CeTrivium:A Stream Cipher Based on Cellular Automata for Securing Real-Time Multimedia Transmission

Due to their significant correlation and redundancy,conventional block cipher cryptosystems are not efficient in encryptingmultimedia data.Streamciphers based onCellularAutomata(CA)can provide amore effective solution.The CA have recently gained recognition as a robust cryptographic primitive,being used as pseudorandom number generators in hash functions,block ciphers and stream ciphers.CA have the ability to perform parallel transformations,resulting in high throughput performance.Additionally,they exhibit a natural tendency to resist fault attacks.Few stream cipher schemes based on CA have been proposed in the literature.Though,their encryption/decryption throughput is relatively low,which makes them unsuitable formultimedia communication.Trivium and Grain are efficient stream ciphers that were selected as finalists in the eSTREAM project,but they have proven to be vulnerable to differential fault attacks.This work introduces a novel and scalable stream cipher named CeTrivium,whose design is based on CA.CeTrivium is a 5-neighborhood CA-based streamcipher inspired by the designs of Trivium and Grain.It is constructed using three building blocks:the Trivium(Tr)block,the Nonlinear-CA(NCA)block,and the Nonlinear Mixing(NM)block.The NCA block is a 64-bit nonlinear hybrid 5-neighborhood CA,while the Tr block has the same structure as the Trivium stream cipher.The NM block is a nonlinear,balanced,and reversible Boolean function that mixes the outputs of the Tr and NCA blocks to produce a keystream.Cryptanalysis of CeTrivium has indicated that it can resist various attacks,including correlation,algebraic,fault,cube,Meier and Staffelbach,and side channel attacks.Moreover,the scheme is evaluated using histogramand spectrogramanalysis,aswell as several differentmeasurements,including the correlation coefficient,number of samples change rate,signal-to-noise ratio,entropy,and peak signal-to-noise ratio.The performance of CeTrivium is evaluated and compared with other state-of-the-art techniques.CeTrivium outperforms them in terms of encryption throughput while maintaining high security.CeTrivium has high encryption and decryption speeds,is scalable,and resists various attacks,making it suitable for multimedia communication.

Improvement Design for Distributed Real-Time Stream Processing Systems

In the era of Big Data, typical architecture of distributed real-time stream processing systems is the combination of Flume, Kafka, and Storm. As a kind of distributed message system, Kafka has the characteristics of horizontal scalability and high throughput, which is manly deployed in many areas in order to address the problem of speed mismatch between message producers and consumers. When using Kafka, we need to quickly receive data sent by producers. In addition, we need to send data to consumers quickly. Therefore, the performance of Kafka is of critical importance to the performance of the whole stream processing system. In this paper, we propose the improved design of real-time stream processing systems, and focus on improving the Kafka's data loading process.We use Kafka cat to transfer data from the source to Kafka topic directly, which can reduce the network transmission. We also utilize the memory file system to accelerate the process of data loading, which can address the bottleneck and performance problems caused by disk I/O. Extensive experiments are conducted to evaluate the performance, which show the superiority of our improved design.

A Real-Time TCP Stream Reassembly Mechanism in High-Speed Network

With the continual growth of the variety and complexity of network crime means, the traditional packet feature matching cannot detect all kinds of intrusion behaviors completely. It is urgent to reassemble network stream to perform packet processing at a semantic level above the network layer. This paper presents an efficient TCP stream reassembly mechanism for real-time processing of high-speed network traffic. By analyzing the characteristics of network stream in high-speed network and TCP connection establishment process, several polices for designing the reassembly mechanism are built. Then, the reassembly implementation is elaborated in accordance with the policies. Finally, the reassembly mechanism is compared with the traditional reassembly mechanism by the network traffic captured in a typical gigabit gateway. Experiment results illustrate that the reassembly mechanism is efficient and can satisfy the real-time property requirement of traffic analysis system in high-speed network.

Stream-computing of High Accuracy On-board Real-time Cloud Detection for High Resolution Optical Satellite Imagery

This paper focuses on the time efficiency for machine vision and intelligent photogrammetry, especially high accuracy on-board real-time cloud detection method. With the development of technology, the data acquisition ability is growing continuously and the volume of raw data is increasing explosively. Meanwhile, because of the higher requirement of data accuracy, the computation load is also becoming heavier. This situation makes time efficiency extremely important. Moreover, the cloud cover rate of optical satellite imagery is up to approximately 50%, which is seriously restricting the applications of on-board intelligent photogrammetry services. To meet the on-board cloud detection requirements and offer valid input data to subsequent processing, this paper presents a stream-computing of high accuracy on-board real-time cloud detection solution which follows the “bottom-up” understanding strategy of machine vision and uses multiple embedded GPU with significant potential to be applied on-board. Without external memory, the data parallel pipeline system based on multiple processing modules of this solution could afford the “stream-in, processing, stream-out” real-time stream computing. In experiments, images of GF-2 satellite are used to validate the accuracy and performance of this approach, and the experimental results show that this solution could not only bring up cloud detection accuracy, but also match the on-board real-time processing requirements.

