PSD estimation in Cognitive Radio systems: a performance analysis

Peer Reviewe

Reliable and Repeatable Power Measurements in DVB-T Systems

Non

On Employing a Savitzky-Golay Filtering Stage to Improve Performance of Spectrum Sensing in CR Applications Concerning VDSA Approach

Abstract In this paper, a filtering stage based on employing a Savitzky-Golay (SG) filter is proposed to be used in the spectrum sensing phase of a Cognitive Radio (CR) communication paradigm for Vehicular Dynamic Spectrum Access (VDSA). It is used to smooth the acquired spectra, which constitute the input for a spectrum sensing algorithm. The sensing phase is necessary, since VDSA is based on an opportunistic approach to the spectral resource, and the opportunities are represented by the user-free spectrum zones, to be detected through the sensing phase. Each filter typology presents peculiarities in terms of its computational cost, de-noising ability and signal shape reconstruction. The SG filtering properties are compared with those of the linear Moving Average (MA) filter, widely used in the CR framework. Important improvements are proposed

Development and Assessment of a Movement Disorder Simulator Based on Inertial Data

The detection analysis of neurodegenerative diseases by means of low-cost sensors and suitable classification algorithms is a key part of the widely spreading telemedicine techniques. The choice of suitable sensors and the tuning of analysis algorithms require a large amount of data, which could be derived from a large experimental measurement campaign involving voluntary patients. This process requires a prior approval phase for the processing and the use of sensitive data in order to respect patient privacy and ethical aspects. To obtain clearance from an ethics committee, it is necessary to submit a protocol describing tests and wait for approval, which can take place after a typical period of six months. An alternative consists of structuring, implementing, validating, and adopting a software simulator at most for the initial stage of the research. To this end, the paper proposes the development, validation, and usage of a software simulator able to generate movement disorders-related data, for both healthy and pathological conditions, based on raw inertial measurement data, and give tri-axial acceleration and angular velocity as output. To present a possible operating scenario of the developed software, this work focuses on a specific case study, i.e., the Parkinson’s disease-related tremor, one of the main disorders of the homonym pathology. The full framework is reported, from raw data availability to pathological data generation, along with a common machine learning method implementation to evaluate data suitability to be distinguished and classified. Due to the development of a flexible and easy-to-use simulator, the paper also analyses and discusses the data quality, described with typical measurement features, as a metric to allow accurate classification under a low-performance sensing device. The simulator’s validation results show a correlation coefficient greater than 0.94 for angular velocity and 0.93 regarding acceleration data. Classification performance on Parkinson’s disease tremor was greater than 98% in the best test conditions

On the use of complex excitation sequences for eddy current testing

Peer Reviewe

Low-cost multi-channel synchronous data acquisition system based on compressive sampling approach

Peer Reviewe

A deep learning approach to organic pollutants classification using voltammetry

This paper proposes a deep leaning technique for accurate detection and reliable classification of organic pollutants in water. The pollutants are detected by means of cyclic voltammetry characterizations made by using low-cost disposable screen-printed electrodes. The paper demonstrates the possibility of strongly improving the detection of such platforms by modifying them with nanomaterials. The classification is addressed by using a deep learning approach with convolutional neural networks. To this end, the results of the voltammetry analysis are transformed into equivalent RGB images by means of Gramian angular field transformations. The proposed technique is applied to the detection and classification of hydroquinone and benzoquinone, which are particularly challenging since these two pollutants have a similar electroactivity and thus the voltammetry curves exhibit overlapping peaks. The modification of electrodes by carbon nanotubes improves the sensitivity of a factor of about x25, whereas the convolution neural network after Gramian transformation correctly classifies 100% of the experiments

A shift from distal to proximal neoplasia in the colon: a decade of polyps and CRC in Italy

<p>Abstract</p> <p>Background</p> <p>In the last years a trend towards proximalization of colorectal carcinomas (CRC) has been reported. This study aims to evaluate the distribution of CRC and adenomatous polyps (ADP) to establish the presence of proximalization and to assess the potential predictors.</p> <p>Methods</p> <p>We retrieved histology reports of colonic specimens excised during colonoscopy, considering the exams performed between 1997 and 2006 at Cuneo Hospital, Italy. We compared the proportion of proximal lesions in the period 1997-2001 and in the period 2002-2006.</p> <p>Results</p> <p>Neoplastic lesions were detected in 3087 people. Proximal CRC moved from 25.9% (1997-2001) to 30.0% (2002-2006). Adjusting for sex and age, the difference was not significant (OR 1.23; 95% CI: 0,95-1,58). The proximal ADP proportion increased from 19.2% (1997-2001) to 26.0% (2002-2006) (OR: 1.43; 95% CI: 1.17-1.89). The corresponding figures for advanced proximal ADP were 6.6% and 9.5% (OR: 1.48; 95% CI: 1.02-2.17). Adjusting for gender, age, diagnostic period, symptoms and number of polyps the prevalence of proximal advanced ADP was increased among people ≥ 70 years compared to those aged 55-69 years (OR 1.49; 95% CI: 1.032.16). The main predictor of proximal advanced neoplasia was the number of polyps detected per exam (> 1 polyp versus 1 polyp: considering all ADP: OR 2.16; 95% CI: 1.59-2.93; considering advanced ADP OR 1.63; 95% CI: 1.08-2.46). Adjusting for these factors, the difference between the two periods was no longer significant.</p> <p>Conclusions</p> <p>CRC do not proximalize while a trend towards a proximal shift in adenomas was observed among people ≥ 70 years.</p