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One-dimensional nanobelt with inner nanopores has been considered as an ideal electrode material for electric energy storage. Herein iron manganese trioxide (FeMnO3) nanobelts have been prepared by a facile solvothermal approach. The phase, composition, structure and morphology of the as-synthesized sample are characterized by several techniques. Benefiting from the unique structure, the as-prepared FeMnO3 nanobelts achieve a superior lithium storage capacity with 635.7 mAh g−1 after 200 cycles, excellent cyclability and good kinetic properties, as well as a high specific capacitance of 209 F g−1 after 2000 cycles for supercapacitor. These results show a great application in large-scale energy storage.

The efficient synthesis of quantum materials is becoming a research hotspot as it determines their successful application in the fields of biomedicine, illumination, energy, sensors, information, and communication. Among the quantum materials, it is still a challenge to synthesize quantum wires (QWs) with surfactants due to the inevitable radial growth of QWs in the soft template method. In this paper, amphipathic graphene oxide (GO) was adopted as a macromolecular surfactant to limit the radial growth instead of the commonly used surfactant. GO could roll up under its electrostatic interaction with a cuprous oxide (Cu2O) quantum dot (QD) and then form a tubular template for the growth of the Cu2O QW, which was named herein as the nanoparticle-induced graphene oxide rolling (NIGOR) procedure. The NIGOR procedure was confirmed by the molecular dynamics results by simulating systems consisting of GO and Cu2O nanoparticles. An intermediate with a necklace morphology corresponding to the simulation result was also observed experimentally during the formation of the QW. Meanwhile, the formation mechanism of the QW was demonstrated rationally. Furthermore, increasing the dosage of the reactant, reaction time, and temperature altered the diameter of the QW from 2 to 4 nm and also changed the morphology of the final products from a QD to a QW and then to a bundle of QWs. This was attributed to the aggregation of materials for the lowest surface energy in the system. Additionally, the universality of NIGOR was manifested via the synthesis of other metal oxides as well. The NIGOR strategy provided an alternative, convenient, and mass production method for synthesizing QWs.

Mesoporous WS

Metasurfaces provide a new approach for planar optics and thus have realized multifunctional meta-devices with different multiplexing strategies, among which polarization multiplexing has received much attention due to its convenience. At present, a variety of design methods of polarization multiplexed metasurfaces have been developed based on different meta-atoms. However, as the number of polarization states increases, the response space of meta-atoms becomes more and more complex, and it is difficult for these methods to explore the limit of polarization multiplexing. Deep learning is one of the important routes to solve this problem because it can realize the effective exploration of huge data space. In this work, a design scheme for polarization multiplexed metasurfaces based on deep learning is proposed. The scheme uses a conditional variational autoencoder as an inverse network to generate structural designs and combines a forward network that can predict meta-atoms’ responses to improve the accuracy of designs. The cross-shaped structure is used to establish a complicated response space containing different polarization state combinations of incident and outgoing light. The multiplexing effects of the combinations with different numbers of polarization states are tested by utilizing the proposed scheme to design nanoprinting and holographic images. The polarization multiplexing capability limit of four channels (a nanoprinting image and three holographic images) is determined. The proposed scheme lays the foundation for exploring the limits of metasurface polarization multiplexing capability.

Chemiresistive gas sensors have been extensively explored for hazardous gas detection. Currently, an overwheming majority of previous attention was focused on the parameter improvement of sensor performance while the impact of carrier gas species on the performance was severely ignored. Aiming to a deep insight into this issue, in this work we prepared zinc oxide (ZnO) nanowire-network sensor and explored its UV-activated sensing performance toward trace nitrogen dioxide gas (NO2) at room temperature (25 °C) under two carrier gases, i.e., dry nitrogen (N2) and air. Within N2, the sensor exhibited a reversible response of 157 toward 50 ppb NO2 and a sensitivity of 7.8/ppb, which was not only among the best showcases of the existing work, but much larger than those within air (11 and 0.091/ppb, respectively). Moreover, decent selectivity and long-term stability were demonstrated. Far more UV irradiation-induced electrons which reacted with adsorbed NO2 molecules on ZnO surface as well as smaller baseline resistance under N2 than those under air jointly led to the superior response and sensitivity. After long-time UV exposure prior to gas-sensing tests within both carrier gas cases, the remaining oxygen ions (O2−) were weakly bonded on ZnO surface, contributing to the reversible behaviors at room temperature. The interconversion between physisorbed O2 molecules and ionic O2− on ZnO surface was proposed to rationalize the sensing phenomena especially when no continuous oxygen was supplied under N2 atmosphere, which enriched the current transduction mechanisms.

