Bitstream Research Articles

On the construction of stationary processes and random fields

Abstract We propose a new method to construct a stationary process and random field with a given decreasing covariance function and any one-dimensional marginal distribution. The result is a new class of stationary processes and random fields. The construction method utilizes a correlated binary sequence, and it allows a simple and practical way to model dependence structures in a stationary process and random field as its dependence structure is induced by the correlation structure of a few disjoint sets in the support set of the marginal distribution. Simulation results of the proposed models are provided, which show the empirical behavior of a sample path.

Maximum length binary sequences and spectral power distribution of periodic signals

The maximum length binary sequence (MLBS) is widely used as a broadband pseudo-random noise excitation signal, for example, for system identification. Although its properties have been known for decades, misleading or inaccurate statements can be found in many references. For example, it is sometimes stated that the spectrum of the MLBS is white, whereas in other references a sinc behavior is stated. In this paper, we therefore analyze the MLBS properties based on precise definitions for the given context (time-discrete vs. time-continuous, periodic vs. non-periodic, etc.), especially with respect to Fourier analysis. Another difficulty arises from the fact that in the literature the mathematical definitions are often simplified by means of normalizations which makes the physical interpretation difficult. Therefore, special emphasis is put on scaling factors which allow such a physical interpretation.

The role of Markov chain in Monte Carlo simulation

The Markov Chain Monte Carlo method has been applied in several fields and has thrived in academic research over the past few decades. The method is utilized to estimate the interest parameters posterior distribution through random sampling in the probability space. Monte Carlo method alone can accomplish random simulations for some complicated continuous integrals or discrete summations. However, the method requires the sample set corresponding to the distribution, which demands the application of Markov chain. Nevertheless, problems occur when the probability function of the samples remain unknown, under which circumstance the Markov Chain Monte Carlo method is no longer available. Therefore, the article introduced Metropolis-Hastings Algorithm as a resolution. The paper is intended to provide specific insights about Metropolis-Hastings Algorithm for Markov Chain Monte Carlo and the corresponding interdisciplinary applications. Specifically, the paper will discuss about using the theory mentioned above to check the relationships among species in a common ecosystem. The paper will cover several prepositive theories, including the Law of Large Numbers for Markov Chains, binary sequences, and Metropolis-Hastings Algorithm. Overall, specific cases and the way to draw correct conclusions will be most concerned.

Block Cipher Nonlinear Component Generation via Hybrid Pseudo-Random Binary Sequence for Image Encryption

To analyze the security of encryption, an effectual encryption scheme based on colored images utilizing the hybrid pseudo-random binary sequence (HPRBS) and substitution boxes, known as S-boxes, is proposed. The presented work aims to design S-boxes using pseudo-random binary numbers acquired by Linear Feedback Shift Registers (LFSRs) in combination with a modified quadratic chaotic map. Firstly, cryptographically robust S-boxes are constructed by using binary pseudo-random number sequences, and then the cryptographic properties of the presented S-boxes are tested. The suggested S-boxes showed good results. Secondly, an RGB image encryption algorithm utilizing sequences generated by modified quadratic chaotic maps and S-boxes is offered. The new color image encryption techniques comprise two steps, including a permutation and a substitution step. The key association with the content of the image is also addressed. This strategy can result in a “one-time pad” effect and make the algorithm resistant to chosen-plaintext attack (CPA). The proposed scheme has been confirmed to be more valuable than most of the existing schemes. S-boxes are analyzed by the nonlinearity test, bit independence criterion (BIC), linear and differential approximation probabilities (LPs; DPs), and Strict-Avalanche Criterion (SAC) tests. A comparison with different S-boxes presented in the literature is also carried out. The comparison shows encouraging results about the quality of the proposed box. From security and experimental outcomes, the effectiveness of the presented color image encryption technique is verified. The proposed scheme has evident efficiency benefits, which implies that the proposed colored encryption of the image scheme has better potential for application in encryption schemes in real-time.

