Estimation of Energy Losses in Nanocrystalline FINEMET Alloys Working at High Frequency.

Soft magnetic materials are at the core of electromagnetic devices. Planar transformers are essential pieces of equipment working at high frequency. Usually, their magnetic core is made of various types of ferrites or iron-based alloys. An upcoming alternative might be the replacement the ferrites with FINEMET-type alloys, of nominal composition of Fe73.5Si13.5B9Cu3Nb1 (at. %). FINEMET is a nanocrystalline material exhibiting excellent magnetic properties at high frequencies, a soft magnetic alloy that has been in the focus of interest in the last years thanks to its high saturation magnetization, high permeability, and low core loss. Here, we present and discuss the measured and modelled properties of this material. Owing to the limits of the experimental set-up, an estimate of the total magnetic losses within this magnetic material is made, for values greater than the measurement limits of the magnetic flux density and frequency, with reasonable results for potential applications of FINMET-type alloys and thin films in high frequency planar transformer cores.

A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction.

Pedestrian trajectory prediction is one of the main concerns of computer vision problems in the automotive industry, especially in the field of advanced driver assistance systems. The ability to anticipate the next movements of pedestrians on the street is a key task in many areas, e.g., self-driving auto vehicles, mobile robots or advanced surveillance systems, and they still represent a technological challenge. The performance of state-of-the-art pedestrian trajectory prediction methods currently benefits from the advancements in sensors and associated signal processing technologies. The current paper reviews the most recent deep learning-based solutions for the problem of pedestrian trajectory prediction along with employed sensors and afferent processing methodologies, and it performs an overview of the available datasets, performance metrics used in the evaluation process, and practical applications. Finally, the current work exposes the research gaps from the literature and outlines potential new research directions.

Deep Neural Architectures for Contrast Enhanced Ultrasound (CEUS) Focal Liver Lesions Automated Diagnosis.

Computer vision, biomedical image processing and deep learning are related fields with a tremendous impact on the interpretation of medical images today. Among biomedical image sensing modalities, ultrasound (US) is one of the most widely used in practice, since it is noninvasive, accessible, and cheap. Its main drawback, compared to other imaging modalities, like computed tomography (CT) or magnetic resonance imaging (MRI), consists of the increased dependence on the human operator. One important step toward reducing this dependence is the implementation of a computer-aided diagnosis (CAD) system for US imaging. The aim of the paper is to examine the application of contrast enhanced ultrasound imaging (CEUS) to the problem of automated focal liver lesion (FLL) diagnosis using deep neural networks (DNN). Custom DNN designs are compared with state-of-the-art architectures, either pre-trained or trained from scratch. Our work improves on and broadens previous work in the field in several aspects, e.g., a novel leave-one-patient-out evaluation procedure, which further enabled us to formulate a hard-voting classification scheme. We show the effectiveness of our models, i.e., 88% accuracy reported against a higher number of liver lesion types: hepatocellular carcinomas (HCC), hypervascular metastases (HYPERM), hypovascular metastases (HYPOM), hemangiomas (HEM), and focal nodular hyperplasia (FNH).

A PointNet-Based Solution for 3D Hand Gesture Recognition.

Gesture recognition is an intensively researched area for several reasons. One of the most important reasons is because of this technology's numerous application in various domains (e.g., robotics, games, medicine, automotive, etc.) Additionally, the introduction of three-dimensional (3D) image acquisition techniques (e.g., stereovision, projected-light, time-of-flight, etc.) overcomes the limitations of traditional two-dimensional (2D) approaches. Combined with the larger availability of 3D sensors (e.g., Microsoft Kinect, Intel RealSense, photonic mixer device (PMD), CamCube, etc.), recent interest in this domain has sparked. Moreover, in many computer vision tasks, the traditional statistic top approaches were outperformed by deep neural network-based solutions. In view of these considerations, we proposed a deep neural network solution by employing PointNet architecture for the problem of hand gesture recognition using depth data produced by a time of flight (ToF) sensor. We created a custom hand gesture dataset, then proposed a multistage hand segmentation by designing filtering, clustering, and finding the hand in the volume of interest and hand-forearm segmentation. For comparison purpose, two equivalent datasets were tested: a 3D point cloud dataset and a 2D image dataset, both obtained from the same stream. Besides the advantages of the 3D technology, the accuracy of the 3D method using PointNet is proven to outperform the 2D method in all circumstances, even the 2D method that employs a deep neural network.

