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In this letter, we investigate covert communication in relay networks with relay selection. We consider the scenario that while forwarding the source’s message, the selected relay opportunistically transmits its own message to the destination covertly. We derive the probability of detection error (PDE) and the average covert rate (ACR) in a closed form, based on which we analyse the effects of system parameters on the performance of the covert communication. Our analysis indicates that applying relay selection causes a decrease in the PDE, however, it can provide an ACR gain when the transmission rate of the source increases.

In the underwater sound source localisation systems, the sound wave signal is accompanied by time delay and Doppler shift, where the cross-ambiguity function (CAF) is commonly applied as a method of estimating them. This study proposes a novel method for calculating the exact value of the CAF,based on the theory of curved surface interpolation. Firstly, two optimisation problems with equality and inequality constraints are developed, where the penalty function method is employed to turn the equality constraint problem into an unconstrained problem. Secondly, the time delay and Doppler shift of the CAF solution are processed to obtain the measurement values required for accurate localisation. Furthermore, the authors propose an improved time difference of arrival and frequency difference of arrival (TDOA/FDOA)-based joint localisation algorithm, which changes the structure of traditional localisation equations and eliminates the localisation imprecision caused by neglecting the square term of the noise. The performance of the proposed algorithm is verified by extensive simulations and comparisons with several established methods. Remarkably, the proposed localisation method is confirmed to be noticeably superior and effective for the considered application.

Scattering of electromagnetic plane waves from normal chiral materials, chiral metamaterials, chiral nihility materials are presented. Based on the volume equivalence principle, the integral equati...

RAPL (Running Average Power Limit) is a hardware feature introduced by Intel to facilitate power management. Even though RAPL and its supporting software interfaces can benefit power management significantly, they are unfortunately designed without taking certain security issues into careful consideration. In this paper, we demonstrate that information leaked through RAPL-induced side channels can be exploited to mount realistic attacks. Specifically, we have constructed a new RAPL-based covert channel using a single AVX instruction, which can exfiltrate data across different boundaries (e.g., those established by containers in software or even CPUs in hardware); and, we have investigated the first RAPL-based website fingerprinting technique that can identify visited webpages with a high accuracy (up to 99% in the case of the regular network using a browser like Chrome or Safari, and up to 81% in the case of the anonymity network using Tor). These two studies form a preliminary examination into RAPL-imposed security implications. In addition, we discuss some possible countermeasures.

In this paper, we present a novel improved wind-driven optimization (IWDO) for transmitbeamforming of phased array radar, which is assumed as a multiple constraints optimizationproblem. First, the emitted signal model and objective functions of optimization with constraints arepresented. Second, all air parcels are divided into feasible and infeasible parcels, and then the target functionand constraint violation of air parcels are evaluated. Finally, the constrained optimization problem isconverted into the nonconstraint problem, and the proposed modified wind-driven optimization (WDO) hasbeen applied for the optimal design of beamforming with null steering. The simulation results show thatthe proposed method outperforms conventional optimization methods in the optimal beamforming byachieving more reduction in the null steering and obtaining accurate peak power.

An air-gapped computer is physically isolated from unsecured networks to guarantee effective protection against data exfiltration. Due to air gaps, unauthorized data transfer seems impossible over legitimate communication channels, but in reality many so-called physical covert channels can be constructed to allow data exfiltration across the air gaps. Most of such covert channels are very slow and often require certain strict conditions to work (e.g., no physical obstacles between the sender and the receiver). In this paper, we introduce a new physical covert channel named BitJabber that is extremely fast and strong enough to even penetrate concrete walls. We show that this covert channel can be easily created by an unprivileged sender running on a victim’s computer. Specifically, the sender constructs the channel by using only memory accesses to modulate the electromagnetic (EM) signals generated by the DRAM clock. While possessing a very high bandwidth (up to 300,000 bps), this new covert channel is also very reliable (less than 1% error rate). More importantly, this covert channel can enable data exfiltration from an air-gapped computer enclosed in a room with thick concrete walls up to 15 cm.

