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Abstract Substance use disorders (SUDs) imposes profound physical, psychological, and socioeconomic burdens on individuals, families, communities, and society as a whole, but the available treatment options remain limited. Deep brain-machine interfaces (DBMIs) provide an innovative approach by facilitating efficient interactions between external devices and deep brain structures, thereby enabling the meticulous monitoring and precise modulation of neural activity in these regions. This pioneering paradigm holds significant promise for revolutionizing the treatment landscape of addictive disorders. In this review, we carefully examine the potential of closed-loop DBMIs for addressing SUDs, with a specific emphasis on three fundamental aspects: addictive behaviors-related biomarkers, neuromodulation techniques, and control policies. Although direct empirical evidence is still somewhat limited, rapid advancements in cutting-edge technologies such as electrophysiological and neurochemical recordings, deep brain stimulation, optogenetics, microfluidics, and control theory offer fertile ground for exploring the transformative potential of closed-loop DBMIs for ameliorating symptoms and enhancing the overall well-being of individuals struggling with SUDs.

Abstract Magnetic resonance imaging (MRI) is a crucial medical imaging modality, with parallel MRI accelerating scans but often reducing the signal-to-noise ratio (SNR). Recent advances in metamaterials have shown considerable potential in enhancing the SNR of MRI and consequently improving the quality of parallel MRI. In this study, we present a nonlinear metamaterial comprising nonlinear meta-atoms designed to selectively enhance the radio-frequency reception field in MRI, while minimizing interference with the radio-frequency transmission field. We develop an electromagnetic field-circuit joint simulation process for analyzing and optimizing the nonlinear response. Experimental validation confirms that nonlinear metamaterial integration in a surface coil array delivers a 3-fold SNR increase for parallel MRI compared to using the surface coil array alone. This research advances our understanding of such metamaterials and demonstrates their potential for practical utilization in MRI and clinical settings, thereby promising substantial enhancements in imaging capabilities.

Abstract Three-dimensional (3D) panoramic vision system plays a fundamental role in the biological perception of external information, and naturally becomes a key system for embodied intelligence to interact with the outside world. A binocular vision system with rotating eyeball has long baseline, large volume and weak sensitivity to motion. A compound eye system has small volume, high sensitivity to motion but poor precision. Here, a planar compound eye microsystem for high precision 3D perception is proposed by combining semiconductor manufacturing process and biological compound eye structure. Using a semiconductor planar image sensor as the sensing unit, a space-coded planar sub-eye array is designed and its sub field of view (FOV) is dynamically mapped to the image sensor. It solves the problem that a traditional vision system cannot simultaneously accommodate wide FOV with long focal length and high sensitivity to motion with high resolution. The parallax among different sub-eyes enables the system to accurately perceive and dynamically track the 3D position of the target in the range of 10 m and within the FOV of 120 ° in a single compound eye. This system is of great significance in the fields of intelligent robot and intelligent perception.

Abstract Flexible sensors based on laser‐induced graphene (LIG) are widely used in wearable personal devices, with the morphology and lattice arrangement of LIG the key factors affecting their performance in various applications. In this study, femtosecond‐laser‐induced MXene‐composited graphene (LIMG) is used to improve the electrical conductivity of graphene by incorporating MXene, a 2D material with a high concentration of free electrons, into the LIG structure. By combining pump‐probe detection, laser‐induced breakdown spectroscopy (LIBS), and density functional theory (DFT) calculations, the morphogenesis and lattice structuring principles of LIMG is explored, with the results indicating that MXene materials are successfully embedded in the graphene lattice, altering both their morphology and electrical properties. The structural sparsity and electrical conductivity of LIMG composites (up to 3187 S m−1) are significantly enhanced compared to those of LIG. Based on these findings, LIMG has been used in wearable electronics. LIMG electrodes are used to detect uric acid, with a minimum detection limit of 2.48 µM. Additionally, LIMG‐based pressure and bending sensors have been successfully used to monitor human limb movement and pulse. The direct in situ femtosecond laser patterning synthesis of LIMG has significant implications for developing flexible wearable electronic sensors.

Identifying ecological protection priority areas (EPPAs) in municipal areas is essential for accurate ecological restoration and ensuring sustainable regional development. Xianyang represents a typical medium-sized city that is currently grappling with the dual challenges of ecological restoration and urban development. This study employed a multi-criteria method to evaluate ecological importance, considering both ecosystem services and ecological vulnerability. Based on this, ecological corridors and pinch points that enhance ecological connectivity were delineated using the circuit theory. Subsequently, the integrity of ecosystems is assessed from structural and functional perspectives to ensure their health and stability. With the support of Geographic Information System (GIS) technology, the three important areas for ecological protection were integrated to designate EPPAs. The results showed that: (1) the extremely important area spans 1956.44 km2, primarily made up of forests and grasslands. (2) There were found to be 19 ecological sources, 26 ecological corridors, and 23 ecological pinch points. (3) With excellent ecosystem integrity, the region was dominated by eco-lands, which accounted for 2692.71 km2. (4) In the end, this study identified 3328.83 km2 of EPPAs, 32.65 % of the study area. These are primarily located in the northern mountains and major watersheds. Human activities are found as the most important factor for the overall spatial variations in the area. A multi-level framework was developed to establish a continuous, comprehensive, and systematic method for identifying EPPAs, thereby enhancing ecological restoration, protection, and the sustainable use of regional ecosystems.

For the autonomous surface vehicle (ASV) planning problem, an enhanced A* method incorporating encrypted memory database for ASV efficient local path planning is proposed. Considering the current various path planning problems mostly use methods with high time complexity, such as neural networks, we select the A* algorithm with low time complexity as the basis. To speed up the path planning rate and further improve the real-time and realistic algorithm, this paper modifies the heuristic function of the A* algorithm by combining the motion mode of ASV. In response to the problem that the target point is far from the detection, we improve the target point design mechanism and create a new temporary target point within the detection range. In addition, the algorithm incorporates a memory database, which can record commonly used waters or retain the environmental path of navigated waters as a priori information. When the same waters are reencountered, the memory database information can be read directly to complete the navigation. Moreover, the memory database is encrypted to prevent information leakage. Finally, a simulation environment is built to verify the effectiveness of the proposed algorithm by comparison with some existing algorithms.

