Code Stream Research Articles

Embedded System Based on Task Interval Data Interaction Model

In the Internet of Things era, more intelligent systems can communicate with each other. Embedded system combined with network communication applications has become the basis for Internet of Things research. The programmable logic unit designed by ARM architecture has great advantages in running speed, power control and so on. In the paper, to solve the problems of common embedded MCU control resources occupying a large amount of memory and the slow speed of building engineering simulation model, it is necessary to use the queuing connection algorithm model to directly input the timing physical characteristics of the code stream in the embedded system to calculate and match the timing physical characteristics of the output code stream. The optimization algorithm of DTF-MARTE to detect the probability of timing deviation is used in the paper. It is to detect the problem of inaccurate timing information in the demand. We compare the expected physical characteristics of the timing sequence and obtain the timing deviation probability of the output data stream. The model developed in this paper has the characteristic of dynamic reconfiguration of the task interval. The design of monotonically decreasing data tasks can be realized, and the reconfigured task modules are used for interacting the data buffer area and dynamically reconstructing the instruction overhead and transmission. We analyze the performance comparison between the proposed model and the traditional communication connection model. It proves that the proposed model can further improve the priority queue and guide the data flow. According to that method, the problem of asynchronous spatial data interaction by controlling and combining different communication modes in a large scene can be solved. Data interaction can be triggered at a fixed time, and mutual interference of randomly triggered wireless communication and data acquisition modules can be avoided. It can solve the problem of insufficient computing power when future embedded devices need massive data encryption in the Internet of Things era, and provide a new way of thinking for fast, safe and efficient implementation.

PEPesc: A TCP Performance Enhancing Proxy for Non-Terrestrial Networks

Non-terrestrial networks (NTNs) using flying objects such as satellites play key roles in the next-generation wireless system (6 G). The NTN links with long propagation delay and random packet losses pose a great challenge to the performance of Transport Control Protocol (TCP), which many Internet applications rely on. Performance enhancing proxy (PEP) is an easy-to-deploy approach for improving TCP's performance. In this paper, we design and implement a novel PEP called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PEPesc</i> which has two distinctive features. First, it features <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">retransmission-free</i> loss recovery, using an adaptive packet-level forward erasure correction method called streaming coding (SC). Second, as packet losses are recovered by SC, the congestion control problem is simplified to rate control and local acknowledgement between entities based on bandwidth estimation. Based on a queueing theoretic analysis of the design, we carefully devise a protocol and implement PEPesc as an open-source application. Extensive evaluations show that PEPesc can achieve much higher <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">and</i> smoother goodput than the canonical TCP variants and than other existing open-source PEPs in applications including <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iperf</i> and HTTP-based adaptive streaming, and achieves similar performance in web browsing. Finally, we also present a deployment case over a real-world geostationary satellite link.

Quantum-enhanced Chaotic Image Encryption: Strengthening Digital Data Security With 1-D Sine-based Chaotic Maps and Quantum Coding

The proposed work delves into the utilization of chaotic maps along with the quantum mechanisms to strengthen the security of the digital images along with addressing the limitations posed by conventional 1-D chaotic systems characterized by their pseudorandom and periodic attributes. Leveraging the inherent uncertainty in quantum theory, the proposed research introduces a new image encryption scheme. This method is built upon the concepts of two-dimensional quantum coding and the 1-D sine-based chaotic map (1-D SBCM). In the proposed encryption scheme, initially, a random sequence is created using 1-D SBCM by varying the seed parameters. This sequence is subsequently utilized for the purpose of scrambling. After that, a pseudorandom number generator (PRNG) is meticulously devised, drawing inspiration from the concepts of quantum coherence. This PRNG generates a confidential code stream that defies predictability and remains incongruent with the plaintext image, thereby contributing to heightened security levels. Subsequently, the research employs the novel enhanced quantum representation (NEQR) model, capitalizing on its advanced capabilities. Within this framework, the study introduces a quantum right cyclic shift operator, as well as a quantum XOR operator. These operators play a vital role in the formation of highly robust encrypted images. The application of these quantum operators not only enhances the security measures but also increases the complexity of the encryption procedure Statistical evidence derived from comprehensive experimentation corroborates the efficacy of the proposed image encryption scheme. Through rigorous analysis, it becomes evident that the system exhibits a robust performance in terms of strong security. The integration of quantum principles and quantum coding demonstrates that the proposed encryption scheme is resilient against potential threats.