A novel dynamic physical model for a magnetorheological (MR) damper is proposed. In this model, the hysteretic properties are considered to result from the difference between the working me...

A radar and communication integration (RCI) system has great flexibility in allocating antenna resources to guarantee both radar and communication performance. This paper considers the array allocation problems for multiple target localization and multiple platforms communication in an RCI network. The objective of array allocation is to maximize the communication capacity for each channel and to minimize the localization error for each target. In this paper, we firstly build a localization and communication model for array allocation in an RCI network. Minorization maximization (MM) is then applied to create surrogate functions for multiple objective optimization problems. The projected gradient descent (PGD) method is further employed to solve two array allocation problems with and without a certain communication capacity constraint. Computer simulations are conducted to evaluate the performance of the proposed algorithms. The results show that the proposed algorithms have improved localization and communication performance after efficiently allocating the array resource in the RCI network., 31 pages

Semiactive control has received extensive attention because it can attain a similar control gain as active control while requiring a smaller external power supply. The critical step of semi...

The rowhammer bug belongs to software-induced hardware faults, and has been exploited to form a wide range of powerful rowhammer attacks. Yet, how to effectively detect such attacks remains a challenging problem. In this paper, we propose a novel approach named RADAR (Rowhammer Attack Detection via A Radio) that leverages certain electromagnetic (EM) signals to detect rowhammer attacks. In particular, we have found that there are recognizable hammering-correlated sideband patterns in the spectrum of the DRAM clock signal. As such patterns are inevitable physical side effects of hammering the DRAM, they can "expose" any potential rowhammer attacks including the extremely elusive ones hidden inside encrypted and isolated environments like Intel SGX enclaves. However, the patterns of interest may become unapparent due to the common use of spread-spectrum clocking (SSC) in computer systems. We propose a de-spreading method that can reassemble the hammering-correlated sideband patterns scattered by SSC. Using a common classification technique, we can achieve both effective and robust detection-based defense against rowhammer attacks, as evaluated on a RADAR prototype under various scenarios. In addition, our RADAR does not impose any performance overhead on the protected system. There has been little prior work that uses physical side-channel information to perform rowhammer defenses, and to the best of our knowledge, this is the first investigation on leveraging EM side-channel information for this purpose.

Memory corruption attacks such as code injection, code reuse, and non-control data attacks have become widely popular for compromising safety-critical Cyber-Physical Systems (CPS). Moving target defense (MTD) techniques such as instruction set randomization (ISR), address space randomization (ASR), and data space randomization (DSR) can be used to protect systems against such attacks. CPS often use time-triggered architectures to guarantee predictable and reliable operation. MTD techniques can cause time delays with unpredictable behavior. To protect CPS against memory corruption attacks, MTD techniques can be implemented in a mixed time and event-triggered architecture that provides capabilities for maintaining safety and availability during an attack. This paper presents a mixed time and event-triggered MTD security approach based on the ARINC 653 architecture that provides predictable and reliable operation during normal operation and rapid detection and reconfiguration upon detection of attacks. We leverage a hardware-in-the-loop testbed and an advanced emergency braking system (AEBS) case study to show the effectiveness of our approach.

In this paper, a new security and green communication scheme is proposed to the interference-alignment (IA) based networks. To achieve a secured communication, full-duplex receivers are utilized to transmit artificial noise (AN). Both the signals and the ANs are used to harvest energy to realize green communication. For these reasons, the feasible conditions of this scheme are analyzed first. Secondly, the average transmission rate, the secrecy performance and the harvested energy are investigated. Thirdly, an optimization scheme of simultaneous wireless information and power transfer (SWIPT) is given to optimize the information transmission and the energy harvesting efficiency. Meanwhile, an improved IA iteration algorithm is designed to eliminate both the AN and the interference. Furthermore, relay cooperation is considered and its system performance is analyzed. The simulations show that the target average transmission rate is not affected by AN, while the secrecy performance can be greatly improved. The energy harvesting efficiency is also better than the traditional schemes. As expected, the average transmission rate further is improved with the relay cooperation.

