Low complexity frequency monitoring filter for fast exon prediction sequence analysis

Over the last few years, the application of Digital Signal Processing (DSP) techniques for genomic sequence analysis has received great interest. Indeed, among its applications in genomic analysis, it has been demonstrated that DSP can be used to detect protein coding regions (exons) among non-coding regions in a DNA sequence. The period-3 behavior exhibited by exons is one of its features that has been exploited in several developed algorithms for exon prediction. Identification of this periodicity in genomic sequences can be done by using different methods such as the well-known Fast Fourier Transform (FFT) and the Goertzel algorithm for complexity reduction in which the reduction of computational time is a great challenge in genomic analysis. Therefore, this paper presents a novel one frequency analysis by using half of the arithmetic complexity of the Goertzel algorithm for gene prediction. Compared to the Intel®’s FFT (MKL) optimized function, the Goertzel’s (IPP) and the dedicated Goertzel compiled function with ICC on Xeon CPU (24 cores), the proposed method conserves the same accuracy provided by the referenced methods which will manifest a speedup of 3000, 10 and 2 compared to MKL FFT, IPP Goertzel and the dedicated Goertzel with ICC, respectively

ECMO Biocompatibility: Surface Coatings, Anticoagulation, and Coagulation Monitoring

The interaction between the patient and the ECMO (extracorporeal membrane oxygenation) circuit initiates a significant coagulation and inflammatory response due to the large surface area of foreign material contained within the circuit. This response can be blunted with the appropriate mix of biocompatible materials and anticoagulation therapy. The use of anticoagulants, in turn, requires appropriate laboratory testing to determine whether the patient is appropriately anticoagulated. Physicians must balance the risks of bleeding with the risks of thrombosis; the proper interpretation of these tests is often shrouded in mystery. It is the purpose of this chapter to help demystify the coagulation system, anticoagulants, biocompatible surfaces, and coagulation testing so that ECMO practitioners can make informed decisions about their patients and to spur coordinated efforts for future research to improve our understanding of these complex processes

Decadal changes in Arctic Ocean Chlorophyll a: Bridging ocean color observations from the 1980s to present time

Remotely-sensed Ocean color data offer a unique opportunity for studying variations of bio-optical properties which is especially valuable in the Arctic Ocean (AO) where in situ data are sparse. In this study, we re-processed the raw data from the Sea-viewing Wide Field-of-View (SeaWiFS, 1998–2010) and the MODerate resolution Imaging Spectroradiometer (MODIS, 2003–2016) ocean-color sensors to ensure compatibility with the first ocean color sensor, namely, the Coastal Zone Color Scanner (CZCS, 1979–1986). Based on a bio-regional approach, this study assesses the quality of this new homogeneous pan-Arctic Chl a dataset, which provides the longest (but non-continuous) ocean color time-series ever produced for the AO (37 years long between 1979 and 2016). We show that despite the temporal gaps between 1986 and 1998 due to the absence of ocean color satellite, the time series is suitable to establish a baseline of phytoplankton biomass for the early 1980s, before sea-ice loss accelerated in the AO. More importantly, it provides the opportunity to quantify decadal changes over the AO revealing for instance the continuous Chl a increase in the inflow shelves such as the Barents Sea since the CZCS era

Design Considerations for High-purity Heralded Single Photon Sources

When building a parametric downconversion photon-pair source with spectrally separable photons, e.g. for making high-purity heralded single photons, two practical issues must be accounted for: the design of the experiment, and its characterization. To address experiment design, we study the impact on spectral separability of realistic (sech shaped and chirped) pump fields, realistic nonlinear crystals with fabrication imperfections, and undesirable PDC generation far from the central PMF peak coming from nonlinearity shaping methods. To address experiment characterization, we study the effect of discretization and spectral range of the measured bi-photon joint spectrum, the difference between inferring separability from the joint spectral amplitude vs. the joint spectral intensity, and advantages of interference experiments for purity characterization over methods based on the joint spectral intensity. This study will be of practical interest to researchers building the next generation of nonlinear sources of separable photon pairs.Comment: 13 pages, 14 figure