Multiplexed Streaming Codes for Messages With Different Decoding Delays in Channel with Burst and Random Erasures

Multiplexed Streaming Codes for Messages With Different Decoding Delays in Channel with Burst and Random Erasures

A Robust Image Encryption Scheme Based on Block Compressive Sensing and Wavelet Transform

In this paper, a modified robust image encryption scheme is developed by combining block compressive sensing (BCS) and Wavelet Transform. It was achieved with a balanced performance of security, compression, robustness and running efficiency. First, the plain image is divided equally and sparsely represented in discrete wavelet transform (DWT) domain, and the coefficient vectors are confused using the coefficient random permutation strategy and encrypted into a secret image by compressive sensing. In pursuit of superior security, the hyper-chaotic Lorenz system is utilized to generate the updated secret code streams for encryption and embedding with assistance from the counter mode. This scheme is suitable for processing the medium and large images in parallel. Additionally, it exhibits superior robustness and efficiency compared with existing related schemes. Simulation results and comprehensive performance analyses are presented to demonstrate the effectiveness, secrecy and robustness of the proposed scheme. The compressive encryption model using BCS with Walsh transform as sensing matrix and WAM chaos system, the scrambling technique and diffusion succeeded in enhancement of secure performance.

A visually meaningful image encryption algorithm based on P-tensor product compressive sensing and newly-designed 2D memristive chaotic map

The noise-like visual feature of cipher images produced by using the traditional image encryption technology explicitly reflects the presence of secret information. To overcome this issue, a visually meaningful image encryption algorithm is proposed based on a newly designed 2D memristive chaotic map, P-tensor product compressive sensing (PTP-CS) and discrete Hartley transform (DHT). For concreteness, a new two-dimensional discrete memristive chaotic map is first designed to provide highly unpredictable secret code streams while maintaining low time consumption. Second, the threshold processing and zigzag confusion operations are performed on the discrete wavelet coefficients of the plain image to meet the prerequisites for effective compression. Third, the intermediate secret information is obtained by utilizing PTP-CS in the compression layer. Information entropy and edge entropy are employed to adaptively identify the complex regions that are suitable for embedding due to inconspicuous visual degradation in the carrier image. Finally, the embedding of the secret information in the DHT domain of these regions is accomplished. Security test and performance analysis confirm that our algorithm has the advantage of a high balance between the encryption security and the decryption recovery, and exhibits excellent performance in important indicators such as visual quality, robustness and timeliness.

High-Fidelity Transient Analysis with STREAM Based on the Predictor-Corrector Quasi-Static Method

The predictor-corrector quasi-static method (PCQM) is used to solve the transient problem in the STREAM code, a steady-state and transient reactor analysis code with the method of characteristics. In PCQM, the angular neutron flux undergoes a factorized split to form the product of shape and amplitude functions. The time-dependent neutron transport equation is solved to obtain the shape function whereas the amplitude function is obtained by resolving the exact point kinetics equations (EPKEs). A two-level coarse mesh finite difference technique is implemented to reduce the transient running time of the transport solution. Moreover, high-order polynomial interpolation is applied to the kinetics parameters utilized in EPKEs to reduce the error when the reactivity insertion is nonlinear. Several numerical benchmarks are solved to justify the application of the procedure, proving that the method maintains solution accuracy.

Online Versus Offline Rate in Streaming Codes for Variable-Size Messages

One pervasive challenge in providing a high quality-of-service for live communication is to recover lost packets in real-time. Streaming codes are a class of erasure codes that are designed for such strict, low-latency streaming communication settings. Motivated by applications that transmit messages whose sizes vary over time, such as live video streaming, this paper considers the setting of streaming codes under variable-size messages. In practice, streaming codes operate in an “online” setting where the sizes of the future messages are unknown. “Offline” codes, in contrast, have access to the sizes of all messages, including future ones. This paper introduces the first online rate-optimal streaming codes for communicating over a burst-only packet loss channel for two broad parameter regimes. These two online codes match the rates of optimal offline codes for the two settings despite the apparent advantage of the offline setting. This paper further establishes that online codes cannot attain the optimal rate for offline codes for all remaining parameter settings.

