Novel electromagnetic radiation in a semi-infinite space filled with a double-negative metamaterial

We have theoretically investigated the electromagnetic radiation excited by a charged particle moving along a semi-infinite space filled with a double-negative metamaterial (DNM). Cherenkov radiation in the double-negative region exhibits reversed or backward radiation behavior. The spectral density of reversed Cherenkov radiation has a continuous distribution over the radiation frequency region. The influence of some important parameters on the Cherenkov radiation energy per unit length has been discussed. The surface wave in the vacuum region presented here also is investigated. We conclude that the amplitude of the surface wave is greatly enhanced over some normal dielectric material cases. The enhanced surface wave may be useful for high frequency and high power vacuum electron devices with the DNM.National Natural Science Foundation (China) (Grant 60971031)National Natural Science Foundation (China) (Grant 61125103)Sichuan Youth Foundation (Grant No. 2010JQ0005)Foundation of the National Key Laboratory of Science and Technology on Vacuum Electronics (Grant No. 9140C050102100C05)Fundamental Research Funds for the Central Universities of China (Grant No. ZYGX2010X010

Face Recognition with Facial Occlusion Based on Local Cycle Graph Structure Operator

Facial occlusion is a difficulty in the field of face recognition. The lack of features caused by occlusion may reduce the face recognition rate greatly. How to extract the identified features from the occluded faces has a profound effect on face recognition. This chapter presents a Local Cycle Graph Structure (LCGS) operator, which makes full use of the information of the pixels around the target pixel with its neighborhood of 3 × 3. Thus, the recognition with the extracted features is more efficient. We apply the extreme learning machine (ELM) classifier to train and test the features extracted by LCGS algorithm. In the experiment, we use the olivetti research laboratory (ORL) database to simulate occlusion randomly and use the AR database for physical occlusion. Physical coverings include scarves and sunglasses. Experimental results demonstrate that our algorithm yields a state-of-the-art performance

Effects of different treatment frequencies of electromagnetic stimulation for urinary incontinence in women:study protocol for a randomized controlled trial

Background: Urinary incontinence is highly prevalent in women while pelvic floor muscle training is recommended as the first-line therapy. However, the exact treatment regimen is poorly understood. Also, patients with pelvic floor muscle damage may have decreased muscle proprioception and cannot contract their muscles properly. Other conservative treatments including electromagnetic stimulation are suggested by several guidelines. Thus, the present study aims to compare the effectiveness of electromagnetic stimulation combined with pelvic floor muscle training as a conjunct treatment for urinary incontinence and different treatment frequencies will be investigated.Methods/design: This is a randomized, controlled clinical trial. We will include 165 patients with urinary incontinence from the outpatient center. Participants who meet the inclusion criteria will be randomly allocated to three groups: the pelvic floor muscle training group (active control group), the low-frequency electromagnetic stimulation group (group 1), and the high-frequency electromagnetic stimulation group (group 2). Both group 1 and group 2 will receive ten sessions of electromagnetic stimulation. Group 1 will be treated twice per week for 5 weeks while group 2 will receive 10 days of continuous treatment. The primary outcome is the change in International Consultation on Incontinence Questionnaire–Short Form cores after the ten sessions of the treatment, while the secondary outcomes include a 3-day bladder diary, pelvic floor muscle function, pelvic organ prolapse quantification, and quality of life assessed by SF-12. All the measurements will be assessed at baseline, after the intervention, and after 3 months of follow-up.Discussion: The present trial is designed to investigate the effects of a conjunct physiotherapy program for urinary incontinence in women. We hypothesize that this strategy is more effective than pelvic floor muscle training alone, and high-frequency electromagnetic stimulation will be superior to the low-frequency magnetic stimulation group

Consistency in Geometry Among Coronary Atherosclerotic Plaques Extracted From Computed Tomography Angiography

