Mammogram Enhancement Using Lifting Dyadic Wavelet Transform and Normalized Tsallis Entropy

In this paper, we present a new technique for mammogram enhancement using fast dyadic wavelet transform （FDyWT） based on lifted spline dyadic wavelets and normalized Tsallis entropy. First, a mammogram image is decom- posed into a multiscale hierarchy of low-subband and high-subband images using FDyWT. Then noise is suppressed using normalized Tsallis entropy of the local variance of the modulus of oriented high-subband images. After that, the wavelet coefficients of high-subbands are modified using a non-linear operator and finally the low-subband image at the first scale is modified with power law transformation to suppress background. Though FDyWT is shift-invariant and has better poten- tial for detecting singularities like edges, its performance depends on the choice of dyadic wavclcts. On the other hand, the nulnber of vanishing moments is an important characteristic of dyadic wavelets for singularity analysis because it provides an upper bound measurement for singularity characterization. Using lifting dyadic schemes, we construct lifted spline dyadic wavelets of different degrees with increased number of vanishing moments. We also examine the effect of these wavelets on mammogram enhancement. The method is tested on mammogram images, taken from MIAS （Mammographic Image Analysis Society） database, having various background tissue types and containing different abnormalities. The comparison with tile state-of-the-art contrast enhancement methods reveals that the proposed method performs better and the difference is statistically significant.     

A data mining approach for machine fault diagnosis based on associated frequency patterns

Bearings play a crucial role in rotational machines and their failure is one of the foremost causes of breakdowns in rotary machinery. Their functionality is directly relevant to the operational performance, service life and efficiency of these machines. Therefore, bearing fault identification is very significant. The accuracy of fault or anomaly detection by the current techniques is not adequate. We propose a data mining-based framework for fault identification and anomaly detection from machine vibration data. In this framework, to capture the useful knowledge from the vibration data stream (VDS), we first pre-process the data using Fast Fourier Transform (FFT) to extract the frequency signature and then build a compact tree called SAFP-tree (sliding window associated frequency pattern tree), and propose a mining algorithm called SAFP. Our SAFP algorithm can mine associated frequency patterns (i.e., fault frequency signatures) in the current window of VDS and use them to identify faults in the bearing data. Finally, SAFP is further enhanced to SAFP-AD for anomaly detection by determining the normal behavior measure (NBM) from the extracted frequency patterns. The results show that our technique is very efficient in identifying faults and detecting anomalies over VDS and can be used for remote machine health diagnosis.     

Deep features-based speech emotion recognition for smart affective services

Emotion recognition from speech signals is an interesting research with several applications like smart healthcare, autonomous voice response systems, assessing situational seriousness by caller affective state analysis in emergency centers, and other smart affective services. In this paper, we present a study of speech emotion recognition based on the features extracted from spectrograms using a deep convolutional neural network (CNN) with rectangular kernels. Typically, CNNs have square shaped kernels and pooling operators at various layers, which are suited for 2D image data. However, in case of spectrograms, the information is encoded in a slightly different manner. Time is represented along the x-axis and y-axis shows frequency of the speech signal, whereas, the amplitude is indicated by the intensity value in the spectrogram at a particular position. To analyze speech through spectrograms, we propose rectangular kernels of varying shapes and sizes, along with max pooling in rectangular neighborhoods, to extract discriminative features. The proposed scheme effectively learns discriminative features from speech spectrograms and performs better than many state-of-the-art techniques when evaluated its performance on Emo-DB and Korean speech dataset.

     

Zr‐Doped Indium Oxide (IZRO) Transparent Electrodes for Perovskite‐Based Tandem Solar Cells

Parasitic absorption in transparent electrodes is one of the main roadblocks to enabling power conversion efficiencies (PCEs) for perovskite‐based tandem solar cells beyond 30%. To reduce such losses and maximize light coupling, the broadband transparency of such electrodes should be improved, especially at the front of the device. Here, the excellent properties of Zr‐doped indium oxide (IZRO) transparent electrodes for such applications, with improved near‐infrared (NIR) response, compared to conventional tin‐doped indium oxide (ITO) electrodes, are shown. Optimized IZRO films feature a very high electron mobility (up to ≈77 cm² V^?1 s^?1), enabling highly infrared transparent films with a very low sheet resistance (≈18 Ω □^?1 for annealed 100 nm films). For devices, this translates in a parasitic absorption of only ≈5% for IZRO within the solar spectrum (250–2500 nm range), to be compared with ≈10% for commercial ITO. Fundamentally, it is found that the high conductivity of annealed IZRO films is directly linked to promoted crystallinity of the indium oxide (In₂O₃) films due to Zr‐doping. Overall, on a four‐terminal perovskite/silicon tandem device level, an absolute 3.5 mA cm^?2 short‐circuit current improvement in silicon bottom cells is obtained by replacing commercial ITO electrodes with IZRO, resulting in improving the PCE from 23.3% to 26.2%.     