High fidelity transient solver in STREAM based on multigroup coarse-mesh finite difference method

This study incorporates a high-fidelity transient analysis solver based on multigroup CMFD in the MOC code STREAM. Transport modeling with heterogeneous geometries of the reactor core increases computational cost in terms of memory and time, whereas the multigroup CMFD reduces the computational cost. The reactor condition does not change at every time step, which is a vital point for the utilization of CMFD. CMFD correction factors are updated from the transport solution whenever the reactor core condition changes, and the simulation continues until the end. The transport solution is adjusted once CMFD achieves the solution. The flux-weighted method is used for rod decusping to update the partially inserted control rod cell material, which maintains the solution's stability. A smaller time-step size is needed to obtain an accurate solution, which increases the computational cost. The adaptive step-size control algorithm is robust for controlling the time step size. This algorithm is based on local errors and has the potential capability to accept or reject the solution. Several numerical problems are selected to analyze the performance and numerical accuracy of parallel computing, rod decusping, and adaptive time step control. Lastly, a typical pressurized LWR was chosen to study the rod-ejection accident.

Double Color Image Visual Encryption Based on Digital Chaos and Compressed Sensing

Image encryption is an effective way to protect images in secure transmission or storage. In this paper, we propose a novel double color image visual encryption algorithm based on the improved Chebyshev map (ICM) and compressed sensing. Firstly, a new nonlinear term is introduced into the classical one-dimensional Chebyshev map, and then the ICM is used to generate the secret code stream for the encryption algorithm. Next, the key-controlled sensing measurement matrices are constructed through the ICM, and they are used to compress the integer wavelet coefficients of two plain images. Subsequently, the compressed images are dislocated by dislocation matrices and diffused by an ICM-generated diffusion matrix, respectively. Finally, the encrypted images are embedded into the carrier image using the least significant bit embedding algorithm. Experimental results demonstrate that the proposed method has good visual safety, large key space, and high key sensitivity.

Multi-Modality Deep Restoration of Extremely Compressed Face Videos.

Arguably the most common and salient object in daily video communications is the talking head, as encountered in social media, virtual classrooms, teleconferences, news broadcasting, talk shows, etc. When communication bandwidth is limited by network congestions or cost effectiveness, compression artifacts in talking head videos are inevitable. The resulting video quality degradation is highly visible and objectionable due to high acuity of human visual system to faces. To solve this problem, we develop a multi-modality deep convolutional neural network method for restoring face videos that are aggressively compressed. The main innovation is a new DCNN architecture that incorporates known priors of multiple modalities: the video-synchronized speech signal and semantic elements of the compression code stream, including motion vectors, code partition map and quantization parameters. These priors strongly correlate with the latent video and hence they are able to enhance the capability of deep learning to remove compression artifacts. Ample empirical evidences are presented to validate the superior performance of the proposed DCNN method on face videos over the existing state-of-the-art methods.

Computationally efficient adaptive decompression for whole slide image processing.

Whole slide image (WSI) analysis is increasingly being adopted as an important tool in modern pathology. Recent deep learning-based methods have achieved state-of-the-art performance on WSI analysis tasks such as WSI classification, segmentation, and retrieval. However, WSI analysis requires a significant amount of computation resources and computation time due to the large dimensions of WSIs. Most of the existing analysis approaches require the complete decompression of the whole image exhaustively, which limits the practical usage of these methods, especially for deep learning-based workflows. In this paper, we present compression domain processing-based computation efficient analysis workflows for WSIs classification that can be applied to state-of-the-art WSI classification models. The approaches leverage the pyramidal magnification structure of WSI files and compression domain features that are available from the raw code stream. The methods assign different decompression depths to the patches of WSIs based on the features directly retained from compressed patches or partially decompressed patches. Patches from the low-magnification level are screened by attention-based clustering, resulting in different decompression depths assigned to the high-magnification level patches at different locations. A finer-grained selection based on compression domain features from the file code stream is applied to select further a subset of the high-magnification patches that undergo a full decompression. The resulting patches are fed to the downstream attention network for final classification. Computation efficiency is achieved by reducing unnecessary access to the high zoom level and expensive full decompression. With the number of decompressed patches reduced, the time and memory costs of downstream training and inference procedures are also significantly reduced. Our approach achieves a 7.2× overall speedup, and the memory cost is reduced by 1.1 orders of magnitudes, while the resulting model accuracy is comparable to the original workflow.

A hybrid NEQR image encryption cryptosystem using two-dimensional quantum walks and quantum coding

Quantum mechanisms can provide strong security in image processing against the disadvantages of pseudorandom and periodic of traditional chaotic systems. Based on its uncertainty and measurement collapse principle, this study proposes an image encryption system based on two-dimensional quantum walks and quantum coding. First, a pseudorandom number generator (PRNG) based on quantum entanglement and coherence principles is designed to generate a secret code stream inconsistent with the original image. Second, using the new enhanced quantum representation (NEQR) model, a quantum right cyclic shift operator and a quantum XOR operator are designed to obtain the final encrypted image. Experimental results and analysis show that the image encryption scheme has high security performance.

