This paper reports a spatial extrapolation of the sound field with a physics-informed neural network. We investigate the spatial extrapolation of the room impulse responses with physics-informed SIREN architecture. Furthermore, we proposed a noise-robust extrapolation method by introducing a tolerance term to the loss function.

We propose a pre-T event-triggered controller (ETC) for the stabilization of a chain of integrators. Our per-T event-triggered controller is a modified event-triggered controller by adding a pre-defined positive constant T to the event-triggering condition. With this pre-T, the immediate advantages are (i) the often complicated additional analysis regarding the Zeno behavior is no longer needed, (ii) the positive lower bound of interexecution times can be specified, (iii) the number of control input updates can be further reduced. We carry out the rigorous system analysis and simulations to illustrate the advantages of our proposed method over the traditional event-triggered control method.

In this letter, we give a characterization for a generic construction of bent functions. This characterization enables us to obtain another efficient construction of bent functions and to give a positive answer on a problem of bent functions.

This letter proposes a scheme for the backward transmission of the propagation-delay based three-user X channel, which is reciprocal to the forward transmission. The given scheme successfully delivers 10 expected messages in 6 time-slots by cyclic interference alignment without loss of degrees of freedom, which supports efficient bidirectional transmission between the two ends of the three-user X channel.

A novel temporal convolution network-gated recurrent unit (NTCN-GRU) algorithm is proposed for the greatest of constant false alarm rate (GO-CFAR) frequency hopping (FH) prediction, integrating GRU and Bayesian optimization (BO). GRU efficiently captures the semantic associations among long FH sequences, and mitigates the phenomenon of gradient vanishing or explosion. BO improves extracting data features by optimizing hyperparameters besides. Simulations demonstrate that the proposed algorithm effectively reduces the loss in the training process, greatly improves the FH prediction effect, and outperforms the existing FH sequence prediction model. The model runtime is also reduced by three-quarters compared with others FH sequence prediction models.

In recent years, blockchain has been widely applied in the Internet of Things (IoT). Blockchain oracle, as a bridge for data communication between blockchain and off-chain, has also received significant attention. However, the numerous and heterogeneous devices in the IoT pose great challenges to the efficiency and security of data acquisition for oracles. We find that the matching relationship between data sources and oracle nodes greatly affects the efficiency and service quality of the entire oracle system. To address these issues, this paper proposes a distributed and efficient oracle solution tailored for the IoT, enabling fast acquisition of real-time off-chain data. Specifically, we first design a distributed oracle architecture that combines both Trusted Execution Environment (TEE) devices and ordinary devices to improve system scalability, considering the heterogeneity of IoT devices. Secondly, based on the trusted node information provided by TEE, we determine the matching relationship between nodes and data sources, assigning appropriate nodes for tasks to enhance system efficiency. Through simulation experiments, our proposed solution has been shown to effectively improve the efficiency and service quality of the system, reducing the average response time by approximately 9.92% compared to conventional approaches.

Smart cities aim to improve the quality of life of citizens and efficiency of city operations through utilization of 5G communication technology. Based on various technologies such as IoT, cloud computing, artificial intelligence, and big data, they provide smart services in terms of urban planning, development, and management for solving problems such as fine dust, traffic congestion and safety, energy efficiency, water shortage, and an aging population. However, as smart city has an open network structure, an adversary can easily try to gain illegal access and perform denial of service and sniffing attacks that can threaten the safety and privacy of citizens. In smart cities, the global mobility network (GLOMONET) supports mobile services between heterogeneous networks of mobile devices such as autonomous vehicles and drones. Recently, Chen et al. proposed a user authentication scheme for GLOMONET in smart cities. Nevertheless, we found some weaknesses in the scheme proposed by them. In this study, we propose a secure lightweight authentication for roaming services in a smart city, called SLARS, to enhance security. We proved that SLARS is more secure and efficient than the related authentication scheme for GLOMONET through security and performance analysis. Our analysis results show that SLARS satisfies all security requirements in GLOMONET and saves 72.7% of computation time compared to that of Chen et al.’s scheme.