Abstract In this paper, an angular position sensor (APS) designed for a resonant miniaturized scanning mirror (M-SM) is presented. The APS operates based on the principle of differential variable capacitance, significantly expanding the detectable bandwidth from a few hertz to several kilohertz. By modeling the motion characteristics, the sampling rates of the biaxial scanning angles are 1473.6 times and 539.4 times higher than those of conventional sensors. Initially, the motion characteristics model is presented as a simple harmonic motion, converting sampled capacitance into continuous capacitance. Subsequently, the nonparallel state of the M-SM and sensor is transformed into a parallel state through the space coordinate system transformation. Furthermore, a 2D nonlinear angle transfer function is developed to convert the differential capacitance into an angle, thereby mitigating the nonlinear errors resulting from large angles. Achieving an accuracy better than 0.014°, the measuring range expands from ±0.5729° (±10 mrad) to ±5.026° ( ± 87 mrad). Additionally, the capturing mode and tracking mode are proposed to monitor real-time angular changes of the M-SM with an accuracy of 0.017°. High-precision APSs have enhanced beam pointing accuracy and resolution and can thereby be used to advance the development of laser components, including light detection and ranging (LiDAR).

Subpixel localization techniques for estimating the positions of point-like images captured by pixelated image sensors have been widely used in diverse optical measurement fields. With unavoidable imaging noise, there is a precision limit (PL) when estimating the target positions on image sensors, which depends on the detected photon count, noise, point spread function (PSF) radius, and PSF’s intra-pixel position. Previous studies have clearly reported the effects of the first three parameters on the PL but have neglected the intra-pixel position information. Here, we develop a localization PL analysis framework for revealing the effect of the intra-pixel position of small PSFs. To accurately estimate the PL in practical applications, we provide effective PSF (ePSF) modeling approaches and apply the Cramér–Rao lower bound. Based on the characteristics of small PSFs, we first derive simplified equations for finding the best PL and the best intra-pixel region for an arbitrary small PSF; we then verify these equations on real PSFs. Next, we use the typical Gaussian PSF to perform a further analysis and find that the final optimum of the PL is achieved at the pixel boundaries when the Gaussian radius is as small as possible, indicating that the optimum is ultimately limited by light diffraction. Finally, we apply the maximum likelihood method. Its combination with ePSF modeling allows us to successfully reach the PL in experiments, making the above theoretical analysis effective. This work provides a new perspective on combining image sensor position control with PSF engineering to make full use of information theory, thereby paving the way for thoroughly understanding and achieving the final optimum of the PL in optical localization.

Gas temperature is one of the critical parameters that affects the velocity, temperature of particle and the consequent coating quality in cold spray additive manufacturing. The accurate control of the gas temperature is of great importance to the stability of the gas heating efficiency, deposition efficiency, and microstructure of coatings. Therefore, a numerical model was established using ANSYS-FLUENT to study the influence of PID strategy and traditional switch control strategy on the precise temperature of cold spraying gas. Results showed that both the stable time and the overshoot of gas temperature were significantly optimized when the PID strategy was applied. Moreover, the influences of the three factors (proportional, integral and derivative) in PID strategy on the stable time and the overshoot of gas temperature were investigated using an orthogonal experiment. The experimental results showed that the stable time and the overshoot were mainly affected by the proportional parameter and the integral parameter, respectively. With the increase of the proportional parameter and the integral parameter, the stable time and the overshoot rate reduced, and the control accuracy of the gas temperature increased.

Amorphous silicon (a-Si) metasurfaces demonstrate remarkable light manipulation capabilities owing to their high refractive index and low loss characteristics. However, the low thermal conductance intrinsic to a-Si leads to substantial heat accumulation during beam modulation, which may cause thermal damage and limit the permissible intensity of the incident beam. Here, laser-induced crystallization to improve the thermal transport properties of the a-Si-based metasurface is proposed. Raman spectroscopy analysis identifies the crystallization thresholds of a-Si at 1.10 × 105 and 9.78 × 104 W/cm2, corresponding to laser spot radii of 1.30 and 0.54 μm, respectively. The maximum temperature rise in the crystallized silicon metasurface is 52% lower than that in the a-Si one under the identical incident power intensity. Furthermore, the crystallized polycrystalline silicon metasurface still maintains excellent optical response with high transmission and 2π phase control. These findings provide an effective strategy for augmenting heat dissipation in amorphous metasurface systems, potentially extending their utility at high laser fluence.

Abstract Cascaded metasurfaces can exhibit powerful dynamic light manipulation by mechanically tuning the far‐field interactions in the layers. However, in most current designs, the metasurfaces are separated by gaps smaller than a wavelength to form a total phase profile, representing the direct accumulation of the phase profiles of each layer. Such small gap sizes may not only conflict with the far‐field conditions but also pose great difficulties for practical implementations. To overcome this limitation, a design paradigm taking advantage of a ray‐tracing scheme that allows the cascaded metasurfaces to operate optimally at easily achievable gap sizes is proposed. Enabled by the relative lateral translation of two cascaded metasurfaces, a continuous two‐dimensional (2D) beam‐steering device for 1064 nm light is designed as a proof of concept. Simulation results demonstrate tuning ranges of ±45° for biaxial deflection angles within ±3.5 mm biaxial translations, while keeping the divergence of deflected light less than 0.007°. The experimental results agree well with theoretical predictions, and a uniform optical efficiency is observed. The generializeddesign paradigm can pave a way towards myriad tunable cascaded metasurface devices for various applications, including but not limited to light detection and ranging (LiDAR) and free space optical communication.