State-Dependent Symbol-Wise Decode and Forward Codes Over Multihop Relay Networks

This paper studies low-latency streaming codes for the multi-hop network. The source transmits a sequence of messages to a destination through a chain of relays, and requires the destination to reconstruct each message by its deadline. We assume that each communication link is subjected to a certain maximum number of packet erasures. The case of a single relay (a three-node network) was considered in Fong et al. (2020). A coding scheme known as symbol-wise decode and forward was proposed. In the present work, we propose an alternative scheme that is different from Fong et al. (2020) and still achieves the same rate as in Fong et al. (2020) for the one hop case as the field-size goes to infinity. Furthermore, our proposed scheme naturally generalizes to the case of multiple-relay nodes yielding new achievable rates for this setting. The main difference with Fong et al. (2020) is that our proposed scheme exploits the ability of the relay nodes to adapt the transmission based on the erasures on the previous link. Hence, we refer to our scheme as “state-dependent” and contrast it with the scheme in Fong et al. (2020) that is state-independent. Our scheme requires the relay nodes to append a header to the transmitted packets, and we show that the size of the header does not depend on the field-size of the code. We also derive an upper bound on the maximal streaming rate achievable over a network with an arbitrary number of relays. We show that this upper bound matches our achievable rate in the special case when the maximal number of erasures on the first link is greater than or equal to the maximal number of erasures on each of the following links, and the field size goes to infinity.

Random Linear Streaming Codes in the Finite Memory Length and Decoding Deadline Regime—Part I: Exact Analysis

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Streaming codes</i> take a string of source symbols as input and output a string of coded symbols in real time, which eliminate the queueing delay of traditional <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">block codes</i> and are thus especially appealing for delay sensitive applications. Existing works on streaming code performance either focused on the asymptotic error-exponent analyses, or on the optimal code construction under <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">deterministic adversarial channel models</i> . In contrast, this work analyzes the exact error probability of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">random linear streaming codes</i> (RLSCs) in the large field size regime over the stochastic i.i.d. symbol erasure channel model. A closed-form expression of the error probability of large-field-size RLSCs is derived under, simultaneously, the finite memory length and decoding deadline constraints. The result is then used to examine the intricate tradeoff between memory length (complexity), decoding deadline (delay), code rate (throughput), and error probability (reliability). Numerical evaluation shows that under the same code rate and error probability requirements, the end-to-end delay of RLSCs is 40–48% of that of the optimal block codes (i.e., MDS codes). This implies that switching from block codes to streaming codes not only eliminates the queueing delay completely (which accounts for the initial 50% of the delay reduction) but also improves the reliability (which accounts for the additional 2–10% delay reduction).

Информационные технологии на основе шумоподобных сигналов: II. Cтатистические и фрактальные свойства хаотических алгоритмов

On the basis of nonlinear systems with dynamic chaos, discrete chaotic signals with high information capacity have been developed and studied. The influence of the main parameters of a generating chaotic algorithm with delay on the statistical, correlation, structural and fractal characteristics of non-periodic pseudo-random integer and binary sequences generated by the algorithm is analyzed by numerical methods. It is shown that non-periodic pseudo-random sequences (PRS) generated by a chaotic algorithm with delay, for all values of the main parameters, have good statistical, correlation, structural and fractal characteristics, close to random sequences of independent trials. It is shown that these characteristics are provided on a long PRS cycle in a multidimensional phase space for all the main parameters of the chaotic algorithm and an arbitrary choice of initial conditions. Such binary PRSs can be quite effectively used in telecommunication systems using streaming coding of large blocks of information messages from the point of view of secrecy, noise immunity and cryptographic stability of the communication channel.

Performance analysis of nuclear reactor core loaded with Accident-Tolerant Fuel: Mo/Cr metallic microcell UO2 pellets and CrAl coating

