PCI接口高速数据采集与处理卡的设计

利用可编程逻辑器件搭建了一个可编程的数据采集平台.     

基于子空间集成学习的3维人体运动识别

如何对3维运动数据进行自动识别,是提高数据利用效率和进行计算机动画创作的前提。为了有效地识别运动数据,需要把运动数据投影到非线性流型低维子空间中,先识别出运动的内在结构,然后对运动的各个关节点分别进行学习,最后基于集成学习的方法产生强的隐马尔可夫学习器,以便能够对一些常见的运动类型进行自动识别。实验结果表明,这种基于子空间集成的人体运动识别方法较传统方法的检索精度、检索速度均有较大提高。     

Promote Intratumoral Drug Release and Penetration to Counteract Docetaxel-Induced Metastasis by Photosensitizer-Modified Red Blood Cell Membrane-Coated Nanoparticle

The red blood cell membrane (RBCm) provides tight protection, lowers the immunogenicity, and prolongs the circulation time of drugs in vivo when acting as the coating of drug delivery systems. However, the cellular uptake and release of drugs are hindered by RBCm. Docetaxel (DTX) is the first-line medicine for treating triple-negative breast cancer (TNBC), but it induces tumor metastasis. To solve these dilemmas, in this study, the photosensitizer 1,1-dioctadecyl-3,3,3,3-tetramethylindotricarbocyanine iodide (DiR)-modified RBCm (DM) is prepared, which is coated onto a hybrid micelle consisting of the prodrugs of DTX and the anti-metastasis agent calcitriol (CTL), obtaining a nanoparticle, named HDC-DM. In a 4T1 tumor-bearing mouse model, after injecting HDC-DM, the intratumoral DTX and CTL concentrations are increased by 1.7 and 2.5 times compared with the free drug groups. After irradiating tumors with near-infrared laser, DiR elicits the photothermal effect, triggering the rupture of RBCm and drug release, promoting drug penetration in tumors, and inducing immunogenic cell death. The tumor growth inhibition rate is 77%, and the formation of lung metastases is reduced by 82%, with good biocompatibility. It is suggested that the combination of phototherapy, chemotherapy, and anti-metastatic therapy using HDC-DM is expected to be a powerful strategy for treating TNBC.     

An Artificial Motion and Tactile Receptor Constructed by Hyperelastic Double Physically Cross-Linked Silk Fibroin Ionoelastomer

Ionotronic artificial motion and tactile receptor (i-AMTR) is essential to realize an interactive human-machine interface. However, an i-AMTR that effectively mimics the composition, structure, mechanics, and multi-functionality of human skin, called humanoid i-AMTR, is yet to be developed. To bridge this technological gap, this study proposes a strategy that combines molecular structure design and function integration to construct a humanoid i-AMTR. Herein, a silk fibroin ionoelastomer (SFIE) with double cross-linked molecular structure is designed to mimic the composition and structure of human skin, thereby resolving the conflict of stretchability, softness, and resilience, suffered by many previously reported ionotronics. Functionally, electromechanical sensing and triboelectricity-based tactile perception are integrated into SFIE, to enable simultaneous perception of both motion and tactile inputs. By further leveraging the machine learning and Internet of Things (IoT) techniques, the proposed SFIE-based humanoid i-AMTR precisely senses the movement of human body and accurately sortball objects made of different materials. Notably, the success rate for 610 sorting tests reaches as high as 92.3%. These promising results essentially demonstrate a massive potential of humanoid i-AMTR in the fields of sorting robots, rehabilitation medicine, and augmented reality.     

Balancing MXene Surface Termination and Interlayer Spacing Enables Superior Microwave Absorption

Surface chemistry and interlayer engineering determines the electrical properties of 2D MXene. However, it remains challenging to regulate the surface and interfacial chemistry of MXene simultaneously. Herein, simultaneous modulation of Ti₃C₂T_x MXene surface termination and layer spacing by alkali treatment are achieved. The electrical and electromagnetic properties of Ti₃C₂T_x are investigated in detail with respect to KOH and ammonia concentration dependence. A high concentration of KOH caused the Ti₃C₂T_x layer spacing to expand to 13.7 Å and the surface O/F ratio to increase to 33.84. Because of its weaker ionization effect, ammonia provides finer tuning compared to the drastic intercalation of KOH with a thorough sweeping of the F-containing groups. Ti₃C₂T_x is enriched with conductive -OH termination after ammonia treatment, which achieves an effective balance with the increased interlayer resistance. Therefore, NH₃H₂O-Ti₃C₂T_x achieves broad-band impedance matching and exhibits an efficient microwave loss of −49.1 dB at a low thickness of 1.7 mm, with an effective frequency bandwidth of 3.9 GHz. The results herein optimize the electrical properties of Ti₃C₂T_x using surface and interfacial chemistry to achieve broad microwave absorption, providing a framework for enhancing the electromagnetic wave loss of intrinsic MXene.     

