Crowd-shipping: a new efficient and eco-friendly delivery strategy

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An aesthetics of touch: investigating the language of design relating to form

How well can designers communicate qualities of touch? This paper presents evidence that they have some capability to do so, much of which appears to have been learned, but at present make limited use of such language. Interviews with graduate designer-makers suggest that they are aware of and value the importance of touch and materiality in their work, but lack a vocabulary to fully relate to their detailed explanations of other aspects such as their intent or selection of materials. We believe that more attention should be paid to the verbal dialogue that happens in the design process, particularly as other researchers show that even making-based learning also has a strong verbal element to it. However, verbal language alone does not appear to be adequate for a comprehensive language of touch. Graduate designers-makers’ descriptive practices combined non-verbal manipulation within verbal accounts. We thus argue that haptic vocabularies do not simply describe material qualities, but rather are situated competences that physically demonstrate the presence of haptic qualities. Such competencies are more important than groups of verbal vocabularies in isolation. Design support for developing and extending haptic competences must take this wide range of considerations into account to comprehensively improve designers’ capabilities

Sustainability in design: now! Challenges and opportunities for design research, education and practice in the XXI century

Copyright @ 2010 Greenleaf PublicationsLeNS project funded by the Asia Link Programme, EuropeAid, European Commission

An aesthetic for sustainable interactions in product-service systems?

Copyright @ 2012 Greenleaf PublishingEco-efficient Product-Service System (PSS) innovations represent a promising approach to sustainability. However the application of this concept is still very limited because its implementation and diffusion is hindered by several barriers (cultural, corporate and regulative ones). The paper investigates the barriers that affect the attractiveness and acceptation of eco-efficient PSS alternatives, and opens the debate on the aesthetic of eco-efficient PSS, and the way in which aesthetic could enhance some specific inner qualities of this kinds of innovations. Integrating insights from semiotics, the paper outlines some first research hypothesis on how the aesthetic elements of an eco-efficient PSS could facilitate user attraction, acceptation and satisfaction

Introduction to Sustainable Urban Renewal: CO2 Reduction and the Use of Performance Agreements: Experience from the Netherlands

As in other European countries, the renewal of post-war housing estates is a major policy issue in the Netherlands. The aim is to upgrade neighbourhoods by means of demolition, renovation of social rented housing and construction of new owner-occupied dwellings. The existing housing stock is a key factor in attaining the greenhouse gas reduction targets in the Kyoto Protocol. This process of renewal will pursue high targets and ambitions as it gets underway in the coming years. It will lead to major interventions in the housing stock and - at the same time - rigorously challenge the market players to make existing dwellings more environmentally-friendly and to develop sustainability in post-war housing. By presenting two case studies this book offers insight into the environmental policies of Dutch housing associations and municipalities. Focusing specifically on the role of performance agreements in realising sustainability ambitions, it also assesses the feasibility of C02 reductions and examines the costs and decision-making processes

