Explore the vast range of titles available.

\"This book overviews the possibilities and limitations offered by various types of computer programs, such as database management systems, diagramming software, CAD, and BIM, offering a practical approach in the accommodation of these programs and as an essential reference for architectural educators, students, and practitioners\"-- Provided by publisher.

A smart campus is an intelligent infrastructure where smart sensors and actuators collaborate to collect information and interact with the machines, tools, and users of a university campus. As in a smart city, a smart campus represents a challenging scenario for Internet of Things (IoT) networks, especially in terms of cost, coverage, availability, latency, power consumption, and scalability. The technologies employed so far to cope with such a scenario are not yet able to manage simultaneously all the previously mentioned demanding requirements. Nevertheless, recent paradigms such as fog computing, which extends cloud computing to the edge of a network, make possible low-latency and location-aware IoT applications. Moreover, technologies such as Low-Power Wide-Area Networks (LPWANs) have emerged as a promising solution to provide low-cost and low-power consumption connectivity to nodes spread throughout a wide area. Specifically, the Long-Range Wide-Area Network (LoRaWAN) standard is one of the most recent developments, receiving attention both from industry and academia. In this article, the use of a LoRaWAN fog computing-based architecture is proposed for providing connectivity to IoT nodes deployed in a campus of the University of A Coruña (UDC), Spain. To validate the proposed system, the smart campus has been recreated realistically through an in-house developed 3D Ray-Launching radio-planning simulator that is able to take into consideration even small details, such as traffic lights, vehicles, people, buildings, urban furniture, or vegetation. The developed tool can provide accurate radio propagation estimations within the smart campus scenario in terms of coverage, capacity, and energy efficiency of the network. The results obtained with the planning simulator can then be compared with empirical measurements to assess the operating conditions and the system accuracy. Specifically, this article presents experiments that show the accurate results obtained by the planning simulator in the largest scenario ever built for it (a campus that covers an area of 26,000 m 2 ), which are corroborated with empirical measurements. Then, how the tool can be used to design the deployment of LoRaWAN infrastructure for three smart campus outdoor applications is explained: a mobility pattern detection system, a smart irrigation solution, and a smart traffic-monitoring deployment. Consequently, the presented results provide guidelines to smart campus designers and developers, and for easing LoRaWAN network deployment and research in other smart campuses and large environments such as smart cities.

Artificial intelligence (AI), which has a strong potential to assist architects in conceptual and visualization stages, has been increasingly used in the field of design and architecture. This study, focusing on the AI tools that generate images from texts and offer innovative solutions to design problems, aims to evaluate the use of AI for the reinterpretation of traditional Iznik tile patterns and colors in the context of architectural design and modern interiors. The methodology consists of four stages, which are the selection of AI tools (Copilot, DALL-E 2, DALL-E 3, Midjourney), the preparation of textual prompts for testing “çini” (tile) expression, testing of the AI tools’ perception of the concepts related to Iznik tile motifs, and the creation of prompt series. The findings of our study provide evidence that current AI tools exhibit distinct features in terms of variety, conceptualization, artistic visualization, and image production, while they are hardly equipped with the necessary conceptual background to communicate with the designers for the interpretation of the traditional Iznik tiles in contemporary architectural design. Specifically, Midjourney, which could produce historically referenced contemporary designs in response to textual expressions, was more successful than other AI tools. DALL-E 2 could not visualize the expressions concerning the placement of the Iznik tile surfaces in interior spaces but was quite inspiring in terms of the images regarding the tile pattern and color. DALL-E 3 and Copilot tools produced similar images in terms of color palette and patterns, whereas DALL-E 3 was better at visualizing spatial data. Our results reveal that AI tools still need to be developed for analyzing traditional patterns, styles, and forms for contemporary design purposes. On the other hand, AI tools can develop innovative approaches, optimize the tile production procedure, and have the potential to accelerate the design process for designers by generating new and diverse ideas.

There is a need for adaptive technology to enhance indoor wayfinding by visually-impaired people. To address this need, we have developed and tested a Digital Sign System. The hardware and software consist of digitally-encoded signs widely distributed throughout a building, a handheld sign-reader based on an infrared camera, image-processing software, and a talking digital map running on a mobile device. Four groups of subjects-blind, low vision, blindfolded sighted, and normally sighted controls-were evaluated on three navigation tasks. The results demonstrate that the technology can be used reliably in retrieving information from the signs during active mobility, in finding nearby points of interest, and following routes in a building from a starting location to a destination. The visually impaired subjects accurately and independently completed the navigation tasks, but took substantially longer than normally sighted controls. This fully functional prototype system demonstrates the feasibility of technology enabling independent indoor navigation by people with visual impairment.

