This paper studies ΔΣ modulators for discrete-time closed loop systems. ΔΣ modulators have been originally developed as efficient analog-to-digital converters (ADCs). Recently, ΔΣ modulators are designed based on the characteristics of the system that uses the ΔΣ modulator. For example in a control system, quantization may degrade control performance due to quantization errors, while the input to any practical system is limited to a range. Then, the saturation of the control input may cause windup phenomena such as overshoots of the system outputs and instability of the control system. In this paper, we propose a design of ΔΣ modulators to mitigate the effects of quantization and saturation in a discrete-time closed loop system. We design the ΔΣ modulator to minimize the norm of the quantization error at the system output to reduce the effects of the quantization error under a stability condition to avoid the saturation of the input on the closed-loop system. Numerical examples are provided to see the effectiveness of our proposed design.

With the rapid rise of the electric vehicle industry, the gap between electric vehicle ownership and available charging pile is becoming increasingly large. In order to ensure the automatic charging efficiency of electric vehicles, it becomes crucial to achieve identification and location of electric vehicle charging ports efficiently and accurately. However, existing technologies face numerous challenges, such as noise interference, large data volumes, and low registration efficiency, which lead to suboptimal performance in charging port identification and positioning. Existing point cloud data noise reduction, feature point extraction and registration techniques for charging port identification and location have problems such as low noise reduction accuracy, poor quality of extracted points and low registration efficiency. Therefore, this paper proposes an optimization strategy for electric vehicle charging port identification and location based on improved point cloud registration. Firstly, the adaptive K-dimensional tree (K-D Tree) method is used to reduce the noise for point cloud data by dynamically selecting the optimal splitting dimension and value. Next, using the geometric feature information of the point cloud data, high quality feature key points are extracted by clustering analysis. Then, a feedback updating mechanism based on the registration loss function is proposed, which updates the K-D Tree model in real-time by the calculation results of the loss function to improve the registration efficiency as well as the charging port identification accuracy. Finally, simulation experiments are conducted to verify the performance of the proposed method in the identification and location of electric vehicle charging ports. The simulation results indicate that, compared with baseline 1 and baseline 2, the intersection over union (IOU) of proposed algorithm is increased by 43.54% and 55.46%, respectively.

Double-layer coils (DLCs) have been extensively investigated for compact inductive power transfer (IPT) systems operating in the MHz frequency range. Different from previous studies which focus mainly on realizing self-resonance and enhancing the Q factor, this paper enhances both the Q factor and the self inductance to achieve high efficiency and guarantee target output voltage when deployed in IPT applications. By using a lumped-element model derived from transmission line concept, this paper shows that the self inductance when the two layers are serially connected is approximately more than 3 times of that when the two layers are open-ended. Motivated by this feature, we focus on the series-connected DLC and investigate a resonance scheme using two external capacitors: one inserted between the two layers and the other inserted outside the coil. In this resonance scheme, parameters of the capacitors are chosen not only to enhance the Q factor but also to maintain the self inductance. Our air-cored sample coils of 100 mm outer diameter exhibit self inductance of 7.69 μH, Q of 308 at 6.78 MHz, and 94% power transfer efficiency at 50 mm transmission distance. These results are attractive when compared to recent self-resonant open-ended DLCs having similar dimensions.

This paper proposes standard cell layout style to reduce the block area in double-diffusion break FinFET process. The first generation of FinFET process technology requires a double-diffusion break to shutdown the leakage current under the dummy gate. Double-diffusion break at the edge of the standard cell requires two additional unit cells for the dummy gates and it results in a large block area. We propose a FinFET cell layout style which VDD/VSS diffusions can be shared with adjacent cells. The proposed layout structure places the VDD/VSS-diffusions at the cell edge to place these nodes adjacently, and it eliminates the use of a double diffusion break. We also propose a diffusion reorder algorithm to improve the use of common potential node sharing. Experimental results show that the proposed cell library with a new layout style and reordering algorithm achieves an 8.39% area reduction in on average.