A novel optimization method for multiple beamforming with null steering is proposed. Firstly, an invasive weed optimization method is improved in order to optimize the search depth. In the process of the seed diffusion, the crossover operation of genetic algorithm is introduced to modify the seed individuals, which can meliorate the searching ability and convergence speed of the algorithm. Then, the harmonic beamforming technology based on time modulated antenna array is presented, and multiple objectives about peak power and null steering are built. Finally, the improved invasive weed optimization algorithm is used in null forming based on pattern synthesis of antenna array, and the element excitation amplitude of time modulated antenna array is set to the excitation value obtained by the improved optimization algorithm. The simulation results show that the improved invasive weed optimization algorithm is superior to the conventional algorithm in performance. The simulation results show that forming null in the specified direction can be achieved while generating multiple beams.

In order to save the radar resources and obtain the better low probability intercept ability in the network, a novel radar selection method for target tracking based on improved interacting multiple model information filtering (IMM-IF) is presented. Firstly, the relationship model between radar resource and tracking accuracy is built, and the IMM-IF method is presented. Then, the information gain of every radar is predicted according to the IMM-IF, and the radars with larger information gain are selected to track target. Finally, the weight parameters for the tracking fusion are designed after the error covariance prediction of every working radar, in order to improve the IMM-IF. Simulation results show that the proposed algorithm not only saves much more radar resources than other methods but also has excellent tracking accuracy.

Marine communication and navigation technologies go hand-in-hand in order to provide a safe and secure marine travel on those who are on board a vessel or a ship. These tools are used together to establish rapport between sea ports and nearby vessels, as well as determining a sea or ocean’s characteristics and weather patterns. Unmanned Surface Vehicle (USV) for maritime salvage is designed to mitigate the menaces associated with marine navigation. The technique involves intelligent navigation and communication, intelligent collision avoidance and intelligent rheostat systems. This paper seeks to explore the technological advancements in marine communication and navigation used in the maritime environment, with veneration to both hardware and software. This review would invariable serve as a source of information pool for further studies. We also present some current technological challenges the marine industry is antagonized with.

In this paper, we consider a joint array resource allocation and transmit beamforming approach to track multiple targets using phased array radar. Firstly, array allocation is accomplished by solving a constrained optimization problem formulated to minimize total tracking Cramer-Rao Lower Bound with antenna number as a constraint. A procedure to solve this problem is developed by checking Karush-Kuhn-Tucker conditions. Based on the allocated array resource, we develop a novel transmit beampattern design with null steering capability. The emitted signal model and objective functions of optimization are presented with a given power budget and null steering constraint. The resulted problem is revealed to be hard to tackle, therefore, we propose a cyclic algorithm scheme for solving it. The scheme separates the optimization into two problems, where each problem is transformed into simpler convex problems. Lastly, the projected gradient decent method with a penalty function is applied for the optimal design of beamforming with null steering. Simulation results show that the proposed array allocation solution provides better tracking accuracy, and the proposed beampattern design method outperforms conventional optimization methods in the optimal beamforming by achieving much deeper null steering and more accurate peak power in the given directions.

Cyber–physical systems (CPS) such as automotive control systems consist of various interacting cyber and physical components. Heterogeneous domains, composition of multiple components, complex dynamics, and nonlinearities result in significant challenges for design, modeling, and simulation of CPS. Model-based design can be used to address such challenges, but it is very important to use physically accurate heterogeneous models that can be composed to represent the overall system behavior. Further, it is important to preserve the properties derived from analyses based on the mathematical models in the control system implementation in order to reduce costly testing and design changes late in the development cycle. This paper proposes a model-based design methodology for automotive control software using port-Hamiltonian systems (PHS). PHS are used to model the vehicle dynamics, speed and steering control systems, and the interactions between physical and cyber components. Passivity analysis is used to design the controllers and ensure system stability. More importantly, the proposed approach guarantees that passivity is preserved after time-discretization and quantization of the controllers. The models are then used for code generation and compilation, scheduling, and software deployment, ensuring that passivity is preserved by the control system implementation. We evaluate the methodology using an automotive control design case study implemented on a hardware-in-the-loop simulation platform and present simulation results to demonstrate its effectiveness.

