Audio-Text Multimodal Speech Recognition via Dual-Tower Architecture for Mandarin Air Traffic Control Communications

In air traffic control communications (ATCC), misunderstandings between pilots and controllers could result in fatal aviation accidents. Fortunately, advanced automatic speech recognition technology has emerged as a promising means of preventing miscommunications and enhancing aviation safety. However, most existing speech recognition methods merely incorporate external language models on the decoder side, leading to insufficient semantic alignment between speech and text modalities during the encoding phase. Furthermore, it is challenging to model acoustic context dependencies over long distances due to the longer speech sequences than text, especially for the extended ATCC data. To address these issues, we propose a speech-text multimodal dual-tower architecture for speech recognition. It employs cross-modal interactions to achieve close semantic alignment during the encoding stage and strengthen its capabilities in modeling auditory long-distance context dependencies. In addition, a two-stage training strategy is elaborately devised to derive semantics-aware acoustic representations effectively. The first stage focuses on pre-training the speech-text multimodal encoding module to enhance inter-modal semantic alignment and aural long-distance context dependencies. The second stage fine-tunes the entire network to bridge the input modality variation gap between the training and inference phases and boost generalization performance. Extensive experiments demonstrate the effectiveness of the proposed speech-text multimodal speech recognition method on the ATCC and AISHELL-1 datasets. It reduces the character error rate to 6.54% and 8.73%, respectively, and exhibits substantial performance gains of 28.76% and 23.82% compared with the best baseline model. The case studies indicate that the obtained semantics-aware acoustic representations aid in accurately recognizing terms with similar pronunciations but distinctive semantics. The research provides a novel modeling paradigm for semantics-aware speech recognition in air traffic control communications, which could contribute to the advancement of intelligent and efficient aviation safety management.

Beyond 5G Networks: Integration of Communication, Computing, Caching, and Control

In recent years,the exponential proliferation of smart devices with their intelligent applications poses severe challenges on conventional cellular networks.Such challenges can be potentially overcome by integrating communication,computing,caching,and control(i4C)technologies.In this survey,we first give a snapshot of different aspects of the i4C,comprising background,motivation,leading technological enablers,potential applications,and use cases.Next,we describe different models of communication,computing,caching,and control(4C)to lay the foundation of the integration approach.We review current stateof-the-art research efforts related to the i4C,focusing on recent trends of both conventional and artificial intelligence(AI)-based integration approaches.We also highlight the need for intelligence in resources integration.Then,we discuss the integration of sensing and communication(ISAC)and classify the integration approaches into various classes.Finally,we propose open challenges and present future research directions for beyond 5G networks,such as 6G.

Controlled Secure Direct Communication Protocol via the Three-Qubit Partially Entangled Set of States

In this paper,we first re-examine the previous protocol of controlled quantum secure direct communication of Zhang et al.’s scheme,which was found insecure under two kinds of attacks,fake entangled particles attack and disentanglement attack.Then,by changing the party of the preparation of cluster states and using unitary operations,we present an improved protocol which can avoid these two kinds of attacks.Moreover,the protocol is proposed using the three-qubit partially entangled set of states.It is more efficient by only using three particles rather than four or even more to transmit one bit secret information.Given our using state is much easier to prepare for multiqubit states and our protocol needs less measurement resource,it makes this protocol more convenient from an applied point of view.

Analysis and Improvement of an Efficient Controlled Quantum Secure Direct Communication and Authentication Protocol

The controlled quantum secure direct communication(CQSDC)with authentication protocol based on four particle cluster states via quantum one-time pad and local unitary operations is cryptanalyzed.It is found that there are some serious security issues in this protocol.An eavesdropper(Eve)can eavesdrop on some information of the identity strings of the receiver and the controller without being detected by the selective-CNOT-operation(SCNO)attack.By the same attack,Eve can also steal some information of the secret message that the sender transmits.In addition,the receiver can take the same kind of attack to eavesdrop on some information of the secret message out of the control of the controller.This means that the requirements of CQSDC are not satisfied.At last,we improve the original CQSDC protocol to a secure one.