This paper presents a performance analysis of a commercial pressurized water reactor (PWR) core loaded with accident-tolerant fuel (ATF) containing Mo/Cr metallic microcell UO2 pellets and a CrAl coating on the cladding. Metallic microcell UO2 pellets using additive materials, such as Mo and Cr, with coated cladding have been developed by the Korea Atomic Energy Research Institute (KAERI) for an OPR-1000 reactor. As demonstrated by the fuel pellet developed at KAERI, a metallic microcell UO2 pellet can increase the thermal conductivity of fuel pellets by forming a metallic film that encapsulates the UO2 to connect the metallic microcell and UO2 pellets; however, there has been no systematic core performance and safety analysis of the core with ATFs. In this study, core analysis with ATFs was conducted from a neutronic perspective. 1) The fuel assembly (FA) containing Mo/Cr metallic microcell UO2 pellets was analyzed using the lattice code STREAM developed by Ulsan National Institute of Science and Technology in terms of the Mo depletion chain and the impact of resonance treatment. 2) The initial and equilibrium cores were analyzed using STREAM/RAST-K 2.0 with respect to the impact of the additives and CrAl coating on the cycle length, power peaking, and fuel temperatures, etc. The reductions in the equilibrium core cycle length were 61, 44, and 65 effective full power days for each material type. 3) Finally, a rod ejection accident (REA) analysis was performed on the core using a Mo metallic microcell UO2 pellet and CrAl-coated ATF. Moreover, the maximum fuel centerline temperature of the ATF core was determined to be approximately 200 ℃ lower than that of the reference UO2 core. Furthermore, the presence of additional thermal margins in ATF cores for steady-state and accident transient scenarios was verified through a systematic neutronic analysis, and future studies will focus on the optimization of the ATF-loaded core by exploiting the extra margins achieved by ATFs and increasing the cycle length.

Propagation of radiation source uncertainties in spent fuel cask shielding calculations

The propagation of radiation source uncertainties in spent nuclear fuel (SNF) cask shielding calculations is presented in this paper. The uncertainty propagation employs the depletion and source term outputs of the deterministic code STREAM as input to the transport simulation of the Monte Carlo (MC) codes MCS and MCNP6. The uncertainties of dose rate coming from two sources: nuclear data and modeling parameters, are quantified. The nuclear data uncertainties are obtained from the stochastic sampling of the cross-section covariance and perturbed fission product yields. Uncertainties induced by perturbed modeling parameters consider the design parameters and operating conditions. Uncertainties coming from the two sources result in perturbed depleted nuclide inventories and radiation source terms which are then propagated to the dose rate on the cask surface. The uncertainty analysis results show that the neutron and secondary photon dose have uncertainties which are dominated by the cross section and modeling parameters, while the fission yields have relatively insignificant effect. Besides, the primary photon dose is mostly influenced by the fission yield and modeling parameters, while the cross-section data have a relatively negligible effect. Moreover, the neutron, secondary photon, and primary photon dose can have uncertainties up to about 13%, 14%, and 6%, respectively.

A stable meaningful image encryption scheme using the newly-designed 2D discrete fractional-order chaotic map and Bayesian compressive sensing

In order to safely and covertly protect the digital image information during public channel transmission and localized storage, a stable image visually secure encryption algorithm by using 2D discrete fractional-order chaotic map (FOCM), Bayesian compressive sensing (BCS), and discrete V transform (DVT) is presented in this paper. First, the key-controlled measurement matrix constructed by the infinite collapse-Chebyshev-coupling chaotic map is employed to measure the scrambled wavelet packet coefficients of plain image in parallel. Then, the fractional-order chaotic map designed through piece-wise constant arguments method is employed to generate the secret code streams with assistance of the counter mode, and under its control, the compressed image is re-encrypted into the intermediate secret image. Next, the DVT-based embedding technology is proposed to stochastically embed the secret data into the non-secret-involved host image. Eventually, experimental results show that under the premise of ensuring security, the proposed algorithm improves the visual security around 1∼9 dB, and stabilizes its compression performance within 1 dB, compared with other existing related encryption algorithms.

Study on the detection and recovery algorithm of important financial information tampering

A self-recovery watermarking algorithm for important financial information content authentication is proposed in this paper. The algorithm firstly pre-processes the source image with wavelet transform and divides sub-band image for low frequency into sub-blocks. Secondly, in order to ensure the accuracy of the localisation of the tampered area, a dual authentication watermarking mechanism called 'SVD-position recording' is adopted for each image block. Thirdly, the source images are down-scaled and segmented based on K-mediods clustering to generate a binary code stream of vital information, taking into account the watermark capacity and recovery quality. Finally, the binary code stream is knight's parade scrambled and RS error-corrected coded to be embedded in the high-frequency wavelet coefficients which reduce the possibility of recovery watermark tampering. Experimental results show that the peak signal-to-noise ratio (PSNR) of the financial document images with embedded watermark by the algorithm in this paper is above 43 dB, which has excellent visual quality. At the same time, it achieves accurate localisation and precise recovery for tampering attacks such as add, copy, and delete, which is suitable for tampering detection and recovery of important financial information.