Physical layer security enhanced scheme based on joint encryption of DNA and RNA data perturbation in CO-OFDM system

—In the work, we propose a new chaotic encryption algorithm for optical physical layer security enhanced in coherent optical orthogonal frequency division multiplexing (CO-OFDM) systems. The proposed algorithm adopts the six-dimensional (6D) hyperchaotic to achieve the generation of key sequences, which can effectively improve the unpredictability and security of the system. The encryption scheme includes two steps, first, the binary sequence is disturbed by DNA and RNA two-stage encoding and RNA mutation, and then the initial encrypted data is injected into the algorithm module with two-dimensional continuous chaotic mapping (2D-SCL) and Brownian motion to scramble successively. Simulations in a 16QAM CO-OFDM system at a symbol rate of 30 Gbaud over 80 km of standard single-mode fiber (SSMF) show that a legitimate user is able to successfully decrypt the received signals, and an illegal eavesdropper obtains a useless signal with a bit error rate (BER) of about 0.5. The scheme can tolerate very small deviations and has a key space of approximately 10294, which can effectively withstand illegal attacks. And the peak-to-average power ratio (PAPR) can be reduced by about 1.01 dB.

Analysing All-Optical Random Bit Sequences Using Gap-Based Approaches.

Quantum mechanical phenomena are revolutionizing classical engineering fields such as signal processing or cryptography. When randomness plays an important role, like in cryptography where random bit sequences guarantee certain levels of security, quantum mechanical phenomena allow new ways of generating random bit sequences. Such sequences have a lot of applications in the communication sector, e.g., regarding data transmission, simulation, sensors or radars, and beyond. They can be generated deterministically (e.g., by using polynomials, resulting in pseudo-random sequences) or in a non-deterministic way (e.g., by using physical noise sources like external devices or sensors, resulting in random sequences). Important characteristics of such binary sequences can be modelled by gap processes in conjunction with the probability theory. Recently, all-optical approaches have attracted a lot of research interest. In this work, an adaptation of the quantum key distribution setup is utilized for generating randomised bit sequences. The simulation results show that all-optically generated sequences very well resemble the theoretically ideal probability density characteristic. Additionally, an experimental optical setup is developed that confirms the simulation results. Furthermore, m-sequences show very promising results as well as Gold sequences. Additionally, the level of burstiness, i.e., the distribution of ones and zeros throughout the sequence, is studied for the different sequences. The results enable the finding that generator polynomials with concentrated non-zero coefficients lead to more bursty bit sequences.

A high sensitivity digital giant magneto-impedance (GMI) sensor for magnetic communication

Abstract A high sensitivity Magneto-Impedance (GMI) sensor for magnetic communication systems in harsh environments is presented. In this system, the receiving coil of a conventional magnetic communication system is replaced by a high sensitivity GMI sensor and its digital electronic conditioning as well as digital signal processing. The whole architecture of the sensor is described in detail. It achieves two digital amplitude demodulations to recover, in real time, the binary sequence of the message sent by the transmitter. The demonstration and the validation of the reliable functioning of the system are illustrated through the transmission of messages using On-Off-Keying modulation method. The measured equivalent magnetic noise of the sensor was about noise 2 p T / Hz at 60 kHz, which is largely lower than the noise of other potential types of magnetic sensors such as Anisotropic Magneto-Resistive sensors for this application. This noise level suggests a potential higher communication range. When compared with an inductive coil, the developed sensor also presented an almost flat and sufficiently wide bandwidth compatible with magnetic communication. This bandwidth could potentially be extended according to the application.

Head tracking using an optical soft tactile sensing surface.

