Tackling the impact of noise in the productivity of collaborative software development projects located in open spaces

In a context of open space office environments, with multiple teams in the same office room working on different projects, a commonly reported issue is the disruptive amount of noise generated by the occupants. High noise levels are mainly attributed to communication necessities both among members of the same team and among members of different teams. Despite the fact that coexistence rules can be established among occupants, with time, the communication needs and stress levels lead to the disrespect of the agreed rules and the increase of the room’s noise level. Frequently a third party needs to be put in place to help enforcing acceptable noise level, e.g. a librarian or a teacher. To ensure the best work conditions for all office occupants, as well as keeping their productivity high, the noise level should be as low as possible while still allowing communication. To control the noise level inside an office, we propose the implementation of a Cyber- Physical System that utilises the Internet of Things technologies to detect the team(s) responsible for producing disruptively-high noise levels. By enriching the office’s physical environment with sensors and the use of sound source location techniques, we can identify the workspace(s) from which the noise was generated, and then reach its occupants through the system’s actuators. Upon identifying the responsible team, the system communicates with it through a LED lamp, using a colour and intensity code, informing them about their noise level. The goals of this work are to design and implement a Cyber-Physical System to address the problem of high noise levels in software development office environments, and to study the behaviour of the workers when prompted by the system actuators about their high noise level. Through the usage of functionality tests, we can ensure the functionality of the system and designed simulations and experimental guidelines for enabling future works to measure its efficiency at keeping the overall noise level low and for assess the correlation of its efficiency with its occupants’ productivity.Num contexto de ambientes de escritórios open space, com múltiplas equipas a trabalhar em projetos diferentes, um dos problemas reportados regularmente é a quantidade disruptiva de ruído gerada pelos ocupantes. Níveis elevados de ruído são atribuídos principalmente à necessidades de comunicação dentro de uma equipa, ou entre equipas. Apesar de poderem ser estabelecidas regras de coexistência entre ocupantes, com o tempo, as necessidades de comunicação e os níveis de stress levam ao desrespeito dessas regras e ao aumento do nível de ruído da sala. É frequente o recurso a uma pessoa para ajudar a manter um nível de ruido aceitável. Para assegurar as melhores condições de trabalho, tal como manter os níveis de produtividade altos, o nível de ruído deve ser o mais baixo possível, tendo em conta as necessidades de comunicação entre ocupantes. Para controlar os níveis de ruido dentro de um escritório, propomos a implementação de um Sistema Ciber Físico que utilize tecnologias Internet of Things para detetar o(s) responsável( eis) pela produção dos níveis de ruido disruptivos. Através do enriquecimento do ambiente físico do escritório com sensores e a utilização de técnicas de localização de origem de som, é possível identificar o(s) espaço(s) de trabalho onde o ruido teve origem, e alcançar os utilizadores desse espaço através dos atuadores do sistema. Após identificar a(s) equipa(s) responsável(eis), o sistema comunica com eles através de luzes LED, usando um código de cor e intensidade para os informar sobre o seu elevado nível de ruido. Os objetivos deste trabalho são o de conceber e implementar um Sistema Ciber Físico que tenta resolver o problema dos altos níveis de ruido num ambiente de escritório de desenvolvimento de software e o de estudar o comportamento dos trabalhadores quando notificados pelo sistema sobre o seu elevado nível de ruido. Garantimos que o nosso sistema funciona como suposto através de testes de funcionalidade e apresentamos simulações e diretrizes experimentais para permitir que futuros trabalhos possam medir da sua eficiência em reduzir os níveis de ruido produzidos e avaliar a correlação entre esta eficiência e a produtividade dos ocupantes

The University Defence Research Collaboration In Signal Processing

This chapter describes the development of algorithms for automatic detection of anomalies from multi-dimensional, undersampled and incomplete datasets. The challenge in this work is to identify and classify behaviours as normal or abnormal, safe or threatening, from an irregular and often heterogeneous sensor network. Many defence and civilian applications can be modelled as complex networks of interconnected nodes with unknown or uncertain spatio-temporal relations. The behavior of such heterogeneous networks can exhibit dynamic properties, reflecting evolution in both network structure (new nodes appearing and existing nodes disappearing), as well as inter-node relations. The UDRC work has addressed not only the detection of anomalies, but also the identification of their nature and their statistical characteristics. Normal patterns and changes in behavior have been incorporated to provide an acceptable balance between true positive rate, false positive rate, performance and computational cost. Data quality measures have been used to ensure the models of normality are not corrupted by unreliable and ambiguous data. The context for the activity of each node in complex networks offers an even more efficient anomaly detection mechanism. This has allowed the development of efficient approaches which not only detect anomalies but which also go on to classify their behaviour

A receding horizon event-driven control strategy for intelligent traffic management

AbstractIn this paper, the intelligent traffic management within a smart city environment is addressed by developing an ad-hoc model predictive control strategy based on an event-driven formulation. To this end, a constrained hybrid system description is considered for safety verification purposes and a low-demanding receding horizon controller is then derived by exploiting set-theoretic arguments. Simulations are performed on the train-gate benchmark system to show the effectiveness and benefits of the proposed methodology