Priority-Based Real-Time Stream Coding over Multi-Channel with Packet Erasures

Multi-channel can be used to provide higher transmission ability to the bandwidth-intensive and delay-sensitive real-time streams. However, traditional channel capacity theories and coding schemes are seldom designed for the real-time streams with strict delay constraint, especially in multi-channel context. This paper considers a real-time stream system, where real-time messages with different importance should be transmitted through several packet erasure channels, and be decoded by the receiver within a fixed delay. Based on window erasure channels and i.i.d.(identically and independently distributed) erasure channels, we derive the Multi-channel Real-time Stream Transmission(MRST) capacity models for Symmetric Real-time(SR) streams and Asymmetric Real-time(AR) streams respectively. Moreover, for window erasures, a Maximum Equilibrium Intra-session Code(MEIC) is presented for SR and AR streams, and is shown able to asymptotically achieve the theoretical MRST capacity. For i.i.d. erasures, we propose an Adaptive Maximum Equilibrium Intra-session Code(AMEIC), and then prove AMEIC can closely approach the MRST transmission capacity. Finally, the performances of the proposed codes are verified by simulations.

Adaptive Learning Video Streaming with QoE in Multi-Home Heterogeneous Networks

In recent years,real-time video streaming has grown in popularity.The growing popularity of the Internet of Things(IoT)and other wireless heterogeneous networks mandates that network resources be carefully apportioned among versatile users in order to achieve the best Quality of Experience(QoE)and performance objectives.Most researchers focused on Forward Error Correction(FEC)techniques when attempting to strike a balance between QoE and performance.However,as network capacity increases,the performance degrades,impacting the live visual experience.Recently,Deep Learning(DL)algorithms have been successfully integrated with FEC to stream videos across multiple heterogeneous networks.But these algorithms need to be changed to make the experience better without sacrificing packet loss and delay time.To address the previous challenge,this paper proposes a novel intelligent algorithm that streams video in multi-home heterogeneous networks based on network-centric characteristics.The proposed framework contains modules such as Intelligent Content Extraction Module(ICEM),Channel Status Monitor(CSM),and Adaptive FEC(AFEC).This framework adopts the Cognitive Learning-based Scheduling(CLS)Module,which works on the deep Reinforced Gated Recurrent Networks(RGRN)principle and embeds them along with the FEC to achieve better performances.The complete framework was developed using the Objective Modular Network Testbed in C++(OMNET++),Internet networking(INET),and Python 3.10,with Keras as the front end and Tensorflow 2.10 as the back end.With extensive experimentation,the proposed model outperforms the other existing intelligentmodels in terms of improving the QoE,minimizing the End-to-End Delay(EED),and maintaining the highest accuracy(98%)and a lower Root Mean Square Error(RMSE)value of 0.001.

基于声流的非接触超声电机理论分析与试验研究

针对传统超声电机存在严重的摩擦磨损及难以长时间连续工作的问题,本文中基于声流原理设计了1种新型的非接触式超声电机.建立电机定子谐响应有限元分析模型,获得其工作频率及结构振型,并对悬浮间隙内的声流场进行建模,采用雷诺应力法求解声流场驱动力,分析不同激励电压下转子转速以及驱动力的变化规律.为验证模型的合理性,搭建试验测试系统并进行性能测试对比分析.结果表明,该电机在声流驱动下能够稳定地运转,转速随着电压的升高而单调上升,驱动电压为1430 V时,转子转速可以达到28 r/min.理论结果与试验结果具有较好的一致性,验证了模型的合理性.

NFA:A neural factorization autoencoder based online telephony fraud detection

The proliferation of internet communication channels has increased telecom fraud,causing billions of euros in losses for customers and the industry each year.Fraudsters constantly find new ways to engage in illegal activity on the network.To reduce these losses,a new fraud detection approach is required.Telecom fraud detection involves identifying a small number of fraudulent calls from a vast amount of call traffic.Developing an effective strategy to combat fraud has become challenging.Although much effort has been made to detect fraud,most existing methods are designed for batch processing,not real-time detection.To solve this problem,we propose an online fraud detection model using a Neural Factorization Autoencoder(NFA),which analyzes customer calling patterns to detect fraudulent calls.The model employs Neural Factorization Machines(NFM)and an Autoencoder(AE)to model calling patterns and a memory module to adapt to changing customer behaviour.We evaluate our approach on a large dataset of real-world call detail records and compare it with several state-of-the-art methods.Our results show that our approach outperforms the baselines,with an AUC of 91.06%,a TPR of 91.89%,an FPR of 14.76%,and an F1-score of 95.45%.These results demonstrate the effectiveness of our approach in detecting fraud in real-time and suggest that it can be a valuable tool for preventing fraud in telecommunications networks.

