Transaction-level power analysis of VLSI digital systems

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Revealing spectrum features of stochastic neuron spike trains

Power spectra of spike trains reveal important properties of neuronal behavior. They exhibit several peaks, whose shape and position depend on applied stimuli and intrinsic biophysical properties, such as input current density and channel noise. The position of the spectral peaks in the frequency domain is not straightforwardly predictable from statistical averages of the interspike intervals, especially when stochastic behavior prevails. In this work, we provide a model for the neuronal power spectrum, obtained from Discrete Fourier Transform and expressed as a series of expected value of sinusoidal terms. The first term of the series allows us to estimate the frequencies of the spectral peaks to a maximum error of a few Hz, and to interpret why they are not harmonics of the first peak frequency. Thus, the simple expression of the proposed power spectral density (PSD) model makes it a powerful interpretative tool of PSD shape, and also useful for neurophysiological studies aimed at extracting information on neuronal behavior from spike train spectra

Overview of the Low Complexity Enhancement Video Coding (LCEVC) Standard

The Low Complexity Enhancement Video Coding (LCEVC) specification is a recent standard approved by the ISO/IEC JTC 1/SC 29/WG04 (MPEG) Video Coding. The main goal of LCEVC is to provide a standalone toolset for the enhancement of any other existing codec. It works on top of other coding schemes, resulting in a multi-layer video coding technology, but unlike existing scalable video codecs, adds enhancement layers completely independent from the base video. The LCEVC technology takes as input the decoded video at lower resolution and adds up to two enhancement sub-layers of residuals encoded with specialized low-complexity coding tools, such as simple temporal prediction, frequency transform, quantization, and entropy encoding. This paper provides an overview of the main features of the LCEVC standard: high compression efficiency, low complexity, minimized requirements of memory and processing power

Improving the approximation ability of Volterra series identified with a cross-correlation method

This paper proposes an improvement in cross-correlation methods derived from the Lee–Schetzen method, in order to obtain a lower mean square error in the output for a wider range of the input variances. In particular, each Wiener kernel is identified with a different input variance and new formulas for conversion from Wiener to Volterra representation are presented

Reproducibility of the WHO histological criteria for the diagnosis of Philadelphia chromosome-negative myeloproliferative neoplasms.

This study, performed on behalf of the Italian Registry of Thrombocythaemias (Registro Italiano Trombocitemie), aimed to test the inter-observer reproducibility of the histological parameters proposed by the WHO classification for the diagnosis of the Philadelphia chromosome-negative myeloproliferative neoplasms. A series of 103 bone marrow biopsy samples of Philadelphia chromosome-negative myeloproliferative neoplasms consecutively collected in 2004 were classified according to the WHO criteria as follows: essential thrombocythaemia (n=34), primary myelofibrosis (n=44) and polycythaemia vera (n=25). Two independent groups of pathologists reviewed the bone marrow biopsies. The first group was asked to reach a collegial 'consensus' diagnosis. The second group reviewed individually all the cases to recognize the main morphological parameters indicated by the WHO classification and report their results in a database. They were subsequently instructed to individually build a 'personal' diagnosis of myeloproliferative neoplasms subtype just assembling the parameters collected in the database. Our results indicate that high levels of agreement ( 6570%) have been reached for about all of the morphological features. Moreover, among the 18 evaluated histological features, 11 resulted statistically more useful for the differential diagnosis among the different Philadelphia chromosome-negative myeloproliferative neoplasms. Finally, we found a high percentage of agreement (76%) between the 'personal' and 'consensus' diagnosis (Cohen's kappa statistic >0.40). In conclusion, our results support the use of the histological criteria proposed by the WHO classification for the Philadelphia chromosome-negative myeloproliferative neoplasms to ensure a more precise and early diagnosis for these patients

Audio-based identification of beehive states

The absence of the queen in a beehive is a very strong indicator of the need for beekeeper intervention. Manually searching for the queen is an arduous recurrent task for beekeepers that disrupts the normal life cycle of the beehive and can be a source of stress for bees. Sound is an indicator for signalling different states of the beehive, including the absence of the queen bee. In this work, we apply machine learning methods to automatically recognise different states in a beehive using audio as input. We investigate both support vector machines and convolutional neural networks for beehive state recognition, using audio data of beehives collected from the NU-Hive project. Results indicate the potential of machine learning methods as well as the challenges of generalizing the system to new hives