Real-world Machine Learning Systems: A survey from a Data-Oriented Architecture Perspective

Machine Learning models are being deployed as parts of real-world systems with the upsurge of interest in artificial intelligence. The design, implementation, and maintenance of such systems are challenged by real-world environments that produce larger amounts of heterogeneous data and users requiring increasingly faster responses with efficient resource consumption. These requirements push prevalent software architectures to the limit when deploying ML-based systems. Data-oriented Architecture (DOA) is an emerging concept that equips systems better for integrating ML models. DOA extends current architectures to create data-driven, loosely coupled, decentralised, open systems. Even though papers on deployed ML-based systems do not mention DOA, their authors made design decisions that implicitly follow DOA. The reasons why, how, and the extent to which DOA is adopted in these systems are unclear. Implicit design decisions limit the practitioners' knowledge of DOA to design ML-based systems in the real world. This paper answers these questions by surveying real-world deployments of ML-based systems. The survey shows the design decisions of the systems and the requirements these satisfy. Based on the survey findings, we also formulate practical advice to facilitate the deployment of ML-based systems. Finally, we outline open challenges to deploying DOA-based systems that integrate ML models.Comment: Under revie

Enabling 5G Technologies

The increasing demand for connectivity and broadband wireless access is leading to the fifth generation (5G) of cellular networks. The overall scope of 5G is greater in client width and diversity than in previous generations, requiring substantial changes to network topologies and air interfaces. This divergence from existing network designs is prompting a massive growth in research, with the U.S. government alone investing $400 million in advanced wireless technologies. 5G is projected to enable the connectivity of 20 billion devices by 2020, and dominate such areas as vehicular networking and the Internet of Things. However, many challenges exist to enable large scale deployment and general adoption of the cellular industries. In this dissertation, we propose three new additions to the literature to further the progression 5G development. These additions approach 5G from top down and bottom up perspectives considering interference modeling and physical layer prototyping. Heterogeneous deployments are considered from a purely analytical perspective, modeling co-channel interference between and among both macrocell and femtocell tiers. We further enhance these models with parameterized directional antennas and integrate them into a novel mixed point process study of the network. At the air interface, we examine Software-Defined Radio (SDR) development of physical link level simulations. First, we introduce a new algorithm acceleration framework for MATLAB, enabling real-time and concurrent applications. Extensible beyond SDR alone, this dataflow framework can provide application speedup for stream-based or data dependent processing. Furthermore, using SDRs we develop a localization testbed for dense deployments of 5G smallcells. Providing real-time tracking of targets using foundational direction of arrival estimation techniques, including a new OFDM based correlation implementation

The assessment and development of methods in (spatial) sound ecology

As vital ecosystems across the globe enter unchartered pressure from climate change industrial land use, understanding the processes driving ecosystem viability has never been more critical. Nuanced ecosystem understanding comes from well-collected field data and a wealth of associated interpretations. In recent years the most popular methods of ecosystem monitoring have revolutionised from often damaging and labour-intensive manual data collection to automated methods of data collection and analysis. Sound ecology describes the school of research that uses information transmitted through sound to infer properties about an area's species, biodiversity, and health. In this thesis, we explore and develop state-of-the-art automated monitoring with sound, specifically relating to data storage practice and spatial acoustic recording and data analysis. In the first chapter, we explore the necessity and methods of ecosystem monitoring, focusing on acoustic monitoring, later exploring how and why sound is recorded and the current state-of-the-art in acoustic monitoring. Chapter one concludes with us setting out the aims and overall content of the following chapters. We begin the second chapter by exploring methods used to mitigate data storage expense, a widespread issue as automated methods quickly amass vast amounts of data which can be expensive and impractical to manage. Importantly I explain how these data management practices are often used without known consequence, something I then address. Specifically, I present evidence that the most used data reduction methods (namely compression and temporal subsetting) have a surprisingly small impact on the information content of recorded sound compared to the method of analysis. This work also adds to the increasing evidence that deep learning-based methods of environmental sound quantification are more powerful and robust to experimental variation than more traditional acoustic indices. In the latter chapters, I focus on using multichannel acoustic recording for sound-source localisation. Knowing where a sound originated has a range of ecological uses, including counting individuals, locating threats, and monitoring habitat use. While an exciting application of acoustic technology, spatial acoustics has had minimal uptake owing to the expense, impracticality and inaccessibility of equipment. In my third chapter, I introduce MAARU (Multichannel Acoustic Autonomous Recording Unit), a low-cost, easy-to-use and accessible solution to this problem. I explain the software and hardware necessary for spatial recording and show how MAARU can be used to localise the direction of a sound to within ±10˚ accurately. In the fourth chapter, I explore how MAARU devices deployed in the field can be used for enhanced ecosystem monitoring by spatially clustering individuals by calling directions for more accurate abundance approximations and crude species-specific habitat usage monitoring. Most literature on spatial acoustics cites the need for many accurately synced recording devices over an area. This chapter provides the first evidence of advances made with just one recorder. Finally, I conclude this thesis by restating my aims and discussing my success in achieving them. Specifically, in the thesis’ conclusion, I reiterate the contributions made to the field as a direct result of this work and outline some possible development avenues.Open Acces