Micro-emboli detection: an ultrasound Doppler signal processing viewpoint

Several studies have been carried out in the last twenty years on the characterization and detection of cerebral artery emboli. From the detection point of view, the existing methods are largely based on the classical Fourier analysis of which the well known limitations provide poor accuracy. This paper first recalls existing methods based on Fourier, Wigner-Ville and wavelet approaches. It then presents new emboli detection methods based on parametric signal processing approaches. The basic idea of these parametric methods is to compare the Doppler embolic signal to its autoregressive model. The detection principle consists in constructing a decision information which contains the signature of the micro-embolus being sought. The detection is finally evaluated using receiver operating characteristic (ROC) curves. Comparison between the new methods and classical approaches is performed using a realistic embolic signal simulation. Furthermore, to validate our theoretical study, we tested our new algorithms using in vivo signals. This comparison shows the significant inaccuracy of existing methods to detect micro-emboli.     

Detection process approach of tool wear in high speed milling

The cutting tool wear degrades the quality of the product in the manufacturing process, for this reason an on-line monitoring of the cutting tool wear level is very necessary to prevent any deterioration. Unfortunately there is no direct manner to measure the cutting tool wear on-line. Consequently we must adopt an indirect method where wear will be estimated from the measurement of one or more physical parameters appearing during the machining process such as the cutting force, the vibrations, or the acoustic emission, etc. The main objective of this work is to establish a relationship between the acquired signals variation and the tool wear in high speed milling process; so an experimental setup was carried out using a horizontal high speed milling machine. Thus, the cutting forces were measured by means of a dynamometer whereas; the tool wear was measured in an off-line manner using a binocular microscope. Furthermore, we analysed cutting force signatures during milling operation throughout the tool life. This analysis was based on both temporal and frequential signal processing techniques in order to extract the relevant indicators of cutting tool state. Our results have shown that the variation of the variance and the first harmonic amplitudes were linked to the flank wear evolution. These parameters show the best behavior of the tool wear state while providing relevant information of this later.     

A Highly Efficient Algorithm for Phased-Array mmWave Massive MIMO Beamforming

With the rapid development of the mobile internet and the internet of things (IoT), the fifth generation (5G) mobile communication system is seeing explosive growth in data traffic. In addition, low-frequency spectrum resources are becoming increasingly scarce and there is now an urgent need to switch to higher frequency bands. Millimeter wave (mmWave) technology has several outstanding features—it is one of the most well-known 5G technologies and has the capacity to fulfil many of the requirements of future wireless networks. Importantly, it has an abundant resource spectrum, which can significantly increase the communication rate of a mobile communication system. As such, it is now considered a key technology for future mobile communications. MmWave communication technology also has a more open network architecture; it can deliver varied services and be applied in many scenarios. By contrast, traditional, all-digital precoding systems have the drawbacks of high computational complexity and higher power consumption. This paper examines the implementation of a new hybrid precoding system that significantly reduces both calculational complexity and energy consumption. The primary idea is to generate several sub-channels with equal gain by dividing the channel by the geometric mean decomposition (GMD). In this process, the objective function of the spectral efficiency is derived, then the basic tracking principle and least square (LS) techniques are deployed to design the proposed hybrid precoding. Simulation results show that the proposed algorithm significantly improves system performance and reduces computational complexity by more than 45% compared to traditional algorithms.     

Time-varying autoregressive spectral estimation for ultrasound attenuation in tissue characterization

In the field of biological tissue characterization, fundamental acoustic attenuation properties have been demonstrated to have diagnostic importance. Attenuation caused by scattering and absorption shifts the instantaneous spectrum to the lower frequencies. Due to the time-dependence of the spectrum, the attenuation phenomenon is a time-variant process. This downward shift may be evaluated either by the maximum energy frequency of the spectrum or by the center frequency. In order to improve, in strongly attenuating media, the results given by the short-time Fourier analysis and the short-time parametric analysis, we propose two approaches adapted to this time-variant process: an adaptive method and a time-varying method. Signals backscattered by an homogeneous medium of scatterers are modeled by a computer algorithm with attenuation values ranging from 1 to 5 dB/cm MHz and a 45 MHz transducer center frequency. Under these conditions, the preliminary results obtained with the proposed time-variant methods, compared with the classical short-time Fourier analysis and the short-time auto-regressive (AR) analysis, are superior in terms of standard deviation (SD) of the attenuation coefficient estimate. This study, based on nonstationary AR spectral estimation, promises encouraging perspectives for in vitro and in vivo applications both in weakly and highly attenuating media.     