In this paper, a cluster-based three-dimensional (3D) non-stationary vehicle-to-vehicle (V2V) channel model with circular arc motions and antenna rotates is proposed. The channel model simulates the complex urban communication scenario where clusters move with arbitrary velocities and directions. A novel cluster evolution algorithm with time-array consistency is developed to capture the non-stationarity. For time evolution, the birth-and-death (BD) property of clusters including birth, death, and rebirth are taken into account. Additionally, a visibility region (VR) method is proposed for array evolution, which is verified to be applicable to circular motions. Based on the Taylor expansion formula, a detailed derivation of space-time correlation function (ST-CF) with circular arc motions is shown. Statistical properties including ST-CF, Doppler power spectrum density (PSD), quasi-stationary interval, instantaneous Doppler frequency, root mean square delay spread (RMS-DS), delay PSD, and angular PSD are derived and analyzed. According to the simulated results, the non-stationarity in time, space, delay, and angular domains is captured. The presented results show that motion modes including linear motions as well as circular motions, the dynamic property of the scattering environment, and the velocity of the vehicle all have significant impacts on the statistical properties.

To improve the recognition rate of the end-to-end modulation recognition method based on deep learning, a modulation recognition method of communication signals based on a cascade network is proposed, which is composed of two networks: Stacked Denoising Auto Encoder (SDAE) network and DCELDNN (Dilated Convolution, ECA Mechanism, Long Short-Term Memory, Deep Neural Networks) network. SDAE network is used to denoise the data, reconstruct the input data through encoding and decoding, and extract deep information from the data. DCELDNN network is constructed based on the CLDNN (Convolutional, Long Short-Term Memory, Fully Connected Deep Neural Networks) network. In the DCELDNN network, dilated convolution is used instead of normal convolution to enlarge the receptive field and extract signal features, the Efficient Channel Attention (ECA) mechanism is introduced to enhance the expression ability of the features, the feature vector information is integrated by a Global Average Pooling (GAP) layer, and signal features are extracted by the DCELDNN network efficiently. Finally, end-to-end classification recognition of communication signals is realized. The test results on the RadioML2018.01a dataset show that the average recognition accuracy of the proposed method reaches 63.1% at SNR of -10 to 15 dB, compared with CNN, LSTM, and CLDNN models, the recognition accuracy is improved by 25.8%, 12.3%, and 4.8% respectively at 10 dB SNR.

In this research, we investigated the digital/analog-operation utilizing ferroelectric nondoped HfO2 (FeND-HfO2) as a blocking layer (BL) in the Hf-based metal/oxide/nitride/oxide/Si (MONOS) nonvolatile memory (NVM), so called FeNOS NVM. The Al/HfN0.5/HfN1.1/HfO2/p-Si(100) FeNOS diodes realized small equivalent oxide thickness (EOT) of 4.5 nm with the density of interface states (Dit) of 5.3 × 1010 eV-1cm-2 which were suitable for high-speed and low-voltage operation. The flat-band voltage (VFB) was well controlled as 80-100 mV with the input pulses of ±3 V/100 ms controlled by the partial polarization of FeND-HfO2 BL at each 2-bit state operated by the charge injection with the input pulses of +8 V/1-100 ms.

Radiation-induced temporal errors become a significant issue for circuit reliability. We measured the pulse widths of radiation-induced single event transients (SETs) from pMOSFETs and nMOSFETs separately. Test results show that heavy-ion induced SET rates of nMOSFETs were twice as high as those of pMOSFETs and that neutron-induced SETs occurred only in nMOSFETs. It was confirmed that the SET distribution from inverter chains can be estimated using the SET distribution from pMOSFETs and nMOSFETs by considering the difference in load capacitance of the measurement circuits.