Background: The three-dimensional (3D) geometry of coronary atherosclerotic plaques is associated with plaque growth and the occurrence of coronary artery disease. However, there is a lack of studies on the 3D geometric properties of coronary plaques. We aim to investigate if coronary plaques of different sizes are consistent in geometric properties.Methods: Nineteen cases with symptomatic stenosis caused by atherosclerotic plaques in the left coronary artery were included. Based on attenuation values on computed tomography angiography images, coronary atherosclerotic plaques and calcifications were identified, 3D reconstructed, and manually revised. Multidimensional geometric parameters were measured on the 3D models of plaques and calcifications. Linear and non-linear (i.e., power function) fittings were used to investigate the relationship between multidimensional geometric parameters (length, surface area, volume, etc.). Pearson correlation coefficient (r), R-squared, and p-values were used to evaluate the significance of the relationship. The analysis was performed based on cases and plaques, respectively. Significant linear relationship was defined as R-squared > 0.25 and p < 0.05.Results: In total, 49 atherosclerotic plaques and 56 calcifications were extracted. In the case-based analysis, significant linear relationships were found between number of plaques and number of calcifications (r = 0.650, p = 0.003) as well as total volume of plaques (r = 0.538, p = 0.018), between number of calcifications and total volume of plaques (r = 0.703, p = 0.001) as well as total volume of calcification (r = 0.646, p = 0.003), and between the total volumes of plaques and calcifications (r = 0.872, p < 0.001). In plaque-based analysis, the power function showed higher R-squared values than the linear function in fitting the relationships of multidimensional geometric parameters. Two presumptions of plaque geometry in different growth stages were proposed with simplified geometric models developed. In the proposed models, the exponents in the power functions of geometric parameters were in accordance with the fitted values.Conclusion: In patients with coronary artery disease, coronary plaques and calcifications are positively related in number and volume. Different coronary plaques are consistent in the relationship between geometry parameters in different dimensions

Machine Learning and Biometrics

We are entering the era of big data, and machine learning can be used to analyze this deluge of data automatically. Machine learning has been used to solve many interesting and often difficult real-world problems, and the biometrics is one of the leading applications of machine learning. This book introduces some new techniques on biometrics and machine learning, and new proposals of using machine learning techniques for biometrics as well. This book consists of two parts: ""Biometrics"" and ""Machine Learning for Biometrics."" Parts I and II contain four and three chapters, respectively. The book is reviewed by editors: Prof. Jucheng Yang, Prof. Dong Sun Park, Prof. Sook Yoon, Dr. Yarui Chen, and Dr. Chuanlei Zhang

A Novel Multimodal Biometrics Recognition Model Based on Stacked ELM and CCA Methods

Multimodal biometrics combine a variety of biological features to have a significant impact on identification performance, which is a newly developed trend in biometrics identification technology. This study proposes a novel multimodal biometrics recognition model based on the stacked extreme learning machines (ELMs) and canonical correlation analysis (CCA) methods. The model, which has a symmetric structure, is found to have high potential for multimodal biometrics. The model works as follows. First, it learns the hidden-layer representation of biological images using extreme learning machines layer by layer. Second, the canonical correlation analysis method is applied to map the representation to a feature space, which is used to reconstruct the multimodal image feature representation. Third, the reconstructed features are used as the input of a classifier for supervised training and output. To verify the validity and efficiency of the method, we adopt it for new hybrid datasets obtained from typical face image datasets and finger-vein image datasets. Our experimental results demonstrate that our model performs better than traditional methods

An Effective Multi-Task Two-Stage Network with the Cross-Scale Training Strategy for Multi-Scale Image Super Resolution

Convolutional neural networks and the per-pixel loss function have shown their potential to be the best combination for super-resolving severely degraded images. However, there are still challenges, such as the massive number of parameters requiring prohibitive memory and vast computing and storage resources as well as time-consuming training and testing. What is more, the per-pixel loss measured by L2 and the Peak Signal-to-Noise Ratio do not correlate well with human perception of image quality, since L2 simply does not capture the intricate characteristics of human visual systems. To address these issues, we propose an effective two-stage hourglass network with multi-task co-optimization, which enables the entire network to focus on training and testing time and inherent image patterns such as local luminance, contrast, structure and data distribution. Moreover, to avoid overwhelming memory overheads, our model is capable of performing real-time single image multi-scale super-resolution, so it is memory-friendly, meaning that memory space is utilized efficiently. In addition, in order to best use the underlying structure and perception of image quality and the intermediate estimates during the inference process, we introduce a cross-scale training strategy with 2×, 3× and 4× image super-resolution. This effective multi-task two-stage network with the cross-scale strategy for multi-scale image super-resolution is named EMTCM. Quantitative and qualitative experiment results show that the proposed EMTCM network outperforms state-of-the-art methods in recovering high-quality images