The efficient synthesis of quantum materials is becoming a research hotspot as it determines their successful application in the fields of biomedicine, illumination, energy, sensors, information, and communication. Among the quantum materials, it is still a challenge to synthesize quantum wires (QWs) with surfactants due to the inevitable radial growth of QWs in the soft template method. In this paper, amphipathic graphene oxide (GO) was adopted as a macromolecular surfactant to limit the radial growth instead of the commonly used surfactant. GO could roll up under its electrostatic interaction with a cuprous oxide (Cu

Hydrogen sulfide (H2S) is a colorless gas with a smell of rotten eggs which poses great threat on human health and environmental protection. Traditional metal oxide based H2S sensors always suffer from undesirable selectivity and elevated operation temperature. To overcome these obstacles, in this report cuprous oxide nanowires/nanoparticles (Cu2O) anchored onto reduced graphene oxide nanosheets (rGO) were prepared to serve as the sensing layer. Multiple characterization techniques such as SEM, TEM, FTIR, and XRD were utilized to explore the sensor performance toward H2S gas. Compared to the partial recovery at 25 °C, the prepared rGO/Cu2O sensor showed a response of 20% toward 1 ppm H2S at 40 °C, together with full restoration, excellent repeatability, long-term stability and selectivity. Favorable film porosity, highly conductive rGO nanosheets and nanometer size effect of Cu2O materials were responsible for these inspiring results, paving the avenue for future trace H2S detection in the aspects of higher sensitivity and lower power-consumption.

This paper considers the secrecy outage performance of a multiple-relay assisted non-orthogonal multiple access (NOMA) network over Nakagami- $m$ fading channels. Two time slots are utilized to transmit signals from the base station to destination. At the first time slot, the base station broadcasts the superposition signal of the two users to all decode-and-forward relays by message mapping strategy. Subsequently, the selected relay transmits superposition signal to the two users via power-domain NOMA technology. Three relay selection schemes, i.e., optimal single relay selection (OSRS) scheme, two-step single relay selection (TSRS) scheme, and optimal dual relay selection (ODRS) scheme are proposed and the secrecy outage performance is analyzed. As a benchmark, we also examine the secrecy outage performance of the NOMA systems with traditional multiple relay forwarding (TMRF) scheme in which all the relay that successfully decode signals from the source forward signals to the NOMA users with equal power. Considering the correlation between the secrecy capacity of two users and different secrecy requirement for two NOMA users, the analytical expressions for the security outage probability (SOP) of the proposed OSRS, TSRS, and ODRS schemes along with the TMRF scheme are derived and validated via simulations. To get more insights, we also derive the analytical expressions for the asymptotic SOP for all the schemes with fixed and dynamic power allocations. Furthermore, the secrecy diversity order (SDO) and secrecy array gain of cooperative NOMA systems are obtained. The results demonstrate that our proposed schemes can significantly enhance the secrecy performance compared to the TMRF scheme and that all the schemes with fixed power allocation obtain zero SDO and the OSRS scheme with dynamic power allocation obtains the same SDO as TMRF.

In order to address the parameter estimation problem of pulse coupled neural network (PCNN) in the infrared human segmentation, this paper proposes an improved search method of optimal parameters. First, based on a simple PCNN model, the cuckoo search (CS) algorithm, instead of the existing swarm intelligent methods, is introduced to deal with this multi-parameter optimization problem. Next, the Kent chaotic map is applied to the local search process of CS algorithm, so as to enhance search precision. Furthermore, to cope with the search blindness, a guidance mechanism by the k dimensional-tree (KD-Tree) is presented to accelerate the convergence speed of parameter search based on the current search history. The proposed method is applied to a set of benchmark test functions and a series of infrared human segmentation experiments, and the experimental results demonstrate that the simple PCNN Model with this proposed parameter optimization method is superior to other PCNN segmentation models with existing swarm intelligent optimization algorithms both in the visual segmentation effect and the objective quantitative evaluations of accuracy and computing speed.