Boosting Piezo-Catalytic Activity of KNN-Based Materials with Phase Boundary and Defect Engineering

Although the piezo-catalysis is promising for the environmental remediation and biomedicine, the piezo-catalytic properties of various piezoelectric materials are limited by low carrier concentrations and mobility, and rapid electron-hole pair recombination, and reported regulating strategies are quite complex and difficult. Herein, a new and simple strategy, integrating phase boundary engineering and defect engineering, to boost the piezo-catalytic activity of potassium sodium niobate ((K, Na)NbO₃, KNN) based materials is innovatively proposed. Tur strategy is validated by exampling 0.96(K_0.48Na_0.52)Nb_0.955Sb_0.045O₃-0.04(Bi_xNa_4-3x)_0.5ZrO₃-0.3%Fe₂O₃ material having phase boundary engineering and conducted the defect engineering via the high-energy sand-grinding. A high reaction rate constant k of 92.49 × 10⁻³ min⁻¹ in the sand-grinding sample is obtained, which is 2.40 times than that of non-sand-grinding one and superior to those of other representative lead-free perovskite piezoelectric materials. Meanwhile, the sand-grinding sample has remarkable bactericidal properties against Escherichia coli and Staphylococcus aureus. Superior piezo-catalytic activities originate from the enhanced electron-hole pair separation and the increased carrier concentration. This study provides a novel method for improving the piezo-catalytic activities of lead-free piezoelectric materials and holds great promise for harnessing natural energy and disease treatment.     

Intercalative Motifs-Induced Space Confinement and Bonding Covalency Enhancement Enable Ultrafast and Large Sodium Storage

Alloying-type metal sulfides with high theoretical capacities are promising anodes for sodium-ion batteries, but suffer from sluggish sodiation kinetics and huge volume expansion. Introducing intercalative motifs into alloying-type metal sulfides is an efficient strategy to solve the above issues. Herein, robust intercalative In S motifs are grafted to high-capacity layered Bi₂S₃ to form a cation-disordered (BiIn)₂S₃, synergistically realizing high-rate and large-capacity sodium storage. The In S motif with strong bonding serves as a space-confinement unit to buffer the volume expansion, maintaining superior structural stability. Moreover, the grafted high-metallicity Indium increases the bonding covalency of Bi S, realizing controllable reconstruction of Bi S bond during cycling to effectively prevent the migration and aggregation of atomic Bi. The novel (BiIn)₂S₃ anode delivers a high capacity of 537 mAh g⁻¹ at 0.4 C and a superior high-rate stability of 247 mAh g⁻¹ at 40 C over 10000 cycles. Further in situ and ex situ characterizations reveal the in-depth reaction mechanism and the breakage and formation of reversible Bi S bonds. The proposed space confinement and bonding covalency enhancement strategy via grafting intercalative motifs can be conducive to developing novel high-rate and large-capacity anodes.     

Functional Materials for Memristor-Based Reservoir Computing: Dynamics and Applications

The booming development of artificial intelligence (AI) requires faster physical processing units as well as more efficient algorithms. Recently, reservoir computing (RC) has emerged as an alternative brain-inspired framework for fast learning with low training cost, since only the weights associated with the output layers should be trained. Physical RC becomes one of the leading paradigms for computation using high-dimensional, nonlinear, dynamic substrates. Among them, memristor appears to be a simple, adaptable, and efficient framework for constructing physical RC since they exhibit nonlinear features and memory behavior, while memristor-implemented artificial neural networks display increasing popularity towards neuromorphic computing. In this review, the memristor-implemented RC systems from the following aspects: architectures, materials, and applications are summarized. It starts with an introduction to the RC structures that can be simulated with memristor blocks. Specific interest then focuses on the dynamic memory behaviors of memristors based on various material systems, optimizing the understanding of the relationship between the relaxation behaviors and materials, which provides guidance and references for building RC systems coped with on-demand application scenarios. Furthermore, recent advances in the application of memristor-based physical RC systems are surveyed. In the end, the further prospects of memristor-implemented RC system in a material view are envisaged.     

Neural network-based event-triggered cluster quasi-consensus for unknown multiagent systems with directed topology

This paper studies cluster quasi-consensus problem for a class of unknown nonlinear multiagent systems (MASs) with directed communication topology. First, a distributed continuous neural network (NN)-based adaptive protocol is presented for solving this problem by introducing reference model to each agent. Then, taking limited communication resource and energy consumption into account, a distributed event-triggered cluster quasi-consensus protocol is proposed. Different from the existing results, two event-triggered mechanisms are constructed in the proposed event-triggered protocol to reduce communication load and control update frequency as possible. The sufficient conditions that guarantee cluster quasi-consensus under the both proposed protocols are obtained, respectively. Zeno behavior is proved to be excluded. Finally, simulation results verify the effectiveness of the proposed protocols.     

Blockchain Security Technology Based on the Asynchronous Transmission Mode of IoT Technology in Smart Cities

Wireless Personal Communications - With the rapid development of information technology, issues such as network security and privacy protection have attracted more and more attention. The...