Ultra-wideband (UWB) technology, with its centimeter-level ranging accuracy, has become a key enabler for Internet of Things (IoT) applications such as asset tracking, smart warehouses, and proximity-aware systems. However, conventional MAC protocols struggle to support real-time, high-density localization in confined environments due to inefficient channel utilization and limited scalability. This paper proposes a novel MAC protocol featuring a layered time slot mechanism that hierarchically decouples logical (macro) and physical (micro) slot allocations. This design enables efficient scheduling of double-sided two-way ranging (DS-TWR) exchanges through non-contiguous resource reuse, without compromising ranging precision. A dynamic superframe structure is introduced, prioritizing deterministic ranging in the Contention-Free Period (CFP) and supporting retries in the Contention Access Period (CAP), and accommodating heterogeneous update rates across tags. Theoretical analysis and simulations demonstrate substantial improvements in tag capacity, bandwidth efficiency, and ranging throughput compared to standard guaranteed time slot (GTS)-based protocols. Furthermore, a hardware implementation using DecaWave DWM1000 modules validates the protocol’s feasibility, confirming effective multi-tag localization under real-world conditions. Beyond positioning, this layered time slot mechanism can also be applied to other domains besides positioning where several transmissions and receptions are necessary for one transaction.

The blockchain has been the main computing scenario for smart contracts, and the decentralized infrastructure of the blockchain is effectively implemented in a de-trusted and executable environment. However, vulnerabilities in smart contracts are particularly vulnerable to exploitation by malicious attackers and have always been a key issue in blockchain security. Existing traditional tools are inefficient in detecting vulnerabilities and have a high rate of false positives when detecting contracts. Some neural network methods have improved the detection efficiency, but they are not competent for fine-grained (code line level) vulnerability detection. We proposes the ContractCheck model for detecting contract vulnerabilities based on neural network methods. ContractCheck extracts fine-grained segments from the abstract syntax tree (AST) and function call graph of smart contract source code. Furthermore, the segments are parsed into token flow retaining semantic information as uint, which are used to generate numerical vector sequences that can be trained using neural network methods. We conduct multiple rounds of experiments using a dataset constructed from 36,885 smart contracts and identified the optimal ContractCheck model structure by employing the Fasttext embedding vector algorithm and constructing a composite model using CNN and BiGRU for training the network. Evaluation on other datasets demonstrates that ContractCheck exhibits significant improvement in contract-level detection performance compared to other methods, with an increase of 23.60% in F1 score over the best existing method. Particularly, it achieves fine-grained detection based on neural network methods. The cases provided indicate that ContractCheck can effectively assist developers in accurately locating the presence of vulnerabilities, thereby enhancing the security of Ethereum smart contracts.

Summary Electrocardiogram (ECG) monitoring systems are widely used in tele‐cardiology healthcare and applications of Internet of Medical Things. In order to guarantee the rapidity, stability, and accuracy of ECG signals in acquisition and transmission, the data delay, loss, and destruction must be considered in dynamic ECG monitoring system. Hence, data encryption as a key processing technique must carefully be selected in such applications. In this article, we comparatively studied the data encryption standard (DES) and elliptic curves cryptography (ECC) for applications in remote ECG monitoring. The study revealed that although both the encryption methods could effectively protect the integrity of data and ensure the operation of ECG monitoring system, the ECC method perform better than DES especially from the perspective of rapidity. In conclusion, the ECC is recommended as the candidate of encryption method in remote ECG monitoring systems.

This article presents results on designing the Pareto-optimal strategy under$H_{\infty }$constraint for the linear mean-field stochastic systems disturbed by external disturbances. First, combining the stochastic$H_{\infty }$control theory with the stochastic mean-field theory, we derive the stochastic bounded real lemma (SBRL) of our considered linear mean-field stochastic systems with the stochastic initial condition. Second, we use the mean-field forward–backward stochastic differential equation to solve the mean-field linear quadratic Pareto-optimal problem with indefinite cost functionals. It is proved that the existence of a closed-loop Pareto-optimal strategy is equivalent to the solvability of the coupled generalized differential Riccati equations when some conditions are satisfied. Finally, a necessary and sufficient condition for the Pareto-optimal strategy under the$H_{\infty }$constraint is researched by four-coupled matrix-valued equations. Besides, we also obtain the Pareto frontier for the mean-field stochastic system with only state-dependent noise. A practical example is presented to show the effectiveness of our main results.

Finger vein recognition is an emerging biometric technique for personal authentication that has garnered considerable attention in the past decade. Although shown to be effective, recent studies have revealed that finger vein biometrics is also vulnerable to presentation attacks, i.e., printed versions of authorized individual finger vein images can be used to gain access to facilities or services. In this paper, given that both blurriness and the noise distribution are slightly different between real and forged finger vein images, we propose an efficient and robust method for detecting presentation attacks that use forged finger vein images (print artifacts). First, we use total variation regularization to decompose original finger vein images into structure and noise components, which represent the degrees of blurriness and the noise distribution. Second, a block local binary pattern descriptor is used to encode both structure and noise information in the decomposed components. Finally, we use a cascaded support vector machine model for classification, by which finger vein presentation attacks can be effectively detected. To evaluate the performance of our approach, we constructed a new finger vein presentation attack database. Extensive experimental results gleaned from the two finger vein presentation attack databases and a palm vein presentation attack database show that our method clearly outperforms state-of-the-art methods.

Based on the decomposition of singular system, we propose a P-type iterative learning control law with initial state to track the desired output trajectory of the singular impulsive system. Then the convergence conditions of the open-loop iterative learning schemes is presented.

This paper discusses adaptive filtering algorithms and proposes a fast algorithm based on vector plots analysis, that is different from the previous adaptive filtering algorithms. By introducing approaches of mathematical geometrical analysis to study adaptive filtering, the paper inquires into vector plots structure of least mean squares (LMS) algorithm and geometrical feature of algorithm convergence and seeks effective fast algorithm on the basis of geometrical analysis. Numerical simulations are given to show the efficiency and superiority of the new algorithm.

In this paper, a new algorithm of iterative learning control with forgetting factor is proposed by using a new norm and a new analysis method. The new method applies the whole information of systems to transfer the iterative learning control problem into a stability problem of a discrete system with parameters. This algorithm improves the shortage appeared in some present learning algorithms with forgetting factors. The simulations show the effectiveness of our new algorithm.