The study on soft-sediment deformation structures (SSDS) of Lingshan Island has been one of the hot topics of sedimentology researches in China in recent years, and SSDS developed in turbidite system in the Laiyang Group are widely known by domestic researchers. However, few studies were conducted on the SSDS in fan delta system in the Qingshan Group, Lingshan Island. This study analyzes the classification and characteristics of SSDS especially their lithofacies association and lithologic characteristics through field outcrops investigation and thin section analysis as well. A conclusion was acquired that the paleoenvironment was a fan delta system with occurrence of several volcanic eruptions, where the water became gradually shallower. The SSDS types in the Qingshan Group includes load and flame structure, ball and pillow structure, water-escape structure, hydroplastic deformation structure, plastic sandstone breccia structure, volcanic drop stone and V-shaped ground fissure mainly caused by volcanic earthquakes of three types: (1) seismic waves, (2) gravity and inertia effect of pyroclastic flows, (3) instant differential air pressure; which is different from slumping and tectonic earthquakes occurred in the Laiyang Group. In addition, with the lithofacies association analysis between pyroclastic flow and SSDS beds, a distribution model of SSDS related to volcanic earthquakes can be established: SSDS types changed gradually with their distance further away from the volcanic activity core. Brittle deformation which was common in the proximal zone disappeared gradually; liquefied and plastic SSDS continued to dominate in the medial zone; and slightly liquefied SSDS were developed in the distal zone. Meanwhile, the scale and size of SSDS is negatively correlated with the distance of SSDS depositional locations from the volcanic vent.

In order to solve the problem of multipath propagation of sound waves generated by sea surface reflection, a time difference of arrival (TDOA) localization method based on maximum or minimum value screening is proposed. Firstly, based on the propagation characteristics of underwater acoustic channel, the distance equation of propagation path in the case of sea surface reflection and linear propagation is derived, and four kinds of measurement value equations of TDOA localization in the deepsea multipath environment are established. Then, the measured TDOA values of linear propagation of sound waves are obtained by screening the measured TDOA values for the twice maximum or minimum value. Finally, the underwater target localization is realized based on Chan and Taylor algorithm. Simulation results show that the localization accuracy of this algorithm is within 1 meter in the deep sea multipath environment with the ocean depth of less than 100 meters, which is greatly improved compared with the multipath TDOA algorithm based on statistical characteristics. The proposed algorithm can effectively eliminate the multipath error in the deep-sea environment and has high practicability and reliability.

In a localization system, time of arrival (TOA) and receive signal strength (RSS) technique are widely used to estimate the location of a mobile station (MS) in many sensor networks. To enhance the performance of MS location estimation accuracy, a new ensembled neural network (NN) is employed with the hybrid TOA and RSS measurements. Firstly, the basic least squared based method for the localization is derived, which can be used for obtaining the accurate output for the neural network training. Then TOA and RSS measurements are respectively preprocessed and employed to train the individual Back propagation (BP) neural network. At last, the weighted average method is presented to design total results based on all the individual neural network, in which the weights are adaptively designed by the training errors. The proposed hybrid method is compared with the single method and its Cramer-Rao lower bound(CRLB) in the simulation, which validate that the proposed algorithm has better efficiency and performance for localization than the other methods.

In a radar and infrared sensor synergistic tracking system, an improved radar and infrared sensor cooperative tracking algorithm is proposed based on interacting multiple model and unscented Kalman filter (IMMUKF), which can reduce the radar radiation time. Firstly, the tracking model for the radar and infrared sensor is built and IMMUKF is employed as a filter. Secondly, a novel tracking quality factor is designed for control the radar’s radiation. The residual of the new information obtained by comparing the filtering result with the estimated measurement is selected as a criterion. Finally, the working time of radar and infrared sensor is adaptively controlled. And the simulation results show that the proposed method can reduce the radiation time with excellent tracking accuracy.

