Abstract
The use of high efficiency resonant coupling wireless power systems for subsea operations is here considered for the charging of autonomous underwater vehicles. In this paper, two architectures based on two different inductors are analysed for their potential as resonant wireless power couplers. Both systems were designed and optimised through electromagnetic 3D simulations, upon which two prototypes were constructed and measured. Efficiencies as high as 75% for distances up to 5 cm were achieved on experimental testing.

Abstract
Context recognition is an indispensable functionality of context-aware applications that deals with automatic determination and inference of contextual information from a set of observations captured by sensors. It enables developing applications that can respond and adapt to user's situations. Thus much attention has been paid to developing innovative context recognition capabilities into context-aware systems. However, some existing studies rely on wearable sensors for context recognition and this practice has limited the incorporation of contexts into practical applications. Additionally, contexts are usually provided as low-level data, which are not suitable for more advanced mobile applications. This article explores and evaluates the use of smartphone's built-in sensors and classification algorithms for context recognition. To realize this goal, labeled sensor data were collected as training and test datasets from volunteers' smartphones while performing daily activities. Time series features were then extracted from the collected data, summarizing user's contexts with 50% overlapping slide windows. Context recognition is achieved by inducing a set of classifiers with the extracted features. Using cross validation, experimental results show that instance-based learners and decision trees are best suitable for smart phone -based context recognition, achieving over 90% recognition accuracy. Nevertheless, using leave one -subject-out validation, the performance drops to 79%. The results also show that smartphone's orientation and rotation data can be used to recognize user contexts. Furthermore, using data from multiple sensors, our results indicate improvement in context recognition performance between 1.5% and 5%. To demonstrate its applicability, the context recognition system has been incorporated into a mobile application to support context-aware personalized media recommendations.

Abstract
Multimedia content consumption is very popular nowadays. However, not every content can be consumed in its original format: the combination of content, transport and access networks, consumption device and usage environment characteristics may all pose restrictions to that purpose. One way to provide the best possible quality to the user is to adapt the content according to these restrictions as well as user preferences. This adaptation stage can be best executed if knowledge about the content is known a-priori. In order to provide this knowledge we classify the content based on metrics to define its temporal and spatial complexity. The temporal complexity classification is based on the Motion Vectors of the predictive encoded frames and on the difference between frames. The spatial complexity classification is based on different implementations of an edge detection algorithm and an image activity measure.

Abstract
This paper presents and evaluates a high-precision, one-way, and master-to-slave time synchronization protocol to minimize the clock time skew in low-power wearable sensor networks. The protocol is implemented in the media access control layer, and is based on directly eliminating deterministic delays during transmission from source to destination node, at hardware level. The proposed protocol keeps the one-hop average synchronization error close to the signal propagation delay, and the one-hop peak-to-peak jitter equals to the period of each node's system clock period. Both values grow linearly as the hop count increases. The protocol can achieve synchronization in the range of a few nanoseconds, enough to satisfy the requirements of many applications related to wearable networks, with one-way messages. Both theoretical analysis and experimental results, in wired wearable networks, show that the proposed protocol has a better performance than precision time protocol and a standard timing protocol for both single and multi-hop situations. The proposed approach is simpler, requires no calculations, and exchanges fewer messages. Experimental results obtained with an implementation of the protocol in a 0.35-mu m CMOS technology show that this approach keeps the one-hop average clock skew around 4.6 ns and peak-to-peak skew around 50 ns for a system clock frequency of 20 mh.

Abstract
The Physical layer architectures for the next generation of wireless devices will be characterized by a high degree of flexibility for real-time adaptation to communication conditions variability. OFDM-based architectures are strong candidates for the Physical layer implementation in 5G systems and one of the most important baseband processing operations required by this waveform is the Fast Fourier Transform (FFT). This paper proposes a dynamically reconfigurable FFT processor supporting FFT sizes and throughputs required by the most widely used wireless standards. The FFT reconfiguration was achieved by means of FPGA-based Dynamic Partial Reconfiguration (DPR) techniques, which enables run-time FFT size adaptation according to communication requirements and better resource utilization. The impact of DPR in terms of reconfiguration time and power consumption overhead was evaluated. The obtained results encourage the exploitation of DPR techniques to implement reconfigurable hardware infrastructures for OFDM baseband processing engines.

Abstract
As the number of wireless devices, services, communication standards and respective modes of operation rapidly grows, the design of reconfigurable digital baseband processing systems for radio devices becomes more important and challenging. Long Term Evolution (LTE) is among the most relevant wireless systems in 4G communications and its waveform is OFDM-based. According to the LTE mode of operation, OFDM parameters may change and influence baseband processing operations. This paper presents a dynamically reconfigurable LTE-compliant OFDM modulator for Downlink transmission able to adapt its internal hardware organization on-demand according to the digital modulation scheme and OFDM parameters, such as number of data subcarriers, IFFT size, Cyclic Prefix and window length. System reconfiguration is performed by employing FPGA-based Dynamic Partial Reconfiguration (DPR) techniques. The worst-case DPR latencies measured are 895 mu s and 1.192 ms for digital modulation and channel bandwidth adaptation, respectively. These results show that the adopted design approach is feasible in wireless baseband processing systems. Power estimations suggest that circuit specialization at run-time can potentially improve system power efficiency.