Underwater image clarifying based on human visual colour constancy using double-opponency

Underwater images are often with biased colours and reduced contrast because of the absorption and scattering effects when light propagates in water.Such images with degradation cannot meet the needs of underwater operations.The main problem in classic underwater image restoration or enhancement methods is that they consume long calcu-lation time,and often,the colour or contrast of the result images is still unsatisfied.Instead of using the complicated physical model of underwater imaging degradation,we propose a new method to deal with underwater images by imitating the colour constancy mechanism of human vision using double-opponency.Firstly,the original image is converted to the LMS space.Then the signals are linearly combined,and Gaussian convolutions are per-formed to imitate the function of receptive fields(RFs).Next,two RFs with different sizes work together to constitute the double-opponency response.Finally,the underwater light is estimated to correct the colours in the image.Further contrast stretching on the luminance is optional.Experiments show that the proposed method can obtain clarified underwater images with higher quality than before,and it spends significantly less time cost compared to other previously published typical methods.

A novel medical image data protection scheme for smart healthcare system

The Internet of Multimedia Things(IoMT)refers to a network of interconnected multimedia devices that communicate with each other over the Internet.Recently,smart healthcare has emerged as a significant application of the IoMT,particularly in the context of knowledge‐based learning systems.Smart healthcare systems leverage knowledge‐based learning to become more context‐aware,adaptable,and auditable while maintain-ing the ability to learn from historical data.In smart healthcare systems,devices capture images,such as X‐rays,Magnetic Resonance Imaging.The security and integrity of these images are crucial for the databases used in knowledge‐based learning systems to foster structured decision‐making and enhance the learning abilities of AI.Moreover,in knowledge‐driven systems,the storage and transmission of HD medical images exert a burden on the limited bandwidth of the communication channel,leading to data trans-mission delays.To address the security and latency concerns,this paper presents a lightweight medical image encryption scheme utilising bit‐plane decomposition and chaos theory.The results of the experiment yield entropy,energy,and correlation values of 7.999,0.0156,and 0.0001,respectively.This validates the effectiveness of the encryption system proposed in this paper,which offers high‐quality encryption,a large key space,key sensitivity,and resistance to statistical attacks.

硅烷偶联剂对木粉/HDPE复合材料力学与吸水性能的影响

采用乙烯基三甲氧基硅烷(A-171)、乙烯基三乙氧基硅烷(A-151)、γ-氨丙基三乙氧基硅烷(DB-550)、γ-缩水甘油醚氧基丙基三甲氧基硅烷(DB-560)、γ-甲基丙烯酰氧基丙基三甲氧基硅烷(DB-570)和γ-巯丙基三乙氧基硅烷(DB-580)6种含有不同取代基的硅烷偶联剂对木粉(WF)进行表面处理,然后与高密度聚乙烯(HDPE)混合挤出制备处理木粉/HDPE复合材料。对复合材料的拉伸、弯曲和无缺口冲击强度及吸水率进行测试,采用扫描电子显微镜(SEM)观察其断面微观形态,并对硅烷处理前后的木粉进行红外光谱(FTIR)分析,从而筛选出较适宜的硅烷偶联剂。结果表明:经这6种偶联剂对木粉处理后,复合材料的力学性能、耐水性和界面相容性都有所改善,其中A-171的处理效果最好,处理后复合材料的弯曲、拉伸和无缺口冲击强度分别提高了31.59%,31.26%和49.29%,冷水和沸水吸水率分别降低了59.71%和48.29%。通过对复合材料SEM观察和FTIR分析可知:A-171成功与木粉发生缩聚反应,最有效地改善了复合材料的界面相容性。这应归因于乙烯基三甲氧基硅烷兼有适宜地与木粉发生缩聚接枝反应及可能与HDPE发生加聚接枝反应的综合优势,偶联作用强,使木/塑界面相容性显著改善。

针对混合极性的并行表格技术的遗传算法

针对混合极性的最佳极性优化问题,提出一种基于并行表格技术的遗传算法.在3n混合极性搜索过程中,采用并行表格技术计算遗传算法中种群的适应度函数;并行表格技术不按变量顺序产生on-set项,克服了在传统表格技术中顺序产生相关项造成数据相关性问题,有效地提高了CPU利用率.实验结果表明文中算法在保证最优结果的同时,可平均缩短8%的处理时间.