This study analyzes valuable information (image feature information) from pixel-based images to systematize the form generation process utilizing this information. Information in architecture is mainly used as an analytical tool for functional design optimization. However, the Fourth Industrial Revolution and advances in information and communication technology have positioned information as a core value-creating tool. Consequently, there is a growing need to develop methodologies for effectively applying information to architectural design generation. This study combines digital technology with images, which directly influence architectural design, transforming them into numerical information. By systematizing the three-dimensional form generation process using low-level image feature information, it analyzes the relationship between the characteristics of valuable information and the generated forms. This approach helps identify the interrelationships between the characteristics of valuable information and the generated forms, the complementarity needed to mitigate the limitations of valuable information, and the potential for architectural applications of the three-dimensional form generation methodology utilizing image information. This approach reduces labor and enhances architectural aesthetics by instantly generating diverse early design alternatives. Such research can provide a more logical basis for avoiding intuitive judgments during the design decision-making process. It can also lay the groundwork for information-centric design systems in architecture.

As for the complexity of language structure, the semantic structure, and the relative scarcity of labeled data and context information, sentiment analysis has been regarded as a challenging task in Natural Language Processing especially in the field of short-text processing. Deep learning model need a large scale of training data to overcome data sparseness and the over-fitting problem, we propose multi-granularity text-oriented data augmentation technologies to generate large-scale artificial data for training model, which is compared with Generative adversarial network(GAN). In this paper, a novel hybrid neural network model architecture(LSCNN) was proposed with our data augmentation technology, which is can outperforms many single neural network models. The proposed data augmentation method enhances the generalization ability of the proposed model. Experiment results show that the proposed data augmentation method in combination with the neural networks model can achieve astonishing performance without any handcrafted features on sentiment analysis or short text classification. It was validated on a Chinese on-line comment dataset and Chinese news headline corpus, and outperforms many state-of-the-art models. Evidence shows that the proposed data argumentation technology can obtain more accurate distribution representation from data for deep learning, which improves the generalization characteristics of the extracted features. The combination of the data argumentation technology and LSCNN fusion model is well suited to short text sentiment analysis, especially on small scale corpus.

Smart sensors and smartphones are becoming increasingly prevalent. Both can be used to gather environmental data (e.g., noise). Importantly, these devices can be connected to each other as well as to the Internet to collect large amounts of sensor data, which leads to many new opportunities. In particular, mobile crowdsensing techniques can be used to capture phenomena of common interest. Especially valuable insights can be gained if the collected data are additionally related to the time and place of the measurements. However, many technical solutions still use monolithic backends that are not capable of processing crowdsensing data in a flexible, efficient, and scalable manner. In this work, an architectural design was conceived with the goal to manage geospatial data in challenging crowdsensing healthcare scenarios. It will be shown how the proposed approach can be used to provide users with an interactive map of environmental noise, allowing tinnitus patients and other health-conscious people to avoid locations with harmful sound levels. Technically, the shown approach combines cloud-native applications with Big Data and stream processing concepts. In general, the presented architectural design shall serve as a foundation to implement practical and scalable crowdsensing platforms for various healthcare scenarios beyond the addressed use case.

The only Revit tutorial guide based on a real project workflowAutodesk Revit Architecture No Experience Required is the ultimate real-world guide for mastering this increasingly prevalent BIM software package. Using a continuous, step-by-step tutorial, this book walks you through all project phases as you learn the basics of Revit by designing, documenting, and presenting a four-story office building. You'll begin by learning your way around the interface and conventions, then jump right into design by placing walls, doors, and windows. Next you'll work with grids, beams, foundations, dimensions, and text as you build floors layer by layer, join walls, create ceilings and roofs, and place stairs, ramps, and railings. The instruction covers construction documentation, advanced detailing, and families, as well as site considerations including grading and top surface features to provide a well-rounded, real-world Revit skill set. The companion website features downloadable 'before and after' tutorial files that allow you to jump in at any point and compare your work to the pros. The shift from 2D drafting to 3D building information modeling has made Revit a must-have skill for an increasing number of design, engineering, and construction professionals. This book is designed to teach you the basics quickly, using a real-world workflow, process, and pacing. Get acquainted with the Revit interface, then immediately start buildingLearn to place structural components, text, dimensions, and moreUnderstand views, grids, editing, importing, exporting, and work sharingGenerate construction documentation including schedules and material takeoffsThis simple yet engaging tutorial brings together all of the major skills a Revit user needs to know to complete real workplace projects. Whether read from beginning to end as a comprehensive lesson, or used as 'dip-in' reference for unfamiliar tasks, Autodesk Revit Architecture No Experience Required provides invaluable practical BIM instruction for every phase of a project.