Abstract
The signal of the wireless sensor network in grounding grid, owing to energy loss, network congestion, path constraints and other factors, is easy to delay even partially losing. In order to ensure that the signal can be transmitted effectively in grounding grids for the substation, this paper presents a method based on traffic model of back-off balanced multiple sensor network cooperation model. As we all know, cognitive radio (CR) technology is adopted in multi-channel wireless networks to provide enough channels for data transmission. The MAC protocols should enable the secondary users to maintain the accurate channel state information to identify and utilize the leftover frequency spectrum in a way that constrains the level of interference to the primary users. We proposed a novel cooperation spectrum sensing scheme in which the secondary users adopt backoff-based sensing policy based on the traffic model of the primary users to maximum the throughput of the network. To obtain the full accurate information of the spectrum is a difficult task so that we propose the backoff sensing as a sub-optimal strategy. Since the secondary users sense only a subset of the channels in our proposed scheme, less time is spent to get the channel state information as more time is saved for the data transmission. And while dealing the signal data, I combine the intensity transfer method instead of the priority method. This can effectively reduce the network congestion, to ensure that the main information can be transfer well. It is also very useful to signal transmission for the Multi-sensor in Substations Grounding Grid (SGG).

Abstract
In predictive modeling tasks, knowledge about the training examples is neither fully complete nor totally incomplete. Unlike semisupervised learning, where one either has perfect knowledge about the label of the point or is completely ignorant about it, here we address a setting where, for each example, we only possess partial information about the label. Each example is described using two (or more) different feature sets or views, where neither are necessarily observed for a given example. If a single view is observed, then the class is only due to that feature set; if more views are present, the observed class label is the maximum of the values corresponding to the individual views. After formalizing this new learning concept, we propose two new learning methodologies that are adapted to this learning paradigm. We also compare their instantiation in experiments with different base models and with conventional methods. The experimental results made both on real and synthetic data sets verify the usefulness of the proposed approaches.

Abstract
Current maritime communications rely on expensive or proprietary technology, such as satellite, WiMAX, and narrowband HF radios. Broadband communications are limited to the near shore 3G/4G coverage provided by mobile operators. The usage of unlicensed and IEEE 802.11 networks may provide ship owners a low-cost broadband access to the Internet offshore, enabling real-time navigation applications and voice/video communications, while increasing safety onboard. Also, they can support underwater communications by acting as a bridge between shore and devices operating underwater. In this paper we present a performance evaluation of IEEE 802.11n networks in the 5.8 GHz band in a maritime environment. A point-to-point link was established between a fishing ship and shore. From our tests, communication links up to 7 km at 1 Mbit/s are possible, showing the advantage of using long range IEEE 802.11 links for broadband maritime communications.

Abstract
The integrate-and-fire model of a biological neuron is an amplitude to time encoding in the spacing between action potentials (spikes). In principle, this encoding can be used to modulate signals in an Impulse Radio Ultra Wide-Band (IRUWB) transmitter suitable for Wireless Sensor Networks (WSN). This paper presents a system level study on power efficiency using MATLAB/ Simulink to evaluate the required energy for an IR-UWB Transmitter using integrate-and-fire encoding technique. Also, a simple but clear comparison with common systems utilizing Nyquist rate Analog-to-Digital Converters (ADC) is presented. This study has been carried out on a band-limited random Gaussian signal and the results show that IR-IF transmitter consumes roughly seven times less energy than a digital UWB transmitter; moreover, in the proposed architecture the need for power hungry ADC is relaxed.

Abstract
Alzheimer's disease is a major cause of disability in older people and because of its nature and symptoms it is a huge weight on the caregivers and health system. The aim of this project, developed at Fraunhofer Portugal, is to create a remote monitoring system for patients suffering from Alzheimer's disease. This system comprises a device able to monitor the environment temperature and humidity, the patients location, using GPS, as well as the patient's movements, including falls. The device sends this information to the caregiver via SMS, and also to a server, where it is stored in a database developed in this work, and which the caregiver can access via a smartphone, using an application also developed in this project. This system therefore affords a close surveillance of the patient's status as well as access to historical data of the patient's information.

Abstract