A vision inspection system is developed for detecting surface damages on cables in long-span cable-stayed bridges. The system consists of a climbing robot, an image processing platform, and 4 fixed cameras. While the climbing robot loads the whole system and ascends along the cables, 4 cameras can continuously acquire cable surface images. Then, image processing techniques can be employed to achieve defect identification. In our optical system, a single image may only collect a part of the whole defect because of the field of view (FOV) of each camera. In order to obtain the morphology of the whole defect, we present an efficient scale-invariant feature transform (SIFT) algorithm to achieve the multi-image mosaic with partially overlapped regions in different defect images. Experimental results demonstrate that the proposed vision inspection technologies are suitable for application to the bridge cables for defect detection and condition assessment.

Visible-infrared compatible camouflage is significant to enhance the equipment survivability through counteracting the modern detecting and surveillance systems. However, there are still great challenges in simultaneously achieving multispectral camouflage with high transmittance in visible, low emissivity in the atmospheric windows and high emissivity in the non-atmospheric window, which can be attributed to the mutual influence and restriction within these characteristics. Here, we proposed an optically transparent infrared selective emitter (OTISE) composed of three Ag-ZnO-Ag disk sub-cells with anti-reflection layers, which can synchronously improve the visible transmittance and widen absorption bandwidth in the non-atmospheric window by enhancing and merging resonance response of multi-resonators. Test results reveal that low emissivity in infrared atmospheric windows, high emissivity in the 5-8 µm non-atmospheric window and high optical transparency have been obtained. In addition, the radiative flux of OTISE in 3-5 µm and 8-14 µm are respectively 34.2% and 9.3% of that of blackbody and the energy dissipation of OTISE is 117% of that of chromium film. Meanwhile, it keeps good optical transparency due to the ultrathin Ag film. This work provides a novel strategy to design the optically transparent selective emissive materials, implying a promising application potential in visible and infrared camouflage technology.

Surmounting the issues of high-sensitivity and room-temperature detection toward trace NO2 gas is of paramount importance in the fields of human health and ultralow emission. Recently, black phosphorus (BP), a novel two-dimensional material, has gained considerable interest to achieve this goal. However, related work is far from satisfactory due to sluggish response, insufficient recovery, and fragile stability. In this scenario, we report on an inspiring NO2 sensor featuring composite film of few-layer BP nanosheets and zinc oxide (ZnO) nanowires serving as the sensing layer. Compared with BP-only counterpart, BP-ZnO sensor exhibited enhanced performance including boosted response (74% vs. 37.7% toward 50 ppb, which was among the best performances of BP involved NO2 sensors), accelerated response speed, better long-term stability, and strengthened humidity-repelling properties. In addition, excellent selectivity toward trace NO2 gas was revealed. These improvements could be ascribed into porous film, abundant sorption sites, numerous p-n heterojunctions, and passivation effect of ZnO nanowires on BP nanosheets. Furthermore, the proposed basic-solution assisted BP exfoliation favored film deposition, and enabled versatile composition design involving BP nanosheets in the future. In brief, the as-prepared BP-ZnO NO2 sensors paved the avenue for further BP applications and enriched its underlying transduction mechanism in gas sensing.

Odometer is a good information source for land vehicle navigation because it can detect the velocity or mileage of a vehicle. Odometer scale factor, misalignment, and level arm with inertial measurement unit are the three sources of integration inaccuracy. However, they are difficult to be calibrated using a single procedure as they vary with many factors, such as the environment temperature, loading, and mounting factors. The online calibration method of the total odometer error and the odometer-aided inertial navigation system algorithm based on Coriolis Law were derived in this study. Vehicle test results indicated that the proposed calibration method can perform a good estimation of total odometer error. Navigation accuracy can be improved significantly with the calibrated odometer when the Global Positioning System declares loss-of-lock, which is approximately 1‰ of the mileage.

