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.

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.

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.

Equation based rate control scheme for video streaming over wireless channels with link level ARQ

Equation based TCP-friendly rate control (TFRC) protocol has been proposed to support video streaming applications. In order to improve TFRC performance in wireless channels, the link level automatic repeat request (ARQ) scheme is usually deployed. However, ARQ cannot ensure strict delay guarantees, especially over multihop links. This paper introduces a theoretical model to deduce an equation for packet size adjustment in transport layer to minimize retransmission delay by taking into con- sideration the causative reasons inducing retransmission in link layer. An enhanced TFRC (ETFRC) scheme is proposed inte- grating TFRC with variable packet size policy. Simulation results demonstrate that higher goodput, lower packet loss rate (PLR), lower frame transmission delay and jitter with good fairness can be achieved by our proposed mechanism.

A Novel Method for Real-Time Monitoring of Channel Siltation Based on Bistatic Scattering Theory

Monitoring the thickness changes of channel siltation is paramount in safeguarding navigation and guiding dredging, This paper presents a novel method for realizing the field monitoring of channel siltation in real time. The method is based on the bistatic scattering theory and concerned more with the receiving and processing of multipath signal at high-frequency and small grazing angle. By use of the multipath propagation structure of underwater acoustic channel, the method obtains the silt thickness by calculating the relative time delay of acoustic signals between the direct and the shortest bottom reflected paths. Bistatic transducer pairs are employed to transmit and receive the acoustic signals, and the GPS time synchronization technology is introduced to synchronize the transmitter and receiver, The WRELAX （Weighted Fourier transforul and RELAX） algorithm is used to obtain the high resolution estimation of muhipath time delay. To examine the feasibility of the presented method and the accuracy and precision of the developed system, a series of sea trials are conducted in the southwest coast area of Dalian City, north of the Yellow Sea. The experimental results are compared with that using high-resolution dual echo sounder HydroBoxTM, and the uncertainty is smaller than ＋ 0.06 m. Compared with the existing means for measuring the silt thickness, the present method is innovative, and the system is stable, efficient and provides a better real-time performance. It especially suits monitoring the narrow channel with rapid changes of siltation.

APPLICATION OF TURBO CODES IN HIGH-SPEED REAL-TIME CHANNEL

The time delay of Turbo codes due to its iterative decoding is the main bottleneck of its application in real-time channel. However, the time delay can be greatly shortened through the adoption of parallel decod-ing algorithm, dividing the received bits into several sub-blocks and processing in parallel. This letter mainly discusses the applicability of turbo codes in high-speed real-time channel through the study of a parallel turbo decoding algorithm based on 3GPP-proposed turbo encoder and interleaver in various channel. Simulation re-sult shows that, by choosing an appropriate sub-block length, the time delay can be obviously shortened with-out degrading the performance and increasing hardware complexity, and furthermore indicates the applicability of Turbo codes in high-speed real-time channel.

An Efficient Secure Real-Time Concurrency Control Protocol

Secure real-time databases must simultaneously satisfy two requirements in guaranteeing data security and minimizing the missing deadlines ratio of transactions. However, these two requirements can conflict with each other and achieve one requirement is to sacrifice the other. This paper presents a secure real-time concurrency control protocol based on optimistic method. The concurrency control protocol incorporates security constraints in a real-time optimistic concurrency control protocol and makes a suitable tradeoff between security and real-time requirements by introducing secure influence factor and real-time influence factor. The experimental results show the concurrency control protocol achieves data security without degrading real-time perform ance significantly.

Two-Phase Rate Adaptation Strategy for Improving Real-Time Video QoE in Mobile Networks

With the popularity of smart handheld devices, mobile streaming video has multiplied the global network traffic in recent years. A huge concern of users' quality of experience(Qo E) has made rate adaptation methods very attractive. In this paper, we propose a two-phase rate adaptation strategy to improve users' real-time video Qo E. First, to measure and assess video Qo E, we provide a continuous Qo E prediction engine modeled by RNN recurrent neural network. Different from traditional Qo E models which consider the Qo E-aware factors separately or incompletely, our RNN-Qo E model accounts for three descriptive factors(video quality, rebuffering, and rate change) and reflects the impact of cognitive memory and recency. Besides, the video playing is separated into the initial startup phase and the steady playback phase, and we takes different optimization goals for each phase: the former aims at shortening the startup delay while the latter ameliorates the video quality and the rebufferings. Simulation results have shown that RNN-Qo E can follow the subjective Qo E quite well, and the proposed strategy can effectively reduce the occurrence of rebufferings caused by the mismatch between the requested video rates and the fluctuated throughput and attains standout performance on real-time Qo E compared with classical rate adaption methods.

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).

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.