河床沉积物竖管试验求参方法应用与评价

利用和田河干流下游河段河床沉积物竖管试验数据,开展不同竖管试验求参方法的应用与评价工作,选取HTST01试验点进行垂向渗透试验,选取HTST02试验点分别进行水平方向渗透试验和垂向渗透试验,分析传统计算法的渗透系数取值合理性以及其与直线图解法计算结果之间的差异。结果表明:传统计算法获取的各试验工况渗透系数近似服从具有一定偏度和峰度的非标准正态分布,均具有中等变异性;采用算术平均值进行参数取值具有一定的合理性和适用性,而采用随机计算值时参数取值的误差大小具有偶然性和不确定性。直线图解法能够有效避免随机误差的产生,但其获取的计算结果与传统计算法的算术平均值存在一定差异,其相对误差平均值为10.59%,最大相对误差值超过27.00%。在竖管试验应用过程中,不应忽视计算方法求参取值合理性的影响。

基于Spark Streaming的在线KMeans聚类模型研究

针对基于Map Reduce框架处理海量数据实时响应能力较差的问题,设计并实现了基于Spark Streaming的在线计算模型进行大规模的KMeans聚类分析。该模型将整个过程分为数据接入、在线训练等模块,各模块通过数据流连通形成任务实体,提交到Spark分布式集群运行完成。通过比对分析实验和性能检测,验证了该在线KMeans聚类模型具有高吞吐、低延迟的优势,且集群运行状况良好。

Flink和Spark Streaming流式计算模型比较分析

数据时代的到来,使数据成为一种重要的战略资源。如何更好地利用这些数据,成为摆在政府、学者和工程师等人面前的一大难题。随着大数据和物联网的发展进步,数据量呈爆发式增长,随之而来的关于数据存储、计算和分析的问题急需解决。因此,主要对Spark Streaming和Flink两个流式计算模型进行对比研究,从原理、实现机制和使用场景等角度进行多维度对比分析,以期使用者能结合自己的业务场景,更好地选择适合的流式计算框架。

Implementation of Mobile Streaming Media Player Based on BREW

Nowadays mobile streaming service through cell phone is becoming the highlight of new value-added mobile services. Based on the present CDMAlx wireless data network and Binary Runtime Environment for Wireless （BREW） platform, adopting compression technologies of H.264 and QCP, a set of streaming media players are designed and implemented, and the principle, structure, key technologies and performance analysis of this system are introduced. This player works well in practice.

View Direction Adaptive 360 Degree Video Streaming System Based on Projected Area

360 video streaming services over the network are becoming popular. In particular, it is easy to experience 360 video through the already popular smartphone. However, due to the nature of 360 video, it is difficult to provide stable streaming service in general network environment because the size of data to send is larger than that of conventional video. Also, the real user's viewing area is very small compared to the sending amount. In this paper, we propose a system that can provide high quality 360 video streaming services to the users more efficiently in the cloud. In particular, we propose a streaming system focused on using a head mount display (HMD).

试飞遥测实时监控分析报告自动生成系统设计

为提高试飞遥测实时监控系统数据分析综合能力,优化实时监控服务质量,面向遥测数据量大、高实时计算的遥测实时监控场景,设计一种实时监控分析报告自动生成系统。系统基于Hadoop的大数据分布式框架设计,以SpringBoot敏捷开发框架搭建,采用流式计算引擎Storm用于实时数据计算、离线计算引擎Spark用于数据统计分析,具备数据实时采集、数据实时计算、数据统计分析、数据分类存储以及监控报告管理核心功能。目前,系统已经在国产某大型客机飞行试验过程中使用,满足在飞行结束后3 min内自动生成本架次全历程监控分析报告,可作为快速评价试飞过程的辅助决策手段,对于缩短试飞周期、提高试飞效率具有重要意义。

Operational guidance for aeration and flow augmentation for the Chicago Area Waterway System—A case study