International Journal of Systems, Control and Communications(IJSCC)

ISSN (Online): 1755-9359; ISSN (Print): 1755-9340Published in 4 issues per year. Description One of the most dramatic technological developments in the era of information is the deployment of communication networks. This on-going revolution

Predictive Decision and Reliable Accessing for UAV Communication in Space-Air-Ground Integrated Networks

The cooperation of multiple Unmanned Aerial Vehicles(UAVs) has become a promising scenario in Space-Air-Ground Integrated Networks(SAGINs) recently due to their widespread applications,where wireless communication is a basic necessity and is normally categorized into control and nonpayload communication(CNPC) as well as payload communication. In this paper, we attempt to tackle two challenges of UAV communication respectively on establishing reliable CNPC links against the high mobility of UAVs as well as changeable communication conditions, and on offering dynamic resource optimization for Quality-of-Service(QoS) guaranteed payload communication with variable link connectivity. Firstly, we propose the concept of air controlling center(ACC), a virtual application equipped on the infrastructure in SAGINs, which can collect global information for estimating UAV trajectory and communication channels. We then introduce the knapsack problem for modelling resource optimization of UAV communication in order to provide optimal access points for both CNPC and payload communication. Meanwhile, using the air controlling information, predictive decision algorithm and handover strategy are introduced for the reliable connection with multiple access points. Simulation results demonstrate that our proposal ensures an approximate always-on reliable accessing of communication links and outperforms the existing methods against high mobility,sparse distribution, and physical obstacles.

ASIC Design of High-Speed Low-Power HDLC Controller

Combined with the engineering requirement, a high-speed low-power ASIC design of HDLC controller based on RS-485 bus is given in this paper. On principle of Top-Down design, this ASIC design uses multi-techniques to reduce its die area and dynamic power, and overcomes some problems appeared frequently in application systems of the RS-485 circuits formed by the Standard Interface Chips. This design also improves the system reliability and reduces the system area.

MULTIPLE ELECTRONIC CONTROL UNITS CALIBRATION SYSTEM BASED ON EXPLICIT CALIBRATION PROTOCOL AND J1939 PROTOCOL

The rising number of electronic control units （ECUs） in vehicles and the decreasing time to market have led to the need for advanced methods of calibration. A multi-ECU calibration system was developed based on the explicit calibration protocol （XCP） and J1939 communication protocol to satisfy the need of calibrating multiple ECUs simultaneously. The messages in the controller area network （CAN） are defined in the J1939 protocol. Each CAN node can get its own calibration messages and information from other ECUs, and block other messages by qualifying the CAN messages with priority, source or destination address. The data field of the calibration message is designed with the XCP, with CAN acting as the transport layer. The calibration sessions are setup with the event-triggered XCP driver in the master node and the responding XCP driver in the slave nodes. Mirroring calibration variables from ROM to RAM enables the user to calibrate ECUs online. The application example shows that the multi-ECU calibration system can calibrate multiple ECUs simultaneously, and the main program can also accomplish its calculation and send commands to the actuators in time. By the multi-ECU calibration system, the calibration effort and time can be reduced and the variables in ECU can get a better match with the variables of other ECUs.

Flow Control Strategy for the High Speed Network Based on Control Theory

Design of an effective congestion control scheme is a hot topic in the development of compute network. The flow control scheme can adjust the packet sending rate in source host, thus effectively avoiding the network congestion. This paper proposes a flow control scheme based on discrete control theory. The simulation results show that this method can adjust the sending rate and queue level in buffer rapidly and effectively. The method is easy to implement and applicable to high speed networks.

Optimization of a Heat Exchanger Using an ARM Core Intelligent Algorithm

In order to optimize heat transfer in a heat exchanger using an ARM(advanced RISC machine)core intelligent computer algorithm,a new type of controller has been designed.The whole control structure of the heat exchange unit has been conceived on the basis of seven functional modules,including data processing and output,human-computer interaction,alarm,and data communication.The main controller and communication controller have been used in a combined fashion and a new MCU(micro control unit)system scheme has been proposed accordingly.A fuzzy controller has been designed by using a fuzzy control algorithm,and a new mode of heat transfer for the heat exchanger has been implemented by combining the fuzzy controller and the PID(proportioning integral derivative)controller.Finally,the model has been applied to an actual heat exchange station to test and verify the performances of the new approach.