With the increasing number of tasks undertaken by unmanned aerial vehicle (UAV) clusters, the corresponding flight ad hoc networks must process an increasing amount of service data, and different service types have different transmission requirements. According to these requirements, existing methods often prioritize high-priority packets at the expense of low-priority packets. To overcome this limitation, this paper proposes a dynamic time-division multiple access (TDMA) protocol based on service priority called the SPD-TDMA protocol. To meet the requirements of packet transmission with different priorities, a queue-scheduling algorithm based on maximum waiting time was designed. In this algorithm, packets received from upper-layer applications are allocated to different queues according to their priority, and a corresponding waiting time step is allocated to each queue. Packets with higher priority have shorter waiting times. When the transmission time slot arrives, the packets with the shortest remaining waiting time are sent preferentially, which not only meets the transmission requirements of high-priority packets but also effectively avoids the starvation of low-priority packets. On this basis, to optimize the usage of time-slot resources further, the SPD-TDMA protocol adopts a priority predictive time-slot scheduling mechanism to avoid overbooking time slots or wasting idle time-slot resources to improve the overall efficiency and throughput of the network. Simulation results based on OMNeT++ indicated that compared with the IEEE 802.11DCF, TDMA, and P-TDMA protocols, the proposed method increases the packet delivery rate by 80%, 58%, and 27%, and throughput by 85%, 58%, and 29%, respectively.

As low-latency designs tend to have a small number of rounds to decrease latency, the differential-type cryptanalysis can become a significant threat to them. In particular, since a multiple-branch-based design, such as Orthros can have the strong clustering effect on differential attacks due to its large internal state, it is crucial to investigate the impact of the clustering effect in such a design. In this paper, we present a new SAT-based automatic search method for evaluating the clustering effect in the multiple-branch-based design. By exploiting an inherent trait of multiple-branch-based designs, our method enables highly efficient evaluations of clustering effects on this-type designs. We apply our method to the low-latency PRF Orthros, and show a best differential distinguisher reaching up to 7 rounds of Orthros with 2^116.806 time/data complexity and 9-round distinguisher for each underlying permutation which is 2 more rounds than known longest distinguishers. Besides, we update the designer’s security bound for differential attacks based on the lower bounds for the number of active S-boxes, and obtain the optimal differential characteristic of Orthros, Branch 1, and Branch 2 for the first time. Consequently, we improve the designer’s security bound from 9/12/12 to 7/10/10 rounds for Orthros/Branch 1/Branch 2 based on a single differential characteristic. Moreover, we define Orthros-like three-branch-based PRF in order to investigate the impact of the clustering effect when increasing the number of branches. Based on the results of our evaluation, we show that adding one more branch makes the clustering effect easy to happen, but is promising to enhance the security against differential cryptanalysis.

Cyber Mimic Defense (CMD) is an active defense theory emerging in recent years, and CMD improves system robustness and security by inherent uncertainty, heterogeneity, redundancy, and other characteristics. Among them, scheduling methods, which are the key technologies of CMD, directly affect the ability of mimic systems to resist vulnerabilities and backdoor attacks. However, most of the existing scheduling methods lack a careful study of executor similarity and high-order heterogeneity. Based on this, a fine-grained heterogeneity metric method that considers high-order common vulnerabilities is proposed. Then, an adaptive scheduling method that combines actuator heterogeneity and historical confidence is proposed, and the dynamics and reliability of this scheduling method are verified by simulation experiments. Specifically, under the experimental conditions of 4 and 5 executor redundancy, the experimental experiments were compared with the CRS, TIRTS and RSMHS methods. Through 80 tests, 80 scheduling cycles and the average failure probability of the system were obtained. Experimental results show that compared with the RSMHS scheduling method, the average scheduling cycle of the HCVCS scheduling method proposed in this paper increases by 42.8% and 45.3%, and the average failure probability of the system decreases by 30.4% and 24.8%.

A low-cost super-resolution frequency modulated continuous wave (FMCW) phased array radar employing a single radio-frequency (RF) channel sampling scheme is proposed. Since the conventional phased array radar lacks element-level array signals, it cannot achieve angle super-resolution. By exploiting time sequence phase weighting (TSPW) technology, the proposed radar can obtain the element-level array signals without connecting additional transmit/receive (T/R) components to each antenna element, as that in element-level digital arrays. Furthermore, the freedom of the array has been further enhanced by exploiting the nested array structure. Numerical simulation results are presented to demonstrate the effectiveness of the proposed system.

This letter emphasizes memory-based computing’s potential in enhancing efficiency for wearable devices with limited resources, focusing on active noise control (ANC) in wireless earbuds. Our proposed memory-based selective fixed-filter ANC (MSFANC) scheme reduces power consumption and delay through lookup table (LUT) computation and simplified filter matching. Theoretical analysis and simulations demonstrate MSFANC’s effectiveness, offering a new paradigm for resource-constrained wearable systems with non-volatile memories’ (NVM) support.