Ultra low-power, high-performance accelerator for speech recognition

Automatic Speech Recognition (ASR) is undoubtedly one of the most important and interesting applications in the cutting-edge era of Deep-learning deployment, especially in the mobile segment. Fast and accurate ASR comes at a high energy cost, requiring huge memory storage and computational power, which is not affordable for the tiny power budget of mobile devices. Hardware acceleration can reduce power consumption of ASR systems as well as reducing its memory pressure, while delivering high-performance. In this thesis, we present a customized accelerator for large-vocabulary, speaker-independent, continuous speech recognition. A state-of-the-art ASR system consists of two major components: acoustic-scoring using DNN and speech-graph decoding using Viterbi search. As the first step, we focus on the Viterbi search algorithm, that represents the main bottleneck in the ASR system. The accelerator includes some innovative techniques to improve the memory subsystem, which is the main bottleneck for performance and power, such as a prefetching scheme and a novel bandwidth saving technique tailored to the needs of ASR. Furthermore, as the speech graph is vast taking more than 1-Gigabyte memory space, we propose to change its representation by partitioning it into several sub-graphs and perform an on-the-fly composition during the Viterbi run-time. This approach together with some simple yet efficient compression techniques result in 31x memory footprint reduction, providing 155x real-time speedup and orders of magnitude power and energy saving compared to CPUs and GPUs. In the next step, we propose a novel hardware-based ASR system that effectively integrates a DNN accelerator for the pruned/quantized models with the Viterbi accelerator. We show that, when either pruning or quantizing the DNN model used for acoustic scoring, ASR accuracy is maintained but the execution time of the ASR system is increased by 33%. Although pruning and quantization improves the efficiency of the DNN, they result in a huge increase of activity in the Viterbi search since the output scores of the pruned model are less reliable. In order to avoid the aforementioned increase in Viterbi search workload, our system loosely selects the N-best hypotheses at every time step, exploring only the N most likely paths. Our final solution manages to efficiently combine both DNN and Viterbi accelerators using all their optimizations, delivering 222x real-time ASR with a small power budget of 1.26 Watt, small memory footprint of 41 MB, and a peak memory bandwidth of 381 MB/s, being amenable for low-power mobile platforms.Los sistemas de reconocimiento automático del habla (ASR por sus siglas en inglés, Automatic Speech Recognition) son sin lugar a dudas una de las aplicaciones más relevantes en el área emergente de aprendizaje profundo (Deep Learning), specialmente en el segmento de los dispositivos móviles. Realizar el reconocimiento del habla de forma rápida y precisa tiene un elevado coste en energía, requiere de gran capacidad de memoria y de cómputo, lo cual no es deseable en sistemas móviles que tienen severas restricciones de consumo energético y disipación de potencia. El uso de arquitecturas específicas en forma de aceleradores hardware permite reducir el consumo energético de los sistemas de reconocimiento del habla, al tiempo que mejora el rendimiento y reduce la presión en el sistema de memoria. En esta tesis presentamos un acelerador específicamente diseñado para sistemas de reconocimiento del habla de gran vocabulario, independientes del orador y que funcionan en tiempo real. Un sistema de reconocimiento del habla estado del arte consiste principalmente en dos componentes: el modelo acústico basado en una red neuronal profunda (DNN, Deep Neural Network) y la búsqueda de Viterbi basada en un grafo que representa el lenguaje. Como primer objetivo nos centramos en la búsqueda de Viterbi, ya que representa el principal cuello de botella en los sistemas ASR. El acelerador para el algoritmo de Viterbi incluye técnicas innovadoras para mejorar el sistema de memoria, que es el mayor cuello de botella en rendimiento y energía, incluyendo técnicas de pre-búsqueda y una nueva técnica de ahorro de ancho de banda a memoria principal específicamente diseñada para sistemas ASR. Además, como el grafo que representa el lenguaje requiere de gran capacidad de almacenamiento en memoria (más de 1 GB), proponemos cambiar su representación y dividirlo en distintos grafos que se componen en tiempo de ejecución durante la búsqueda de Viterbi. De esta forma conseguimos reducir el almacenamiento en memoria principal en un factor de 31x, alcanzar un rendimiento 155 veces superior a tiempo real y reducir el consumo energético y la disipación de potencia en varios órdenes de magnitud comparado con las CPUs y las GPUs. En el siguiente paso, proponemos un novedoso sistema hardware para reconocimiento del habla que integra de forma efectiva un acelerador para DNNs podadas y cuantizadas con el acelerador de Viterbi. Nuestros resultados muestran que podar y/o cuantizar el DNN para el modelo acústico permite mantener la precisión pero causa un incremento en el tiempo de ejecución del sistema completo de hasta el 33%. Aunque podar/cuantizar mejora la eficiencia del DNN, éstas técnicas producen un gran incremento en la carga de trabajo de la búsqueda de Viterbi ya que las probabilidades calculadas por el DNN son menos fiables, es decir, se reduce la confianza en las predicciones del modelo acústico. Con el fin de evitar un incremento inaceptable en la carga de trabajo de la búsqueda de Viterbi, nuestro sistema restringe la búsqueda a las N hipótesis más probables en cada paso de la búsqueda. Nuestra solución permite combinar de forma efectiva un acelerador de DNNs con un acelerador de Viterbi incluyendo todas las optimizaciones de poda/cuantización. Nuestro resultados experimentales muestran que dicho sistema alcanza un rendimiento 222 veces superior a tiempo real con una disipación de potencia de 1.26 vatios, unos requisitos de memoria modestos de 41 MB y un uso de ancho de banda a memoria principal de, como máximo, 381 MB/s, ofreciendo una solución adecuada para dispositivos móviles

