Abstract
Location data plays an important role in several applications embedded in our digital living. These applications, usually, take advantage of Global Positioning System (GPS). However, GPS is not targeted for indoor location, therefore this paper presents an alternative system, based on Bluetooth Low Energy (BLE) beacons that together with bluetooth-enabled Smartphones, allows the development of low cost and accurate location-aware applications for indoor scenarios. The paper describes the challenges associated with the system deployment and presents algorithms to improve the distance estimation process as the user moves around the smart space. The evaluation performed shows that this approach has good results on noise reduction and movement adaptation allowing a close tracking of the indoor user position. © Springer International Publishing AG, part of Springer Nature 2018.

Abstract
IEEE 1687 standard (IJTAG), as an extension to the IEEE 1149.1, facilitates efficient access to embedded instruments by supporting reconfigurable scan networks. Specifically, IJTAG allows each IP to be wrapped by a test data register (TDR) whose access is controlled by a segment insertion bit (SIB) or a scan-mux control bit (SCB). Because the TDRs and the SIB/SCB network are typically not public, but critical for accessing embedded instruments, they might be used for illegitimate purposes, such as dumping credential data and reverse engineering IP design. Machine learning has been proposed to detect such attacks, but the large number of instruments and parallel execution enabled by the IJTAG produce high-dimensional data, which poses a challenge to on-chip detection. In this paper, we propose to reduce the high-dimensional but sparse data using a low-density parity-check (LDPC) matrix. Experiments using a modified version of the OpenSPARC T2 to include IJTAG functionality demonstrate that the use of feature reduction eliminates 91% of the features, leading to 43% reduction in circuit size without affecting detection accuracy. Also, the on-chip detector adds moderate overhead (similar to 8%) to the IJTAG.

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 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
Breast cancer treatments can have a negative impact on breast aesthetics, in case when surgery is intended to intersect tumor. For many years mastectomy was the only surgical option, but more recently breast conserving surgery (BCS) has been promoted as a liable alternative to treat cancer while preserving most part of the breast. However, there is still a significant number of BCS intervened patients who are unpleasant with the result of the treatment, which leads to self-image issues and emotional overloads. Surgeons recognize the value of a tool to predict the breast shape after BCS to facilitate surgeon/patient communication and allow more educated decisions; however, no such tool is available that is suited for clinical usage. These tools could serve as a way of visually sensing the aesthetic consequences of the treatment. In this research, it is intended to propose a methodology for predict the deformation after BCS by using machine learning techniques. Nonetheless, there is no appropriate dataset containing breast data before and after surgery in order to train a learning model. Therefore, an in-house semi-synthetic dataset is proposed to fulfill the requirement of this research. Using the proposed dataset, several learning methodologies were investigated, and promising outcomes are obtained.

Abstract