This article describes the idea of utilizing Attested Execution Secure Processors (AESPs) that fit into building a secure Self-Sovereign Identity (SSI) system satisfying Sybil-resistance under permissionless blockchains. Today’s circumstances requiring people to be more online have encouraged us to address digital identity preserving privacy. There is a momentum of research addressing SSI, and many researchers approach blockchain technology as a foundation. SSI brings natural persons various benefits such as owning controls; on the other side, digital identity systems in the real world require Sybil-resistance to comply with Anti-Money-Laundering (AML) and other needs. The main idea in our proposal is to utilize AESPs for three reasons: first is the use of attested execution capability along with tamper-resistance, which is a strong assumption; second is powerfulness and flexibility, allowing various open-source programs to be executed within a secure enclave, and the third is that equipping hardware-assisted security in mobile devices has become a norm. Rafael Pass et al.’s formal abstraction of AESPs and the ideal functionality $\color{brown}{\mathcal{G}_\mathtt{att}}$ enable us to formulate how hardware-assisted security works for secure digital identity systems preserving privacy under permissionless blockchains mathematically. Our proposal of the AESP-based SSI architecture and system protocols, $\color{blue}{\Pi^{\mathcal{G}_\mathtt{att}}}$, demonstrates the advantages of building a proper SSI system that satisfies the Sybil-resistant requirement. The protocols may eliminate the online distributed committee assumed in other research, such as CanDID, because of assuming AESPs; thus, $\color{blue}{\Pi^{\mathcal{G}_\mathtt{att}}}$ allows not to rely on multi-party computation (MPC), bringing drastic flexibility and efficiency compared with the existing SSI systems.

Emergency Medical Services (EMS) play a crucial role in healthcare systems, managing pre-hospital or out-of-hospital emergencies from the onset of an emergency call to the patient’s arrival at a healthcare facility. The design of an efficient ambulance location model is pivotal in enhancing survival rates, controlling morbidity, and preventing disability. Key factors in the classical models typically include travel time, demand zones, and the number of stations. While urban EMS systems have received extensive examination due to their centralized populations, rural areas pose distinct challenges. These include lower population density and longer response distances, contributing to a higher fatality rate due to sparse population distribution, limited EMS stations, and extended travel times. To address these challenges, we introduce a novel mathematical model that aims to optimize coverage and equity. A distinctive feature of our model is the integration of equity within the objective function, coupled with a focus on practical response time that includes the period required for personal protective equipment procedures, ensuring the model’s applicability and realism in emergency response scenarios. We tackle the proposed problem using a tailored genetic algorithm and propose a greedy algorithm for solution construction. The implementation of our tailored Genetic Algorithm promises efficient and effective EMS solutions, potentially enhancing emergency care and health outcomes in rural communities.

In this paper, we propose two algorithms, B-N2N and B-N2S, that solve the node-to-node and node-to-set disjoint paths problems in the bicube, respectively. We prove their correctness and that the time complexities of the B-N2N and B-N2S algorithms are O(n2) and O(n2 log n), respectively, if they are applied in an n-dimensional bicube with n ≥ 5. Also, we prove that the maximum lengths of the paths generated by B-N2N and B-N2S are both n + 2. Furthermore, we have shown that the algorithms can be applied in the locally twisted cube, too, with the same performance.

Aiming at the problem that large-scale traffic data lack labels and take too long for feature extraction in network intrusion detection, an unsupervised intrusion detection method ACOPOD based on Adam asymmetric autoencoder and COPOD (Copula-Based Outlier Detection) algorithm is proposed. This method uses the Adam asymmetric autoencoder with a reduced structure to extract features from the network data and reduce the data dimension. Then, based on the Copula function, the joint probability distribution of all features is represented by the edge probability of each feature, and then the outliers are detected. Experiments on the published NSL-KDD dataset with six other traditional unsupervised anomaly detection methods show that ACOPOD achieves higher precision and has obvious advantages in running speed. Experiments on the real civil aviation air traffic management network dataset further prove that the method can effectively detect intrusion behavior in the real network environment, and the results are interpretable and helpful for attack source tracing.

Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) can provide mobile users (MU) with additional computing services and a wide range of connectivity. This paper investigates the joint optimization strategy of task offloading and resource allocation for UAV-assisted MEC systems in complex scenarios with the goal of reducing the total system cost, consisting of task execution latency and energy consumption. We adopt a game theoretic approach to model the interaction process between the MEC server and the MU Stackelberg bilayer game model. Then, the original problem with complex multi-constraints is transformed into a duality problem using the Lagrangian duality method. Furthermore, we prove that the modeled Stackelberg bilayer game has a unique Nash equilibrium solution. In order to obtain an approximate optimal solution to the proposed problem, we propose a two-stage alternating iteration (TASR) algorithm based on the subgradient method and the marginal revenue optimization method. We evaluate the effective performance of the proposed algorithm through detailed simulation experiments. The simulation results show that the proposed algorithm is superior and robust compared to other benchmark methods and can effectively reduce the task execution latency and total system cost in different scenarios.

