22 research outputs found

    Chapter 34 - Biocompatibility of nanocellulose: Emerging biomedical applications

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    Nanocellulose already proved to be a highly relevant material for biomedical applications, ensued by its outstanding mechanical properties and, more importantly, its biocompatibility. Nevertheless, despite their previous intensive research, a notable number of emerging applications are still being developed. Interestingly, this drive is not solely based on the nanocellulose features, but also heavily dependent on sustainability. The three core nanocelluloses encompass cellulose nanocrystals (CNCs), cellulose nanofibrils (CNFs), and bacterial nanocellulose (BNC). All these different types of nanocellulose display highly interesting biomedical properties per se, after modification and when used in composite formulations. Novel applications that use nanocellulose includewell-known areas, namely, wound dressings, implants, indwelling medical devices, scaffolds, and novel printed scaffolds. Their cytotoxicity and biocompatibility using recent methodologies are thoroughly analyzed to reinforce their near future applicability. By analyzing the pristine core nanocellulose, none display cytotoxicity. However, CNF has the highest potential to fail long-term biocompatibility since it tends to trigger inflammation. On the other hand, neverdried BNC displays a remarkable biocompatibility. Despite this, all nanocelluloses clearly represent a flag bearer of future superior biomaterials, being elite materials in the urgent replacement of our petrochemical dependence

    Sparse Signal Processing and Statistical Inference for Internet of Things

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    Data originating from many devices within the Internet of Things (IoT) framework can be modeled as sparse signals. Efficient compression techniques of such data are essential to reduce the memory storage, bandwidth, and transmission power. In this thesis, I develop some theory and propose practical schemes for IoT applications to exploit the signal sparsity for efficient data acquisition and compression under the frameworks of compressed sensing (CS) and transform coding. In the context of CS, the restricted isometry constant of finite Gaussian measurement matrices is investigated, based on the exact distributions of the extreme eigenvalues of Wishart matrices. The analysis determines how aggressively the signal can be sub-sampled and recovered from a small number of linear measurements. The signal reconstruction is guaranteed, with a predefined probability, via various recovery algorithms. Moreover, the measurement matrix design for simultaneously acquiring multiple signals is considered. This problem is important for IoT networks, where a huge number of nodes are involved. In this scenario, the presented analytical methods provide limits on the compression of joint sparse sources by analyzing the weak restricted isometry constant of Gaussian measurement matrices. Regarding transform coding, two efficient source encoders for noisy sparse sources are proposed, based on channel coding theory. The analytical performance is derived in terms of the operational rate-distortion and energy-distortion. Furthermore, a case study for the compression of real signals from a wireless sensor network using the proposed encoders is considered. These techniques can reduce the power consumption and increase the lifetime of IoT networks. Finally, a frame synchronization mechanism has been designed to achieve reliable radio links for IoT devices, where optimal and suboptimal metrics for noncoherent frame synchronization are derived. The proposed tests outperform the commonly used correlation detector, leading to accurate data extraction and reduced power consumption

    Big data-driven multimodal traffic management : trends and challenges

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    Modern Random Access for Satellite Communications

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    The present PhD dissertation focuses on modern random access (RA) techniques. In the first part an slot- and frame-asynchronous RA scheme adopting replicas, successive interference cancellation and combining techniques is presented and its performance analysed. The comparison of both slot-synchronous and asynchronous RA at higher layer, follows. Next, the optimization procedure, for slot-synchronous RA with irregular repetitions, is extended to the Rayleigh block fading channel. Finally, random access with multiple receivers is considered.Comment: PhD Thesis, 196 page

    RMER: Reliable and Energy-Efficient Data Collection for Large-Scale Wireless Sensor Networks

