Sensing for HOV/HOT Lanes Enforcement

The use and creation of combined high-occupancy vehicle/high-occupancy toll (HOV/HOT Lanes) have become more common in urban areas since all types of road users can take advantage of the lane either as a high- occupancy vehicle or opting in to pay a congestion adjusted free. However, to maintain working integrity of the lanes for all users, stepped enforcement to discourage cheating has been needed as more lanes are added. This study evaluated the capability of a novel image sensor device to automate detection of in-vehicle occupants to flag law enforcement of HOV/HOT lane violators. The sensor device synchronously captures three co-registered images, one in the visible spectrum and two others in the infrared bands. The key idea is that the infrared bands can enhance correct occupancy detection through known phenomenological spectral properties of objects and humans residing inside the vehicle. Several experiments were conducted to determine this capability across varied conditions and scenarios to assess detection segmentation algorithms of vehicle passengers and drivers. Although occupancy detection through vehicle glass could be achieved in many cases, improvements must be made to such a detection system to increase robustness and reliability as a law enforcement tool. These improvements were guided by the experimental results, as well as suggested methods for deployment if this or similar technologies were to be deployed in the future

Sensing for HOV/HOT Lanes Enforcement

The use and creation of combined high-occupancy vehicle/high-occupancy toll (HOV/HOT Lanes) have become more common in urban areas since all types of road users can take advantage of the lane either as a high- occupancy vehicle or opting in to pay a congestion adjusted free. However, to maintain working integrity of the lanes for all users, stepped enforcement to discourage cheating has been needed as more lanes are added. This study evaluated the capability of a novel image sensor device to automate detection of in-vehicle occupants to flag law enforcement of HOV/HOT lane violators. The sensor device synchronously captures three co-registered images, one in the visible spectrum and two others in the infrared bands. The key idea is that the infrared bands can enhance correct occupancy detection through known phenomenological spectral properties of objects and humans residing inside the vehicle. Several experiments were conducted to determine this capability across varied conditions and scenarios to assess detection segmentation algorithms of vehicle passengers and drivers. Although occupancy detection through vehicle glass could be achieved in many cases, improvements must be made to such a detection system to increase robustness and reliability as a law enforcement tool. These improvements were guided by the experimental results, as well as suggested methods for deployment if this or similar technologies were to be deployed in the future

Persistent homology approach for human presence detection from 60 GHz OTFS transmissions

Orthogonal Time Frequency Space (OTFS) is a new, promising modulation waveform candidate for the next generation integrated sensing and communication (ISAC) systems, providing the environment-awareness capabilities together with high speed wireless data communications. This paper presents original results of OTFS-based person monitoring measurements in the 60 GHz millimeter-wave frequency band under realistic conditions, without the assumption of an integer ratio between the actual delays and Doppler shifts of the reflected components and the corresponding resolution of the OTFS grid. As the main contribution of the paper, we propose the use of the persistent homology technique as a method for processing of gathered delay-Doppler responses. We highlight the advantages of persistence homology approach over the standard constant false alarm rate target detector for selected scenarios

Spatiotemporal occupancy in building settings

This thesis presents an investigation of methods to capture and analyze spatiotemporal occupancy patterns of high resolution, demonstrating their value by measuring behavioral outcomes over time. Obtaining fine-grain occupancy patterns is particularly useful since it gives researchers an ability to study such patterns not just with respect to the geometry of the space in which they occur, but also to study how they change dynamically in time, in response to the behavior itself. This research has three parts: The first is a review of the traditional methods of behavioral mapping utilized in architecture research, as well as the existing indoor positioning systems, offering an assessment of their comparative potential, and a selection for the current scenario. The second is an implementation of scene analysis analyses using computer vision to capture occupancy patterns on one week of surveillance videos over twelve corridors in a hospital in Chile. The data outcome is occupancy in a set of hospital corridors at a resolution of one square foot per second. Due to the practical detection errors, a two-part statistical model was developed to compute the accuracy on recognition and precision of location, given certain scenario conditions. These error rates models can be then used to predict estimates of patterns of occupancy in an actual scenario. The third is a proof-of-concept study of the usefulness of a new spatiotemporal metric called the Isovist-minute, which describes the actual occupancy of an Isovist, over a specified period of time. Occupancy data obtained using scene-analyses, updated with error-rate models of the previous study, are used to compute Isovist-minute values per square feet. The Isovist-minute is shown to capture significant differences in the patient surveillance outcome in the same spatial layout, but different organizational schedule and program.Ph.D

Critical review and research roadmap of office building energy management based on occupancy monitoring

Buildings are responsible for a large portion of global energy consumption. Therefore, a detailed investigation towards a more effective energy performance of buildings is needed. Building energy performance is mature in terms of parameters related to the buildings’ physical characteristics, and their attributes are easily collectable. However, the poor ability of emulating reality pertinent to time-dependent parameters, such as occupancy parameters, may result in large discrepancies between estimated and actual energy consumption. Although efforts are being made to minimize energy waste in buildings by applying different control strategies based on occupancy information, new practices should be examined to achieve fully smart buildings by providing more realistic occupancy models to reflect their energy usage. This paper provides a comprehensive review of the methods for collection and application of occupancy-related parameters affecting total building energy consumption. Different occupancy-based control strategies are investigated with emphasis on heating, ventilation, and air conditioning (HVAC) and lighting systems. The advantages and limitations of existing methods are outlined to identify the gaps for future research