Effects of Multiple Cleaning and Disinfection Interventions on Infectious Diseases in Children: A Group Randomized Trial in China

Objective To assess the effectiveness of multiple cleaning and disinfection interventions in the homes and kindergartens, in reducing gastrointestinal and respiratory illnesses of children. Methods From October 2010 to September 2011, we performed a prospective, controlled study in China. 408 children under 5 years old were recruited and group randomized into intervention and control groups. Families and kindergartens in the intervention group were provided with antibacterial products for hand hygiene and surface cleaning or disinfection for one year. Each child＇s illness symptoms and sick leave were recorded every day. Results A total of 393 children completed the study, with similar baseline demographics in each of the 2 groups. Except for abdominal pain, the odds of symptoms （fever, cough and expectoration, runny nose and nasal congestion, diarrhea）, illness （acute respiratory illness and gastrointestinal illness）, and sick leave per person each month were significantly reduced by interventions. The rates of fever, diarrhea, acute respiratory illness, gastrointestinal illness and sick leave per person per year were significantly decreased as well. Conclusion Not only the acute respiratory children were significantly reduced by multiple and gastrointestinal illness but the sick leave rate in interventions.

Robust attitude coordinated control for spacecraft formation with communication delays

In this paper,attitude coordinated tracking control algorithms for multiple spacecraft formation are investigated with consideration of parametric uncertainties,external disturbances,communication delays and actuator saturation.Initially,a sliding mode delay-dependent attitude coordinated controller is proposed under bounded external disturbances.However,neither inertia uncertainty nor actuator constraint has been taken into account.Then,a robust saturated delaydependent attitude coordinated control law is further derived,where uncertainties and external disturbances are handled by Chebyshev neural networks（CNN）.In addition,command filter technique is introduced to facilitate the backstepping design procedure,through which actuator saturation problem is solved.Thus the spacecraft in the formation are able to track the reference attitude trajectory even in the presence of time-varying communication delays.Rigorous analysis is presented by using Lyapunov-Krasovskii approach to demonstrate the stability of the closed-loop system under both control algorithms.Finally,the numerical examples are carried out to illustrate the efficiency of the theoretical results.

Improving TCP Performance over Wireless Links Using Link-Layer Retransmission and Explicit Loss Notification

In order to support the mobility of computers during communication, the transport control protocol (TCP) connections between fixed host and mobile host often traverse wired and wireless networks, and the recovery of the losses due to wireless transmission error is much different from congestion control. This paper analyzes the interaction between TCP and link layer retransmission scheme when the correlated packet are losses handled, indicates that a higher value of the maximum number of successive link layer timeout retransmissions has an adverse effect on TCP ability to perform congestion control rapidly. To achieve a better TCP performance, the paper proposes a strategy combining link-layer selective-reject automatic repeat request (ARQ) with explicit loss notification mechanism, which can respond to congestion quickly while keeping wireless link more reliable, and make TCP react to the different packet losses more suitably.

Performance Analysis of TCP-Reno and TCP-Sack in the Case of a Single Source

This work explores the behavior of both TCP-Reno and TCP-Sack under a simple scenario, where a single TCP source transmits the packets continuously over a single bottleneck node characterized by its queue size, bandwidth and propagation delay. The analysis allows to derive the performance of TCP, the utilization tends to 75% of the bottleneck throughput when the bandwidth×propagation delay pipe becomes very large, while it tends to 100% when the queuing delays are predominant because the queue is never empty. In the transient analysis we show how the initial phase of the session can degrade the performances. These results are proved through simulation.