Abstract Optical measurement systems suffer from a fundamental tradeoff between the field of view (FOV), the resolution and the update rate. A compound eye has the advantages of a wide FOV, high update rate and high sensitivity to motion, providing inspiration for breaking through the constraint and realizing high-performance optical systems. However, most existing studies on artificial compound eyes are limited by complex structure and low resolution, and they focus on imaging instead of precise measurement. Here, a high-performance lensless compound eye microsystem is developed to realize target motion perception through precise and fast orientation measurement. The microsystem splices multiple sub-FOVs formed by long-focal subeyes, images targets distributed in a panoramic range into a single multiplexing image sensor, and codes the subeye aperture array for distinguishing the targets from different sub-FOVs. A wide-field and high resolution are simultaneously realized in a simple and easy-to-manufacture microelectromechanical system (MEMS) aperture array. Moreover, based on the electronic rolling shutter technique of the image sensor, a hyperframe update rate is achieved by the precise measurement of multiple time-shifted spots of one target. The microsystem achieves an orientation measurement accuracy of 0.0023° (3σ) in the x direction and 0.0028° (3σ) in the y direction in a cone FOV of 120° with an update rate ~20 times higher than the frame rate. This study provides a promising approach for achieving optical measurements with comprehensive high performance and may have great significance in various applications, such as vision-controlled directional navigation and high-dynamic target tracking, formation and obstacle avoidance of unmanned aerial vehicles.

High-temperature photodetectors are desired for aerospace applications and harsh-environment robotics, but their responsivity is usually limited. Here, the authors report flexible hBN-encapsulated graphite/WSe2 photodetectors which can endure temperatures up to 700 °C in air with enhanced photoresponsivity.

Abstract MEMS light detection and ranging (LiDAR) is becoming an indispensable sensor in vehicle environment sensing systems due to its low cost and high performance. The beam scanning trajectory, sampling scheme and gridding are the key technologies of MEMS LiDAR imaging. In Lissajous scanning mode, this paper improves the sampling scheme, through which a denser Cartesian grid of point cloud data at the same scanning frequency can be obtained. By summarizing the rules of the Cartesian grid, a general sampling scheme independent of the beam scanning trajectory patterns is proposed. Simulation and experiment results show that compared with the existing sampling scheme, the resolution and the number of points per frame are both increased by 2 times with the same hardware configuration and scanning frequencies for a MEMS scanning mirror (MEMS-SM). This is beneficial for improving the point cloud imaging performance of MEMS LiDAR.

Abstract Precise and high-speed sorting of individual target cells from heterogeneous populations plays an imperative role in cell research. Although the conventional fluorescence-activated cell sorter (FACS) is capable of rapid and accurate cell sorting, it occupies a large volume of the instrument and inherently brings in aerosol generation as well as cross-contamination among samples. The sorting completed in a fully enclosed and disposable microfluidic chip has the potential to eliminate the above concerns. However, current microfluidic cell sorters are hindered by the high complexities of the fabrication procedure and the off-chip setup. In this paper, a spark-cavitation-bubble-based fluorescence-activated cell sorter is developed to perform fast and accurate sorting in a microfluidic chip. It features a simple structure and an easy operation. This microfluidic sorter comprises a positive electrode of platinum and a negative electrode of tungsten, which are placed on the side of the main channel. By applying a high-voltage discharge on the pair of electrodes, a single spark cavitation bubble is created to deflect the target particle into the downstream collection channel. The sorter has a short switching time of 150 μs and a long lifespan of more than 100 million workable actions. In addition, a novel control strategy is proposed to dynamically adjust the discharge time to stabilize the size of the cavitation bubble for continuous sorting. The dynamic control of continuously triggering the sorter, the optimal delay time between fluorescence detection and cell sorting, and a theoretical model to predict the ideal sorting recovery and purity are studied to improve and evaluate the sorter performance. The experiments demonstrate that the sorting rate of target particles achieves 1200 eps, the total analysis throughput is up to 10,000 eps, the particles sorted at 4000 eps exhibit a purity greater than 80% and a recovery rate greater than 90%, and the sorting effect on the viability of HeLa cells is negligible.

CubeSats have extensive applications in the realm of remote sensing. However, due to size constraints, attitude determination and control systems (ADCS) often adopt an integrated and densely packed structure. This leads to simple interference in the magnetic field measurements from components like reaction wheels and magnetorquers, resulting in magnetometer failures. Furthermore, the magnetic interference compromises the ability to dump the angular momentum of CubeSats. This leads to attitude control and remote sensing mission failures. To address these challenges, we introduce a dynamics-sensing, magnetic, fault-tolerant attitude control method that achieves attitude control without a priori magnetic field information generated from a magnetometer, restoring the remote sensing capabilities of CubeSats under magnetic failure. The proposed geomagnetic field sensing method, based on the temporal expansion geomagnetic vector calculate algorithm (GVCA), decouples the observation equations in three axes through control segmentation in the time domain to address the singularity issue of the Kalman coefficient matrix. Additionally, the magnetic field vector can be obtained in real time with acceptable computational consumption. Moreover, utilizing this real-time magnetic field information, the fault-tolerant control strategies and multi-mode control laws can progressively restore destabilized CubeSats to their regular states without performance degradation. We conducted numerical simulation experiments to evaluate the effectiveness of our method and system. Beyond the numerical simulations, we also built hardware systems. We designed and implemented a plug-and-play (PnP) ADCS to apply our methodology, further supporting CubeSats’ high-precision remote sensing. Furthermore, with the aid of a space environment simulation platform, we verified the performance of our system and method under conditions simulating the actual space environment. Ground testing demonstrated that our proposed method was able to identify the magnetic field and achieve high-performance attitude control with magnetic field measurement failures. The system’s pointing accuracy was better than 0.02 degrees, and the attitude stability surpassed 0.003 degrees per second.

