All existing blind equalization algorithms rely on the non-Gaussianity of the input sequence. In recent years, a technique called shaping has been used in data transmission, in order to approach the capacity of Gaussian channels. The transmitted signal then has a Gaussian-like distribution and this will affect blind equalization algorithms. In this paper, we address the problem of blind equalization where the input of the unknown system is a shaped data signal. In particular, we demonstrate the effects of shaping on some of the widely known blind equalization schemes. Our results point to the need for more powerful blind equalization algorithms for shaped signals.

In spite of the overwhelming high-tech marvels and applications that rule our digital lives, the use of the handwritten signature is still recognized worldwide in government, personal and legal entities to be the most important behavioral biometric trait. A number of notable research approaches provide advanced results up to a certain point which allow us to assert with confidence that the performance attained by signature verification (SV) systems is comparable to those provided by any other biometric modality. Up to now, the mainstream trend for offline SV is shared between standard -or handcrafted- feature extraction methods and popular machine learning techniques, with typical examples ranging from sparse representation to Deep Learning. Recent progress in graph mining algorithms provide us with the prospect to re-evaluate the opportunity of utilizing graph representations by exploring corresponding graph features for offline SV. In this paper, inspired by the recent use of image visibility graphs for mapping images into networks, we introduce for the first time in offline SV literature their use as a parameter free, agnostic representation for exploring global as well as local information. Global properties of the sparsely located content of the shape of the signature image are encoded with topological information of the whole graph. In addition, local pixel patches are encoded by sequential visibility motifs-subgraphs of size four, to a low six dimensional motif profile vector. A number of pooling functions operate on the motif codes in a spatial pyramid context in order to create the final feature vector. The effectiveness of the proposed method is evaluated with the use of two popular datasets. The local visibility graph features are considered to be highly informative for SV; this is sustained by the corresponding results which are at least comparable with other classic state-of-the-art approaches.

The use of a wireless sensor network to monitor an area of interest for possible hazardous events has become a common practice. The difficulty of replacing or recharging sensor batteries dictates the use of energy harvesting as a means to extend the network’s lifetime. To this end, energy beamforming is used in a millimeter wave wireless power sensor network with randomly deployed nodes. A simple protocol is proposed that allows nodes to report their charging conditions in an effort to select efficient energy-beamforming strategies. Analytical expressions for the probability of successful information reception and successful reporting are provided for two benchmark schemes: the random and the circular energy-beamforming scheme. A Markov chain is used for the former to model the energy level of sensor nodes. Simple sector selection strategies are presented and their performance, in terms of delay and failure information delivery, is assessed through simulations.

Energy consumption has become a very important case due to the global energy issue. Therefore, there is an increasing demand for efficient energy control in Heat Ventilation and Air Conditioning (HVAC) Systems. An effective method for controlling the heat energy in a multiple room building is the multizone approach, where each room is treated as a single zone and temperature control is applied separately for each zone. In the proposed technique, the multizone approach is adopted along with a Wireless Sensor Network (WSN) of temperature nodes, where each node covers a single zone and communicates its sensed values to adjacent nodes. Each node is responsible for the tuning of a heating source so that a preset zone temperature value is maintained. Moreover, each node utilizes a simple lumped capacity model for its controlling zone and based on the received neighbor temperature values detects possible divergences from the anticipated energy consumption. Simulation results using a simple weather temperature model and Proportional Integral (PI) controllers verify the effectiveness of the proposed method.

Various aspects of the detection of events in wireless powered communication networks (WPCN) are studied and analyzed under the assumption of highly noisy sensor measurements. In WPCN, networks sensor nodes’ stored energy is a scarce resource and must be treated sparingly. Frequent false alarm detections force superfluous transmissions, thus depleting nodes’ energy storage. This has an adverse effect on the probability of successful transmission of the information message and its delay in case of a true positive detection. In this work, the detection problem is approached using an optimal stopping framework, where the involved likelihoods are highly unstable due to the noisy measurements. A classical AR filter is adopted in order to smooth the posterior likelihoods prior to their usage in the detection phase and its performance is contrasted to that of a novel Beta Particle Filter smoother. The effects of the smoothing filters on the achieved false alarm rate and detection delay are examined using numerical and simulation results. Moreover, the assessment of the detection process takes into account critical WPCN parameters, such as the charging efficiency and the location of the sensors, thus aiding the system design.