Windowing compensation in Fourier based Surrogate Analysis

This paper shows how adding a second step of windowing after each phase randomization can reduce the False Rejection Rate in Fourier based Surrogate Analysis. Windowing techniques improve the resolution of the Power Spectrum estimation by reducing the sampling gap caused by the periodic extension of the Fourier Series. However, it adds a time domain non-stationarity which affects the Surrogate Analysis. This effect is particularly problematic for short lowpass signals. Applying the same window to the surrogate data allows having the same non-stationarity. The method is tested on order 1 autoregressive process null hypothesis by Monte Carlo simulations. Previous methods were not able to yield good performances for left-sided and right-sided tests at the same time, even less with bilateral tests. It is shown that the new method is conservative for unilateral tests as well as bilateral tests

Early results on deep unfolded conjugate gradient-based large-scale MIMO detection

Deep learning (DL) is attracting considerable attention in the design of communication systems. This paper derives a deep unfolded conjugate gradient (CG) architecture for large-scale multiple-input multiple-output detection. The proposed technique combines the advantages of a model-driven approach in readily incorporating domain knowledge and deep learning in effective parameters learning. The parameters are trained via backpropagation over a data flow graph inspired from the iterative conjugate gradient method. We derive the closed-form expressions for the gradients for parameters training and discuss early results on the performance in a statistically identical and independent distributed channel where the training overhead is considerably low. It is worth noting that the loss function is based on the residual error that is not an explicit function of the desired signal, which makes the proposed algorithm blind. As an initial framework, we will point to the inherent issues and future directions

Low group delay interpolation filter for Delta-Sigma converters

This paper shows how a relaxation of the high frequency requirements can help reducing the latency in linear phase interpolation filter, with an audio production system perspective. The reduced need for attenuation is justified when the interpolation filter is followed by a noise-shaping Delta-Sigma loop and an analog filtering stage. This is done by using a non-constant error weight of the stop-band. In order to use the Parks-McClellan method for finite impulse response filter design from Matlab, the stop-band is divided and weighted logarithmically. Quantitative results are shown for different example filter design, limited to situations where the Parks-McClellan converges well. It has been found that the shorter the filter length needed to respect a given filter template, the more relative group delay reduction can be achieved by relaxing the high frequency requirement. For filter size of the order of 100, reduction of group delay of 30% can be expected. For sake of simplicity, the Delta-Sigma loop is discussed but not analysed here. The idea is demonstrated in the context of Digital-to-Analog converters (DAC) but by duality could be applicable also to Analog-to-Digital converters (ADC). The main performance metric used is a relative reduction of the impulse response group delay. The results are also presented as impulse responses and power spectrum examples. The presented approach may be generalised to complex and non-linear phase filters and does not prevent the use of polyphase structures

Windowing compensation in Fourier based Surrogate Analysis and application to EEG signal classification

This paper shows how adding a second step of windowing after each phase randomization can reduce the False Rejection Rate in Fourier based Surrogate Analysis. Windowing techniques reduce the discontinuities at the boundaries of the periodically extended data sequence in Fourier Series. However, they add a time domain non-stationarity which affects the Surrogate Analysis. This effect is particularly problematic for short low-pass signals. Applying the same window to the surrogate data allows having the same non-stationarity. The method is tested on order 1 autoregressive process null hypothesis by Monte-Carlo simulations. Previous methods were not able to yield good performances for left-sided and right-sided tests at the same time, even less with bilateral tests. It is shown that the new method is conservative for unilateral tests as well as bilateral tests. In order to show that the proposed windowing method can be useful in real context, in this extended paper, it was applied for an EEG diagnostic problem. A dataset comprising the EEG measurements of 15 subject distributed in three groups: attention-deficit disorder primarily hyperactive-impulsive (ADHD), attention-deficit disorder primarily inattentive (ADD); and anxiety with attentional fragility (ANX) was used. Both statistical and machine learning (Naïve Bayesian) approaches were considered. The Mean Short Windowed SA (MSWSA) was used as a signal feature and its performances was studied with respect to the windowing systems. The main findings were that (i) the MSWSA feature has less variability for ADD than for ADHD or ANX, (ii) the proposed windowing method reduces bias and non-normality of the SA feature, (iii) with the proposed method and a naïve Bayesian classifier, a 93% success rate of discriminating ADD from ADHD and ANX was achieved with leave-one-out cross-validation, and (iv) the new feature could not have yielded interesting results without the proposed windowing system