Terahertz (THz) wave manipulation, especially the beam deflection, plays an essential role in various applications, such as next-generation communication, space exploration, and high-resolution imaging. Current THz optical components and devices are hampered by their large bulk sizes and passive responses, limiting the development of high-performance, miniaturized THz microsystems. Tunable metasurfaces offer a powerful dynamic optical platform for controlling the propagation of electromagnetic waves. In this article, we presented a mechanically tunable metasurface (MTM), which can achieve terahertz beam deflection and vary the intensity of the anomalous reflected terahertz wave by changing the air gap between the metallic resonator (MR) array with phase discontinuities and Au ground plane. The absence of lossy spacer materials substantially enhances deflection efficiency. The device was fabricated by a combination of the surface and bulk-micromachining processes. The THz beam steering capability was characterized using terahertz time domain spectroscopy. When the air gap is 50 μm, the maximum deflection coefficient reaches 0.60 at 0.61 THz with a deflection angle of ~44.5°, consistent with theoretical predictions. We further established an electrically tunable miniaturized THz device for dynamic beam steering by introducing a micro voice coil motor to control the air gap continuously. It is shown that our designed MTM demonstrates a high modulation depth of deflection coefficient (~ 62.5%) in the target steered angle at the operating frequency. Our results showcase the potential of the proposed MTM as a platform for high-efficiency THz beam manipulation.

BACKGROUND There is a certain deviation between the on-site analysis results of hand-held XRF and the laboratory analysis results due to the unique technical difficulties in field X-ray fluorescence measurement such as roughness effect, uneven effect and humidity effect of samples. OBJECTIVES To seek economic, rapid and practical test conditions of hand-held XRF for rapid exploration of porphyry copper ore in practical work. METHODS Influence of test conditions, such as analytical time, sample content, test distance, sample dry humidity and sample particle size on the analytical results of Niton XL3t GOLDD+ hand-held XRF was quantitatively studied. RESULTS The results showed that, under the best test time of 90s, the determination limits of Cu, Mo, Pb, and Zn were 57μg/g, 14μg/g, 24μg/g, and 38μg/g, respectively, and the calculation formula for the determination limit was given. The field analysis test distance should be less than 5mm and the sample surface should be kept dry. The test method for bulk sample was the simplest but the test results varied the most. When sample was crushed to a particle size of 80 mesh or above, the stability and accuracy of the results were best. Considering that it took a long time to crush sample into 80 mesh, the mixed state of the sample after crushing was better strategy for field testing. CONCLUSIONS The practical application of core scanning in Qulong porphyry copper deposit in Tibet shows that the handheld XRF analyzer is suitable for in-situ analysis and can meet the requirements of rapid exploration and evaluation such as delineation of copper ore bodies in field, which can greatly improve work efficiency and reduce exploration cost.

The educational concepts upholding the theory of brain dominance have been developed for more than 30 years. Some academicians developed a series of the syllabus to exploit the brain capability of students by training their weaker hemisphere of the brain. Prior to training the weaker side of the brain with the developed syllabus, the brain dominance of the student shall be determined. All the current methods used to determine brain dominance are questionnaire-based assessments. There is a possibility that questionnaire biases could exist and lead to inaccurate results. In this research, we introduce a deep-learning method to classify brain dominance based on the electroencephalogram (EEG) signal that reflects the bio-information of the brain. In this paper, we employ a series of EEG signal processing techniques and a state-of-the-art deep learning neural network namely Metric Learning Based Convolutional Neural Network (MLBCNN) to determine brain dominance. We prove that the brain dominance theory is valid and it can be determined by applying machine learning from the EEG signals. We also present the results that show the MLBCNN system can give the best performance as compared to the other benchmark neural network models of which its classification accuracy is 97.44%. Hence, this proposed method can contribute to the education field by providing a system to discover students’ brain dominance and keep track of their brain training progress. In this way, the potential and capability of their brain can be fully unleashed.

This paper reviews the-state-of-the-art of electromagnetic (EM) metasurfaces and emergent applications in advanced integrated devices and instruments from the design method to physical implementation. The design method includes the analytical coupled mode theory model and commonly used building blocks to construct functional metasurfaces. The modeling approach creates a common design basis of metasurface devices for optical beam steering, focusing, modulation, lasing, and detection. The proof of concept of metasurfaces has been established and is translating to practical applications. Previous studies demonstrated promising applications of metasurfaces including but not limited to optical imaging instruments, biochemical sensing devices, and multifunctional microoptoelectromechanical systems (MOEMS). Significant performance improvement of devices and instruments has been achieved due to the implementation of specially tailored metasurfaces. This review provides an alternative for researchers to step forward on the way of advancing devices and instruments by the deployment of metasurfaces.

Flow cytometers are instruments used for the rapid quantitative analysis of cell suspension. Traditional flow cytometry uses multi-channel filters to detect fluorescence, whereas full-spectrum fluorescence based on dispersion detection is a more effective and accurate method. The application of various dispersion schemes in flow cytometry spectroscopy has been studied. From the perspective of modern detectors and demand for the miniaturization of flow cytometry, prism dispersion exhibits higher and more uniform light energy utilization, meaning that it is a more suitable dispersion method for small flow cytometers, such as microfluidic flow cytometers. Prism dispersion designs include the size, number, and placement of prisms. By deducing the formula of the final position of light passing through the prism and combining it with the formula of transmittance, the design criteria of the top angle and the incident angle of the prism in pursuit of the optimum transmittance and dispersion index can be obtained. Considering the case of multiple prisms, under the premise of pursuing a smaller size, the optimal design criteria for dispersion system composed of multiple prisms can be obtained. The design of prism dispersion fluorescence detection was demonstrated with a microfluidic flow cytometer, and the effectiveness of the design results was verified by microsphere experiments and practical biological experiments. This design criterion developed in this study is generally applicable to spectral flow cytometers.

