This paper proposes a kind of programmable logic element (PLE) based on Sense-Switch pFLASH technology. By programming Sense-Switch pFLASH, all three-bit look-up table (LUT3) functions, partial four-bit look-up table (LUT4) functions, latch functions, and d flip flop (DFF) with enable and reset functions can be realized. Because PLE uses a choice of operational logic (COOL) approach for the operation of logic functions, it allows any logic circuit to be implemented at any ratio of combinatorial logic to register. This intrinsic property makes it close to the basic application specific integrated circuit (ASIC) cell in terms of fine granularity, thus allowing ASIC-like cell-based mappers to apply all their optimization potential. By measuring Sense-Switch pFLASH and PLE circuits, the results show that the “on” state driving current of the Sense-Switch pFLASH is about 245.52 μA, and that the “off” state leakage current is about 0.1 pA. The programmable function of PLE works normally. The delay of the typical combinatorial logic operation AND3 is 0.69 ns, and the delay of the sequential logic operation DFF is 0.65 ns, both of which meet the requirements of the design technical index.
We investigate the resource allocation problem of a cell-free massive multiple-input multiple-output system under the condition of colluding eavesdropping by multiple passive eavesdroppers. To address the problem of limited pilot resources, a scheme is proposed to allocate the pilot with the minimum pollution to users based on access point selection and optimize the pilot transmission power to improve the accuracy of channel estimation. Aiming at the secure transmission problem under a colluding eavesdropping environment by multiple passive eavesdroppers, based on the local partial zero-forcing precoding scheme, a transmission power optimization scheme is formulated to maximize the system’s minimum security spectral efficiency. Simulation results show that the proposed scheme can effectively reduce channel estimation error and improve system security.
Due to factors such as motion blur, video out-of-focus, and occlusion, multi-frame human pose estimation is a challenging task. Exploiting temporal consistency between consecutive frames is an efficient approach for addressing this issue. Currently, most methods explore temporal consistency through refinements of the final heatmaps. The heatmaps contain the semantics information of key points, and can improve the detection quality to a certain extent. However, they are generated by features, and feature-level refinements are rarely considered. In this paper, we propose a human pose estimation framework with refinements at the feature and semantics levels. We align auxiliary features with the features of the current frame to reduce the loss caused by different feature distributions. An attention mechanism is then used to fuse auxiliary features with current features. In terms of semantics, we use the difference information between adjacent heatmaps as auxiliary features to refine the current heatmaps. The method is validated on the large-scale benchmark datasets PoseTrack2017 and PoseTrack2018, and the results demonstrate the effectiveness of our method.
Adversarial training with online-generated adversarial examples has achieved promising performance in defending adversarial attacks and improving robustness of convolutional neural network models. However, most existing adversarial training methods are dedicated to finding strong adversarial examples for forcing the model to learn the adversarial data distribution, which inevitably imposes a large computational overhead and results in a decrease in the generalization performance on clean data. In this paper, we show that progressively enhancing the adversarial strength of adversarial examples across training epochs can effectively improve the model robustness, and appropriate model shifting can preserve the generalization performance of models in conjunction with negligible computational cost. To this end, we propose a successive perturbation generation scheme for adversarial training (SPGAT), which progressively strengthens the adversarial examples by adding the perturbations on adversarial examples transferred from the previous epoch and shifts models across the epochs to improve the efficiency of adversarial training. The proposed SPGAT is both efficient and effective; e.g., the computation time of our method is 900 min as against the 4100 min duration observed in the case of standard adversarial training, and the performance boost is more than 7% and 3% in terms of adversarial accuracy and clean accuracy, respectively. We extensively evaluate the SPGAT on various datasets, including small-scale MNIST, middle-scale CIFAR-10, and large-scale CIFAR-100. The experimental results show that our method is more efficient while performing favorably against state-of-the-art methods.
As cross-chain technologies enable interactions among different blockchains (hereinafter “chains”), multi-chain consensus is becoming increasingly important in blockchain networks. However, more attention has been paid to single-chain consensus schemes. Multi-chain consensus schemes with trusted miner participation have not been considered, thus offering opportunities for malicious users to launch diverse miner behavior (DMB) attacks on different chains. DMB attackers can be friendly in the consensus process on some chains, called mask chains, to enhance their trust value, while on others, called kill chains, they engage in destructive behaviors on the network. In this paper, we propose a multi-chain consensus scheme named Proof-of-DiscTrust (PoDT) to defend against DMB attacks. The idea of distinctive trust (DiscTrust) is introduced to evaluate the trust value of each user across different chains. The trustworthiness of a user is split into local and global trust values. A dynamic behavior prediction scheme is designed to enforce DiscTrust to prevent an intensive DMB attacker from maintaining strong trust by alternately creating true or false blocks on the kill chain. Three trusted miner selection algorithms for multi-chain environments can be implemented to select network miners, chain miners, and chain miner leaders, separately. Simulation results show that PoDT is secure against DMB attacks and more effective than traditional consensus schemes in multi-chain environments.
