Message Signs Research Articles

Design and implementation of ECC combined with OPT encryption algorithm

【Objective】 Combining ECC and OPT, a novel elliptic curve algorithm was proposed, which can effectively resist the reverse calculation of quantum computer and ensure the security of private key. 【Method】 On the basis of elliptic curve encryption algorithm, OPT algorithm was introduced to protect the private key. 【Result】 The signature and verification of messages can be successfully completed by the repeated algorithm on Python platform. Finally, the algorithm is proved to be safe under Shor algorithm and quantum computer attack through algorithm theory. 【Conclusion】 ECC combined with OPT encryption algorithm can effectively resist reversible computation of functional quantum devices, prevent eavesdroppers from forging signatures, and ensure the security of modern public key cryptosystems.

Estimating Driver Response Rates to Variable Message Signage at Seattle-Tacoma International Airport

We apply Bayesian Linear Regression to estimate the response rate of drivers to variable message signs at Seattle-Tacoma International Airport, or SEA. Our approach uses vehicle speed and flow data measured at the entrances of the arrival and departure-ways of the airport terminal, and sign message data. Depending on the time of day, we estimate that between 5.5 and 9.1% of drivers divert from departures to arrivals when the sign reads “departures full, use arrivals”, and conversely, between 1.9 and 4.2% of drivers divert from arrivals to departures. Though we lack counterfactual data (i.e., what would have happened had the diversionary treatment not been active), adopting a causal model that encodes time dependency with prior distributions rate can yield a measurable effect.

Computer Multimedia-Oriented Chinese Language and Literature Distance Teaching System

The traditional Chinese language and literature distance teaching system has the problem of incomplete instruction recognition function, which leads to the slow connection speed of the system. This paper designs a computer multimedia Chinese language and literature distance teaching system. For the hardware part, strain gauge and sensor are combined, connected to VCC through 10 K ohm pull-up resistance, and connected to other components. The software part obtains the expression form of Chinese language and literature; formulates the training target, according to the antimonotone characteristics of the support degree; adopts the association rule algorithm to build the remote teaching system database, combined with the client to parse the signaling message; and designs the software instruction recognition function by using the computer multimedia technology. Experimental Results. The average connection speeds of the Chinese language and literature distance teaching system in this paper and the other two kinds of Chinese language and literature distance teaching system are 8.737 s, 13.140 s, and 13.156 s, respectively, which proves that the performance of the Chinese language and literature distance teaching system combined with computer multimedia technology is more perfect.

The Quantum Internet: Enhancing Classical Internet Services One Qubit at A Time

Nowadays, the classical Internet is mainly envisioned as the underlying communication infrastructure of the Quantum Internet, aimed at providing services such as signaling and coordination messages. However, the interplay between the classical and the quantum Internet is complex and its understanding is pivotal for an effective design of the Quantum Internet protocol stack. The aim of the article is to shed light on this interplay, by highlighting the fact that such an interplay is indeed bidirectional rather than unidirectional. The Quantum Internet exhibits the potential of supporting and even enhancing classical Internet functionalities.

Time-Synchronized Control of Chaotic Systems in Secure Communication

High-quality data transmission synchronization process is frequently expected in light of secure communication mechanisms (SCMs), especially for space laser communication among the satellite constellation. To improve security and reliability during the data transmission processes prominently, control problems of chaotic synchronization synchronously <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">at the same time</i> are explored. In this paper, several novel error synchronization control protocols are proposed to solve these problems. First, by introducing a norm-normalized sign function (NNSF), unique (fixed-) time-synchronized stability is manifested, such that all non-zero state elements reach the origin synchronously at the same time. And upper bounds of synchronous resident time calculated by offered protocols are irrelevant/relevant to initial states of the error systems. Second, integrated with the (fixed-) time-synchronized stability theories, the (fixed-) time-synchronized sliding mode controllers with special convergent performance are established for two representative types of chaotic systems. Third, the ratio-persistent performance plays a dominating role for simultaneous convergence of the errors. Further, the innovation of the algorithms is reflected in that the decrypted signal is completely consistent with the transmitted message signal within synchronized settling time. Finally, in the simulation, not only theoretical verifications, but also practical verifications of image encryption and decryption processes verify the effectiveness of the SCMs.