216 Jewish Hospital of St. Louis

https://digitalcommons.wustl.edu/bjc_216/1165/thumbnail.jp

FOUNDRIES OF THE FUTURE:

Since the 1970s, cities world-wide have been witness to radical de-industrialisation. Manufacturing was considered incompatible with urban life and was actively pushed out. As economies have grown, public officials and developers have instinctively shifted their priorities to short-term, high-yielding land uses such as offices, retail space and housing. Inner-city growth from New York to London and even Seoul have generally come at the expense of land uses such as manufacturing or logistics. Despite the odds, manufacturing is not in terminal decay in western cities. On the contrary, it is at the opening of a new chapter. Urban manufacturing can help cities to be more innovative, circular, inclusive and resilient. Recently, with increasing interest in the circular economy, with cleaner and more compact technology, with more progressive building codes for mixed use, with increasing awareness of the impacts of social inequality and with a clearer understanding of the value chains between the trade of material and immaterial goods, cities across the world are realising that manufacturing has an important place in the 21st century urban economy. While both enthusiasm for making is increasing and the value of manufacturing is becoming increasingly evident in cities, the topic remains extremely complex and challenging to manage. This book attempts to shed light on the ways manufacturing can address urban challenges, it exposes constraints for the manufacturing sector and provides fifty patterns for working with urban manufacturing. This book has been written as a manual to help politicians, public authorities, planners, designers and community organisations to be able to plan, discuss and collaborate by developing more productive urban manufacturing. The book is split into two parts.  We first cover an abridged history of the late nineteenth and early twentieth centuries, noting how European cities evolved rapidly by harnessing manufacturing, and then how the late twentieth century led to a radical shift in how cities work and think. We’re now at a crossroads between actors that do not see the need for manufacturing in cities and those that consider it vital for a prosperous urban future. Part of the tension comes from the fact that manufacturing is considered a ‘weak land use’ compared to activities such as real-estate development, which has been considered more financially attractive by many actors in the private and public sector. This real estate-oriented development narrative is increasingly regarded as short-sighted, but will not change without an alternative vision. We have therefore elaborated a narrative on how urban manufacturing responds to four specific challenges facing cities and how in turn manufacturing needs cities. In practice, planning and design for a topic like this is highly challenging. The second part of the book is intended as a handbook. By synthesising our research and fieldwork conducted in a number of cities, we have encountered many similarities in terms of problems, challenges and solutions for urban manufacturing. Inspired by the seminal 1977 book, ‘A Pattern Language’ we have translated our findings into fifty patterns which help render the diversity of issues concerning manufacturing more tangible. As both teamwork and negotiation are necessary, exercises and methods are provided to use the patterns. Finally, we have set out twelve key action areas as possible starting points for supporting urban manufacturing

The end of stigma? Understanding the dynamics of legitimisation in the context of TV series consumption

This research contributes to prior work on stigmatisation by looking at stigmatisation and legitimisation as social processes in the context of TV series consumption. Using in-depth interviews, we show that the dynamics of legitimisation are complex and accompanied by the reproduction of existing stigmas and creation of new stigmas

Becoming a contract controller : tips for a thriving career

https://egrove.olemiss.edu/aicpa_guides/1808/thumbnail.jp