Symbolic execution is a well-known program analysis technique that explores multiple program paths simultaneously. Among other things, it is used to uncover subtle bugs and corner cases in programs, as well as to produce high-coverage test suites. Even though symbolic execution has seen successful use in practice, there remain challenges in applying it to programs like web servers that use features such as multithreading and callbacks. This paper describes our dynamic symbolic execution framework for Java that was designed with these types of features in mind. Our framework uses bytecode instrumentation combined with a run-time agent to perform the symbolic execution. We give a detailed description of the challenges we faced along with our design choices. We also present benchmark results on various examples including programs that use web server frameworks.

In this paper, pattern synthesis through time-modulated linear array is studied, and a novel strategy for harmonic beamforming in time-modulated array is proposed. The peak side lobe level is designed as optimization objective function, and the switch-on time sequence of each element is selected as optimization variable. An improved invasive weed optimization (IWO) algorithm is developed in order to determine the optimal parameters describing the pulse sequence used to modulate the excitation weights of array elements. Representative results are reported and discussed to point out potentialities and advantages of the proposed approach, which can obtain lower objective function values.

With the increasingly connected nature of Cyber-Physical Systems (CPS), new attack vectors are emerging that were previously not considered in the design process. Specifically, autonomous vehicles are one of the most at risk CPS applications, including challenges such as a large amount of legacy software, non-trusted third party applications, and remote communication interfaces. With zero day vulnerabilities constantly being discovered, an attacker can exploit such vulnerabilities to inject malicious code or even leverage existing legitimate code to take over the cyber part of a CPS. Due to the tightly coupled nature of CPS, this can lead to altering physical behavior in an undesirable or devastating manner. Therefore, it is no longer effective to reactively harden systems, but a more proactive approach must be taken. Moving target defense (MTD) techniques such as instruction set randomization (ISR), and address space randomization (ASR) have been shown to be effective against code injection and code reuse attacks. However, these MTD techniques can result in control system crashing which is unacceptable in CPS applications since such crashing may cause catastrophic consequences. Therefore, it is crucial for MTD techniques to be complemented by control reconfiguration to maintain system availability in the event of a cyber-attack. This paper addresses the problem of maintaining system and security properties of a CPS under attack by integrating moving target defense techniques, as well as detection, and recovery mechanisms to ensure safe, reliable, and predictable system operation. Specifically, we consider the problem of detecting code injection as well as code reuse attacks, and reconfiguring fast enough to ensure the safety and stability of autonomous vehicle controllers are maintained. By using MTD such as ISR, and ASR, our approach provides the advantage of preventing attackers from obtaining the reconnaissance knowledge necessary to perform code injection and code reuse attacks, making sure attackers can’t find vulnerabilities in the first place. Our system implementation includes a combination of runtime MTD utilizing AES 256 ISR and fine grained ASR, as well as control management that utilizes attack detection, and reconfiguration capabilities. We evaluate the developed security architecture in an autonomous vehicle case study, utilizing a custom developed hardware-in-the-loop testbed.

In a localization system, time difference of arrival (TDOA) technique is widely used to estimate the location of a mobile station (MS). To improve the estimation performance of MS location, a novel algorithm based a constrained ant lion optimization method is presented in non-line-of-sight (NLOS) environments. Firstly, two objective functions are designed according to the TDOA and angle of arrival(AOA) information, then the MS positions are obtained by the ant lion optimization method which is novel and effective. Simulations are conducted to compare with other localization methods and evaluate the performance of the algorithm. The simulation results show that the proposed algorithm can obtain more precise location accuracy.

