Utilizing an Adaptive Neuro-Fuzzy Inference System (ANFIS) for overcrowding level risk assessment in railway stations

The railway network plays a significant role (both economically and socially) in assisting the reduction of urban traffic congestion. It also accelerates the decarbonization in cities, societies and built environments. To ensure the safe and secure operation of stations and capture the real-time risk status, it is imperative to consider a dynamic and smart method for managing risk factors in stations. In this research, a framework to develop an intelligent system for managing risk is suggested. The adaptive neuro-fuzzy inference system (ANFIS) is proposed as a powerful, intelligently selected model to improve risk management and manage uncertainties in risk variables. The objective of this study is twofold. First, we review current methods applied to predict the risk level in the flow. Second, we develop smart risk assessment and management measures (or indicators) to improve our understanding of the safety of railway stations in real-time. Two parameters are selected as input for the risk level relating to overcrowding: the transfer efficiency and retention rate of the platform. This study is the world’s first to establish the hybrid artificial intelligence (AI) model, which has the potency to manage risk uncertainties and learns through artificial neural networks (ANNs) by integrated training processes. The prediction result shows very high accuracy in predicting the risk level performance, and proves the AI model capabilities to learn, to make predictions, and to capture risk level values in real time. Such risk information is extremely critical for decision making processes in managing safety and risks, especially when uncertain disruptions incur (e.g., COVID-19, disasters, etc.). The novel insights stemmed from this study will lead to more effective and efficient risk management for single and clustered railway station facilities towards safer, smarter, and more resilient transportation systems

Video based vehicle detection for advance warning Intelligent Transportation System

Video based vehicle detection and surveillance technologies are an integral part of Intelligent Transportation System (ITS), due to its non-intrusiveness and capability or capturing global and specific vehicle behavior data. The initial goal of this thesis is to develop an efficient advance warning ITS system for detection of congestion at work zones and special events based on video detection. The goals accomplished by this thesis are: (1) successfully developed the advance warning ITS system using off-the-shelf components and, (2) Develop and evaluate an improved vehicle detection and tracking algorithm. The advance warning ITS system developed includes many off-the-shelf equipments like Autoscope (video based vehicle detector), Digital Video Recorders, RF transceivers, high gain Yagi antennas, variable message signs and interface processors. The video based detection system used requires calibration and fine tuning of configuration parameters for accurate results. Therefore, an in-house video based vehicle detection system was developed using the Corner Harris algorithm to eliminate the need of complex calibration and contrasts modifications. The algorithm was implemented using OpenCV library on a Arcom\u27s Olympus Windows XP Embedded development kit running WinXPE operating system. The algorithm performance is for accuracy in vehicle speed and count is evaluated. The performance of the proposed algorithm is equivalent or better to the Autoscope system without any modifications to calibration and lamination adjustments

Vision Science and Technology at NASA: Results of a Workshop

A broad review is given of vision science and technology within NASA. The subject is defined and its applications in both NASA and the nation at large are noted. A survey of current NASA efforts is given, noting strengths and weaknesses of the NASA program

Sound Synthesis Using Programmable System-On-Chip Devices

The last 20 years has witnessed a resurgence of interest in analogue synthesisers 1 . Manufacturers, such as Moog and Sequential Circuits, that had disappeared from the commercial marketplace by the end of the 1980’s, have reappeared with an impressive line of products. Other established companies such as Korg and Roland, as well as entrants that had made their name with digital technology, such as Novation and Arturia, have released analogue instruments. Although the feature set of digital synthesisers is extensive and with a falling comparative cost, the analogue market has continued to grow with more and more devices coming available. They are perceived to be of superior sound quality by users, but their primary drawback is price, as numerous discrete components or specialist integrated circuits are required. This thesis introduces two novel low-cost approaches to building analogue-type synthesisers. Such a low-cost instrument could have applications in an educational laboratory environment for synthesisers. The first approach is to exploit a new mixed-signal technology called the Programmable System-on-Chip (PSoC), which includes a CPU core and mixed-signal arrays of configurable integrated analogue and digital peripherals. The second exploits a System on Chip (SoC) comprising an ARM-based (Acorn RISC Machine) processor and a Field-Programmable Gate Array (FPGA). Two synthesisers were built and were evaluated for difficulty of implementation and assessed for their sound quality. The design and testing process was recorded and documented in detail. The mixed-signal approach was found to be cheaper than the FPGA-approach both in terms of component costs and development time compared to the FPGA-based approach. Actually, the FPGA-approach was determined to be prohibitively expensive in terms of the development time incurred. The sound quality analysis demonstrated that both instruments were perceived by users to be of high quality, achieving a noticeable analogue sound. Future work would be to repackage the PSoC system and modules into rack-mounted form for use in an educational synthesiser laboratory environment