This research proposes a sensor for tracking the motion of a human head via optical tactile sensing. It implements the use of a fibrescope a non-metal alternative to a webcam. Previous works have included robotics grippers to mimic the sensory features of human skin, that used monochrome cameras and depth cameras. Tactile sensing has shown advantages in feedback-based interactions between robots and their environment. The methodology in this paper is utilised to track motion of objects in physical contact with these sensors to replace external camera based motion capture systems. Our immediate application is related to detection of human head motion during radiotherapy procedures. The motion was analysed in two degrees of freedom, respective to the tactile sensor (translational in z-axis, and rotational around y-axis), to produce repeatable and accurate results. The movements were stimulated by a robot arm, which also provided ground truth values from its end-effector. The fibrescope was implemented to ensure the device's compatibility with electromagnetic waves. The cameras and the ground truth values were time synchronised using robotics operating systems tools. Image processing methods were compared between grayscale and binary image sequences, followed by motion tracking estimation using deterministic approaches. These included Lukas-Kanade Optical Flow and Simple Blob Detection, by OpenCV. The results showed that the grayscale image processing along with the Lukas-Kanade algorithm for motion tracking can produce better tracking abilities, although further exploration to improve the accuracy is still required.

Rapid Lithium-Ion Battery Impedance Measurements Using Binary Sequence With Optimized Frequency Components

Rapid Lithium-Ion Battery Impedance Measurements Using Binary Sequence With Optimized Frequency Components

Large Sets of Binary Spreading Sequences With Low Correlation and Low PAPR via Gold Functions

Large Sets of Binary Spreading Sequences With Low Correlation and Low PAPR via Gold Functions

Further Investigations on Nonlinear Complexity of Periodic Binary Sequences

Further Investigations on Nonlinear Complexity of Periodic Binary Sequences

Dissimilarity measures based on the application of Hamming distance to generate controlled probabilistic tests

Objectives. The problem of constructing dissimilarity measures based on the application of the Hamming distance to generate controlled random binary test sets is solved. The main goal of this article is to develop methods for determining the Hamming distance for the achievability of finding the difference between test sets when they coincide according to estimates of other difference measures.Methods. Based on the Hamming distance used in the theory and practice of generating controlled random tests, new dissimilarity measures are proposed for two binary test n-bit patterns. The basis of the proposed dissimilarity measures is the formation of sets of Hamming distances for initial sets, represented as sequences of characters from different alphabets.Results. The indistinguishability of pairs of binary test sets Ti and Tk is shown using a dissimilarity measure based on the application of the Hamming distance. In this case, different pairs of sets may have identical Hamming distance values. To construct new measures of difference, the original binary test sequences are represented as sequences consisting of characters belonging to different alphabets. Various strategies are proposed for applying new measures of difference based on the use of one of three rules in generating controlled probability tests. It is shown that in all three cases of dissimilarity measures, only the first few of their components areinformative, as a rule, no more than two or three. Accordingly, the computational complexity for all three options is comparable and does not exceed 3n comparison operations. The experimental studies carried out confirm the effectiveness of the proposed dissimilarity measures and their low computational complexity.Conclusion. The proposed dissimilarity measures expand the possibilities of generating test sets when forming controlled random tests. It is shown that test sets that are indistinguishable when using the Hamming distance as a dissimilarity measure have different values of the proposed dissimilarity measures, which makes it possible to more accurately classify randomly generated sets that are candidate test cases

Bounds on the number of squares in recurrence sequences

We investigate the number of squares in a very broad family of binary recurrence sequences with u0=1. We show that there are at most two distinct squares in such sequences (the best possible result), except under very special conditions where we prove there are at most three such squares.

