enables the wireless sensor to be operated for up to 3 years from two 1200 mAh capacity batteries.A grant (No. SEN-10/15) from the Research Council of Lithuania. Project acronym: “CaSpine”.http://www.journals.elsevier.com/locate/qeue2019-02-20hj2018Electrical, Electronic and Computer Engineerin

Understanding driving stress in urban Bangladesh: An exploratory study, wearable development and experiment

Driving stress significantly impacts driving behavior primarily from roadside factors, where driving is more challenging in developing countries (i.e., Bangladesh) for unique cultural and infrastructural setups. We conduct an exploratory study (Qualitative n=26, and Subjective Feedback n= 80) and a correlational analysis involving professional and private car drivers in urban Bangladesh. The study reveals drivers' demography and driving stress factors on the road. These findings motivate us to identify driving stress from physiological factors by developing a low-cost wearable, Stress Wear. This can detect stress from varying Heart Rates, validated by expensive commercial wearables. Between subject experiments on drivers (total n=14 in two phases) with wearables, we also found that road factors are responsible for driving stress. Therefore, the developed system is helpful for these drivers to self-sensing their stress

A Review on Vehicle Classification and Potential Use of Smart Vehicle-Assisted Techniques

Vehicle classification (VC) is an underlying approach in an intelligent transportation system and is widely used in various applications like the monitoring of traffic flow, automated parking systems, and security enforcement. The existing VC methods generally have a local nature and can classify the vehicles if the target vehicle passes through fixed sensors, passes through the short-range coverage monitoring area, or a hybrid of these methods. Using global positioning system (GPS) can provide reliable global information regarding kinematic characteristics; however, the methods lack information about the physical parameter of vehicles. Furthermore, in the available studies, smartphone or portable GPS apparatuses are used as the source of the extraction vehicle’s kinematic characteristics, which are not dependable for the tracking and classification of vehicles in real time. To deal with the limitation of the available VC methods, potential global methods to identify physical and kinematic characteristics in real time states are investigated. Vehicular Ad Hoc Networks (VANETs) are networks of intelligent interconnected vehicles that can provide traffic parameters such as type, velocity, direction, and position of each vehicle in a real time manner. In this study, VANETs are introduced for VC and their capabilities, which can be used for the above purpose, are presented from the available literature. To the best of the authors’ knowledge, this is the first study that introduces VANETs for VC purposes. Finally, a comparison is conducted that shows that VANETs outperform the conventional techniques

A Review on Vehicle Classification and Potential Use of Smart Vehicle-Assisted Techniques

Vehicle classification (VC) is an underlying approach in an intelligent transportation system and is widely used in various applications like the monitoring of traffic flow, automated parking systems, and security enforcement. The existing VC methods generally have a local nature and can classify the vehicles if the target vehicle passes through fixed sensors, passes through the short-range coverage monitoring area, or a hybrid of these methods. Using global positioning system (GPS) can provide reliable global information regarding kinematic characteristics; however, the methods lack information about the physical parameter of vehicles. Furthermore, in the available studies, smartphone or portable GPS apparatuses are used as the source of the extraction vehicle’s kinematic characteristics, which are not dependable for the tracking and classification of vehicles in real time. To deal with the limitation of the available VC methods, potential global methods to identify physical and kinematic characteristics in real time states are investigated. Vehicular Ad Hoc Networks (VANETs) are networks of intelligent interconnected vehicles that can provide traffic parameters such as type, velocity, direction, and position of each vehicle in a real time manner. In this study, VANETs are introduced for VC and their capabilities, which can be used for the above purpose, are presented from the available literature. To the best of the authors’ knowledge, this is the first study that introduces VANETs for VC purposes. Finally, a comparison is conducted that shows that VANETs outperform the conventional techniques

Predicting space occupancy for street paid parking

This dissertation discusses how to develop a prediction method for on-street parking space availability, using only historical occupancy data collected from on-street multi-space parking meters. It is analyzed how to transform the raw data into a dataset representing the occupancy and how can this information be used to detect when the parking spaces on a street are Vacant or Full. Attributes like weather conditions and holidays are added to the data, giving them more context and comprehension. After the data preparation and analysis, a prediction model is developed using machinelearning techniques that can forecast the availability of the parking spaces on a street at a specific day and on a given moment. For that, a classification method is implemented based on decision trees and neural networks, comparing both methods regarding results and development time. Particular attention is given to the algorithm parameters, to achieve the right balance between accuracy and computational time. The developed model proved effective, correctly capturing the different behavior of each street through the different weeks, and returning results useful to drivers searching for parking and to the business owners while monitoring their parking investments and returns.Esta dissertação apresenta como pode ser desenvolvido um método para previsão de disponibilidade de lugares de estacionamento em rua, utilizando dados históricos obtidos através de parquímetros de controlo a múltiplos lugares. É analisado como os dados em bruto dos parquímetros podem ser transformados num conjunto de dados que represente qual a ocupação dos lugares, e posteriormente como esta informação pode ser utilizada para detetar se o estacionamento em uma rua está livre ou ocupado. São adicionados também mais alguns atributos, como por exemplo informação sobre as condições meteorológicas ou que dias são feriados, dando mais algum contexto e compreensão à informação já existente. Após a preparação e análise dos dados, é desenvolvido um método de previsão utilizando técnicas de aprendizagem automática de modo a que seja possível saber qual a disponibilidade de estacionamento em uma rua, a um dia específico e a um determinado momento. Para isso, foi implementado um método de classificação baseado em árvores de decisão e redes neuronais, comparando ambos os métodos do ponto de vista dos resultados e do tempo de desenvolvimento. Foi dada especial atenção aos parâmetros utilizados em cada algoritmo, de modo a que haja um balanço entre a precisão e tempo de computação. O modelo desenvolvido mostrou ser eficaz, captando corretamente o comportamento de cada rua nas diferentes semanas, devolvendo resultados uteis aos condutores que procurem lugares de estacionamento e aos proprietários do negócio por lhes permitir monitorizar o desempenho dos seus investimentos em parques de estacionamento e qual o retorno