Rosgen stream classification and fluvial processes of the Shiyang River,China

The Shiyang River is an important ecological pillar in northwest China,sustaining Minqin oasis and its surrounding society.However,the basin has long been plagued by water scarcity and ecological fragility.Although the river classification is critical for understanding the complexity,diversity,and ecological functions of rivers,and the foundation of river management and watershed ecological restoration,it has not received adequate attention in this region.To obtain a deeper and comprehensive understanding of the Shiyang River,this study utilizes the Rosgen stream classification system to assess the river morphology,geomorphic features,and hydrologic processes.The results showed that seven first-level and fourteen second-level river types can be identified along 53 river sections of the Shiyang River.Further comparison analysis on the hydrologic parameters for each river type demonstrated a strong positive correlation between discharge and all river parameters.As discharge increased,channels with moderate to high width/depth ratios experienced significant lateral adjustments.A consistent channel gradient,coupled with higher discharge,facilitated the transition from single to multiple channels.Braiding tendencies were more pronounced in rivers where riverbeds were wider and shallower with higher stream power.Additionally,water-flow shear stress decreased with the increase in the width/depth ratio.This study offered critical insights into the Shiyang River’s forms and processes and for the river management and ecological restoration practices.

Heat and Mass Transfer on Non-Newtonian Peristaltic Flow in a Channel in Presence of Magnetic Field: Analytical and Numerical Analysis

A study has been arranged to investigate the flow of non-Newtonian fluid in a vertical asymmetrical channel using peristalsis. The porous medium allows the electrically conductive fluid to flow in the channel, while a uniform magnetic field is applied perpendicular to the flow direction. The analysis takes into account the combined influence of heat and mass transfer, including the effects of Soret and Dufour. The flow’s non-Newtonian behavior is characterized using a Casson rheological model. The fluid flow equations are examined within a wave frame of reference that has a wave velocity. The analytic solution is examined using long wavelengths and a small Reynolds number assumption. The stream function, temperature, concentration and heat transfer coefficient expressions are derived. The bvp4c function from MATLAB has been used to numerically solve the transformed equations. The flow characteristics have been analyzed using graphs to demonstrate the impacts of different parameters.

Credit Determination of Fibre Channel in Avionics Environment

Fibre channel （FC） is the main candidate architecture for ＂unified network＂. Flow control deals with the problem in which a device receives frames faster than it can process them. Credit is an important service parameter for fibre channel flow control. Configuring the credit reasonably can avoid buffer overflow in nodes. This paper derives the mathematic relationships among credit, bandwidth and message sets under real-time condition according as three main topologies of fibre channel, and proposes the credit determination and the optimal credit for typical message sets. This study is based on the features of hard real-time communications in avionics environment.

Quantitative Methods for Comparing Different Polyline Stream Network Models

Two techniques for exploring relative horizontal accuracy of complex linear spatial features are described and sample source code (pseudo code) is presented for this purpose. The first technique, relative sinuosity, is presented as a measure of the complexity or detail of a polyline network in comparison to a reference network. We term the second technique longitudinal root mean squared error (LRMSE) and present it as a means for quantitatively assessing the horizontal variance between two polyline data sets representing digitized (reference) and derived stream and river networks. Both relative sinuosity and LRMSE are shown to be suitable measures of horizontal stream network accuracy for assessing quality and variation in linear features. Both techniques have been used in two recent investigations involving extraction of hydrographic features from LiDAR elevation data. One confirmed that, with the greatly increased resolution of LiDAR data, smaller cell sizes yielded better stream network delineations, based on sinuosity and LRMSE, when using LiDAR-derived DEMs. The other demonstrated a new method of delineating stream channels directly from LiDAR point clouds, without the intermediate step of deriving a DEM, showing that the direct delineation from LiDAR point clouds yielded an excellent and much better match, as indicated by the LRMSE.

EFFECTS OF ORIGINAL PHASE AND TIME-DELAY ON THE HF CHANNEL PARAMETER ESTIMATION

It is proposed firstly that the original phase and the time-delay are the main factors which affect the measuring resolution of the multitone complex envelope method. The effects of these factors are analysed and checked by the computer simulation. Finally, three possible ways to eliminate these effects are given.

T-type calcium channel expression in cultured human neuroblastoma cells

Human neuroblastoma cells （SH-SY5Y） have similar structures and functions as neural cells and have been frequently used for cell culture studies of neural cell functions.Previous studies have revealed L-and N-type calcium channels in SH-SY5Y cells.However,the distribution of the low-voltage activated calcium channel （namely called T-type calcium channel,including Cav3.1,Cav3.2,and Cav3.3） in SH-SY5Y cells remains poorly understood.The present study detected mRNA and protein expres-sion of the T-type calcium channel （Cav3.1,Cav3.2,and Cav3.3） in cultured SH-SY5Y cells using real-time polymerase chain reaction （PCR） and western blot analysis.Results revealed mRNA and protein expression from all three T-type calcium channel subtypes in SH-SY5Y cells.Moreover,Cav3.1 was the predominant T-type calcium channel subtype in SH-SY5Y cells.

A stream layered cooperative relay multicast scheme and capacity analysis in cellular systems

It is widely believed that cooperative relay technologies can improve the throughput of multicasting in mobile cellular networks significantly, however, the mobility of the relay terrninals may cause frequent relay link outage. This paper proposes a stream layered cooperative relay scheme to deal with this problem. In order to study the characteristics of layered relay channels in the scheme, the capacity region is determined based on a single and a multi relay abstract model with streams layering. Besides, to satisfy the cellular network scenario, the results are extended to a wireless Gaussian channel model. The analysis and simulation results show that the scheme guarantees the continuity of the multicast streams, and maintains the high bandwidth of relay channel, with a slight loss on system capacity.