In this paper, we presented a new method to directly fabricate 3D microlens array (MLA) without the pattern transfer and substrate reshaping process. Gas-assist deformed concave Polydimethylsiloxane (PDMS) film was used as the molding template. The template was fixed on the curved micro-hole array with a micro cavity during the injection molding process. The curved micro-hole array was fabricated by 3D printing in order to avoid the interference from microlens nearby. In addition, the micro-hole array is detachable, thus allowing it to be assembled onto the MLA automatically. Less than few minutes, the formation of 3D MLA can be realized by which the polymeric lens material is capable of being cured within dozens of seconds. Multi-focusing MLA was designed to reduce the defocus. And the focal length of each microlens varied according to the different diameter of micro-holes. This novel method is time-saving, cost-effective and precision. A single microlens has a radius of 250–500 μm, and the accuracy can reach 30 μm. Performance of the 3D MLA has been optically characterized. The introduction of micro-holes array can effectively reduce the crosstalk between the eyes and the influence of stray light. The curved MLA have has a FOV over 70°.

This paper puts forward a novel method for fabrication of sandwich-structured BCE using a detachable micro-hole array (MHA) prepared by 3D printing. Compared with most traditional methods, 3D print...

Nitrogen dioxide (NO2) is a hazardous gas species that could impose a great threat on environmental protection and human health even at very low doses. Thus, it is of great importance to selectively detect trace NO2 gas below the ppm level. This has been a serious challenge so far, especially in the presence of other interfering gases. Herein, we report ultrasensitive, room-temperature and UV light-assisted NO2 gas sensing based on few-layer MoS2 nanosheet/ZnO nanowire composites serving as the sensing layer. A series of characterization techniques, such as scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), X-ray powder diffraction (XRD), Raman, energy-dispersive X-ray spectroscopy (EDS), UV-Visual and ultraviolet photoelectron spectroscopy (UPS), were employed to explore the componential and structural properties of the obtained sensitive materials. Initially, the as-prepared MoS2/ZnO composites showed a tiny response (40%) and an incomplete recovery to 10 ppm NO2 gas in dark condition. While under UV illumination, the sensing response attained 8.4 and 188, toward 50 ppb and 200 ppb NO2 with a full recovery. Meanwhile, a sensitivity of 0.93 ppb−1, a detection limit of 50 ppq (10−15), and excellent repeatability and selectivity were also achieved. The experimental results were better than most previous work on NO2 detection to the best of our knowledge. Two main aspects are responsible for the outstanding performance. One is that a mass of photo-excited electron–hole pairs participated in the reaction with NO2 molecules under UV illumination. The other lies in numerous p–n MoS2/ZnO nanojunctions, favorable for the extension of the depletion region and the separation of the charge carrier. Additionally, long-term stability, as well as the effect of film thickness and carrier gas species on sensor performance, were simply investigated. We combined p–n nanojunctions with the UV illumination method, providing an alternative strategy to realize room-temperature operation and high sensitivity in the field of gas sensors.

Pulse coupled neural network (PCNN) has become a significant tool for the infrared pedestrian segmentation, and a variety of relevant methods have been developed at present. However, these existing models commonly have several problems of the poor adaptability of infrared noise, the inaccuracy of segmentation results, and the fairly complex determination of parameters in current methods. This paper presents an improved PCNN model that integrates the simplified framework and spectral residual to alleviate the above problem. In this model, firstly, the weight matrix of the feeding input field is designed by the anisotropic Gaussian kernels (ANGKs), in order to suppress the infrared noise effectively. Secondly, the normalized spectral residual saliency is introduced as linking coefficient to enhance the edges and structural characteristics of segmented pedestrians remarkably. Finally, the improved dynamic threshold based on the average gray values of the iterative segmentation is employed to simplify the original PCNN model. Experiments on the IEEE OTCBVS benchmark and the infrared pedestrian image database built by our laboratory, demonstrate that the superiority of both subjective visual effects and objective quantitative evaluations in information differences and segmentation errors in our model, compared with other classic segmentation methods.