Visual/Inertial/GNSS (VIG) integrated navigation and positioning systems are widely used in unmanned vehicles and other systems. This VIG system is vulnerable to of GNSS spoofing attacks. Relevant research on the harm that spoofing causes to the system and performance analyses of VIG systems under GNSS spoofing are not sufficient. In this paper, an open-source VIG algorithm, VINS-Fusion, based on nonlinear optimization, is used to analyze the performance of the VIG system under a GNSS spoofing attack. The influence of the visual inertial odometer (VIO) scale estimation error and the transformation matrix deviation in the transition period of spoofing detection is analyzed. Deviation correction methods based on the GNSS-assisted scale compensation coefficient estimation method and optimal pose transformation matrix selection are proposed for VIG-integrated system in spoofing areas. For an area that the integrated system can revisit many times, a global pose map-matching method is proposed. An outfield experiment with a GNSS spoofing attack is carried out in this paper. The experiment result shows that, even if the GNSS measurements are seriously affected by the spoofing, the integrated system still can run independently, following the preset waypoint. The scale compensation coefficient estimation method, the optimal pose transformation matrix selection method and the global pose map-matching method can depress the estimation error under the circumstances of a spoofing attack.

The integrated device for energy supply and sensing (IDESS) is a potential candidate for relieving the energy and space burdens caused by the rising integration degrees of microsystems. In this article, we propose a force sensor based on an interdigital supercapacitor (IDTSC). The capacitance and internal resistance of the IDTSC change under external loads, resulting in a transient current fluctuation at a constant bias voltage, which can be used to sense external force/acceleration. The IDTSC showed a specific energy and specific power of 4.16 Wh/kg and 22.26 W/kg (at 0.1 A/g), respectively, which could maintain an essential energy supply. According to the simulation analysis, the designed IDTSC’s current response exhibited good linearity with the external force. In addition, benefiting from its light weight and the applied gel electrolytes, the IDTSC showed good high-g impact sensing performance (from 9.9 × 103× g to 3.2 × 104× g). This work demonstrated the feasibility of realizing an integrated energy supply and force-sensing device by empowering energy storage devices with sensing capabilities.

In aerospace engineering, the telemetry sequence is a vital reference for evaluating the performance of each component. The segmentation algorithm is an effective method for processing telemetry sequence, which lays a solid foundation for obtaining feature indicators. For the Momentum Wheel, rotational speed stability and acceleration characteristic are two crucial feature indicators. This paper presents a novel method of Adaptive Segmentation and Feature Extraction (ASFE) for processing the rotational speed sequence of the Momentum Wheel, which can realize the requirements of online testing and automatic acquisition of these two indicators. Firstly, based on the two different working modes of constant speed and adjusting speed, the rotational speed sequence of Momentum Wheel is identified as smooth and transitional. Secondly, the improved Adaptive Piecewise Constant Approximation (APCA) algorithm is proposed to identify the smooth segmentation, and the feature extraction sub-algorithm with online processing capability is also proposed to obtain the indicator of rotational speed stability. Thirdly, the fitting method based on slope threshold is used for the data of the transition segmentation, and the indicator of acceleration characteristic is extracted. The experiment results with the X Momentum Wheel shows that the accuracy of the data measurement processing is not less than 0.96 of the theoretical true value. This indicates that ASFE can significantly improve the test efficiency and meet the requirements of the Momentum Wheel test.

Traditional energy management strategies of wireless sensor network (WSN) nodes with hybrid energy storage do not adequately combine the merits of supercapacitors (SCs) and batteries, and thus, battery degradation remains a problem that restricts the lifetime of the WSN nodes. In this paper, an adaptive rule-based energy management strategy is proposed to extend the battery lifetime of a solar-powered wireless sensor node. The energy models are constructed with the consideration of the battery degradation, and Poisson distribution is utilized to simulate the power consumption variation of the node. A rule-based energy management strategy that is adaptive with the SC capacity and load conditions is designed. Traditional energy management strategies of the WSN nodes are compared with the proposed one. The simulation results indicate that the proposed strategy can improve the node lifetime from several tens of days to more than ten years. Meanwhile, the strategy ensures the continuous availability of the solar-powered WSN nodes even at night throughout the long lifetime. The robustness of the strategy is also tested. It is revealed that the strategy is adaptive with the SC capacity and workload conditions. The proposed strategy is broadly applicable and promising to resolve the problem of limited battery lifetime for the solar-powered WSN nodes.

A low-g triggered micro-electromechanical system (MEMS) resonant acceleration switch is designed, fabricated and tested in this paper for near-zero power wake-up applications. The switch is actuated by ambient low-g vibration, consuming zero power while waiting for vibration at its resonant frequency. A cantilever beam and proof mass structure is adopted in the switch. The patterns of spiral cantilever beams are designed for low resonant frequency and threshold. Once the vibration with resonant frequency exceeds the acceleration threshold of the switch, the movable electrode becomes sufficiently displaced to contact the fixed electrodes and causes them to trigger. The dynamic responses of the switch are tested on a piezoelectric stack. The experimental results show that the switch closes under vibration at a frequency as low as 39.3 Hz and at an acceleration threshold of 0.074 g. A wake-up sensor node connected to the switch can awaken when the switch is under vibration as an intended characteristics.

WiFi energy harvesting is a promising solution for powering microsensors and microsystems through collecting electromagnetic (EM) energies that exist everywhere in modern daily lives. In order to harvest EM energy, we proposed a metamaterial-inspired antenna (MIA) based on the resonant magnetic dipole operating in the WiFi bands. The MIA consists of two metallic split-ring resonators (SRRs), separated by an FR4 dielectric layer, in the broadside coupled configuration. The incident EM waves excite surface currents in the coupled SRRs, and the energy is oscillating between them due to near-field coupling. By varying the vertical distance of the two SRRs, we may achieve impedance matching without complicated matching networks. Collected EM energy can be converted to DC voltages via a rectifier circuit at the output of the coupling coil. Measured results demonstrate that the designed MIA may resonate at 2.4 GHz with a deep-subwavelength form factor (14 mm×14 mm×1.6 mm). The WiFi energy-harvesting capability of the proposed MIA with an embedded one-stage Dickson voltage multiplier has also been evaluated. A rectified DC voltage is approximately 500 mV when the MIA is placed at a distance of 2 cm from the WiFi transmit antenna with a 9 dBm transmitting power. The proposed compact MIA in this paper is of great importance for powering future distributed microsystems.