The verbiage in variable message signs and traffic diversion during crash incidents

Purpose WIth limited research on the effects of variable message sign (VMS) message content and verbiage on revealed driver behavior, this study aims to investigate how different verbiage of crash-related messages are related to the diversion rate. Design/methodology/approach Using ordered logit models, the associations of message verbiage with diversion rates during crash incidents were assessed using five years of VMS message history within a section of I-15 in the state of Utah. Findings A significant impact of message verbiage on the diversion rate was observed. Based on the analysis results, the crash message verbiage with the highest diversion was found to be miles to crash + “prepare to stop,” followed by crash location + delay information, miles to crash + “use caution” + lane of the crash, etc. In addition, the diversion rate was found to be correlated to some roadway characteristics (e.g. occupancy in mainline, weather condition and light condition) along with the temporal variations. Research limitations/implications These findings could be used by transportation agencies (e.g. state department of transportation [DOTs]) to make informed decisions about choosing the message verbiage during future crash incidents. This study also revealed that higher diversion rates are associated with a shorter distance between the crash location and VMS device location, recommending increasing the number of VMS devices, particularly in crash-prone areas.

Full Duplex Relaying with Intelligent Reflecting Surface: Joint Beamforming and Phase Adjustment

In this paper, an optimization algorithm is proposed for the system, where a decode-and-forward (DaF) protocol based full duplex relay (FDR) and an intelligent reflecting surface (IRS) work together to deliver message signals from a source to a destination. The transmission is carried out in a single time frame due to the full duplex nature of the FDR and IRS. The joint optimization of the beamformers for the multiple antenna sets at the source along with the FDR antenna sets and the phase adjustment of the reflecting elements at the IRS is proposed. Though separate optimizations of the beamformers and the phase adjustment were available previously, the joint one is more challenging due to the increased number of signal paths and the impacts from these new paths. To accommodate conflicting requirements from different paths, the reflecting elements of the IRS are cleverly grouped into the corresponding number of sets, where the requirements are met separately through the optimizations of the phase values of the corresponding group. For the beamformer optimization, we parameterize the beamforming vectors, which results in two concave optimization problems for the source and for the FDR relay. These optimization sets are implemented in an alternating manner to reach a reasonable solution. Simulation results are provided to illustrate the impacts of the proposed algorithm in various implementation scenarios and parameter sets. These results identify the gains the proposed algorithm provides and the scenarios where these gains are expected.

Efficient Hybrid Iterative Method for Signal Detection in Massive MIMO Uplink System over AWGN Channel

Massive multiple input multiple output (massive MIMO) is a key technology in fifth-generation (5G) and beyond fifth-generation (B5G) networks. It improves performance metrics such as gain, energy efficiency, spectral efficiency, and bit error rate (BER). Because of the large number of users and antennas, sophisticated processing is required to detect the transmitted message signal. One of the challenges in massive MIMO systems is transmitted message signal detection. To respond to these challenges, several detection algorithms have been developed, including minimum mean squared error (MMSE), zero forcing (ZF), matched filter (MF), conjugate-gradient (CG), gauss-seidel (GS), and optimized coordinate descent (OCD). Although the ZF and MMSE algorithms perform well, their computational complexity is high due to direct matrix inversion. When the number of users is much lower than the number of antennas, the MF algorithm performs well. However, as the number of users increases, the performance of the MF algorithm degrades. Although the OCD, CG, and GS algorithms have less computational complexity than the MMSE algorithm, they perform poorly in comparison. To address and resolve the shortcomings of existing methods, an efficient iterative algorithm has been proposed in this manuscript, which is a hybrid method possessing the combination of MMSE with the alternating direction method of multipliers (ADMM) technique and Gauss-Seidel method. The initial vector has a large influence on the performance, complexity, and convergence rate of such iterative algorithms. The proposed detector’s initial solution is determined using the diagonal matrix and MMSE with the ADMM technique. The proposed algorithm’s performance and complexity are compared with existing algorithms based on BER and the real number of multiplications, respectively. The numerical results revealed that the proposed algorithm achieves the desired performance with a small number of iterations and a significant reduction in computational complexity. At 8QAM, SNR = 20 dB, 80 × 120 massive MIMO antenna configuration, and n = 2, the percentage performance improvement of the proposed detector from the GS detector is 99.82%. At 32QAM, SNR = 25 dB, 120 × 180 antenna configuration, and n = 5, performance improvement of the proposed detector is 99.89%. At 64QAM, SNR = 28 dB, 80 × 120 antenna configuration, and n = 3, performance improvement of the proposed detector is 99.93%.

