The electroluminescence (EL) of isotactic and syndiotactic poly(N-pentenyl-carbazole) (PPK), achieved by coordination polymerization, is studied in order to investigate the interrelation between the polymer tacticity and their physical-chemical properties. The use of these polymers in organic light-emitting diode (OLED) fabrication is also explored. Thermal and x-ray diffraction analyses of PPKs show that the isotactic stereoisomer is semicrystalline, whereas the syndiotactic one is amorphous. Optical analysis of both stereoisomers, carried out on film samples, reveals the presence of two different excimers: ‘sandwich-like’ and ‘partially overlapping’. Nevertheless, the emission intensity ratio between ‘sandwich-like’ and ‘partially overlapping’ excimers is higher in the isotactic than in the syndiotactic stereoisomer. Using the synthesized polymers as OLED emitting layers, the influence of the polymer tacticity on the EL properties of the device is highlighted. In detail, while blue OLEDs are obtained by using the syndiotactic stereoisomer, OLEDs with a multilayer structure fabricated with the isotactic stereoisomer emit white light. The contribution of three different emissions (fluorescence, phosphorescence and electromer emissions) with comparable intensities to the detected white light is discussed.

A custom HW design of a Fully Convolutional Neural Network (FCN) is presented in this paper to implement an embeddable Human Posture Recognition (HPR) system capable of very high accuracy both for laying and sitting posture recognition. The FCN exploits a new base-2 quantization scheme for weight and binarized activations to meet the optimal trade-off between low power dissipation, a very reduced set of instantiated physical resources and state-of-the-art accuracy to classify human postures. By using a limited number of pressure sensors only, the optimized HW implementation allows keeping the computation close to the data sources according to the edge computing paradigm and enables the design of embedded HP systems. The FCN can be simply reconfigured to be used for laying and sitting posture recognition. Tested on a public dataset for in-bed posture classification, the proposed FCN obtains a mean accuracy value of 96.77% to recognize 17 different postures, while a small custom dataset has been used for training and testing for sitting posture recognition, where the FCN achieves 98.88% accuracy to recognize eight positions. The FCN has been prototyped on a Xilinx Artix 7 FPGA where it exhibits a dynamic power dissipation lower than 11 $\mathrm{m}\mathrm{W}$ and 7 $\mathrm{m}\mathrm{W}$ for laying and sitting posture recognition, respectively, and a maximum operation frequency of 47.64 $\mathrm{M}\mathrm{Hz}$ and 26.6 $\mathrm{M}\mathrm{Hz}$, corresponding to an Output Data Rate (ODR) of the sensors of 16.50 $\mathrm{k}\mathrm{Hz}$ and 9.13 $\mathrm{k}\mathrm{Hz}$, respectively. Furthermore, synthesis results with a CMOS 130 $\mathrm{n}\mathrm{m}$ technology have been reported, to give an estimation about the possibility of an in-sensor circuital implementation.

This work focused on the development and optimization of an impedimetric label-free immunosensor for detecting deoxynivalenol (DON). A monoclonal antibody for DON detection was immobilized on a modified gold electrode with a cysteamine layer and polyamidoamine (PAMAM) dendrimers. Cyclic voltammetry and electrochemical impedance spectroscopy techniques were used to monitor the layer-by-layer development of the immunosensor design, while electrochemical impedance spectroscopy and differential pulse voltammetry were employed to investigate the antigen/antibody interaction. The PAMAM dendrimers, allowing to immobilize a large number of monoclonal antibodies, permitted reaching, through the DPV technique, a high sensitivity and a low limit of detection equal to 1 ppb. The evaluation of the possible reuse of the immunosensors highlighted a decrease in the analytical performances of the regenerated immunosensors. After evaluating the matrix effect, the developed immunosensor was used to quantify DON in pasta samples spiked with a known mycotoxin concentration. Taking into consideration the DON extraction procedure used for the pasta samples and the matrix effect related to the sample, the proposed immunosensor showed a limit of detection of 50 ppb, which is lower than the maximum residual limit imposed by European Regulation for DON in dry pasta (750 ppb).

The accuracy and computational complexity of keyword spotting (KWS) systems are heavily influenced by the choice of audio features in speech signals. This paper introduces a novel approach for audio feature extraction in KWS by leveraging a convolutional autoencoder, which has not been explored in the existing literature. Strengths of the proposed approach are in the ability to automate the extraction of the audio features, keep its computational complexity low, and allow accuracy values of the overall KWS systems comparable with the state of the art. To evaluate the effectiveness of our proposal, we compared it with the widely-used Mel Frequency Cepstrum (MFC) method in terms of classification metrics in noisy conditions and the number of required operators, using the public Google speech command dataset. Results demonstrate that the proposed audio feature extractor achieves an average classification accuracy on 12 classes ranging from 81.84% to 90.36% when the signal-tonoise ratio spans from 0 to 40 dB, outperforming the MFC up to 5.2%. Furthermore, the required number of operations is one order of magnitude lower than that of the MFC, resulting in a reduction in computational complexity and processing time, which makes it well-suited for integration with KWS systems in resource-constrained edge devices

In this paper, we propose a temperature sensor based on a 4H-SiC CMOS oscillator circuit and that is able to operate in the temperature range between 298 K and 573 K. The circuit is developed on Fraunhofer IISB’s 2 μm 4H-SiC CMOS technology and is designed for a bias voltage of 20 V and an oscillation frequency of 90 kHz at room temperature. The possibility to relate the absolute temperature with the oscillation frequency is due to the temperature dependency of the threshold voltage and of the channel mobility of the transistors. An analytical model of the frequency-temperature dependency has been developed and is used as a starting point for the design of the circuit. Once the circuit has been designed, numerical simulations are performed with the Verilog-A BSIM4SiC model, which has been opportunely tuned on Fraunhofer IISB’s 2 μm 4H-SiC CMOS technology, and their results showed almost linear frequency-temperature characteristics with a coefficient of determination that was higher than 0.9681 for all of the bias conditions, whose maximum is 0.9992 at a $V_{DD}$ = 12.5 V. Moreover, we considered the effects of the fabrication process through a Monte Carlo analysis, where we varied the threshold voltage and the channel mobility with different values of the Gaussian distribution variance. For example, at $V_{DD}$ = 20 V, a deviation of $17.4\%$ from the nominal characteristic is obtained for a Gaussian distribution variance of $20\%$. Finally, we applied the one-point calibration procedure, and temperature errors of +8.8 K and −5.8 K were observed at $V_{DD}$ = 15 V.