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Ferroelectric (FE) and solid polymer electrolytes (SPEs) are being explored for memory and for a variety of purposes in transistors. While considering the dynamics of emerging memories, we note some interesting implications for transistors and transistor memories. In this paper we consider the dynamics of subthreshold swing (SS) in transistors and show that under certain conditions SS can be lowered due to the delay associated with their complex impedance. We show that even a conventional metal oxide semiconductor field-effect transistor (MOSFETs) can exhibit a dynamic SS, lowered by introducing a series resistance in the gate. We show that the when the gate dielectric has a complex response, SS can be decomposed into static and dynamic factors with the static factor given by the usual thermal SS and the dynamic factor related to dynamic gate amplification. In the measurement of SS, slew rate and slew direction can be readily utilized to reveal the SS dependence on dynamics.

Complex gate dielectrics are being widely explored to provide voltage amplification leading to steep subthreshold swing (SS) in transistors [1]. Here we show that hysteretic steep SS can arise simply from the introduction of a series resistance in the gate of a metal-oxide-semiconductor field-effect transistor (MOSFET), i.e. as a dynamic effect. The choice of measurement slew rate (SR) and slew direction then directly affects the measured SS as has been previously reported [2]. We use an ionic polymer gate dielectric to illustrate how in this case the series resistance arises from the ionic conductance, which controls the time response of the electric double layer (EDL) at the polymer/metal [3] or polymer/semiconductor interface [4]. While the ionic polymer in an EDL transistor is used to illustrate the effects, the principles apply generally to other complex insulating gates including ferroelectrics and piezoelectrics.

In this paper, we focus on the parameter estimation of dynamic state-space models using privacy-protected data. We consider an scenario with two parties: on one side, the data owner, which provides privacy-protected observations to, on the other side, an algorithm owner, that processes them to learn the system’s state vector. We combine additive homomorphic encryption and Secure Multiparty Computation protocols to develop secure functions (multiplication, division, matrix inversion) that keep all the intermediate values encrypted in order to effectively preserve the data privacy. As an application, we consider a tracking problem, in which a Extended Kalman Filter estimates the position, velocity and acceleration of a moving target in a collaborative environment where encrypted distance measurements are used.

This paper tries to reduce the Peak-to-Average Power Ratio (PAPR) of Orthogonal Frequency Division Multiplexing (OFDM) signal using a method that combines the use of Convex Optimization and Amplitude Predistortion. To reduce the peak of the OFDM signal, the proposed method applies one of the techniques on free carriers and the other one on the data carriers. Depending on the method used in each carrier, two different variants of it can be identify. Throughout this article, it is demonstrated that a significant improvement of ht performance can be obtained with a combination of the two methods with a slight increase in the computational complexity.

In this paper, we address the problem of tracking multiple targets trajectories that potentially cross each other. We propose a solution to this problem by using the support vector machines (SVM) to predict the position of the targets from the past history of the measurements. We will predict the dynamic behaviour of the targets using the SVM method, after turning off the sensing mode. By this way, we will avoid wrong measures that might take sensors and which are due to the superposition of physical signals. The simulations results demonstrate the potential advantage of this approach.

In this article we address the localization and tracking of multiple targets coincident in the same area. We propose a distributed method that enables us to simplify the signal processing and make the system more robust. Also we address a cluster of sensors, which are led by one of them (leader node). It is responsible to estimate the target?s location. To control the coincidence of multiple objectives in the same area, we propose an algorithm that disconnect the sensing mode and predict the target position. It permits to avoid wrong measures of the sensors and we continue with a correctly tracking. We also suggest a classification method based in the speed of the target, and it permits to reduce the uncertainty of the estimation. Simulations results show the tracking of different targets and demonstrate the reliability of these strategies.

Localization and tracking of target is an important application of the wireless sensor network. In this paper, we propose to apply a classification algorithm to sensor network nodes aimed at target tracking. Localization and tracking is based on a distributed method that enables us to simplify the signal processing and makes a more robust system. Also we address to group sensors, each group or cluster is led by one of them (leader node/sensor). This sensor is responsible for processing all the information about the target and estimate its position. Simulation results show that this classification algorithm reduces the estimate error in tracking targets.

Predistortion in personal mobile telephony systems has been proposed to deal with saturation in amplification at the transmission stage. This paper discusses the elements and architectures in signal and data predistortion for GSM and UMTS. The GCMAC neural network has been introduced as a predistorter to provide the design with adaptive, digital and practical features. Modulation aspects of the technologies involved define the models to substitute the amplifiers in the proposed schemes. The results included show how the predistortion architectures developed allow working linearly in saturation regimen. This point is important since it provides base transceiver stations (BTS), repeaters or even terminals with a higher gain. A performance evaluation including convergence and spectrum characteristics is carried out.

The cerebellar model arithmetic controller (CMAC) is a neurological model for human-like nonlinear controller design. The CMAC can approximate a wide variety of nonlinear functions by local training. The conventional CMAC uses receptive fields with equal width along all dimensions of input space without considering the variation of the target function in different areas. To avoid this limitation, we propose to adapt the width of the receptive fields depending an the input data distribution. The present paper describes this strategy and explores the application of the proposed algorithm in a nonlinear digital communications framework.

This paper deals with the compensation for the nonlinear distortion introduced by power-efficient amplifiers on linear modulation by means of equalization. We propose a new equalizer based on a reduced-complexity network called the generalized cerebellar model arithmetic computer (GMAC) GCMAC. The GCMAC-based equalizer is compared with other well-known structures such as the Volterra filter and the multi-layer perceptron. Extensive computer simulations have been carried out. The obtained results show the effectiveness of the proposed structure to compensate for strong nonlinearities.