Design and implementation of a physical layer optical fiber security communication system based on a ZUC stream cipher

In this work, we have proposed a new physical layer encryption scheme to improve the security of optical fiber communication systems. The secrecy scheme is based on disturbance of the data cluster under the ZUC algorithm (named for Zu Chongzhi, a famous mathematician in ancient China), which is generated by optical XOR using a dual-drive Mach–Zehnder modulator (DD-MZM). A base scrambling pseudo random binary sequence (PRBS) generated by the ZUC stream cipher is introduced, which results in a better scrambling effect and randomness in the key stream of the block encryption structure. A simulation setup conducted in an encrypted transmission of the 64×64 grayscale image over 80 km of optical fiber shows that the authorized user can successfully decrypt the received signal, while the eavesdroppers cannot derive useful information with a bit error rate (BER) at approximately 0.5. In the simulation, after setting the delay compensation to 3.125 ps, a correct signal with a Q-factor as high as 11.3642 and a BER as low as 3.14295−30 can be obtained.

A crosstalk-free multiple-image encryption scheme based on computational ghost imaging with binarized detection

Crosstalk noise is a main problem limiting the performance of multiple-image encryption (MIE) scheme. In this work, we proposed a crosstalk-free MIE scheme based on computational ghost imaging (CGI) with binarized detection. In the encryption process, the plaintext images are encrypted into intensity sequences by the CGI system and quantified into two levels to obtain binary ciphertext sequences, which does not cause severe degradation in decrypted image quality compared to traditional CGI. Then, for the binary ciphertext sequences, we can combine them into a decimal grayscale ciphertext. To enhance security, a pixel bit layer scrambling (PBLS) algorithm is designed to scramble the grayscale ciphertext to obtain the final ciphertext. In the decryption process, anyone of the plaintext images can be decrypted without being affected by other plaintext images after performing inverse PBLS algorithm on the ciphertext and extracting the binary ciphertext sequence. The effectiveness, robustness, encryption capacity and security of the proposed scheme are demonstrated by numerical simulations and theoretical analysis.

Wideband spectrum compressive sensing utilizing photonic multi-coset sampling with dual low-rate optical pulses

A wideband spectrum compressive sensing approach for sparse multiband signals utilizing photonic multi-coset sampling (MCS) is presented. The method employs dual low-rate optical pulses, initially modulated by separate pseudo-random binary sequences (PRBSs), which are then multiplexed to achieve the desired multi-coset pattern. It results in a substantial reduction of the rate requirements for both the sampling optical pulses and PRBSs, leading to a significant enhancement of system bandwidth. In simulations, an instantaneous system bandwidth of 21 GHz is achieved at a low mean sampling rate of 4.8 GS/s, employing two optical pulses with rates of 8.4 GHz and 6 GHz, respectively. The spectra of a mixed signal with six passbands are accurately identified, illustrating the capability of the scheme to recognize the spectra of various types of multiband signals. Furthermore, a comparative analysis of the performance between our proposed scheme and traditional MCS techniques is conducted.

A cascaded bit-weighted distribution matching for LDPC-coded PS-64QAM DMT in a high-speed Unamplified IM-DD system

We experimentally demonstrate the generation and transmission of probabilistic shaping (PS) 64-ary quadrature amplitude modulation (64-QAM) discrete multi-tone (DMT) signal with low-density parity-check (LDPC)-coded modulation based on two-stage bit-weighted distribution matching (TS-BWDM) in a high-speed unamplified intensity modulation and direct detection system. The TS-BWDM scheme is based on a two-stage bit-weighted inversion algorithm, which has the advantages of no complex multiplication and division operations and low hardware implementation complexity. As the key content of TS-BWDM, before FEC coding, the bit-weighted intervention operation is used to change the bit probability of a specific sequence in the parallel binary sequence. The two-stage bit-weighted inversion operation and one-stage weight-labeling bit insertion is performed for achieving the target symbol probability distribution. As a result, it does not add a significant amount of extra redundant bits. Additionally, the high-accuracy training symbols-based sampling frequency offset (SFO) estimation method and digital interpolation are used to compensate SFO. In our experiments, the TS-BWDM-based PS-64QAM DMT signals with a net rate of 157.3-Gb/s transmission over 10-km non-zero dispersion- shifted fiber can be achieved. The system performance is investigated under two LDPC code rates (3/4 and 9/10) and three PS parameter values (k = 3, 5 and 9). The experimental results show that the receiver power sensitivity and the system fiber nonlinear effect tolerance can be significantly improved compared with uniformly-distributed signals.