The development of a real-time energy prediction framework in domestic buildings

The construction industry consumed 35% of worldwide energy, with domestic buildings accounting for 22%. Providing a healthy, positive environment in domestic buildings raised energy demand by around 80% in building operations, with thermal comfort accounting for about half of that increase. Furthermore, building energy consumption is 5 to 10 times greater than predictions given during the design phase. The discrepancy between the actual and intended design is called the performance gap. Although the term is widely used in the context of energy performance, it can also be found in indoor environmental parameters such as temperature, relative humidity, air quality, noise, and illumination. This thesis connects building performance simulation to building operational performance, focusing on real-time energy prediction for space heating in an indoor environment of domestic buildings. The work presented in this research is a technical implementation framework for examining the energy consumption of indoor space heating in real-time, focusing on energy-related thermal comfort conditions at the zone level. Unlike building performance simulation tools, The developed framework can be used beyond the design phase to encompass operations and assist in diagnosing and detecting building underperformance or performance discrepancy over time. Focusing on zone level can offer a greater understanding of the thermal state and energy usage of specific individual spaces, which can also assist in identifying performance disparity. Buildings with good indoor environmental quality are objectively assessed using simulation tools. However, the indoor environmental quality, especially thermal comfort, is experienced subjectively, making the building energy and thermal performance evaluation task challenging. The developed framework extends the use of the energy model to the operational stage by predicting thermal and energy performance based on indoor and outdoor environmental parameters. Moreover, using a parametric energy simulation and machine learning approach connected to an IoT sensor system enable users to identify thermal comfort conditions in the indoor environment and the amount of energy consumed for space heating. Finally, the research identified several lessons that can potentially inform and improve the existing domestic buildings, especially winter space heating. Following the framework, an innovative device was developed and validated using an experimental approach that focuses on real-time energy prediction of space heating. In this process, the experimental case studies' thermal comfort conditions and energy consumption were monitored and analysed to identify thermal-energy performance-related issues, also used for validating the proposed real-time energy prediction module

Simulation-Based Optimization of Energy Consumption and Occupants Comfort in Open-Plan Office Buildings Using Probabilistic Occupancy Prediction Model

Considering the ever-growing increase in the world energy consumption and the fact that buildings contribute a large portion of the global energy consumption arises a need for detailed investigation towards more effective energy performance of buildings. Thus, monitoring, estimating, and reducing buildings’ energy consumption have always been important concerns for researchers and practitioners in the field of energy management. Since more than 80% of energy consumption happens during the operation phase of a building’s life cycle, efficient management of building operation is a promising way to reduce energy usage in buildings. Among the parameters influencing the total building energy consumption, building occupants’ presence and preferences could have high impacts on the energy usage of a building. To consider the effect of occupancy on building energy performance, different occupancy models, which aim to estimate the space utilization patterns, have been developed by researches. However, providing a comprehensive occupancy model, which could capture all important occupancy features, is still under development. Moreover, researchers investigated the effect of the application of occupancy-centered control strategies on the efficiency of the energy-consuming systems. However, there are still many challenges in this area of research mainly related to collecting, processing, and analyzing the occupancy data and the application of intelligent control strategies. In addition, generally, there is an inverse relationship between the energy consumption of operational systems and the comfort level of occupants using these systems. As a result, finding a balance between these two important concepts is crucial to improve the building operation. The optimal operation of building energy-consuming systems is a complex procedure for decision-makers, especially in terms of minimizing the energy cost and the occupants’ discomfort. On this premise, this research aims to develop a new simulation-based multi-objective optimization model of the energy consumption in open-plan offices based on occupancy dynamic profiles and occupants’ preferences and has the following objectives: (1) developing a method for extracting detailed occupancy information with varying time-steps from collected Real-Time Locating System (RTLS) occupancy data. This method captures different resolution levels required for the application of intelligent, occupancy-centered local control strategies of different building systems; (2) developing a new time-dependent inhomogeneous Markov chain occupancy prediction model based on the derived occupancy information, which distinguishes the temporal behavior of different occupants within an open-plan office; (3) improving the performance of the developed occupancy prediction model by determining the near-optimum length of the data collection period, selecting the near-optimum training dataset, and finding the most satisfying temporal resolution level for analyzing the occupancy data; (4) developing local control algorithms for building energy-consuming systems; and (5) integrating the energy simulation model of an open-plan office with an optimization algorithm to optimally control the building energy-consuming systems and to analyze the trade-off between building energy consumption and occupants’ comfort. It is found that the occupancy perdition model is able to estimate occupancy patterns of the open-plan office with 92% and 86% accuracy at occupant and zone levels, respectively. Also, the proposed integrated model improves the thermal condition by 50% along with 2% savings in energy consumption by developing intelligent, optimal, and occupancy-centered local control strategies