或符合全展开式的分解转换算法

针对现有算法在处理多变量实际电路时间开销较大的问题,提出或符合全展开式的分解转换算法.cj最大项展开式和dj最大项展开式在相同极性下两者之间存在转换矩阵,而矩阵运算复杂度较高,把矩阵的运算简化成与和非的位运算,从而大量地节省了运算时间;在此基础上,将cj最大项展开式分解到不同的分组中,提出了分解算法,避免了矩阵的重复计算,再次缩短了计算时间.为了避免cj最大项展开式中过多最大项而造成转化时间开销增加,还提出了基于cj最小项的分解算法.实验结果表明,包含算法适用于处理小变量,但在处理多变量时时间开销增大,而采用了分解算法后,可极大减少转换时间开销.

中英两国珠宝首饰消费特点研究

英国作为全球主要的珠宝首饰消费大国之一,近年来由于其珠宝首饰需求高于供给的差距急剧扩大,使其在整体上出现了供给赤字。中国是一个珠宝生产大国,应抓住这种时机扩大对英国的出口。通过研究中英两国珠宝首饰消费特点发现,英国从2001年到2005年人均珠宝首饰消费额占个人可支配年收入的0.68%逐年增长到0.77%;其珠宝首饰需求-收入弹性均大于1(平均为2.262),珠宝首饰消费会随着消费者收入的增长而更快地增加。分析认为,中国具有竞争优势的出口珠宝首饰产品为项链、耳饰和珍珠戒指,玉雕产品的题材应以西方文化为主。预计英国2007年的珠宝首饰消费额为130亿美元,到2010年可达到160亿美元,为中国的珠宝首饰出口英国提供有益的参考。

中国国家助学贷款两难问题的信息不对称范式研究

本文从信息不对称的视角分析了国家助学贷款中出现“两难”问题的原因:放贷前提供资助者对申请者中的投机行为采取谨慎态度,使助学贷款业务中出现非贫困学生“驱逐”贫困学生的“逆向选择”,造成贫困学生“贷款难”;学生获贷后受到多种主客观因素影响,可能产生“道德风险”,造成“还款难”;并在此基础上提出了六条政策建议。

面向多优化目标的有限状态机状态分配

针对有限状态机状态分配问题,提出一种不仅考虑面积,同时也考虑功耗的算法.借鉴接力跑算法的基本思想提出了全新的粗略搜索方法、聚焦搜索方法、指引操作和传递操作.为了克服局部最优和快速收敛的问题,算法中分成粗略搜索和聚焦搜索,粗略搜索采用旋转和非邻交换方法大幅度修改解,而聚焦搜索采用相邻交换方法小幅度修改解;指引操作利用概率计算来引导优化取得更佳解,传递操作则通过组合最优解和当前解产生新的解以克服局部最优解问题.实验结果表明,文中算法在面积、功耗和CPU时间三方面性能指标都获得了理想的结果.