In this paper, both reduced graphene oxide (rGO) and rGO/molybdenum disulfide (MoS2) composite films serving as sensing layers were prepared for nitrogen dioxide (NO2) gas detection at low operation temperature, wherein multiple characterization techniques containing TEM, XRD, XPS and Raman were employed. The experimental results showed that rGO/MoS2 composite film possessed a larger exposure area, more sorption sites and a mass of p–n heterojunctions, thereby resulting in a sensing response of 59.8% toward 2 ppm NO2 at 60 °C (optimal operation temperature), which was nearly 200% enhanced in comparison with bare rGO one. Furthermore, we investigated the role of rGO or MoS2 material in the sensing performance as well as the effect of different MoS2 introduction manners. Apart from these, relative humidity was found to pose a small interfering impact on NO2 response, while long-term stability on exposure to 120 ppb NO2 revealed a small response decay after several weeks. Moreover, the composites showed an excellent selectivity toward NO2 gas against other interfering gas species. Through further exploration of ppb-level NO2 detection, the detection limit was calculated to be as low as 5.7 ppb.

Sensitive detection for low-concentration NO 2 gas is imperative in the fields of environmental monitoring and human healthcare. In this work, reduced graphene oxide (rGO)/tin oxide (SnO 2 ) nanocomposites were prepared by a simple blending and deposited onto different microporous substrates to explore their sensing performance to NO 2 gas, combining with multiple characterization techniques such as SEM, TEM, XRD and XPS. The experimental results revealed an increased sensing response with rising SnO 2 concentration ranging from 0 to 10 mg/ml. Especially, the sensing response at 10 mg/ml attained nearly three times larger than that of pure rGO. Investigation on long-term stability manifested a smaller response attenuation of rGO/SnO 2 sensors toward NO 2 gas of high concentration regime than that of low one. Besides, the largest sensing response of rGO film to 1 ppm NO 2 was achieved under 200-μm microporous substrate, which was 350% enhancement of that under flat one. To analyze the dynamic equilibrium of adsorption/desorption process, two kinds of measurement methods were contrastively studied. Additionally, interfering factors including operation temperature and relative humidity were probed as well. The prepared rGO/SnO 2 sensors exhibited an excellent selectivity toward NO 2 gas as well as a nice detection limit as low as 15.7 ppb.

In this paper, we prepared reduced graphene oxide (RGO) thin film by simple airbrush technology and then studied its gas-sensing behaviors at room temperature. On exposure to dynamic NO2 gas, RGO thin film showed a time-resolved n–p transition process (n-type switched to p-type), due to its weak n-type properties arising from hydrazine-reduction method and strong electron-accepting NO2 molecules. As RGO amount was added, the time taken to run through this transition became longer. On consecutive exposures to NO2 and NH3 gases, p–n transition (p-type switched to n-type) emerged as well. To suppress these transition behaviors, CuCl was incorporated into RGO material, resulting in opposite p-type characteristics for the composite film as well as no occurrence of p–n transition during the sensing tests. Compared to pure RGO counterpart, RGO/CuCl film had a six-fold response enhancement and a likewise good repeatability toward 10 ppm NH3 within four cyclic periods. We expect that the proposed research on transition behaviors and relevant suppression methods will shed new light on gas-sensing mechanisms and improve operation stability of RGO based sensors.