Automatic speech recognition (ASR) is popular in our daily lives (e.g., via voice assistants or voice input). Once its security attributes are destroyed, it poses as a severe threat to a user’s life and ‘property safety’. Prior research has demonstrated that ASR systems are vulnerable to backdoor attacks. A model embedded with a backdoor behaves normally on clean samples yet misclassifies malicious samples that contain triggers. Existing backdoor attacks have mostly been conducted in the image domain. However, they can not be applied in the audio domain because of poor transferability. This paper proposes a dynamic backdoor attack method against ASR models, named DriNet. Significantly, we designed a dynamic trigger generation network to craft a variety of audio triggers. It is trained jointly with the discriminative model incorporated with an attack success rate on poisoned samples and accuracy on clean samples. We demonstrate that DriNet achieves an attack success rate of 86.4% when infecting only 0.5% of the training set without reducing its accuracy. DriNet can still achieve comparable attack performance to backdoor attacks using static triggers, further enjoying richer attack patterns. We further evaluated DriNet’s resistance to a current state-of-the-art defense mechanism. The anomaly index of DriNet is more than 37.4% smaller than that of BadNets method. The triggers generated by DriNet are hard reverse, keeping DriNet from the detectors.

Automatic segmentation of brain tumors from multi-modalities magnetic resonance image data has the potential to enable preoperative planning and intraoperative volume measurement. Recent advances in deep convolutional neural network technology have opened up an opportunity to achieve end-to-end segmenting the brain tumor areas. However, the medical image data used in brain tumor segmentation are relatively scarce and the appearance of brain tumors is varied, so that it is difficult to find a learnable pattern to directly describe tumor regions. In this paper, we propose a novel cross-modalities interactive feature learning framework to segment brain tumors from the multi-modalities data. The core idea is that the multi-modality MR data contain rich patterns of the normal brain regions, which can be easily captured and can be potentially used to detect the non-normal brain regions, i.e., brain tumor regions. The proposed multi-modalities interactive feature learning framework consists of two modules: cross-modality feature extracting module and attention guided feature fusing module, which aim at exploring the rich patterns cross multi-modalities and guiding the interacting and the fusing process for the rich features from different modalities. Comprehensive experiments are conducted on the BraTS 2018 benchmark, which show that the proposed cross-modality feature learning framework can effectively improve the brain tumor segmentation performance when compared with the baseline methods and state-of-the-art methods.

Three-dimensional (3D) liver tumor segmentation from Computed Tomography (CT) images is a prerequisite for computer-aided diagnosis, treatment planning, and monitoring of liver cancer. Despite many years of research, 3D liver tumor segmentation remains a challenging task. In this paper, we propose an effective and efficient method for tumor segmentation in liver CT images using encoder-decoder based octave convolution networks. Compared with other convolution networks utilizing standard convolution for feature extraction, the proposed method utilizes octave convolutions for learning multiple-spatial-frequency features, thus can better capture tumors with varying sizes and shapes. The proposed network takes advantage of a fully convolutional architecture which performs efficient end-to-end learning and inference. More importantly, we introduce a deep supervision mechanism during the learning process to combat potential optimization difficulties, and thus the model can acquire a much faster convergence rate and more powerful discrimination capability. Finally, we integrate octave convolutions into the encoder-decoder architecture of UNet, which can generate high resolution tumor segmentation in one single forward feeding without post-processing steps. Both architectures are trained on a subset of the LiTS (Liver Tumor Segmentation) Challenge. The proposed approach is shown to significantly outperform other networks in terms of various accuracy measures and processing speed.

Background Adoptive cell therapy based on the infusion of chimeric antigen receptor (CAR) T cells has shown remarkable efficacy for the treatment of hematologic malignancies. The primary mechanism of action of these infused T cells is the direct killing of tumor cells expressing the cognate antigen. However, understanding why only some T cells are capable of killing, and identifying mechanisms that can improve killing has remained elusive.Methods To identify molecular and cellular mechanisms that can improve T-cell killing, we utilized integrated high-throughput single-cell functional profiling by microscopy, followed by robotic retrieval and transcriptional profiling.Results With the aid of mathematical modeling we demonstrate that non-killer CAR T cells comprise a heterogeneous population that arise from failure in each of the discrete steps leading to the killing. Differential transcriptional single-cell profiling of killers and non-killers identified CD137 as an inducible costimulatory molecule upregulated on killer T cells. Our single-cell profiling results directly demonstrate that inducible CD137 is feature of killer (and serial killer) T cells and this marks a different subset compared with the CD107apos (degranulating) subset of CAR T cells. Ligation of the induced CD137 with CD137 ligand (CD137L) leads to younger CD19 CAR T cells with sustained killing and lower exhaustion. We genetically modified CAR T cells to co-express CD137L, in trans, and this lead to a profound improvement in anti-tumor efficacy in leukemia and refractory ovarian cancer models in mice.Conclusions Broadly, our results illustrate that while non-killer T cells are reflective of population heterogeneity, integrated single-cell profiling can enable identification of mechanisms that can enhance the function/proliferation of killer T cells leading to direct anti-tumor benefit.