消费者脆弱性与寿险非道德销售行为

在区分非道德销售行为事件基础上,借用某寿险公司消费者保单数据,运用稀有事件偏差修正估计(Rare events biased-corrected estimates)模型,可以测度消费者脆弱性(以教育水平为度量指标)对非道德销售行为发生几率的影响。研究发现:消费者教育脆弱性与销售人员的非道德销售行为有显著的正相关关系;对销售人员分类后进一步考察,低学历相对于高学历的销售人员,男性相对女性销售员,更容易实施非道德销售策略。这就从消费者脆弱性视角重新解释了销售人员的非道德销售行为,既可丰富该领域的研究文献,亦可为寿险公司保单管理和监管部门政策制定和实施提供借鉴。

珠宝方向MBA教育探讨

培养具有行业特色是未来MBA教育发展的趋势。珠宝方向MBA教育的创办不仅满足了珠宝行业的巨大需求,也填补了国内外空白,具有重要的现实意义。从师资队伍、课程设置、国际合作等方面的实证分析证明,珠宝方向MBA教育目前在国内是可行的,可推动MBA教育朝着多元化、市场化和国际化方向健康发展。提出成立专业(家)委员会指导教学工作,通过产学合作教育模式、校内产学研结合模式强化实践操作,培养应用型珠宝企业管理人才。

Self-management of coronary heart disease in older patients after elective percutaneous transluminal coronary angioplasty

Objective To explore how older patients self-manage their coronary heart disease （CHD） aider undergoing elective percutaneous transluminal coronary angioplasty （PTCA）. Methods This mixed methods study used a sequential, explanatory design and recruited a convenience sample of patients （n = 93） approximately three months after elective PTCA. The study was conducted in two phases. Quantitative data collected in Phase 1 by means of a self-administered survey were subject to univariate and bivariate analysis. Phase 1 findings in- formed the purposive samplhag for Phase 2 where ten participants were selected from the original sample for an in-depth interview. Qualita- tive data were analysed using thematic analysis. This paper will primarily report the findings from a sub-group of older participants （n = 47） classified as 65 years of age or older. Results 78.7% （n = 37） of participants indicated that they would manage recurring angina symptoms by taking glyceryl trinitrate and 34% （n = 16） thought that resting would help. Regardless of the duration or severity of the symptoms 40.5% （n = 19） would call their general practitioner or an emergency ambulance for assistance during any recurrence of angina symptoms. Older participants weighed less （P = 0.02） and smoked less （P = 0.01） than their younger counterparts in the study. Age did not seem to affect PTCA patients＇ likelihood of altering dietary factors such as fruit, vegetable and saturated fat consumption （P = 0.237）. Conclusions The findings suggest that older people in the study were less likely to know how to correctly manage any recurring angina symptoms than their younger counterparts but they had fewer risk factors for CHD. Age was not a factor that influenced participants＇ likelihood to alter lifestyle factors.

A Novel Automatic Classification System Based on Hybrid Unsupervised and Supervised Machine Learning for Electrospun Nanofibers

The manufacturing of nanomaterials by the electrospinning process requires accurate and meticulous inspection of related scanning electron microscope(SEM)images of the electrospun nanofiber,to ensure that no structural defects are produced.The presence of anomalies prevents practical application of the electrospun nanofibrous material in nanotechnology.Hence,the automatic monitoring and quality control of nanomaterials is a relevant challenge in the context of Industry 4.0.In this paper,a novel automatic classification system for homogenous(anomaly-free)and non-homogenous(with defects)nanofibers is proposed.The inspection procedure aims at avoiding direct processing of the redundant full SEM image.Specifically,the image to be analyzed is first partitioned into subimages(nanopatches)that are then used as input to a hybrid unsupervised and supervised machine learning system.In the first step,an autoencoder(AE)is trained with unsupervised learning to generate a code representing the input image with a vector of relevant features.Next,a multilayer perceptron(MLP),trained with supervised learning,uses the extracted features to classify non-homogenous nanofiber(NH-NF)and homogenous nanofiber(H-NF)patches.The resulting novel AE-MLP system is shown to outperform other standard machine learning models and other recent state-of-the-art techniques,reporting accuracy rate up to92.5%.In addition,the proposed approach leads to model complexity reduction with respect to other deep learning strategies such as convolutional neural networks(CNN).The encouraging performance achieved in this benchmark study can stimulate the application of the proposed scheme in other challenging industrial manufacturing tasks.