The application of deep reinforcement learning (DRL) has become a hot research topic in unmanned aerial vehicle (UAV) path planning and resource allocation. However, current DRL methods do not consider coordination among spectrum, path and power, leading to a waste of spectrum resources. A coordinated routing and resource allocation Q network (CRRQN) algorithm with low computing complexity in multiple UAVs scenarios is proposed, and a co-optimization module is proposed to enhance coordination among path planning, spectrum and power allocation in CRRQN by designing their reward functions. Moreover, double deep Q network (DDQN) is employed to guarantee its stability. The simulation shows that the CRRQN algorithm reduces the flight time by about 4% and improves the channel capacity by about 15% compared to the existing algorithms. The running time per test epoch of CRRQN reduces by about 35%.

We consider a zero-order-hold (ZOH) triggered control problem for a chain of integrators under measurement feedback. Two challenging aspects of our control problems are (i) the feedback information is distorted by uncertain noises, (ii) the control input is only updated discretely. With our ZOH triggered controller, we carry out the the system analysis to show that the control performance and ultimate bounds can be adjusted by control parameters and interexecution time. We provide simulation results to confirm the validity of our control method.

Agile Earth observation satellite (AEOS) mission planning (AEOSMP) problem aims to optimize observation efficiency by selecting and scheduling tasks from the Earth’s surface, subject to complex resource constraints. Increased flexibility of AEOS presents challenges for autonomous mission planning and scheduling. Deep reinforcement learning (DRL) and clustering tasks are two approaches to enhance the autonomy and observation efficiency of AEOSMP. This letter introduces two innovative algorithms to tackle the AEOSMP problem: the Sequential Clique Clustering and PPO Planning algorithm (SCC-PPO) and the Simultaneous Clustering and Planning PPO Algorithm (SCP-PPO). SCC-PPO initially partitions the mission tasks into cliques, followed by planning. In contrast, SCP-PPO combines clustering and planning into a single, concurrent process. Numerical simulations reveal that SCP-PPO enhances the observation reward by 1.01% to 11.43% compared to SCC-PPO.

In recent years, chaos maps and opaque predicates have received widespread attention in the field of code obfuscation. Chaos map is proved opaque in n-state predicates code obfuscation. We use n-state opaque predicates to improve the control flow flattening technique and flatten the control structure of the code. Finally, we demonstrate that Henon map scheme outperforms other obfuscation schemes.

Reconfigurable intelligent surface (RIS) is treated as a promising technology for future wireless communications. In this letter, we investigate the active RIS-assisted MISO systems, which can overcome the multiplicative fading effect introduced by the passive RIS. In particular, the active sub-connection architecture of RIS is used to overcome the high power consumption of the existing fully-connected architecture, where each component independently controls its phase shift but shares the same power amplifier. In order to reduce the power loss of each component, we propose to add a switcher device in the power amplifier to select the most appropriate component. As the component is selected, the active number of components is significantly reduced along with less power consumption. Our analysis shows that the introduced switcher brings less performance loss, indicating that higher energy efficiency (EE) can be achieved. Furthermore, EE maximization problems with power constraints are considered in the active RIS assisted system for the architecture with switcher, and the corresponding joint beamforming design is derived. Simulation results show that the proposed structure and method with switcher can effectively improve EE compared with the schemes without switcher.

Reliability-based belief propagation (RBP) decoding algorithms are used to decode low-density parity-check (LDPC) codes. However, due to the reliability of comparing and sorting in traditional algorithms, conventional RBP decoders significantly lose in resource consumption. This letter presents an enhanced reliability list-based check-belief propagation (RL-CBP) algorithm. The RL-CBP algorithm reduces computational complexity by scheduling a concise list of check-beliefs. Moreover, the list is applied for comparisons and selections of check-beliefs. The selected check-belief transforms the decoding message between edges; all check-beliefs are iteratively enlarged according to the reliabilities, and high-performance decoding will be achieved. The simulation results and analyses show that the proposed method achieves a reliability-list gain compared with the check-belief propagation (CBP) algorithm but consumes much fewer calculations than the traditional RBP algorithm.

In this paper, an area-selective deep reinforcement learning scheme is proposed to achieve high-quality wireless localization. The conventional localization schemes based on deep learning face several challenges that are labor-intensive for data collection and labeling, lack of adaptability to large-scale, etc. To address these issues, the proposed scheme incorporates deep reinforcement learning (DRL) with a reward-setting mechanism The localization problem is modeled as a dynamic decision process to leverage the capabilities of DRL. The device location is determined by an iterative decision-making procedure, which is an area-selective process. The proposed scheme consists of two distinct modes, namely train and deployment modes. During the train mode, an agent learns the optimal actions for a given environment. The learned agent estimates the position of device in deployment mode. Simulations were conducted to demonstrate the advantages of the proposed scheme and the results showed that it offers better localization performance, adaptability, and time complexity than conventional schemes.