Abstract Wake‐up circuits in smart microsystems make huge contributions to energy conservation of electronic networks in unmanned areas, which still require higher pressure‐triggering sensitivity and lower power consumption. In this work, a bionic triboelectric nanogenerator (bTENG) is developed to serve as a self‐powered motion sensor in the wake‐up circuit, which captures slight mechanical disturbances and overcomes the drawback of conventional self‐powered motion sensors in the wake‐up circuit that the circuit can only be triggered when a considerable pressure is applied on the sensor. The bTENG mimics the structure of plants and the addition of the leaf‐shaped tentacle structures can increase the electrical outputs by four times, which largely extends the detection range of the wake‐up circuit. The bTENG can detect both noncontact and contact mechanical disturbances; and voltages generated from both situations can trigger the wake‐up system. Moreover, the specially designed circuit that is compatible with the bTENG can help more accurately control the wake‐up system and prolong the battery life of the electronic networks to 12.4 times. An intrusion detection system is established in the wake‐up circuit to distinguish human motion and judge the scene. This work opens new horizons for wake‐up technologies, and provides new routes for persistent sensing.

In this paper, on the basis of multifunctional reconfigurable pixel antenna (RPA) elements, a novel linear sparse array with an attractive compound reconfigurability is presented. It has the potential advantages of its beam scanning with low gain fluctuation, low sidelobe in two orthogonal planes, and polarization reconfigurable performance. Specifically, an RPA with simultaneous polarization and pattern reconstruction capabilities, consisting of the driven patch and the parasitic pixels on the same layer of dielectric substrate, is firstly designed, which can work in several operation modes corresponding to steerable beam directions θ=0°;θxoz=25°, 45°;θyoz=15° with two circular polarizations in X-band. Cross-slot coupling feed is used to improve polarization reconstruction capability and reduce the complexity of hybrid reconstruction topology optimization. Then, those RPAs are integrated into the 1×8 linear sparse array to realize the reconfiguration of two circular polarizations and beam steering in xoz- and yoz-plane. Simulation results show that the gain fluctuation and sidelobe level of the array during beam scanning have significant advantages over the previous phased array, and the generation of antenna grating lobes is avoided. Moreover, both RPA element and RPA array prototypes have been fabricated and measured to testify the efficiency. The measured results agree well with the simulated ones, which indicates the application potential in the field of modern wireless communication system of the proposed linear sparse array.

Aiming at constant torque output,the constant-torque control strategy of power-shift is proposed. Based on constant-torque control strategy,considering diesel engine speed characteristics and the practical needs of tractor under load condition,the practical control strategy of power-shift is proposed for high-powered tractor. The power-shift process is divided into two periods,which are speed synchronization period and power transfer period respectively. Power transfer period comes first and speed synchronization period comes later when power-shift-up,while speed synchronization period comes first and power transfer period comes later when power-shift-down. Based on the two periods of power-shift,the relationship between actuating pressure of oncoming and off-going clutches and the principles for upper-limit of actuating pressure during power-shift are presented. Based on MATLAB/Simulink,power-shift process is modeled and simulated. The results show that the practical power-shift control strategy can realize no power break and large consistent torque output during shift without coordinating engine,so that the high-powered tractor can adapt to the changes of working resistance and operate continuously without stopping,which improves operation efficiency.

A reliable framework for SINS/SAR/GPS integrated positioning systems is proposed for the case that sensors are in critical environments. Credibility is used to describe the difference between the true error and the initial setting standard deviation. Credibility evaluation methods for inertial measurement unit (IMU), synthetic aperture radar (SAR), and global positioning system (GPS) are presented. In particular, IMU credibility is modeled by noises and constant drifts that are accumulated with time in a strapdown inertial navigation system (SINS). The quality of the SAR image decides the credibility of positioning based on SAR image matching. In addition, a cumulative residual chi-square test is used to evaluate GPS credibility. An extended Kalman filter based on a sensor credibility evaluation is introduced to integrate the measurements. The measurement of a sensor is either discarded when its credibility value is below a threshold or the variance matrix for the estimated state is otherwise adjusted. Simulations show that the final fusion positioning accuracy with credibility evaluation can be improved by 1–2 times compared to that without evaluation. In addition, the derived standard deviation correctly indicates the value of the position error with credibility evaluation. Moreover, the experiments on an unmanned ground vehicle partially verify the proposed evaluation method of GPS and the fusion framework in the actual environment.

The reliability of MEMS in shock environments is a complex area which involves structural dynamics, fracture mechanics, and system reliability theory etc. With growth in the use of MEMS in automotive, IoT, aerospace and other harsh environments, there is a need for an in-depth understanding of the reliability of MEMS in shock environments. Despite the contributions of many articles that have overviewed the reliability of MEMS panoramically, a review paper that specifically focuses on the reliability research of MEMS in shock environments is, to date, absent. This paper reviews studies which examine the reliability of MEMS in shock environments from 2000 to 2020 in six sub-areas, which are: (i) response model of microstructure, (ii) shock experimental progresses, (iii) shock resistant microstructures, (iv) reliability quantification models of microstructure, (v) electronics-system-level reliability, and (vi) the coupling phenomenon of shock with other factors. This paper fills the gap around overviews of MEMS reliability in shock environments. Through the framework of these six sub-areas, we propose some directions potentially worthy of attention for future research.

Spoofing attacks are one of the severest threats for global navigation satellite systems (GNSSs). This kind of attack can damage the navigation systems of unmanned air vehicles (UAVs) and other unmanned vehicles (UVs), which are highly dependent on GNSSs. A novel method for GNSS spoofing detection based on a coupled visual/inertial/GNSS positioning algorithm is proposed in this paper. Visual inertial odometry (VIO) has high accuracy for state estimation in the short term and is a good supplement for GNSSs. Coupled VIO/GNSS navigation systems are, unfortunately, also vulnerable when the GNSS is subject to spoofing attacks. The method proposed in this article involves monitoring the deviation between the VIO and GNSS under an optimization framework. A modified Chi-square test triggers the spoofing alarm when the detection factors become abnormal. After spoofing detection, the optimal estimation algorithm is modified to prevent it being deceived by the spoofed GNSS data and to enable it to carry on positioning. The performance of the proposed spoofing detection method is evaluated through a real-world visual/inertial/GNSS dataset and a real GNSS spoofing attack experiment. The results indicate that the proposed method works well even when the deviation caused by spoofing is small, which proves the efficiency of the method.