FPGA PLACEMENT OPTIMIZATION BY TWO-STEP UNIFIED GENETIC ALGORITHM AND SIMULATED ANNEALING ALGORITHM

Genetic Algorithm (GA) is a biologically inspired technique and widely used to solve numerous combinational optimization problems. It works on a population of individuals, not just one single solution. As a result, it avoids converging to the local optimum. However, it takes too much CPU time in the late process of GA. On the other hand, in the late process Simulated Annealing (SA) converges faster than GA but it is easily trapped to local optimum. In this letter, a useful method that unifies GA and SA is introduced, which utilizes the advantage of the global search ability of GA and fast convergence of SA. The experimental results show that the proposed algorithm outperforms GA in terms of CPU time without degradation of performance. It also achieves highly comparable placement cost compared to the state-of-the-art results obtained by Versatile Place and Route (VPR) Tool.

R2N: A Novel Deep Learning Architecture for Rain Removal from Single Image

Visual degradation of captured images caused by rainy streaks under rainy weather can adversely affect the performance of many open-air vision systems.Hence,it is necessary to address the problem of eliminating rain streaks from the individual rainy image.In this work,a deep convolution neural network(CNN)based method is introduced,called Rain-Removal Net(R2N),to solve the single image de-raining issue.Firstly,we decomposed the rainy image into its high-frequency detail layer and lowfrequency base layer.Then,we used the high-frequency detail layer to input the carefully designed CNN architecture to learn the mapping between it and its corresponding derained high-frequency detail layer.The CNN architecture consists of four convolution layers and four deconvolution layers,as well as three skip connections.The experiments on synthetic and real-world rainy images show that the performance of our architecture outperforms the compared state-of-the-art de-raining models with respects to the quality of de-rained images and computing efficiency.

A Lightweight Deep Autoencoder Scheme for Cyberattack Detection in the Internet of Things

The Internet of things(IoT)is an emerging paradigm that integrates devices and services to collect real-time data from surroundings and process the information at a very high speed to make a decision.Despite several advantages,the resource-constrained and heterogeneous nature of IoT networks makes them a favorite target for cybercriminals.A single successful attempt of network intrusion can compromise the complete IoT network which can lead to unauthorized access to the valuable information of consumers and industries.To overcome the security challenges of IoT networks,this article proposes a lightweight deep autoencoder(DAE)based cyberattack detection framework.The proposed approach learns the normal and anomalous data patterns to identify the various types of network intrusions.The most significant feature of the proposed technique is its lower complexity which is attained by reducing the number of operations.To optimally train the proposed DAE,a range of hyperparameters was determined through extensive experiments that ensure higher attack detection accuracy.The efficacy of the suggested framework is evaluated via two standard and open-source datasets.The proposed DAE achieved the accuracies of 98.86%,and 98.26%for NSL-KDD,99.32%,and 98.79%for the UNSW-NB15 dataset in binary class and multi-class scenarios.The performance of the suggested attack detection framework is also compared with several state-of-the-art intrusion detection schemes.Experimental outcomes proved the promising performance of the proposed scheme for cyberattack detection in IoT networks.

Classification of Citrus Plant Diseases Using Deep Transfer Learning

In recent years,the field of deep learning has played an important role towards automatic detection and classification of diseases in vegetables and fruits.This in turn has helped in improving the quality and production of vegetables and fruits.Citrus fruits arewell known for their taste and nutritional values.They are one of the natural and well known sources of vitamin C and planted worldwide.There are several diseases which severely affect the quality and yield of citrus fruits.In this paper,a new deep learning based technique is proposed for citrus disease classification.Two different pre-trained deep learning models have been used in this work.To increase the size of the citrus dataset used in this paper,image augmentation techniques are used.Moreover,to improve the visual quality of images,hybrid contrast stretching has been adopted.In addition,transfer learning is used to retrain the pre-trainedmodels and the feature set is enriched by using feature fusion.The fused feature set is optimized using a meta-heuristic algorithm,the Whale Optimization Algorithm(WOA).The selected features are used for the classification of six different diseases of citrus plants.The proposed technique attains a classification accuracy of 95.7%with superior results when compared with recent techniques.