OFDM Network Optimization Using a QPSK Based on a Wind-Driven Genetic Algorithm

Quadrature phase shift keying (QPSK) is a digital modulation technique that transmits data at a constant frequency whilst varying the phases of the carrier signal. QPSK is one of the fundamental modulation schemes for orthogonal frequency division multiplexing systems (OFDM). It is a stable modulation technique with good spectral efficiency. However, during transmission, the carrier signal can undergo numerous phase changes. This creates phase ambiguity problems at the receiver end. This results in inter-symbol interference (ISI) and a high bit error rate (BER). In this paper, the wind-driven optimization was incorporated into the genetic algorithm (GA) as its population selection function. This hybrid algorithm was used to determine the phase assignments for the QPSK. The developed QPSK was implemented on an OFDM network and the message signal was recovered at more than 92% accuracy in a noisy Rayleigh fading channel and 100% accuracy in a noiseless channel. The enhancements greatly mitigated phase ambiguity and bit errors.

Public-key quantum signature for classical messages without third-party verification

Public-key quantum signature for classical messages without third-party verification

Provably Secure with Efficient Data Sharing Scheme for Fifth-Generation (5G)-Enabled Vehicular Networks without Road-Side Unit (RSU)

The vehicles in the fifth-generation (5G)-enabled vehicular networks exchange the data about road conditions, since the message transmission rate and the downloading service rate have been considerably brighter. The data shared by vehicles are vulnerable to privacy and security issues. Notably, the existing schemes require expensive components, namely a road-side unit (RSU), to authenticate the messages for the joining process. To cope with these issues, this paper proposes a provably secure efficient data-sharing scheme without RSU for 5G-enabled vehicular networks. Our work included six phases, namely: TA initialization (TASetup) phase, pseudonym-identity generation (PIDGen) phase, key generation (KeyGen) phase, message signing (MsgSign) phase, single verification (SigVerify) phase, and batch signatures verification (BSigVerify) phase. The vehicle in our work has the ability to verify multiple signatures simultaneously. Our work not only achieves privacy and security requirements but also withstands various security attacks on the vehicular network. Ultimately, our work also evaluates favourable performance compared to other existing schemes with regards to costs of communication and computation.

An Improved Lattice-Based Ring Signature With Unclaimable Anonymity in the Standard Model

Abstract Ring signatures enable a user to sign messages on behalf of an arbitrary set of users, called the ring, without revealing exactly which member of that ring actually generated the signature. The signer-anonymity property makes ring signatures have been an active research topic. Recently, Park and Sealfon (PS; CRYPTO’19) presented an important anonymity notion named signer-unclaimability and constructed a lattice-based ring signature scheme with unclaimable anonymity in the standard model; however, it did not consider the unforgeable w.r.t. adversarially chosen-key attack (the public key ring of a signature may contain keys created by an adversary) and the signature size grows quadratically in the size of ring and message. In this work, we propose a new lattice-based ring signature scheme with unclaimable anonymity in the standard model. In particular, our work improves the security and efficiency of PS work, which is unforgeable w.r.t. adversarially chosen-key attack, and the ring signature size grows linearly in the ring size.