Cyber-Physical Systems (CPS) have been increasingly subject to cyber-attacks including code injection attacks. Zero day attacks further exasperate the threat landscape by requiring a shift to defense in depth approaches. With the tightly coupled nature of cyber components with the physical domain, these attacks have the potential to cause significant damage if safety-critical applications such as automobiles are compromised. Moving target defense techniques such as instruction set randomization (ISR) have been commonly proposed to address these types of attacks. However, under current implementations an attack can result in system crashing which is unacceptable in CPS. As such, CPS necessitate proper control reconfiguration mechanisms to prevent a loss of availability in system operation. This paper addresses the problem of maintaining system and security properties of a CPS under attack by integrating ISR, detection, and recovery capabilities that ensure safe, reliable, and predictable system operation. Specifically, we consider the problem of detecting code injection attacks and reconfiguring the controller in real-time. The developed framework is demonstrated with an autonomous vehicle case study.

The rowhammer bug belongs to software-induced hardware faults, and has posed great security challenges to numerous systems. On x86, many approaches to triggering the rowhammer bug have been found; yet, due to several different reasons, the number of discovered approaches on ARM is limited. In this paper, we revisit the problem of how to trigger the rowhammer bug on ARM-based devices by carefully investigating whether it is possible to translate the original x86-oriented rowhammer approaches to ARM. We provide a thorough study of the unprivileged ARMv8-A cache maintenance instructions and give two previously overlooked reasons to support their use in rowhammer attacks. Moreover, we present a previously undiscovered instruction that can be exploited to trigger the rowhammer bug on many ARM-based devices. A potential approach to quickly evicting ARM CPU caches is also discussed, and experimental evaluations are carried out to show the effectiveness of our findings.

As the number of cores and utilization of the system are increasing quickly, shared resources like caches are interfering tasks' execution behaviors more heavily. In order to achieve resource efficiency in both temporal and spatial domains for multi-core real-time systems, caches should be taken into consideration when performing partitions. In this paper, partitioned Earliest Deadline First (EDF) scheduling on a preemptive multi-core platform is considered. We propose a new system model that covers inter-task cache interference and describe some ongoing work in identifying proper partition schemes under such settings.

In this paper, we investigate how to soundly analyze multi-level caches that employ write-back policy at each level for worst-case execution time (WCET) estimation. To the best of our knowledge, there is only one existing approach for dealing with write backs in multi-level cache analysis. However, as shown in the paper, this existing approach is not sound. In order to soundly handle write backs, at a cache level, we need to consider whether a memory block is potentially dirty and when such a potentially dirty block may be evicted from the cache. To this end, we introduce a dirty attribute into persistence analysis for tracking dirty blocks, and over-approximate a write back window for each possible write back. Based on the overestimated write back occurring times, we propose an approach that can soundly deal with write backs in analysis of multi-level (unified) caches for WCET estimation. Possible write back costs are also integrated into path analysis. We evaluate the proposed approach on a set of benchmarks to demonstrate its effectiveness.

Most existing multiprocessor schedulability analysis assumes zero cost for preemptions and migrations. In order for those analysis to be correct, execution time estimations are often inflated by a certain (pessimistic) factor, leading to severe waste of computing resource. In this paper, a novel Global Earliest Deadline First (GEDF) schedulability test is proposed, where Cache-Related Preemption Delay (CRPD) is separately modeled and integrated. Specifically, multiple analyses for estimating CRPD bounds are conducted based on the refined estimation of the maximal number of preemptions, leading to tighter G-EDF schedulability tests. The experimental study is conducted to demonstrate the performance of the proposed methods.

Cache-related preemption delay (CRPD) analysis is crucial when designing embedded control systems that employ preemptive scheduling. CRPD analysis for single-level caches has been studied extensively based on useful cache blocks (UCBs). As high-performance embedded processors are increasingly used, which are often equipped with multi-level caches, CRPD analysis for cache hierarchies also needs to be investigated. Recently, an approach has been proposed to estimate CRPD for multi-level non-inclusive caches. Since multi-level inclusive caches are also commonly used, especially in some multi-core processors, it becomes important to study how to analyze CRPD for inclusive cache hierarchies. However, as shown in this paper, new challenges appear due to the strict inclusion enforcement in the multi-level inclusive caches, which make the traditional UCB concept hard to use. In this paper, we propose a new concept of useful positive references (UPRs) to replace the UCB concept. Based on UPRs, we propose an approach to bound the additional cache misses due to a preemption in a two-level inclusive cache hierarchy. We present theoretical analysis to show the approach is safe, and we evaluate the proposed approach on a set of benchmarks to demonstrate its effectiveness. To the best of our knowledge, this is the first attempt to analyze CRPD for multi-level inclusive caches.