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    We propose a novel event data collection approach named reliability and multipath encounter routing (RMER) for meeting reliability and energy efficiency requirements. The contributions of the RMER approach are as follows. 1) Fewer monitor nodes are selected in hotspot areas that are close to the Sink, and more monitor nodes are selected in nonhotspot areas, which can lead to increased network lifetime and event detection reliability. 2) The RMER approach sends data to the Sink by converging multipath routes of event monitoring nodes into a one-path route to aggregate data. Thus, energy consumption can be greatly reduced, thereby enabling further increased network lifetime. Both theoretical and experimental simulation results show that RMER applied to event detection outperforms other solutions. Our results clearly indicate that RMER increases energy efficiency by 51% and network lifetime by 23% over other solutions while guaranteeing event detection reliability

    Towards Computational Efficiency of Next Generation Multimedia Systems

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    To address throughput demands of complex applications (like Multimedia), a next-generation system designer needs to co-design and co-optimize the hardware and software layers. Hardware/software knobs must be tuned in synergy to increase the throughput efficiency. This thesis provides such algorithmic and architectural solutions, while considering the new technology challenges (power-cap and memory aging). The goal is to maximize the throughput efficiency, under timing- and hardware-constraints

    Low-Power and Programmable Analog Circuitry for Wireless Sensors

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    Embedding networks of secure, wirelessly-connected sensors and actuators will help us to conscientiously manage our local and extended environments. One major challenge for this vision is to create networks of wireless sensor devices that provide maximal knowledge of their environment while using only the energy that is available within that environment. In this work, it is argued that the energy constraints in wireless sensor design are best addressed by incorporating analog signal processors. The low power-consumption of an analog signal processor allows persistent monitoring of multiple sensors while the device\u27s analog-to-digital converter, microcontroller, and transceiver are all in sleep mode. This dissertation describes the development of analog signal processing integrated circuits for wireless sensor networks. Specific technology problems that are addressed include reconfigurable processing architectures for low-power sensing applications, as well as the development of reprogrammable biasing for analog circuits

    Anuário Científico – 2011 Resumos de Artigos, Comunicações, Livros e Monografias de Mestrado

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    Há mais de uma década que o ISEL vem firmando a sua aposta na busca e na divulgação do conhecimento científico na área da Engenharia, assentes na inovação e no desenvolvimento de novas tecnologias, procurando que os resultados alcançados nos projetos de investigação tenham impacto na indústria e na vida dos cidadãos como forma de responder às necessidades cada vez mais complexas e exigentes da sociedade no seu todo. Nesta relação, o ISEL tem contribuído para a evolução da produção e do conhecimento científicos, assumindo, por vezes numa posição de vanguarda, ora em iniciativa própria ora em parceria com diversas instituições, quer de ensino quer do tecido empresarial. Como forma de dar visibilidade ao trabalho desenvolvido pelos docentes (com afiliação ISEL) e alunos do ISEL, o Anuário Científico tornou-se num meio de divulgação privilegiado, estando disponível em acesso livre a toda a comunidade científica mas também a todos os cidadãos, podendo ser consultado em formato eletrónico no sítio institucional do ISEL, bem como no Repositório Científico do Instituto Polítécnico de Lisboa.1 Fazendo uma análise comparativa em relação às publicações referentes a 2009 e a 2010, constata-se que o número de publicações duplicou em 2011

    Low-Power and Programmable Analog Circuitry for Wireless Sensors

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    Embedding networks of secure, wirelessly-connected sensors and actuators will help us to conscientiously manage our local and extended environments. One major challenge for this vision is to create networks of wireless sensor devices that provide maximal knowledge of their environment while using only the energy that is available within that environment. In this work, it is argued that the energy constraints in wireless sensor design are best addressed by incorporating analog signal processors. The low power-consumption of an analog signal processor allows persistent monitoring of multiple sensors while the device\u27s analog-to-digital converter, microcontroller, and transceiver are all in sleep mode. This dissertation describes the development of analog signal processing integrated circuits for wireless sensor networks. Specific technology problems that are addressed include reconfigurable processing architectures for low-power sensing applications, as well as the development of reprogrammable biasing for analog circuits
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