Studies on the curing behavior,thermal, and mechanical properties of epoxy resin-co-amine-functionalized lead phthalocyanine

A high performance copolymer was prepared by using epoxy (EP) resin as matrix and 3,10,17,24-tetra-aminoethoxy lead phthalocyanine (APbPc) as additive with dicyandiamide as curing agent. Fourier-transform infrared spectroscopy, dynamic mechanical analysis (DMA), differential scanning calorimetric analysis (DSC), and thermogravimetric analysis (TGA) were used to study the curing behavior, curing kinetics, dynamic mechanical properties, impact and tensile strength, and thermal stability of EP/APbPc blends. The experimental results show that APbPc, as a synergistic curing agent, can effectively reduce the curing temperature of epoxy resin. The curing kinetics of the copolymer was investigated by non-isothermal DSC to determine kinetic data and measurement of the activation energy. DMA, impact, and tensile strength tests proved that phthalocyanine can significantly improve the toughness and stiffness of epoxy resin. Highest values were seen on the 20 wt% loading of APbPc in the copolymers, energy storage modulus, and impact strength increased respectively 388.46 MPa and 3.6 kJ/m², T_g decreased 19.46°C. TGA curves indicated that the cured copolymers also exhibit excellent thermal properties.     

High‐performance polymeric materials with greatly improved mechanical and thermal properties from cyanate ester/benzoxazine resin reinforced by silane‐treated basalt fibers

This present article investigates the effect of silane‐treated basalt fibers (TBFs) on the morphological, mechanical and thermal properties of cyanate ester/benzoxazine (CE/BOZ) resin composites. The characterization was made using a scanning electron microscopy (SEM), Fourier Transform Infrared Spectroscopy (FTIR), flexural test, impact strength (IS) test, microhardness test, dynamic scanning calorimetry, and thermogravimetric analysis. The mechanical test results inferred the distinctive improvements in the values of the flexural strength and modulus, IS, and microhardness of the CE/BOZ composites. The thermal stabilities in terms of the T_g, T_5%, T_10%, and T_HRI were appreciably improved and were higher than those of the pure CE/BOZ resin. Data from the SEM and FTIR tests ascertained the good dispersion and adhesion between the TBFs and the resin matrix, which might be behind the significant enhancement in the ultimate performances of the composites, with respect to the distinguished properties of BFs. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018 , 135, 46283.     

Wavelet-based multifractal analysis of 1-D and 2-D signals: New results

During the past decade, new tools stemming from fractal geometry and wavelet analysis are meeting with great success in signal image processing. This paper will focus on these two topics: Wavelets and Multifractal. Both themes evolved towards self contained theories, and yet, a host of reasons justify for coupling them in same applications. It is well known that both analyses share the same conceptual backbone of “scale”: it is the “mathematical zoom” commonly associated to wavelet analysis and it is the “scaling laws” that underlie multifractal structures. Very naturally then, wavelets stood as a privileged tool for analyzing and characterizing multifractal signals and images. Hence, the purpose of this paper is to illuminate some of the issue involved in taking advantage of the current advances in wavelets and multifractals analysis. We discuss continuous, discrete, orthogonal wavelets and present applications to fracture processes and medical ultrasound imaging.     

Influence of machining cycle of horizontal milling on the quality of cutting force measurement for the cutting tool wear monitoring

The cutting tools are today used a lot by industry and they are expensive, so it was interesting to optimize their use, by developing a predictive method of their wear, particularly, the flank wear V _b . For this task, the flank tool wear was measured in off-line using a binocular microscope, whereas, the cutting forces are recorded by means of a dynamometer (Kistler 9255B). The acquired signatures are analyzed during the milling operation throughout the tool life. In this paper, we are interested in the extraction of the appropriate indicators which characterize the tool wear by temporal and frequential analyses of the cutting force signals; and highlighting the influence of the clamp holes and the machining cycle to the quality of the measurements.     

Bone microarchitecture characterization based on fractal analysis in spatial frequency domain imaging

This paper suggests a new technique for trabecular bone characterization using fractal analysis of X‐Ray and MRI texture images for osteoporosis diagnosis. Osteoporosis is a chronic disease characterized by a decrease in bone density that can lead to fracture and disability. In essence, the proposed fractal model makes use of the differential box‐counting method (DBCM) to estimate the fractal dimension (FD) after an appropriate image preprocessing stage that ensures a robust estimation process. In this study, we showed that within the frequency domain generated through discrete cosine transform (DCT), only a quarter of DCT coefficients are enough to characterize osteoporotic tissues. The algorithmic complexity of the developed approach is of the order of where N stands for the size of the image, which, in turn, likely yields important gain in terms of medication cost. We report a successful separation of healthy and pathological cases in term of both P ? value (using statistical Wilcoxon rank sum test) and margin difference. A comparative statistical analysis has been performed using a publicly available database that contains a set of MRI and X‐Ray texture images of both healthy and osteoporotic bone tissues. The statistical results demonstrated the feasibility and accepted performance level of our fractal model‐based diagnosis to discriminate healthy and unhealthy trabecular bone tissues. The developed approach has been implemented on a medical device prototype.