Controller area networks (CANs), as one of the widely used fieldbuses in the industry, have been extended to the automation field with strict standards for safety and reliability. In practice, factors such as fatigue and insulation wear of the cables can cause intermittent connection (IC) faults to occur frequently in the CAN, which will affect the dynamic behavior and the safety of the system. Hence, quantitatively evaluating the performance of the CAN under the influence of IC faults is crucial to real-time health monitoring of the system. In this paper, a novel methodology is proposed for real-time quantitative evaluation of CAN availability when considering IC faults, with the system availability parameter being calculated based on the network state transition model. First, the causal relationship between IC fault and network error response is constructed, based on which the IC fault arrival rate is estimated. Second, the states of the network considering IC faults are analyzed, and the deterministic and stochastic Petri net (DSPN) model is applied to describe the transition relationship of the states. Then, the parameters of the DSPN model are determined and the availability of the system is calculated based on the probability distribution and physical meaning of markings in the DSPN model. A testbed is constructed and case studies are conducted to verify the proposed methodology under various experimental setups. Experimental results show that the estimation results obtained using the proposed method agree well with the actual values.
This paper deals with the search-and-rescue tasks of a mobile robot with multiple interesting targets in an unknown dynamic environment. The problem is challenging because the mobile robot needs to search for multiple targets while avoiding obstacles simultaneously. To ensure that the mobile robot avoids obstacles properly, we propose a mixed-strategy Nash equilibrium based Dyna-Q (MNDQ) algorithm. First, a multi-objective layered structure is introduced to simplify the representation of multiple objectives and reduce computational complexity. This structure divides the overall task into subtasks, including searching for targets and avoiding obstacles. Second, a risk-monitoring mechanism is proposed based on the relative positions of dynamic risks. This mechanism helps the robot avoid potential collisions and unnecessary detours. Then, to improve sampling efficiency, MNDQ is presented, which combines Dyna-Q and mixed-strategy Nash equilibrium. By using mixed-strategy Nash equilibrium, the agent makes decisions in the form of probabilities, maximizing the expected rewards and improving the overall performance of the Dyna-Q algorithm. Furthermore, a series of simulations are conducted to verify the effectiveness of the proposed method. The results show that MNDQ performs well and exhibits robustness, providing a competitive solution for future autonomous robot navigation tasks.
Multi-exit architecture allows early-stop inference to reduce computational cost, which can be used in resource-constrained circumstances. Recent works combine the multi-exit architecture with self-distillation to simultaneously achieve high efficiency and decent performance at different network depths. However, existing methods mainly transfer knowledge from deep exits or a single ensemble to guide all exits, without considering that inappropriate learning gaps between students and teachers may degrade the model performance, especially in shallow exits. To address this issue, we propose Multi-exit self-distillation with Appropriate TEachers (MATE) to provide diverse and appropriate teacher knowledge for each exit. In MATE, multiple ensemble teachers are obtained from all exits with different trainable weights. Each exit subsequently receives knowledge from all teachers, while focusing mainly on its primary teacher to keep an appropriate gap for efficient knowledge transfer. In this way, MATE achieves diversity in knowledge distillation while ensuring learning efficiency. Experimental results on CIFAR-100, TinyImageNet, and three fine-grained datasets demonstrate that MATE consistently outperforms state-of-the-art multi-exit self-distillation methods with various network architectures.
With the development of satellite miniaturization and remote sensing, the establishment of microsatellite constellations is an inevitable trend. Due to their limited size, weight, and power, spaceborne storage systems with excellent scalability, performance, and reliability are still one of the technical bottlenecks of remote sensing microsatellites. Based on the commercial off-the-shelf field-programmable gate array and memory devices, a spaceborne advanced storage system (SASS) is proposed in this paper. This work provides a dynamic programming, queue scheduling multiple-input multiple-output cache technique and a high-speed, high-reliability NAND flash controller for multiple microsatellite payload data. Experimental results show that SASS has outstanding scalability with a maximum write rate of 2429 Mb/s and preserves at least 78.53% of the performance when a single NAND flash fails. The scheduling technique effectively shortens the data scheduling time, and the data remapping method of the NAND flash controller can reduce the retention error by at least 50.73% and the program disturbance error by at least 37.80%.
In this study we present the design and realization of a tunable dual band wireless power transfer (TDB-WPT) coupled resonator system. The frequency response of the tunable band can be controlled using a surface-mounted varactor. The transmitter (Tx) and the receiver (Rx) circuits are symmetric. The top layer contains a feed line with an impedance of 50 Ω. Two identical half rings defected ground structures (HR-DGSs) are loaded on the bottom using a varactor diode. We propose a solution for restricted WPT systems working at a single band application according to the operating frequency. The effects of geometry, orientation, relative distance, and misalignments on the coupling coefficients were studied. To validate the simulation results, the proposed TDB-WPT system was fabricated and tested. The system occupied a space of 40 mm×40 mm. It can deliver power to the receiver with an average coupling efficiency of 98% at the tuned band from 817 to 1018 MHz and an efficiency of 95% at a fixed band of 1.6 GHz at a significant transmission distance of 22 mm. The results of the measurements accorded well with those of an equivalent model and the simulation.