双光楔实现外腔半导体激光器波长精密调谐研究

提出利用旋转双光楔的方法来实现外腔半导体激光器波长的精密调谐.双光楔中,较大楔角的光楔用于粗调,较小楔角的光楔用于细调.与传统的Littman结构光栅外腔半导体激光器相比,通过这种方法,波长的精密调谐无需机械调谐装置,手动即可实现;对调谐的机械要求以及对振动的敏感性降低,波长选择精度相应大大提高.     

基于元学习的少样本红外空中目标分类方法

针对空中红外目标样本数目不足、细粒度分类精度低等问题,提出一种基于元学习的少样本红外空中目标分类的方法.该方法以元学习为基础,结合多尺度特征融合,在减少计算量的同时有效提取不同分类任务之间的共性,再利用微调策略实现对不同任务的分类.实验证明,此方法在提升mini-ImageNet数据集分类精度的同时可减少约70％的计算...     

A multiple-pass ring oscillator based dual-loop phase-locked loop

A dual-loop phase-locked loop (PLL) for wideband operation is proposed. The dual-loop architecture combines a coarse-tuning loop with a fine-tuning one, enabling a wide tuning range and low voltage-controlled oscillator (VCO) gain without poisoning phase noise and reference spur suppression performance. An analysis of the phase noise and reference spur of the dual-loop PLL is emphasized. A novel multiple-pass ring VCO is designed for the dual-loop application. It utilizes both voltage-control and current-control simultaneously in the delay cell. The PLL is fabricated in Jazz 0.18-μm RF CMOS technology. The measured tuning range is from 4.2 to 5.9 GHz. It achieves a low phase noise of-99 dBc/Hz @ 1 MHz offset from a 5.5 GHz carrier.     

Model-independent Veltman condition,naturalness and the little hierarchy problem

We adopt a bottom-up Effective Field Theory(EFT)approach to derive a model-independent Veltman condition to cancel out the quadratic divergences in the Higgs mass.We show using the equivalence theorem that all the deviations in the Higgs couplings to the W and Z from the SM predictions should vanish.We argue based on tree-level unitarity that any new physics that naturally cancels out the quadratic divergences should be ≤19 TeV.We show that the level of fine-tuning required is unless the O(0:1%-1%) UV sector has a symmetry that forces the satisfaction of the model-independent Veltman condition,in which case all fine-tuning is eliminated.We also conjecture that,if no new physics that couples to the Higgs is observed up to~19 TeV,or if the Higgs couplings to the SM particles conform to the SM predictions,then the Higgs either does not couple to any UV sector or is fine-tuned.     

A Hybrid Deep Learning Model for Brain Tumour Classification

A brain tumour is one of the major reasons for death in humans, and it is the tenth most common type of tumour that affects people of all ages. However, if detected early, it is one of the most treatable types of tumours. Brain tumours are classified using biopsy, which is not usually performed before definitive brain surgery. An image classification technique for tumour diseases is important for accelerating the treatment process and avoiding surgery and errors from manual diagnosis by radiologists. The advancement of technology and machine learning (ML) can assist radiologists in tumour diagnostics using magnetic resonance imaging (MRI) images without invasive procedures. This work introduced a new hybrid CNN-based architecture to classify three brain tumour types through MRI images. The method suggested in this paper uses hybrid deep learning classification based on CNN with two methods. The first method combines a pre-trained Google-Net model of the CNN algorithm for feature extraction with SVM for pattern classification. The second method integrates a finely tuned Google-Net with a soft-max classifier. The proposed approach was evaluated using MRI brain images that contain a total of 1426 glioma images, 708 meningioma images, 930 pituitary tumour images, and 396 normal brain images. The reported results showed that an accuracy of 93.1% was achieved from the finely tuned Google-Net model. However, the synergy of Google-Net as a feature extractor with an SVM classifier improved recognition accuracy to 98.1%.     