Entity alignment (EA) is a crucial task for integrating cross-lingual and cross-domain knowledge graphs (KGs), which aims to discover entities referring to the same real-world object from different KGs. Most existing embedding-based methods generate aligning entity representation by mining the relevance of triple elements, paying little attention to triple indivisibility and entity role diversity. In this paper, a novel framework named TTEA - Type-enhanced Ensemble Triple Representation via Triple-aware Attention for Cross-lingual Entity Alignment is proposed to overcome the above shortcomings from the perspective of ensemble triple representation considering triple specificity and diversity features of entity role. Specifically, the ensemble triple representation is derived by regarding relation as information carrier between semantic and type spaces, and hence the noise influence during spatial transformation and information propagation can be smoothly controlled via specificity-aware triple attention. Moreover, the role diversity of triple elements is modeled via triple-aware entity enhancement in TTEA for EA-oriented entity representation. Extensive experiments on three real-world cross-lingual datasets demonstrate that our framework makes comparative results.

Automatic detection and recognition systems have numerous applications in smart city implementation. Despite the accuracy and widespread use of device-based and optical methods, several issues remain. These include device limitations, environmental limitations, and privacy concerns. The FMWC sensor can overcome these issues to detect and track moving people accurately in commercial environments. However, single-chip mmWave sensor solutions might struggle to recognize standing and sitting people due to the necessary static removal module. To address these issues, we propose a real-time indoor people detection and tracking fusion system using mmWave radar and cameras. The proposed fusion system approaches an overall detection accuracy of 93.8% with a median position error of 1.7 m in a commercial environment. Compared to our single-chip mmWave radar solution addressing an overall accuracy of 83.5% for walking people, it performs better in detecting individual stillness, which may feed the security needs in retail. This system visualizes customer information, including trajectories and the number of people. It helps commercial environments prevent crowds during the COVID-19 pandemic and analyze customer visiting patterns for efficient management and marketing. Powered by an IoT platform, the system can be deployed in the cloud for easy large-scale implementation.

For flight simulators, it is crucial to create three-dimensional terrain using clear remote sensing images. However, due to haze and other contributing variables, the obtained remote sensing images typically have low contrast and blurry features. In order to build a flight simulator visual system, we propose a deep learning-based dehaze model for remote sensing images dehazing. An encoder-decoder architecture is proposed that consists of a multiscale fusion module and a gated large kernel convolutional attention module. This architecture can fuse multi-resolution global and local semantic features and can adaptively extract image features under complex terrain. The experimental results demonstrate that, with good generality and application, the model outperforms existing comparison techniques and achieves high-confidence dehazing in remote sensing images with a variety of haze concentrations, multi-complex terrains, and multi-spatial resolutions.

In the realm of Single Image Super-Resolution (SISR), the meticulously crafted Nonlocal Sparse Attention-based block demonstrates its efficacy in noise reduction and computational cost reduction for nonlocal (global) features. However, it neglect the traditional Convolutional-based block, which proficient in handling local features. Thus, merging both the Nonlocal Sparse Attention-based block and the Convolutional-based block to concurrently manage local and nonlocal features poses a significant challenge. To tackle the aforementioned issues, this paper introduces the Channel Contrastive Attention-based Local-Nonlocal Mutual block (CCLN) for Super-Resolution (SR). (1) We introduce the CCLN block, encompassing the Local Sparse Convolutional-based block for local features and the Nonlocal Sparse Attention-based network block for nonlocal features. (2) We introduce Channel Contrastive Attention (CCA) blocks, incorporating Sparse Aggregation into Convolutional-based blocks. Additionally, we introduce a robust framework to fuse these two blocks, ensuring that each branch operates according to its respective strengths. (3) The CCLN block can seamlessly integrate into established network backbones like the Enhanced Deep Super-Resolution network (EDSR), achieving in the Channel Attention based Local-Nonlocal Mutual Network (CCLNN). Experimental results show that our CCLNN effectively leverages both local and nonlocal features, outperforming other state-of-the-art algorithms.