Magnetic Properties of Manganese-Zinc Soft Ferrite Ceramic for High Frequency Applications.

A soft magnetic MnZn-type ferrite is considered for high frequency applications. First, the morphological, structural, and chemical composition of the material are presented and discussed. Subsequently, by using a vibrating sample magnetometer (VSM), the hysteresis loops are recorded. The open magnetic circuit measurements are corrected by employing demagnetization factors, and by taking into consideration the local magnetic susceptibility. Finally, the hysteresis losses are estimated by the Steinmetz approach, and the results are compared with available commercial information provided by selected MnZn ferrite manufacturers. Such materials are representative in planar inductor and transformer cores due to their typically low losses at high frequency, i.e., up to several MHz, in low-to-medium power applications and providing high efficiency of up to 97%-99%.

Illumination normalization of face image based on illuminant direction estimation and improved Retinex.

Illumination normalization of face image for face recognition and facial expression recognition is one of the most frequent and difficult problems in image processing. In order to obtain a face image with normal illumination, our method firstly divides the input face image into sixteen local regions and calculates the edge level percentage in each of them. Secondly, three local regions, which meet the requirements of lower complexity and larger average gray value, are selected to calculate the final illuminant direction according to the error function between the measured intensity and the calculated intensity, and the constraint function for an infinite light source model. After knowing the final illuminant direction of the input face image, the Retinex algorithm is improved from two aspects: (1) we optimize the surround function; (2) we intercept the values in both ends of histogram of face image, determine the range of gray levels, and stretch the range of gray levels into the dynamic range of display device. Finally, we achieve illumination normalization and get the final face image. Unlike previous illumination normalization approaches, the method proposed in this paper does not require any training step or any knowledge of 3D face and reflective surface model. The experimental results using extended Yale face database B and CMU-PIE show that our method achieves better normalization effect comparing with the existing techniques.

Organized formation of 2D extended covalent organic frameworks at surfaces.

The development of nanoscale masking for particle deposition is exceedingly important to push the future of nanoelectronics beyond the current limits of lithography. We present the first example of ordered hexagonal covalent nanoporous structures deposited in extended arrays of near monolayer coverage across a Ag(111) surface. The networks were formed from the deposition of the reagents from a heated molybdenum crucible between 370 and 460 K under ultrahigh vacuum (UHV) onto a cleaned Ag(111) substrate and imaged using a scanning tunneling microscope (STM). Two surface covalent organic frameworks (SCOFs) are presented; the first is formed from the deposition of 1,4-benzenediboronic acid (BDBA) and its dehydration to form the boroxine-linked SCOF-1, the second is formed from the co-deposition of BDBA and 2,3,6,7,10,11-hexahydroxytriphenylene (HHTP) to form a dioxaborole-linked SCOF-2 network. The networks were found to produce nanoporous structures of 15 A for SCOF-1 and 29 A for SCOF-2, which agreed with theoretical pore sizes determined from DFT calculations. Both SCOFs were found to have exceptional thermal stability, maintaining their structure until approximately 750 K, which was found to be the polymer degradation temperature from thermal gravimetric analysis (TGA).

Influence of chlorine substitution on the self-assembly of zinc phthalocyanine.

The adsorption and ordering of zinc phthalocyanine (ZnPc) and octachloro zinc phthalocyanine (ZnPcCl(8)) on an Ag(111) surface is studied in situ by scanning tunneling microscopy under ultrahigh vacuum. Two-dimensional self-assembled supramolecular domains are observed for these two molecules. We show how substituting chlorine atoms for half of the peripheral hydrogen atoms on ZnPc influences the self-assembly mechanisms. While intermolecular interactions are dominated by van der Waals forces in ZnPc molecular networks, ZnPcCl(8) molecular packing undergoes a sequential phase evolution driven by the creation of C-Cl...H-C hydrogen bonds between adjacent molecules. At the end of this evolution, the final molecular assembly involves all possible hydrogen bonds. Our study also reveals the influence of molecule-substrate interactions through the presence of fault lines generating a stripe structure in the molecular film.