A Simple and Real-Time Support System for Firefighters Using Low-Cost 3-DOF Accelerometer and CO Sensor

During the operations, firefighters can be injured or killed because of the smoke and heat emission from the fire area, broken structure elements such as floors, walls, or boiling liquid ejection and gas explosion. Therefore, this paper aims to develop an efficient and portable system to monitor falls and high CO level through integrating a three degrees of freedom accelerometer and an MQ7 sensor to recorded acceleration and measured CO concentration with the embedded fall and high CO detection algorithms. The embedded fall detection algorithm can detect fall events with ultra-high accuracy without mistakenly identifying normal activities such as walking, standing, jogging, and jumping as fall events. The posture recognition and cascade posture recognition after three seconds are proposed in this paper to gain the accuracy of our proposed fall detection system. If a firefighter falls and is unable to stand up, the alert signal message will be sent to their commander outside through the GSM/GPRS module. The embedded high CO detection algorithm used to alert the dangerous CO level to recommend using self-contained breathing apparatuses (SCBA) and saving fresh air with acceptable CO level. We carefully investigated the proposed thresholds and window size before embedding them into the microcontroller. The sensitivity and accuracy achieved were around 96.5% and 93% respectively in our recorded data. Furthermore, the proposed fall detection algorithm also achieved higher geometric mean in comparison with Support Vector Machine classifier (SVM) and a nearest neighbor rule (NN) in the public datasets with the achieved around 99.44%, 98.41% and 95.76% respectively.

Privacy Preservation Authentication Model for a Secure Infrastructure over Vehicular Communications

Vehicle Ad-hoc Networks (VANET) are considered among recent wireless communication technologies. Nowadays, vehicles are no more than simple means of transport, they are endowed with a source of intelligence through their interaction with the road environment due to embedded equipment on board vehicles and integrated into stations along roads and highways. The mechanisms of security and protection of messages exchanged in VANET, thus preserving the privacy of users and satisfying the various security requirements, are a prerequisite for the deployment of vehicle networks. Increasingly, several research have been proposed to improve protocols for maintaining security and preserving privacy. This paper presents a hierarchical revocable infrastructure based privacy preservation authentication protocol for vehicles that involves authentication of each vehicle and the corresponding Road Side Unit (RSU) by a Certification Authority (CA). The proposed protocol used Elliptic Curve Diffie Hellman (ECDH) algorithm for reliable key exchange and Edwards-curve Digital Signature Algorithm (EdDSA) to speed up the execution of the authentication process especially at the key management level, message signing and verification of this signature. On the other hand, the creation of sub-lists of revoked certificates based on vehicle type makes it possible to minimize the response time by looking for a certificate if it is revoked or not. Our solution was checked by the security verification tool, Automated Validation of Internet Security Protocols and Applications (AVISPA), which indicated that it is a very secure level. Performance analysis illustrates that the protocol greatly saves computation resources.

A Machine Learning Approach to Predict Channel Stability Using Channel Quality Index

Channel quality feedback is crucial for the operation of 4G and 5G radio networks, as it allows to control User Equipment (UE) connectivity, transmission scheduling, and the modulation and rate of the data transmitted over the wireless link. However, when such feedback is frequent and the number of UEs in a cell is large, the channel may be overloaded by signalling messages, resulting in lower throughput and data loss. Optimizing this signaling process thus represents a key challenge. In this paper, a focus on Channel Quality Indicator (CQI) reports that are periodically sent from a UE to the base station, and propose mechanisms to optimize the reporting process with the aim of reducing signaling overhead and avoiding the associated channel overloads, particularly when channel conditions are stable. To this end, we apply machine learning mechanisms to predict channel stability, which can be used to decide if the CQI of a UE is necessary to be reported, and in turn to control the reporting frequency. For this purpose,four machine learning models,namely Support Vector Machines (SVM),K-Nearest Neighbour (KNN),Random Forest and Neural Networks (ANN). Simulation results shows that both provide a high prediction accuracy, with ANN consistently outperforming, especially as CQI reporting frequency reduces. Keywords: ANN,KNN,SVM,Random Forest,CQI,5G,UE

Collaborative one-shot beamforming under localization errors: A discrete optimization approach

We consider a mobile multi-agent network in which the agents locate themselves in an environment through imperfect measurements and aim to transmit a message signal to a far-field base station via collaborative beamforming. The agents imperfect measurements yield localization errors that degrade the quality of service at the base station due to unknown phase offsets in the channels. Assuming that the localization errors follow Gaussian distributions, we study the design of a one-shot (non-iterative) beamforming strategy that ensures reliable communication between the agents and the base station despite the localization errors. We formulate a risk-sensitive discrete optimization problem to choose an agent subset for transmission so that the desired signal-to-interference-plus-noise ratio (SINR) at the base station is attained with minimum variance. We show that, when the localization errors have small variances characterized in terms of the carrier frequency, greedy algorithms globally minimize the variance of the received SINR. Moreover, when the localization errors have large variances, we show that the variance of the received SINR can be locally minimized by exploiting the supermodularity of the mean and variance of the received SINR. Simulations demonstrate that the proposed algorithms synthesize beamformers orders of magnitude faster than convex optimization-based approaches while achieving comparable performance with fewer agents.