基于肤色模型法的人脸定位技术研究

基于JAVA语言对人脸识别首要环节的定位技术进行代码开发,在传统肤色模型法的基础上,对采集到的大量图像进行深入研究,运用图像精准化微调和图像增强处理的方法,提高了传统肤色模型法在实际运用中的精准率,实现了人脸在视频图像中的定位。     

An Application for Aesthetic Quality Assessment in Photography with Interpretability Features

Automatic aesthetic quality assessment is a computer vision problem in which we quantify the attractiveness or the appealingness of a photograph. This is especially useful in social networks, where the amount of images generated each day requires automation for processing. This work presents Aesthetic Selector, an application able to identify images of high aesthetic quality, showing also relevant information about the decisions and providing the use of the most appropriate filters to enhance a given image. We then analyzed the main proposals in the aesthetic quality field, describing their strengths and weaknesses in order to determine the filters to be included in the application Aesthetic Selector. This proposed application was tested, giving good results, in three different scenarios: image selection, image finding, and filter selection. Besides, we carried out a study of distinct visualization tools to better understand the models’ behavior. These techniques also allow detecting which areas are more relevant within the images when models perform classification. The application also includes this interpretability module. Aesthetic Selector is an innovative and original program, because in the field of aesthetic quality in photography, there are no applications that identify high-quality images and also because it offers the capability of showing information about which parts of the image have affected this decision.     

Preparation and Biomedical Applications of Multicolor Carbon Dots: Recent Advances and Future Challenges

Multicolor carbon dots (CDs) as an emerging subclass of carbonaceous nanomaterials have inspired intensive attention due to the fascinating fluorogenic properties of quantum dots, exhibiting great potential applications in the biomedical field. In some cases, reported CDs with blue or green fluorescence are not desirable for further biological applications owing to the conflicting background autofluorescence, low penetration, and relatively large damage to biological tissue. However, multicolor CDs that can work in the longer wavelength region are being developed to address this issue by overcoming the autofluorescence from the cellular components (usually in the blue and green range). In this review, the development of multicolor CDs is described comprehensively, including for red-emissive or NIR-emissive CDs. Additionally, the preparation methods of multicolor CDs are summarized. Moreover, the factors affecting the luminescence of multicolor CDs are discussed in detail, and the biomedical applications are emphasized from multiple perspectives.     

Assessing,Testing and Estimating the Amount of Fine-Tuning by Means of Active Information

A general framework is introduced to estimate how much external information has been infused into a search algorithm, the so-called active information. This is rephrased as a test of fine-tuning, where tuning corresponds to the amount of pre-specified knowledge that the algorithm makes use of in order to reach a certain target. A function f quantifies specificity for each possible outcome x of a search, so that the target of the algorithm is a set of highly specified states, whereas fine-tuning occurs if it is much more likely for the algorithm to reach the target as intended than by chance. The distribution of a random outcome X of the algorithm involves a parameter θ that quantifies how much background information has been infused. A simple choice of this parameter is to use θf in order to exponentially tilt the distribution of the outcome of the search algorithm under the null distribution of no tuning, so that an exponential family of distributions is obtained. Such algorithms are obtained by iterating a Metropolis–Hastings type of Markov chain, which makes it possible to compute their active information under the equilibrium and non-equilibrium of the Markov chain, with or without stopping when the targeted set of fine-tuned states has been reached. Other choices of tuning parameters θ are discussed as well. Nonparametric and parametric estimators of active information and tests of fine-tuning are developed when repeated and independent outcomes of the algorithm are available. The theory is illustrated with examples from cosmology, student learning, reinforcement learning, a Moran type model of population genetics, and evolutionary programming.     

一种基于环形振荡器的双环路锁相环

A dual-loop phase-locked loop（PLL）for wideband operation is proposed.The dual-loop architecture combines a coarse-tuning loop with a fine-tuning one,enabling a wide tuning range and low voltage-controlled oscillator（VCO）gain without poisoning phase noise and reference spur suppression performance.An analysis of the phase noise and reference spur of the dual-loop PLL is emphasized.A novel multiple-pass ring VCO is designed for the dual-loop application.It utilizes both voltage-control and current-control simultaneously in the delay cell. The PLL is fabricated in Jazz 0.18-μm RF CMOS technology.The measured tuning range is from 4.2 to 5.9 GHz.It achieves a low phase noise of–99 dBc/Hz@1 MHz offset from a 5.5 GHz carrier.