Real-time monitoring of trace NO 2 emission has been an emerging challenge in environment and health sectors lately. Aiming to overcome this challenge, NO 2 gas sensors based on cuprous oxide quantum dots (Cu 2 O QDs) anchored onto reduced graphene oxide (RGO) nanosheets serving as a sensitive layer were prepared in this report. Apart from a series of purposive measurements, various characterization techniques such as XRD, Raman, XPS and TEM were employed as well to assist the exploration of sensors performance to NO 2 gas. The experimental results revealed a 580% response enhancement for prepared RGO/Cu 2 O sensors compared with pure RGO counterparts, as well as an excellent selectivity. In a specific experiment, the sensing response attained 4.8% and 29.3% toward 20 ppb and 100 ppb NO 2 respectively at 60 °C, which was larger than most Cu 2 O based resistive gas sensors. Moreover, further subtle modulation of this RGO/Cu 2 O nanocomposites led to a preferable room-temperature response of 37.8% toward 100 ppb NO 2 , which also offered a favorable stability of 98.1% response retention after four exposures within ten days. The obtained results imply that the prepared RGO/Cu 2 O QDs sensors possess a competitive capability of trace NO 2 detection.

The flower-like nickel-zinc-cobalt (Ni-Zn-Co) mixed metal oxide nanowire arrays have been directly synthesized on nickel foam by using a simple hydrothermal method and subsequent thermal treatment. According to the electrochemical characterization, it is demonstrated that the flower-like Ni-Zn-Co oxide nanowire arrays manifest high specific capacitance (776 F g −1 ) and areal capacitance (1.16 F cm −2 ) at a current density of 2 A g −1 . It should also be noted that the Ni-Zn-Co oxide nanowire arrays exhibit long cycle stability (88.9% of the maximum value after 10000 cycles) and good rate capability (retention of 73.8% at 32 A g −1 ). The assembled aqueous asymmetric supercapacitor shows an energy density of 44.5 Wh kg −1 at a power density of 880 W kg −1 . Furthermore, in terms of the analysis results of material characterization and electrochemical performance, the excellent pseudocapacitive performance could be ascribed to the unique flower-like nanowire architecture with high surface areas, rich redox reaction sites, good mechanical and electrical contacts, as well as high conductivities and transport rates for both electrolyte ions and electrons. This study indicates that the flower-like Ni-Zn-Co oxide nanowire arrays could find opportunities in supercapacitor applications.

In the search robotics field, human target segmentation method plays a basic preprocessing step in the visual guidance. However, with the wide application of the infrared sensor on robot vision, traditional segmentation methods are facing more challenges of low-contrast, overlapping and blurring targets, and complex background. This paper introduces an infrared human segmentation approach that integrates the improved pulse coupled neural network (PCNN), the curvature gravity gradient tensor (CGGT) and the mathematical morphology to address these above problems. This approach starts with an improved PCNN segmentation model. Local dynamic synapse weights are designed to enhance the synchronous pulsing ability of the improved PCNN model with similar inputs, and a reformed threshold is conducted to guide the process of segmentation. Moreover, eigenvalues of CGGT are guaranteed in this model as linking coefficients, in order to capture the edges and details of human target more exactly in segmentation. Lastly, the segmentation result is repaired by morphology operators, to ensure the integrity of the target region and the independent noise removal. Experiments on 200 real infrared images captured by the mobile robot CQSearcher I, demonstrate that our method is superior over the other classic segmentation methods in both the subjective visual performance and the objective indicators of misclassification error and f-measure.

Specific detection of protein biomarkers has a wide range of applications in areas such as medical science, diagnostics, and pharmacology. Quantitative detection of protein biomarkers in biological media, such as serum, is critically important in detecting disease or physiological malfunction, or tracking disease progression. Among various detection methods, electrical detection is particularly well suited for point-of-care (POC) specific protein detection, being of low cost, light weight and small form factor. A portable system for sensitive and quantitative detection of protein biomarkers will be highly valuable in controlling and preventing diseases outbreaks. Recently, an alternating current electrokinetic (ACEK) capacitive sensing method has been reported to demonstrate very promising performance on rapid and sensitive detection of specific protein from serum. In this work, a low cost and portable analyzer with good accuracy is developed to use with ACEK capacitive sensing to produce a true POC technology. The development of a board-level capacitance readout system is presented, as well as the adaption of the protocol for use with ACEK capacitive sensing. Results showed that the developed system could achieve a limit of detection of 10 ng/mL, comparable to a sophisticated benchtop instrument. With its small size and light-weight similar to a smart phone, the developed system is ready to be applicable to POC diagnostics. Further, the readout system can be readily expanded for multichannel monitoring and telecommunication capabilities.