In order to achieve the important tactical requirement of low probability of intercept (LPI) in the radar network, dynamically controlling the emission of the radars is very necessary. Firstly, a data fusion method based on an interacting multiple model and information filtering (IMM-IF) algorithm is presented. Then, the influence of the emitted power is considered in the tracking performance of the radar network, and a novel power scheduling method based IMM-IF is proposed, which controls the power of radar according to predicted covariance matrix during tracking. Finally, taking advantage of the relation model between the emitted power and the tracking performance, the Wind Driven Optimization (WDO) method is used to select the emitted power of the radar network. The tracking accuracy and LPI performance are demonstrated in the Monte Carlo simulations. The results are validated through the comparison with other methods.

In a localization system, time difference of arrival technique is widely used to estimate the location of a mobile station. To improve the performance of mobile station location estimation, a novel algorithm-based artificial neural network ensemble and time difference of arrival information is proposed in non-line-of-sight environments. Back propagation neural network is a classic artificial neural network and may be effectively used for mathematical modeling and prediction, and an artificial neural network ensemble has better generalization ability and stability than a single network. First, the parameters, such as the weights and biases of the single neural network are optimized by the ant lion optimization method which is novel and effective. Then four types of different information from the time difference of arrival measurements are respectively used to train the individual neural network. Finally, the weighted average method is improved to combine the outputs of the different individual neural network, where weights are determined by the training errors. The estimation accuracy of the locating system is evaluated through experimental measurements. The simulation results show that the proposed algorithm is efficient in improving the generalization ability and localization precision of the neural network ensemble model.

When designing cyber-physical systems (CPS), virtual prototyping can discover potential design flaws at early design stages to reduce the difficulties at the integration stage. CPS are typically complex real-time distributed systems which require networks with deterministic end-to-end latency and bounded jitter. Time-triggered Ethernet (TTEthernet) integrates time-triggered and event-triggered traffic, and has been used in many CPS domains, such as automotive, aerospace, and industrial process control. In this paper, a TTEthernet model in SystemC/TLM is developed to facilitate the design and integration of CPS. The model realizes all the necessary features of TTEthernet, and can be integrated with the hardware platform model for design space exploration. We validate the model by comparing latency and jitter with those obtained using a commercial software-based implementation. We also compare our model with the TTEthernet modeled in OMNeT++ INET framework. Our model provides startup and restart services that are necessary for maintaining synchronized operations in TTEthernet. We evaluate these services and also the efficiency of the simulation.

The systematic design of automotive control applications is a challenging problem due to lack of understanding of the complex and tight interactions that often manifest during the integration of components from the control design phase with the components from software generation and deployment on actual platform/network. In order to address this challenge, we present a systematic methodology and a toolchain using well-defined models to integrate components from various design phases with specific emphasis on restricting the complex interactions that manifest during integration such as timing, deployment, and quantization. We present an experimental platform for the evaluation and testing of the design process. The approach is applied to the development of an adaptive cruise control, and we present experimental results that demonstrate the efficacy of the approach.

In order to improve the radio frequency stealth ability of phased array radars, a novel resource scheduling method of the radar network for target tracking in clutter is presented. Firstly, the relationship model between radar resource and tracking accuracy is built, and the sampling interval, power, and waveform will influence predicted error covariance matrix through transition matrix and measurement noise. Then, radar resource scheduling algorithm based on Markov decision process which is converted to be a binary optimization problem is proposed, and an improved binary wind-driven optimization method is presented to solve that problem. The radar and its radiation parameters will be selected for better radio frequency stealth performance and tracking accuracy. Simulation results show that the proposed algorithm not only has excellent tracking accuracy in clutter but also has better radio frequency stealth ability comparing with other methods.