Smart Monitoring and Control in the Future Internet of Things

The Internet of Things (IoT) and related technologies have the promise of realizing pervasive and smart applications which, in turn, have the potential of improving the quality of life of people living in a connected world. According to the IoT vision, all things can cooperate amongst themselves and be managed from anywhere via the Internet, allowing tight integration between the physical and cyber worlds and thus improving efficiency, promoting usability, and opening up new application opportunities. Nowadays, IoT technologies have successfully been exploited in several domains, providing both social and economic benefits. The realization of the full potential of the next generation of the Internet of Things still needs further research efforts concerning, for instance, the identification of new architectures, methodologies, and infrastructures dealing with distributed and decentralized IoT systems; the integration of IoT with cognitive and social capabilities; the enhancement of the sensing–analysis–control cycle; the integration of consciousness and awareness in IoT environments; and the design of new algorithms and techniques for managing IoT big data. This Special Issue is devoted to advancements in technologies, methodologies, and applications for IoT, together with emerging standards and research topics which would lead to realization of the future Internet of Things

Developing a person guidance module for hospital robots

This dissertation describes the design and implementation of the Person Guidance Module (PGM) that enables the IWARD (Intelligent Robot Swarm for attendance, Recognition, Cleaning and delivery) base robot to offer route guidance service to the patients or visitors inside the hospital arena. One of the common problems encountered in huge hospital buildings today is foreigners not being able to find their way around in the hospital. Although there are a variety of guide robots currently existing on the market and offering a wide range of guidance and related activities, they do not fit into the modular concept of the IWARD project. The PGM features a robust and foolproof non-hierarchical sensor fusion approach of an active RFID, stereovision and cricket mote sensor for guiding a patient to the X-ray room, or a visitor to a patient’s ward in every possible scenario in a complex, dynamic and crowded hospital environment. Moreover, the speed of the robot can be adjusted automatically according to the pace of the follower for physical comfort using this system. Furthermore, the module performs these tasks in any unconstructed environment solely from a robot’s onboard perceptual resources in order to limit the hardware installation costs and therefore the indoor setting support. Similar comprehensive solution in one single platform has remained elusive in existing literature. The finished module can be connected to any IWARD base robot using quick-change mechanical connections and standard electrical connections. The PGM module box is equipped with a Gumstix embedded computer for all module computing which is powered up automatically once the module box is inserted into the robot. In line with the general software architecture of the IWARD project, all software modules are developed as Orca2 components and cross-complied for Gumstix’s XScale processor. To support standardized communication between different software components, Internet Communications Engine (Ice) has been used as middleware. Additionally, plug-and-play capabilities have been developed and incorporated so that swarm system is aware at all times of which robot is equipped with PGM. Finally, in several field trials in hospital environments, the person guidance module has shown its suitability for a challenging real-world application as well as the necessary user acceptance

Human Activity Recognition and Fall Detection Using Unobtrusive Technologies

As the population ages, health issues like injurious falls demand more attention. Wearable devices can be used to detect falls. However, despite their commercial success, most wearable devices are obtrusive, and patients generally do not like or may forget to wear them. In this thesis, a monitoring system consisting of two 24×32 thermal array sensors and a millimetre-wave (mmWave) radar sensor was developed to unobtrusively detect locations and recognise human activities such as sitting, standing, walking, lying, and falling. Data were collected by observing healthy young volunteers simulate ten different scenarios. The optimal installation position of the sensors was initially unknown. Therefore, the sensors were mounted on a side wall, a corner, and on the ceiling of the experimental room to allow performance comparison between these sensor placements. Every thermal frame was converted into an image and a set of features was manually extracted or convolutional neural networks (CNNs) were used to automatically extract features. Applying a CNN model on the infrared stereo dataset to recognise five activities (falling plus lying on the floor, lying in bed, sitting on chair, sitting in bed, standing plus walking), overall average accuracy and F1-score were 97.6%, and 0.935, respectively. The scores for detecting falling plus lying on the floor from the remaining activities were 97.9%, and 0.945, respectively. When using radar technology, the generated point clouds were converted into an occupancy grid and a CNN model was used to automatically extract features, or a set of features was manually extracted. Applying several classifiers on the manually extracted features to detect falling plus lying on the floor from the remaining activities, Random Forest (RF) classifier achieved the best results in overhead position (an accuracy of 92.2%, a recall of 0.881, a precision of 0.805, and an F1-score of 0.841). Additionally, the CNN model achieved the best results (an accuracy of 92.3%, a recall of 0.891, a precision of 0.801, and an F1-score of 0.844), in overhead position and slightly outperformed the RF method. Data fusion was performed at a feature level, combining both infrared and radar technologies, however the benefit was not significant. The proposed system was cost, processing time, and space efficient. The system with further development can be utilised as a real-time fall detection system in aged care facilities or at homes of older people