We report the synthesis, characterization and gas sensing performance of reduced graphene oxide (RGO) and RGO/ZnO bilayer thin films prepared by facile sol-airbrush technology at room temperature. We found that RGO/ZnO bilayer films exhibit enhanced surface roughness and more spacing compared to RGO. Sensing performance measurements showed that RGO/ZnO film has 30% enhancement of Δ R/R 0 to chloroform vapor compared to pure RGO film due to the improvement of film structure. A slight enhancement of Δ R/R 0 was also observed on exposure to other vapors (water, ethanol, acetone and formaldehyde), with response and recovery time less than 10 s. To explore the stability of RGO film, we studied the effect of various flow rates of background gas on the electric resistance variation. It was found that electric resistance fell at a larger flow rate, rose at a smaller rate, and Δ R/R 0 became smaller after annealing. These results can be understood by the air pressure-induced deformation of RGO film. We further proposed a new method to calculate the sensing response, which was verified to be more reliable for sensing performance analysis with negligible hysteresis. We expect these findings to shed new light on future development of high-performance gas sensors based on bilayer films.

This paper considers a multiple-input–multiple-output (MIMO) cognitive wiretap system over Nakagami- $m$ channels with generalized selection combining (GSC), where confidential messages transmitted from a multiple-antenna transmitter to a multiple-antenna legitimate receiver are overheard by a multiple-antenna eavesdropper. Depending on whether the source node has the global channel state information (CSI) of both the main and wiretap channels, we investigate the secrecy outage performances of optimal antenna selection (OAS) and suboptimal antenna selection (SAS) schemes for MIMO underlay cognitive radio systems over Nakagami- $m$ channels, and we compare them with the space-time transmission (STT) scheme. The closed-form expressions for the exact and asymptotic secrecy outage probability (SOP) for various schemes are derived. Simulations are conducted to validate the accuracy of our derived analytical results.

This paper considers a single-input-multiple-output underlay cognitive wiretap system over Nakagami-m channels with generalized selection combining, where confidential messages transmitted from a single-antenna transmitter to a multiple-antenna legitimate receiver are overheard by a multiple-antenna eavesdropper. Passive eavesdropping scenario is considered in this work, while the channel state information of the eavesdropping channel is not available at the secondary transmitter. We derived the closed-form expression for the exact secrecy outage probability. Simulations are conducted to validate the accuracy of our proposed analytical models.

Benefiting from advances in nanofabrication technology, emerging metasurfaces are promising for compact and wearable multicolor meta-holograms with large fields of view. However, due to the inherent electromagnetic properties of the structures that are used, current multicolor meta-holograms are often sensitive to the incident light polarization, which greatly restricts the application of meta-holography. Here, we took advantage of the amplitude properties of metasurfaces and the off-axis illumination method to carry out experiments involving polarization-insensitive colorful meta-holography with anisotropic nanostructures. With red, green and blue lasers illuminating the meta-hologram along different angles, a polarization-insensitive colorful holographic image was achieved and the disturbance from zero-order diffraction light was essentially eliminated. To the best of our knowledge, the current work was the first time that a polarization-insensitive colorful meta-hologram with anisotropic nanostructures was experimentally demonstrated. We expect our approach to provide promising prospects for the use of metasurfaces in applications such as flat meta-lenses, data storage and virtual/augmented reality.

A highly sensitive detection system for sulfur dioxide (SO2) and nitric oxide (NO) was developed via deep ultraviolet differential optical absorption spectroscopy (DUV-DOAS). The wavelength range of 200–230 nm was used which was rarely used before as result of severe cross sensitivity to SO2 and NO, in this work, this problem was overcame. A system detection limit (DL) of 60 ppb for SO2 has been reached which was among the best ones. Meanwhile, a novel method based on spectrum superposition theory was proposed to decompose the differential optical density (DOD) of NO from that of gas mixture in cross sensitive band. The advantage of this method is that the most sensitive absorption peak of NO was used, which cannot be used by conventional methods due to the cross sensitive to SO2. A system DL of 7 ppb for NO has been achieved which is among the best ones reported before. Furthermore, the effect of gas temperature and humidity on concentration retrieval has also been studied, gas temperature and humidity compensation models have also been proposed. The experimental results show that the compensation models succeed in compensating the deviation caused by gas temperature and humidity. The environmental adaptability of the system has been enhanced. This work achieves the aim of monitoring ultra-low concentration of SO2 and NO in a complex environment simultaneously.