The Chicago Area Waterway System(CAWS)is a 133.9 km branching network of navigable waterways controlled by hydraulic structures,in which the majority of the flow is treated wastewater effluent and there are periods of substantial combined sewer overflows.The CAWS comprises a network of effluent dominated streams.More stringent dissolved oxygen(DO)standards and a reduced flow augmentation allowance have been recently applied to the CAWS.Therefore,a carefully calibrated and verified one-dimensional flow and water quality model was applied to the CAWS to determine emission-based real-time control guidelines for the operation of flow augmentation and aeration stations.The goal of these guidelines was to attain DO standards at least 95%of the time.The“optimal”guidelines were tested for representative normal,dry,and wet years.The finally proposed guidelines were found in the simulations to attain the 95%target for nearly all locations in the CAWS for the three test years.The developed operational guidelines have been applied since 2018 and have shown improved attainment of the DO standards throughout the CAWS while at the same time achieving similar energy use at the aeration stations on the Calumet River system,greatly lowered energy use on the Chicago River system,and greatly lowered discretionary diversion from Lake Michigan,meeting the recently enacted lower amount of allowed annual discretionary diversion.This case study indicates that emission-based real-time control developed from a well calibrated model holds potential to help many receiving water bodies achieve high attainment of water quality standards.

A distributed real-time data prediction framework for large-scale time-series data using stream processing

Purpose-The purpose of this paper is to propose a data prediction framework for scenarios which require forecasting demand for large-scale data sources,e.g.,sensor networks,securities exchange,electric power secondary system,etc.Concretely,the proposed framework should handle several difficult requirements including the management of gigantic data sources,the need for a fast self-adaptive algorithm,the relatively accurate prediction of multiple time series,and the real-time demand.Design/methodology/approach-First,the autoregressive integrated moving average-based prediction algorithm is introduced.Second,the processing framework is designed,which includes a time-series data storage model based on the HBase,and a real-time distributed prediction platform based on Storm.Then,the work principle of this platform is described.Finally,a proof-of-concept testbed is illustrated to verify the proposed framework.Findings-Several tests based on Power Grid monitoring data are provided for the proposed framework.The experimental results indicate that prediction data are basically consistent with actual data,processing efficiency is relatively high,and resources consumption is reasonable.Originality/value-This paper provides a distributed real-time data prediction framework for large-scale time-series data,which can exactly achieve the requirement of the effective management,prediction efficiency,accuracy,and high concurrency for massive data sources.

A domain-independent methodology to analyze IoT data streams in real-time.A proof of concept implementation for anomaly detection from environmental data

Pushed by the Internet of Things(IoT)paradigm modern sensor networks monitor a wide range of phenomena,in areas such as environmental monitoring,health care,industrial processes,and smart cities.These networks provide a continuous pulse of the almost infinite activities that are happening in the physical space and are thus,key enablers for a Digital Earth Nervous System.Nevertheless,the rapid processing of these sensor data streams still continues to challenge traditional data-handling solutions and new approaches are being requested.We propose a generic answer to this challenge,which has the potential to support any form of distributed real-time analysis.This neutral methodology follows a brokering approach to work with different kinds of data sources and uses web-based standards to achieve interoperability.As a proof of concept,we implemented the methodology to detect anomalies in real-time and applied it to the area of environmental monitoring.The developed system is capable of detecting anomalies,generating notifications,and displaying the recent situation to the user.

Real-time ultra-wideband video streaming in long-reach passive optical networks with wireless radiation in the 10 and 60 GHz bands

Real-time video streaming using ultra-wideband(UWB) technology is experimentally demonstrated along long-reach passive optical networks(LR-PONs) with different wired and wireless reaches. Experimental tests using external and direct modulation with UWB wireless radiation in the 10- and 60-GHz bands are performed. An ultra-bendable fiber is also considered for a last-mile distribution. The video quality at the output of the optical fiber infrastructure of the LR-PON is assessed using the error vector magnitude(EVM), and the link quality indicator(LQI) is used as a figure of merit after wireless radiation. An EVM below –17 dB is achieved for both externally and directly modulated LR-PONs comprising up to 125 km of optical fiber. EVM improvement is observed for longer LR-PONs when directly modulated lasers(DMLs) are used because of the amplitude gain provided by the combined effect of dispersion and DML's chirp. Compared with optical back-to-back operation, the LQI level degrades to the maximum around 20% for LR-PONs ranging between 75 and 125 km of fiber reach and with a wireless coverage of 2 m in the 10-GHz UWB band. The same level of LQI degradation is observed using the 60-GHz UWB band with a LR-PON integrating 101 km of access network, a last-mile distribution using ultra-bendable fiber, and a 5.2-m wireless link.

渭河(陕西段)河道自净需水量研究

建立了河段自净最小需水量计算模型。为了使研究区段内的水质达标,该区段内的自净需水量应该是从区段内所有河段最小需水量中取其最大量。应用该方法分达标排污和现状排污对渭河(陕西段)河道自净需水量做了计算。结果分析表明:渭河河道自净需水量随年径流流量变化不大,自净缺水严重,主要出现在非汛期,达标排污较于现状排污,自净需水量大幅度下降,自净缺水主要出现在1,2,12三个月。