Evidence of scaling advantage for the quantum approximate optimization algorithm on a classically intractable problem.

The quantum approximate optimization algorithm (QAOA) is a leading candidate algorithm for solving optimization problems on quantum computers. However, the potential of QAOA to tackle classically intractable problems remains unclear. Here, we perform an extensive numerical investigation of QAOA on the low autocorrelation binary sequences (LABS) problem, which is classically intractable even for moderately sized instances. We perform noiseless simulations with up to 40 qubits and observe that the runtime of QAOA with fixed parameters scales better than branch-and-bound solvers, which are the state-of-the-art exact solvers for LABS. The combination of QAOA with quantum minimum finding gives the best empirical scaling of any algorithm for the LABS problem. We demonstrate experimental progress in executing QAOA for the LABS problem using an algorithm-specific error detection scheme on Quantinuum trapped-ion processors. Our results provide evidence for the utility of QAOA as an algorithmic component that enables quantum speedups.

High-Density Electroencephalogram Facilitates the Detection of Small Stimuli in Code-Modulated Visual Evoked Potential Brain-Computer Interfaces.

In recent years, there has been a considerable amount of research on visual evoked potential (VEP)-based brain-computer interfaces (BCIs). However, it remains a big challenge to detect VEPs elicited by small visual stimuli. To address this challenge, this study employed a 256-electrode high-density electroencephalogram (EEG) cap with 66 electrodes in the parietal and occipital lobes to record EEG signals. An online BCI system based on code-modulated VEP (C-VEP) was designed and implemented with thirty targets modulated by a time-shifted binary pseudo-random sequence. A task-discriminant component analysis (TDCA) algorithm was employed for feature extraction and classification. The offline and online experiments were designed to assess EEG responses and classification performance for comparison across four different stimulus sizes at visual angles of 0.5°, 1°, 2°, and 3°. By optimizing the data length for each subject in the online experiment, information transfer rates (ITRs) of 126.48 ± 14.14 bits/min, 221.73 ± 15.69 bits/min, 258.39 ± 9.28 bits/min, and 266.40 ± 6.52 bits/min were achieved for 0.5°, 1°, 2°, and 3°, respectively. This study further compared the EEG features and classification performance of the 66-electrode layout from the 256-electrode EEG cap, the 32-electrode layout from the 128-electrode EEG cap, and the 21-electrode layout from the 64-electrode EEG cap, elucidating the pivotal importance of a higher electrode density in enhancing the performance of C-VEP BCI systems using small stimuli.

Distributed l2−l∞ filtering over wireless sensors based on adaptive event triggering

This paper investigates the design problem of a set of distributed filters. These adaptive event-triggered (AET) filters have the [Formula: see text] property and are suitable for sensor networks characterized by random measurement data loss and dynamic topology switching. In a distributed filtering network, each local filter estimates the system’s state not only relying on the node’s individual information but also using information from neighboring nodes in the network topology. The adaptive event triggering condition is determined by the combination of the latest release data of the filter itself, the estimated value of the current moment, and the latest release data of the neighboring nodes. The event triggering mechanism adopts a threshold adaptive adjustment scheme, which dynamically changes the threshold parameter according to the filtering error under the premise of guaranteeing the filter performance, thus maximally saving the network communication resources. Initially, we used a binary sequence to describe the random loss of sensor measurements, and a Markov chain is used to describe the switching of the filtering network topology. Secondly, a Lyapunov function is created to examine the [Formula: see text] performance index of the filtering error system and investigate its mean square exponential stability. Subsequently, the distributed filter is formulated, and its parameters are determined using the linear matrix inequality approach. Ultimately, the validity of the proposed method is substantiated through numerical illustrations.