Sensitive detection of trace nitrogen dioxide (NO2) gas at room temperature is of urgent necessity in the fields of healthcare and environment monitoring. To achieve this goal, we report on a porous composite film featuring reduced graphene oxide (rGO) nanosheets as the template platform of nanostructured cuprous oxide (Cu2O) nanowires and nanoparticles via a hydrothermal method. The sensor performance was investigated in terms of sensing response, optimal operation temperature, repeatability, long-term stability, selectivity and humidity effect on NO2 sensing. The sensor response achieved 0.66 towards 50 ppb NO2 gas with a full recovery at room temperature (25 °C ± 2 °C), which was among the best cases of Cu2O-related NO2 detection concerning sensor response and operation temperature. Moreover, a modest repeatability, stability, selectivity as well as a negligible humidity effect on NO2 sensing were exhibited. A mass of interspaces existing within nanostructured composites as well as the synergistic effect between rGO and Cu2O materials endowed the sensing layer with favorable gas accessibility and sufficient gas-solid interaction. Simultaneously, highly conductive rGO nanosheets facilitated an effective electron transfer and collection. In brief, the as-prepared rGO/Cu2O sensors showed a competitive room temperature detection capability for ppb-level NO2 gas, providing a vast potential in the future applications such as the real-time monitoring of ultralow emission.

In this work, we report on UV illumination-enhanced room-temperature trace NH3 detection based on ternary composites of reduced graphene oxide nanosheets (rGO), titanium dioxide nanoparticles (TiO2), and Au nanoparticles as the sensing layer, which is the first reported so far. The effect of the UV state as well as componential combination and content on the sensing behavior disclosed that rGO nanosheets served not only as a template to attach TiO2 and Au but also as an effective electron collector and transporter, TiO2 nanoparticles acted as a dual UV and NH3 sensitive material, and Au nanoparticles could increase the sorption sites and promote charge separation of photoinduced electron–hole pairs. The as-prepared rGO/TiO2/Au sensors were endowed with a sensing response of 8.9% toward 2 ppm of NH3, a sensitivity of 1.43 × 10–2/ppm within the investigated range, nice selectivity, robust operation repeatability, and stability, which was fairly competitive in comparison with previous work. Meanwhile, the ex...

In this study, the secrecy performance of the classic Wyner's wiretap model over generalised-K fading channels is studied. The closed-form expressions for the average secrecy capacity, the lower bound of secure outage probability, and the probability of strictly positive secrecy capacity are derived. The new expressions provide a unified form, which can handle several of the well-known composite fading environments as special or limiting cases. Moreover, all the derived expressions are given in terms of Meijer's G-function, which can be simply realised in MATLAB and MATHEMATICA. Monte-Carlo simulations are performed to verify the proposed analysis models.

Hydrazine-reduced graphene oxide (RGO) film was further annealed for gas sensing investigation. Pristine RGO film with an initial n-type property underwent an n-p transition on dynamic exposure to NO 2 gas. After annealing at 300 °C, RGO film became p-type semi-conducting while the p-n transition behavior did not appear when immersed into target gases. Sensing mechanisms were studied combining with multiple characterization techniques such as SEM, EDS, XRD, Raman and FTIR by researching film differences before and after annealing. Interfering factors including operational temperature and relative humidity were contrastively analyzed as well. Besides, the effect of thermal annealing on relationship between flow rate of background gas and electric resistance variation was considered closely related to structural deformation of RGO film. The detailed and systematic investigation would enrich available gas-sensing mechanisms of RGO based sensors, and put forward some new scientific issues to be further studied, such as suppression of n-p or p - n transition and the concrete influence mode of film deformation on gas sensing.