Nondestructive fiber optic sensor system for measurement of traffic speed

Disertační práce popisuje návrh, realizaci a otestování nového způsobu detekce a měření rychlosti vozidel s primárním zaměřením na silniční provoz do rychlosti 100km/h, který lze využít v koncepci SMART Cities. Uvedené výsledky v této práci potvrzují, že podobný přístup lze využít rovněž pro monitorování tramvajového provozu, provozu metra a vlakových souprav na železnici. Popsaný měřicí systém je založen na využití interference v optických vláknech. Základem řešení je sériové zapojení senzorických jednotek na bázi Mach-Zehnderova interferometru pracujících s jednovidovými telekomunikačními optickými vlákny standardu G.652.D. a G.653, vlnovou délkou 1550nm a nároky na výkon zdroje záření v řádech jednotek mW. Řešení popsaná v této disertační práci jsou v současné době chráněna autorským osvědčením (patent číslo 306992). Základem tohoto řešení je imunita vůči elektromagnetickým interferencím (EMI) a jednoduchá implementace, protože senzorické jednotky není nutné instalovat destruktivně do vozovky nebo kolejiště. Vzhledem k masivnímu rozšíření optických kabelů podél silnic a železničních tratí, které zabezpečují telekomunikační a bezpečnostní služby, je významnou výhodou i možnost přímého napojení senzorů na stávající infrastrukturu a možnost vzdáleného vyhodnocení. Měřicí systém byl dlouhodobě testován v reálném provozu a je charakterizován chybou v toleranci ± 3km/h udávané u úsekových měřicích systémů do rychlosti 100km/h v České republice.My dissertation thesis describes a design, implementation, and testing of a new way of vehicles detection and speed measurement primarily used in the road transport with the speed limit up to 100kph, which can be utilized in the concept of SMART Cities. Results published in this thesis confirm that a similar approach can be also used for the monitoring of tram, underground and railway transport. The proposed measuring system is based on the interference in optical fibers. The key condition is that sensory units are connected in series on the basis of Mach-Zehnder interferometer working with single-mode optical fibers of G.652.D. and G.653 standards, with the wavelength of 1550nm and demands on the radiation source output in the range of mW. Solutions described in this dissertation thesis are currently protected by copyright (the patent No.306992). The basis of this solution lies in electromagnetic interference immunity (EMI) and simple implementation as the sensory units do not need to be installed destructively into the roadway or railway. With regard to a massive use of optical fibers along roads and railway tracks, which provides telecommunications and security services, the important advantage is also the possibility of direct connections of sensors to existing infrastructure and the possibility of remote evaluation. The measuring system was tested in real traffic over a long period and is characterized by an error with the tolerance of ± 3kph which is given by sectional speed measuring systems up to 100kph in the Czech Republic.440 - Katedra telekomunikační technikyvyhově

Dynamic Vehicle Detection via the Use of Magnetic Field Sensors

The vehicle detection process plays the key role in determining the success of intelligent transport management system solutions. The measurement of distortions of the Earth’s magnetic field using magnetic field sensors served as the basis for designing a solution aimed at vehicle detection. In accordance with the results obtained from research into process modeling and experimentally testing all the relevant hypotheses an algorithm for vehicle detection using the state criteria was proposed. Aiming to evaluate all of the possibilities, as well as pros and cons of the use of anisotropic magnetoresistance (AMR) sensors in the transport flow control process, we have performed a series of experiments with various vehicles (or different series) from several car manufacturers. A comparison of 12 selected methods, based on either the process of determining the peak signal values and their concurrence in time whilst calculating the delay, or by measuring the cross-correlation of these signals, was carried out. It was established that the relative error can be minimized via the Z component cross-correlation and Kz criterion cross-correlation methods. The average relative error of vehicle speed determination in the best case did not exceed 1.5% when the distance between sensors was set to 2 m