dBFS

Abstract: Nowadays, imbalanced data sets are pervasive in real world human practices, and hence, become a very interesting research area within machine learning communities. Imbalanced data sets introduce a significant reduction in performance of standard classifiers when they are invoked to learn data underlying concepts. The problem becomes even more sever when imbalanced data sets are involved with high dimensions. This paper presents a novel feature ranking approach based on the probability density estimation to cope with these issues. The idea behind our approach, named Density Based Feature Selection (DBFS), is that features' distributions over classes can bring significant benefits to feature selection algorithms. In other words, to explore the contribution of each attribute and assign it an appropriate rank, DBFS takes into account features' corresponding distributions over all classes along with their correlations. To show the effectiveness of the presented approach, well-known feature ranking methods are implemented and compared with our approach across varieties of small sample size and high dimensional data sets from microarray, mass spectrometry and text mining domains. Our theoretical analysis and experimental observations reveal that our approach is the method of choice by offering a simple yet effective feature ranking method based on well-known statistical evaluation measures.

Abstract: This paper presents an algorithm to transform and reconstruct diffusion-weighted imaging (DWI) data for alignment of micro-structures in association with spatial transformations. The key idea is to decompose the diffusion-attenuated signal profile, a function defined on a unit sphere, into a series of weighted diffusion basis functions (DBFs), reorient these weighted DBFs independently based on a local affine transformation, and then recompose the reoriented weighted DBFs to obtain the final transformed signal profile. The decomposition is performed in a sparse representation framework in recognition of the fact that each diffusion signal profile is often resulting from a small number of fiber populations. A non-negative constraint is further imposed so that noise-induced negative lobes in the signal profile can be avoided. The proposed framework also explicitly models the isotropic component of the diffusion-attenuated signals to avoid undesirable artifacts during transformation. In contrast to existing methods, the current algorithm executes the transformation directly in the signal space, thus allowing any diffusion models to be fitted to the data after transformation.

Abstract: This paper presents a sub-ranging 6-way time-interleaved pipelined-SAR ADC that achieves 900MS/s and 9.3 ENOB in 65nm CMOS. The architecture optimization is based on a pipelined-SAR structure that obtains high-speed with an optimized number of channels, thus leading to relaxed calibration with higher efficiency in power and area consumption. The proposed channel-selection-embedded bootstrap performs sampling instants synchronization without additional components, thus effectively suppressing the spurs from time skews below -65 dBFS. The mismatch errors due to offset and gain are all solved on-chip, whose spurs are suppressed below -67 dBFS. The prototype achieves 66 dB SFDR and 51.5 dB SNDR with a Nyquist input exhibiting a FoM of 56 fJ/conv.step.

Abstract: The limitation of the dynamic range given by noise and spurious signals is discussed. Attention is paid to the proper unit (dBFS/√Hz) in the case of noise limitation. On the example of the acquisition system it is presented how to determine the limiting part of the system.