Sound Synthesis Using Programmable System-On-Chip Devices

The last 20 years has witnessed a resurgence of interest in analogue synthesisers 1 . Manufacturers, such as Moog and Sequential Circuits, that had disappeared from the commercial marketplace by the end of the 1980’s, have reappeared with an impressive line of products. Other established companies such as Korg and Roland, as well as entrants that had made their name with digital technology, such as Novation and Arturia, have released analogue instruments. Although the feature set of digital synthesisers is extensive and with a falling comparative cost, the analogue market has continued to grow with more and more devices coming available. They are perceived to be of superior sound quality by users, but their primary drawback is price, as numerous discrete components or specialist integrated circuits are required. This thesis introduces two novel low-cost approaches to building analogue-type synthesisers. Such a low-cost instrument could have applications in an educational laboratory environment for synthesisers. The first approach is to exploit a new mixed-signal technology called the Programmable System-on-Chip (PSoC), which includes a CPU core and mixed-signal arrays of configurable integrated analogue and digital peripherals. The second exploits a System on Chip (SoC) comprising an ARM-based (Acorn RISC Machine) processor and a Field-Programmable Gate Array (FPGA). Two synthesisers were built and were evaluated for difficulty of implementation and assessed for their sound quality. The design and testing process was recorded and documented in detail. The mixed-signal approach was found to be cheaper than the FPGA-approach both in terms of component costs and development time compared to the FPGA-based approach. Actually, the FPGA-approach was determined to be prohibitively expensive in terms of the development time incurred. The sound quality analysis demonstrated that both instruments were perceived by users to be of high quality, achieving a noticeable analogue sound. Future work would be to repackage the PSoC system and modules into rack-mounted form for use in an educational synthesiser laboratory environment

Interactive, multi-purpose traffic prediction platform using connected vehicles dataset

Traffic congestion is a perennial issue because of the increasing traffic demand yet limited budget for maintaining current transportation infrastructure; let alone expanding them. Many congestion management techniques require timely and accurate traffic estimation and prediction. Examples of such techniques include incident management, real-time routing, and providing accurate trip information based on historical data. In this dissertation, a speech-powered traffic prediction platform is proposed, which deploys a new deep learning algorithm for traffic prediction using Connected Vehicles (CV) data. To speed-up traffic forecasting, a Graph Convolution -- Gated Recurrent Unit (GC-GRU) architecture is proposed and analysis of its performance on tabular data is compared to state-of-the-art models. GC-GRU's Mean Absolute Percentage Error (MAPE) was very close to Transformer (3.16 vs 3.12) while achieving the fastest inference time and a six-fold faster training time than Transformer, although Long-Short-Term Memory (LSTM) was the fastest in training. Such improved performance in traffic prediction with a shorter inference time and competitive training time allows the proposed architecture to better cater to real-time applications. This is the first study to demonstrate the advantage of using multiscale approach by combining CV data with conventional sources such as Waze and probe data. CV data was better at detecting short duration, Jam and stand-still incidents and detected them earlier as compared to probe. CV data excelled at detecting minor incidents with a 90 percent detection rate versus 20 percent for probes and detecting them 3 minutes faster. To process the big CV data faster, a new algorithm is proposed to extract the spatial and temporal features from the CSV files into a Multiscale Data Analysis (MDA). The algorithm also leverages Graphics Processing Unit (GPU) using the Nvidia Rapids framework and Dask parallel cluster in Python. The results show a seventy-fold speedup in the data Extract, Transform, Load (ETL) of the CV data for the State of Missouri of an entire day for all the unique CV journeys (reducing the processing time from about 48 hours to 25 minutes). The processed data is then fed into a customized UNet model that learns highlevel traffic features from network-level images to predict large-scale, multi-route, speed and volume of CVs. The accuracy and robustness of the proposed model are evaluated by taking different road types, times of day and image snippets of the developed model and comparable benchmarks. To visually analyze the historical traffic data and the results of the prediction model, an interactive web application powered by speech queries is built to offer accurate and fast insights of traffic performance, and thus, allow for better positioning of traffic control strategies. The product of this dissertation can be seamlessly deployed by transportation authorities to understand and manage congestions in a timely manner.Includes bibliographical references

Photography equipment and techniques. A survey of NASA developments

The Apollo program has been the most complex exploration ever attempted by man, requiring extensive research, development, and engineering in most of the sciences before the leap through space could begin. Photography has been used at each step of the way to document the efforts and activities, isolate mistakes, reveal new phenomena, and to record much that cannot be seen by the human eye. At the same time, the capabilities of photography were extended because of the need of meeting space requirements. The results of this work have been applied to community planning and ecology, for example, as well as to space and engineering. Special uses of standard equipment, modifications and new designs, as well as film combinations that indicate actual or potential ecological problems are described

New Trends in Development of Services in the Modern Economy

The services sector strategic development unites a multitude of economic and managerial aspects and is one of the most important problems of economic management. Many researches devoted to this industry study are available. Most of them are performed in the traditional aspect of the voluminous calendar approach to strategic management, characteristic of the national scientific school. Such an approach seems archaic, forming false strategic benchmarks. The services sector is of special scientific interest in this context due to the fact that the social production structure to the services development model attraction in many countries suggests transition to postindustrial economy type where the services sector is a system-supporting sector of the economy. Actively influencing the economy, the services sector in the developed countries dominates in the GDP formation, primary capital accumulation, labor, households final consumption and, finally, citizens comfort of living. However, a clear understanding of the services sector as a hyper-sector permeating all spheres of human activity has not yet been fully developed, although interest in this issue continues to grow among many authors. Target of strategic management of the industry development setting requires substantive content and the services sector target value assessment

Multispectral photography for earth resources

A guide for producing accurate multispectral results for earth resource applications is presented along with theoretical and analytical concepts of color and multispectral photography. Topics discussed include: capabilities and limitations of color and color infrared films; image color measurements; methods of relating ground phenomena to film density and color measurement; sensitometry; considerations in the selection of multispectral cameras and components; and mission planning