Abstract
This paper presents a novel high-gain transimpedance amplifier for flexible radiation sensing systems that can be used as large-area dosimeters. The circuit is implemented with indium-gallium-zinc-oxide thin-film-transistors and uses two stages for the amplification of the sensor signal (current). The first stage consists of cascode current mirrors with a diode connected load that performs current amplification and voltage conversion. Then, the first stage is followed by a voltage amplifier based on a positive feedback topology for gain enhancement. The proposed circuit converts nano-ampere (10 nA) currents into hundreds of millivolts (280 mV), showing a gain around 149 dB and a power consumption of 0.45 mW. The sensed radiation dose level, in voltage terms, can drive the next stages in the radiation sensing system, such as analog to digital converters. These radiation sensing devices can find potential applications in real-time, large area, flexible health, and security systems.

Abstract
This paper proposes a new optimization tool based on the cross-entropy (CE) method to assess security-constrained optimal power flow (SCOPF) solutions. First, the corresponding SCOPF stochastic problem is defined so that the optimum solution is interpreted as a rare event to be reached by a random search. Second, the CE method solves this new problem efficiently by making adaptive changes to the probability density function according to the Kullback-Leibler distance, creating a sequence of density functions that guides the search in the direction of the theoretically degenerate density at the optimal point. Different types of density functions are tested in order to cope with discrete variables present in the SCOPF problem. Two test systems, namely the IEEE 57 bus and the IEEE 300 bus, are used to evaluate the effectiveness of the proposed method in terms of solution quality and computational effort. Comparisons carried out with reference algorithms in the literature demonstrate that the CE method is capable of finding better solutions for the SCOPF problem with fewer evaluations.

Abstract
This paper presents a high speed digitally programmable Ring Oscillator (RO) using Indium-galliumzinc oxide thin-film transistors (IGZO TFTs). Proposed circuit ensures high speed compared to the conventional ROs using negative skewed scheme, in which each inverter delay is reduced by pre-maturely switching on/off the transistors. In addition, by controlling the load capacitance of each inverter through digital control bits, a programmable frequency of oscillation was attained. Proposed RO performance is compared with two conventional designs under same conditions. From simulation, it has been observed that the proposed circuit has shown a higher frequency of oscillations (283 KHz) compared to the conventional designs (76.52 KHz and 144.9 KHz) under same conditions. Due to the programmable feature, the circuit is able to generate 8 different linearly spaced frequencies ranging from 241.2 KHz to 283 KHz depending upon three digital control bits with almost rail-to-rail voltage swing. The circuit is a potential on-chip clock generator in many real-world flexible systems, such as, smart packaging, wearable devices, RFIDs and displays that need multi frequencies.

Abstract
This paper proposes an Artificial Neural Network (ANN) based approach to classify different contexts, with the goal of enhancing the management of residential energy resources. The increasing penetration of renewable based generation has completely changed the paradigm of the power and energy sector. The intermittent nature of these resources requires the system to incentivize the adaptability of consumers in order to guarantee the balance between generation and consumption. This leads to the emergence of several incentives with the objective of increasing the flexibility from the consumer's side. This, allied to the increasing price of electricity, leads to an increasing need for consumers to adapt their consumption in order to improve energy efficiency, decrease energy bills, and achieve a better use of their own generation resources. With this, several House Management Systems (HMS), and Building Energy Management Systems (BEMS) have emerged. These systems allow adapting the consumption (or suggesting changes in consumers' habits) according to several factors. However, in order to make this management truly smart, there is a need for adaptation to different contexts, so that changes can be done accordingly to the different situations that are faced at each time. This paper addresses this problem by proposing a novel methodology that enables classifying different situations in different contexts, according to different contextual variables. © 2017 IEEE.

Abstract

Abstract
This paper proposes a methodology using Hybrid Control System (HS) to manage the integration of Variable Renewable Electricity Sources (VRES). The HS define a combination of discrete and continuous signals, in this case, discrete by Pump-Hydro-Storage (PHS) and continuous performance is the Wind Power (WP). The coupling of Wind Power and PHS to produce a dispatchable power output could be a significant benefit to those in an energy trading system. Improving VRES prediction reduces system dispatch errors, however does not give total economic opportunities to the generator. Increased dispatchable backup power generation can improve the system's ability to handle deviations of WP, as verified when the proposed approach is applied to Brazilian and Portuguese power system. © 2018 IEEE.