Machine learning in vehicular networking: An overview

As vehicle complexity and road congestion increase, combined with the emergence of electric vehicles, the need for intelligent transportation systems to improve on-road safety and transportation efficiency using vehicular networks has become essential. The evolution of high mobility wireless networks will provide improved support for connected vehicles through highly dynamic heterogeneous networks. Particularly, 5G deployment introduces new features and technologies that enable operators to capitalize on emerging infrastructure capabilities. Machine Learning (ML), a powerful methodology for adaptive and predictive system development, has emerged in both vehicular and conventional wireless networks. Adopting data-centric methods enables ML to address highly dynamic vehicular network issues faced by conventional solutions, such as traditional control loop design and optimization techniques. This article provides a short survey of ML applications in vehicular networks from the networking aspect. Research topics covered in this article include network control containing handover management and routing decision making, resource management, and energy efficiency in vehicular networks. The findings of this paper suggest more attention should be paid to network forming/deforming decision making. ML applications in vehicular networks should focus on researching multi-agent cooperated oriented methods and overall complexity reduction while utilizing enabling technologies, such as mobile edge computing for real-world deployment. Research datasets, simulation environment standardization, and method interpretability also require more research attention.     

Impact of N-Terminal Tags on De Novo Vimentin Intermediate Filament Assembly

Vimentin, a type III intermediate filament protein, is found in most cells along with microfilaments and microtubules. It has been shown that the head domain folds back to associate with the rod domain and this association is essential for filament assembly. The N-terminally tagged vimentin has been widely used to label the cytoskeleton in live cell imaging. Although there is previous evidence that EGFP tagged vimentin fails to form filaments but is able to integrate into a pre-existing network, no study has systematically investigated or established a molecular basis for this observation. To determine whether a tag would affect de novo filament assembly, we used vimentin fused at the N-terminus with two different sized tags, AcGFP (239 residues, 27 kDa) and 3 × FLAG (22 residues; 2.4 kDa) to assemble into filaments in two vimentin-deficient epithelial cells, MCF-7 and A431. We showed that regardless of tag size, N-terminally tagged vimentin aggregated into globules with a significant proportion co-aligning with β-catenin at cell–cell junctions. However, the tagged vimentin aggregates could form filaments upon adding untagged vimentin at a ratio of 1:1 or when introduced into cells containing pre-existing filaments. The resultant filament network containing a mixture of tagged and untagged vimentin was less stable compared to that formed by only untagged vimentin. The data suggest that placing a tag at the N-terminus may create steric hinderance in case of a large tag (AcGFP) or electrostatic repulsion in case of highly charged tag (3 × FLAG) perhaps inducing a conformational change, which deleteriously affects the association between head and rod domains. Taken together our results shows that a free N-terminus is essential for filament assembly as N-terminally tagged vimentin is not only incapable of forming filaments, but it also destabilises when integrated into a pre-existing network.     

Gas Sensing of NiO‐SCCNT Core–Shell Heterostructures: Optimization by Radial Modulation of the Hole‐Accumulation Layer

Hierarchical core–shell (C–S) heterostructures composed of a NiO shell deposited onto stacked‐cup carbon nanotubes (SCCNTs) are synthesized by atomic layer deposition (ALD). A film of NiO particles (0.80–21.8 nm in thickness) is uniformly deposited onto the inner and outer walls of the SCCNTs. The electrical resistance of the samples is found to increase of many orders of magnitude with the increasing of the NiO thickness. The response of NiO–SCCNT sensors toward low concentrations of acetone and ethanol at 200 °C is studied. The sensing mechanism is based on the modulation of the hole‐accumulation region in the NiO shell layer upon chemisorption of the reducing gas molecules. The electrical conduction mechanism is further studied by the incorporation of an Al₂O₃ dielectric layer at NiO and SCCNT interfaces. The investigations on NiO–Al₂O₃–SCCNT, Al₂O₃–SCCNT, and NiO–SCCNT coaxial heterostructures reveal that the sensing mechanism is strictly related to the NiO shell layer. The remarkable performance of the NiO–SCCNT sensors toward acetone and ethanol benefits from the conformal coating by ALD, large surface area of the SCCNTs, and the optimized p‐NiO shell layer thickness followed by the radial modulation of the space‐charge region.