Intelligent throughput stabilizer for UDP-based rate-control communication system

Michiko Harayama*and Noboru Miyagawa Abstract:In view of the successful application of deep learning,mainly in the field of image recognition,deep learning applications are now being explored in the fields of communication and computer networks.In these fields,systems have been developed by use of proper theoretical calculations and procedures.However,due to the large amount of data to be processed,proper processing takes time and deviations from the theory sometimes occur due to the inclusion of uncertain disturbances.Therefore,deep learning or nonlinear approximation by neural networks may be useful in some cases.We have studied a user datagram protocol(UDP)based rate-control communication system called the simultaneous multipath communication system(SMPC),which measures throughput by a group of packets at the destination node and feeds it back to the source node continuously.By comparing the throughput with the recorded transmission rate,the source node detects congestion on the transmission route and adjusts the packet transmission interval.However,the throughput fluctuates as packets pass through the route,and if it is fed back directly,the transmission rate fluctuates greatly,causing the fluctuation of the throughput to become even larger.In addition,the average throughput becomes even lower.In this study,we tried to stabilize the transmission rate by incorporating prediction and learning performed by a neural network.The prediction is performed using the throughput measured by the destination node,and the result is learned so as to generate a stabilizer.A simple moving average method and a stabilizer using three types of neural networks,namely multilayer perceptrons,recurrent neural networks,and long short-term memory,were built into the transmission controller of the SMPC.The results showed that not only fluctuation reduced but also the average throughput improved.Together,the results demonstrated that deep learning can be used to predict and output stable values from data with complicated time fluctuations that are difficultly analyzed.

Revisiting classical SIR modelling in light of the COVID-19 pandemic

Background:Classical infectious disease models during epidemics have widespread usage,from predicting the probability of new infections to developing vaccination plans for informing policy decisions and public health responses.However,it is important to correctly classify reported data and understand how this impacts estimation of model parameters.The COVID-19 pandemic has provided an abundant amount of data that allow for thorough testing of disease modelling assumptions,as well as how we think about classical infectious disease modelling paradigms.Objective:We aim to assess the appropriateness of model parameter estimates and preiction results in classical infectious disease compartmental modelling frameworks given available data types(infected,active,quarantined,and recovered cases)for situations where just one data type is available to fit the model.Our main focus is on how model prediction results are dependent on data being assigned to the right model compartment.Methods:We first use simulated data to explore parameter reliability and prediction capability with three formulations of the classical Susceptible-Infected-Removed(SIR)modelling framework.We then explore two applications with reported data to assess which data and models are sufficient for reliable model parameter estimation and prediction accuracy:a classical influenza outbreak in a boarding school in England and COVID-19 data from the fall of 2020 in Missoula County,Montana,USA.Results:We demonstrated the magnitude of parameter estimation errors and subsequent prediction errors resulting from data misclassification to model compartments with simulated data.We showed that prediction accuracy in each formulation of the classical disease modelling framework was largely determined by correct data classification versus misclassification.Using a classical example of influenza epidemics in an England boarding school,we argue that the Susceptible-Infected-Quarantined-Recovered(SIQR)model is more appropriate than the commonly employed SIR model given the data collected(number of active cases).Similarly,we show in the COVID-19 disease model example that reported active cases could be used inappropriately in the SIR modelling framework if treated as infected.Conclusions:We demonstrate the role of misclassification of disease data and thus the importance of correctly classifying reported data to the proper compartment using both simulated and real data.For both a classical influenza data set and a COVID-19 case data set,we demonstrate the implications of using the“right”data in the“wrong”model.The importance of correctly classifying reported data will have downstream impacts on predictions of number of infections,as well as minimal vaccination requirements.

A Modeling and Analysis Approach for C^3I Systems in Conflicting Environment

A modling and analysis approach is proposed for Command,Control,Communication,and inteligence(C3I)systems in complex combat environments.This approach is carried out by characterizing both the basic quantities that are dispeusible for describing the system,and the causal relations among the quantities.The model of the system is determined by identifying the types of the causal relations among the quantities.By using this approach an example for a C3I system in an antiair battle is analyzed in detail.