Dynamic Hand Gesture Recognition Using 3D-CNN and LSTM Networks

Recognition of dynamic hand gestures in real-time is a difficult task because the system can never know when or from where the gesture starts and ends in a video stream.Many researchers have been working on visionbased gesture recognition due to its various applications.This paper proposes a deep learning architecture based on the combination of a 3D Convolutional Neural Network(3D-CNN)and a Long Short-Term Memory(LSTM)network.The proposed architecture extracts spatial-temporal information from video sequences input while avoiding extensive computation.The 3D-CNN is used for the extraction of spectral and spatial features which are then given to the LSTM network through which classification is carried out.The proposed model is a light-weight architecture with only 3.7 million training parameters.The model has been evaluated on 15 classes from the 20BN-jester dataset available publicly.The model was trained on 2000 video-clips per class which were separated into 80%training and 20%validation sets.An accuracy of 99%and 97%was achieved on training and testing data,respectively.We further show that the combination of 3D-CNN with LSTM gives superior results as compared to MobileNetv2+LSTM.

Machine learning empowered COVID-19 patient monitoring using non-contact sensing:An extensive review

The severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),which caused the coronavirus disease 2019(COVID-19)pandemic,has affected more than 400 million people worldwide.With the recent rise of new Delta and Omicron variants,the efficacy of the vaccines has become an important question.The goal of various studies has been to limit the spread of the virus by utilizing wireless sensing technologies to prevent human-to-human interactions,particularly for healthcare workers.In this paper,we discuss the current literature on invasive/contact and non-invasive/noncontact technologies(including Wi-Fi,radar,and software-defined radio)that have been effectively used to detect,diagnose,and monitor human activities and COVID-19 related symptoms,such as irregular respiration.In addition,we focused on cutting-edge machine learning algorithms(such as generative adversarial networks,random forest,multilayer perceptron,support vector machine,extremely randomized trees,and k-nearest neighbors)and their essential role in intelligent healthcare systems.Furthermore,this study highlights the limitations related to non-invasive techniques and prospective research directions.

Password Pattern and Vulnerability Analysis for Web and Mobile Applications

Text-based passwords are heavily used to defense for many web and mobile applications. In this paper, we investigated the patterns and vulnerabilities for both web and mobile applications based on conditions of the Shannon entropy, Guessing entropy and Minimum entropy. We show how to substantially improve upon the strength of passwords based on the analysis of text-password entropies. By analyzing the passwords datasets of Rockyou and 163.com, we believe strong password can be designed based on good usability, deployability, rememberbility, and security entropies.

EEG-Based Neonatal Sleep Stage Classification Using Ensemble Learning

Sleep stage classification can provide important information regarding neonatal brain development and maturation.Visual annotation,using polysomnography(PSG),is considered as a gold standard for neonatal sleep stage classification.However,visual annotation is time consuming and needs professional neurologists.For this reason,an internet of things and ensemblebased automatic sleep stage classification has been proposed in this study.12 EEG features,from 9 bipolar channels,were used to train and test the base classifiers including convolutional neural network,support vector machine,and multilayer perceptron.Bagging and stacking ensembles are then used to combine the outputs for final classification.The proposed algorithm can reach a mean kappa of 0.73 and 0.66 for 2-stage and 3-stage(wake,active sleep,and quiet sleep)classification,respectively.The proposed network works as a semi-real time application because a smoothing filter is used to hold the sleep stage for 3 min.The high-performance parameters and its ability to work in semi real-time makes it a promising candidate for use in hospitalized newborn infants.