Serving as an effective biomarker in liquid biopsy, circulating tumor cells (CTCs) can provide an accessible source for cancer biology study. For the in-depth evaluation of CTCs in cancer analysis, their efficient enrichment is essential, owing to their low abundance in peripheral blood. In this paper, self-assembled immunomagnetic beads were developed to isolate CTCs from the ordered bundles of cells under the assistance of the spiral inertial effect. Parametric numerical simulations were performed to explore the velocity distribution in the cross section. Based on this chip, rare CTCs could be recovered under the throughput of 500 μL/min, making this device a valuable supplement in cancer analysis, diagnostics, and therapeutics.

CubeSats have been widely used in remote sensing applications such as global coverage, hotspots revisited, etc. However, due to the strict size limitation, the high-accuracy measuring instruments such as star tracker are too large to be applied in CubeSat, thus causing insufficient accuracy in satellite attitude and image positioning. In order to reduce the volume of star tracker without compromising the performance, the relationship between the volume and pointing accuracy or dynamic performance is studied and an optimization model of star tracker with a minimum volume is proposed. Compared with the traditional star tracker, a deployable star tracker with a novel deployable baffle and surrounded circuit structure is designed. The baffle consists of nested three-stage sub-baffles with a scientifically analyzed and verified taper to achieve smooth deployment and compression. The special circuit structure surrounds the lens and can be compressed in the inner sub-baffle. Therefore, the deployable star tracker can be compressed to the smallest volume and the sub-baffles can be deployed to the accurate position without self-lock risk. The experimental results verify its deployment accuracy and reliability as well as space environmental adaptability. The deployable star tracker has almost the same results on stray light suppression ability, pointing accuracy (better than 3″ (3σ)) and dynamic performance (up to 3°/s) with the traditional star tracker. Furthermore, an integrated attitude determination and control system based on the deployable star tracker for CubeSat is further designed and implemented to support high-accuracy remote sensing.

In this paper nickel oxide (NiO) nanoflower was synthesized and used for the realization of direct electrochemistry of horseradish peroxidase (HRP). By using carbon ionic liquid electrode (CILE) as the substrate electrode, NiO-HRP composite was casted on the surface of CILE with chitosan (CTS) as the film forming material and the modified electrode was denoted as CTS/NiO-HRP/CILE. UV-Vis absorption and FT-IR spectra confirmed that HRP retained its native structure after mixed with NiO nanoflower. Direct electron transfer of HRP on the modified electrode was investigated by cyclic voltammetry with a pair of quasi-reversible redox waves appeared, indicating that the presence of NiO nanoflower on the electrode surface could accelerate the electron transfer rate between the electroactive center of HRP and the substrate electrode. Electrochemical behaviors of HRP on the modified electrode were carefully investigated. The HRP modified electrode showed excellent electrocatalytic activity to the reduction of trichloroacetic acid with wider linear range and lower detection limit. Therefore the presence of NiO nanoflower could provide a friendly biocompatible interface for immobilizing biomolecules and keeping their native structure. The fabricated electrochemical biosensor displayed the advantages such as high sensitivity, good reproducibility and long-term stability. This work is licensed under a Creative Commons Attribution 4.0 International License.

Compared with conventional solid-state relays, micro-electro mechanical system (MEMS) relays have the advantages of high isolation, low contact resistance, low power consumption, and abrupt switching characteristics. Nevertheless, the widespread application of MEMS relays has been limited due to the issue of the conflict between low actuation voltages and high device performance. This article presents a novel cantilever MEMS relay with an embedded contact electrode which helps to achieve a low actuation voltage (below 8 V) and high restoring force simultaneously. Meanwhile, the contact resistance is as low as around 0.4 Ω and the reliability is verified. To thoroughly investigate and analyze the novel cantilever MEMS relay, a static theoretical model of the structure was developed. Based on the model, the cantilever MEMS relay was designed and optimized. Then, the relays were fabricated by the bulk-silicon micromachining process based on the silicon−glass anodic bonding technology. Finally, the switching performance of the novel cantilever MEMS relay was measured. Experimental results demonstrate that the proposed MEMS relay has a low actuation voltage below 8 V and high performance, which is in good agreement with the simulation results, and shows significant advantages when compared with previous reports. Therefore, the proposed MEMS relay with an embedded contact electrode is promising in practical applications.

We investigated the operation of an all-optical rubidium-87 atomic magnetometer with amplitude-modulated light. To study the suppression of spin-exchange relaxation, three schemes of pumping were implemented with room-temperature and heated paraffin coated vacuum cells. Efficient pumping and accumulation of atoms in the F=2 ground state were obtained. However, the sought-for narrowing of the resonance lines has not been achieved. A theoretical analysis of the polarization degree is presented to illustrate the absence of light narrowing due to radiation trapping at high temperature.

Global navigation satellite systems (GNSS) are the most widely used positioning, navigation, and timing (PNT) technology. However, a GNSS cannot provide effective PNT services in physical blocks, such as in a natural canyon, canyon city, underground, underwater, and indoors. With the development of micro-electromechanical system (MEMS) technology, the chip scale atomic clock (CSAC) gradually matures, and performance is constantly improved. A deep coupled integration of CSAC and GNSS is explored in this thesis to enhance PNT robustness. “Clock coasting” of CSAC provides time synchronized with GNSS and optimizes navigation equations. However, errors of clock coasting increase over time and can be corrected by GNSS time, which is stable but noisy. In this paper, weighted linear optimal estimation algorithm is used for CSAC-aided GNSS, while Kalman filter is used for GNSS-corrected CSAC. Simulations of the model are conducted, and field tests are carried out. Dilution of precision can be improved by integration. Integration is more accurate than traditional GNSS. When only three satellites are visible, the integration still works, whereas the traditional method fails. The deep coupled integration of CSAC and GNSS can improve the accuracy, reliability, and availability of PNT.