"We Understand You Hate Us": Latinx Immigrant-Origin Adolescents' Coping with Social Exclusion.

Little research has focused on how adolescents are faring with the national context of exclusion. Growing evidence suggests that exclusionary efforts signal messages of who belongs (and who does not) in the United States. This study draws from a risk and resilience model to understand how Latinx immigrant-origin adolescents react and adapt to social exclusion. A total of 41 adolescents between the ages of 13 to 24 (Mage = 18.85) completed a semistructured interview. Results revealed participants' use of avoidant and active coping strategies to deal with exclusion. There was a shift in coping responses across developmental stages. Future research and recommendations are provided to address systems that need to be dismantled to foster a sense of belonging for adolescents.

City-Wide NB-IoT Network Monitoring and Diagnosing

NarrowBand-IoT (NB-IoT) is a radio-access technology standardized by 3GPP to support a large set of use cases associated with the rapid deployment of massive machine-type communications. NB-IoT facilitates the connection of devices in inaccessible areas, extends battery life, and reduces device complexity. Unfortunately, the opacity of the underlying schema (i.e., the way that these benefits are achieved) makes it very difficult for most users and developers to manage deployment scenarios. In this study, we built an embedded system comprising a Raspberry Pi with an NB module, referred to as NBPilot, which interacts with NB networks to identify essential signaling messages transmitted by a Qualcomm NB modem. This system gives researchers and developers an unprecedented understanding of network behavior as well as the ability to adjust them to their particular requirements. We employed the-state-of-the-art machine learning techniques for modeling and the analysis of NB performance. The efficacy of the proposed NBPilot system was established by applying it to a metropolitan NB-IoT network with over 2,000 NB sites for the collection and testing of data trace as well as the validation of a cellular station prior to going online. The developed machine learning techniques predict the actual performance (rate, delay, and power consumption) of the user equipment’s connection against the QoS advertised by the operator and pinpoint the affecting factors.

Dataset: Variable Message Signal Annotated Images for Object Detection

This publication presents a dataset consisting of Spanish road images taken from inside a vehicle, as well as annotations in XML files in PASCAL VOC format that indicate the location of Variable Message Signals within them. Additionally, a CSV file is attached with information regarding the geographic position, the folder where the image is located and the text in Spanish. This can be used to train supervised learning computer vision algorithms such as convolutional neural networks. Throughout this work, the process followed to obtain the dataset, image acquisition and labeling and its specifications are detailed. The dataset constitutes 1216 instances, 888 positives and 328 negatives, in 1152 jpg images with a resolution of 1280 × 720 pixels. These are divided into 756 real images and 756 images created from the data-augmentation technique. The purpose of this dataset is to help in road computer vision research since there is not one specifically for VMSs.

Study of Effect of Noise on Multi Input Single Output FLC and AIFLC

Abstract: This paper represents effect of noise on multi input and output FLC and AIFLC. Noise is a term referred as unwanted electrical signals that accompany the message signals. These unwanted signals arise from a variety of sources and can be classified as man-made or naturally. Introduction of noise/disturbance causes instability in the control system. Hence, it is very essential to study the effect of noise. Noise reduction is an important issue in any process control application. Noise contaminates the process control signals. Noise imposed on the process controller may be of entirely unknown composition, which degrades the quality of the controlling signals. The sources of noise may be external to the system or internal to the system. In usual laboratory conditions, feedback control systems do suppress noise. The present paper shows the effects of uniform white noise are studied for the speed control of BLDC motor using 3 input single output FLC and AIFLC and 2 input single output FLC and AIFLC. Keywords: Fuzzy logic Tool kit, Labview,