Raman imaging for a sepsis study is reported here for the first time. We propose a confocal resonance Raman microscopic imager (CRRMI)to measure in vivo the redox state of mitochondria over a surface area of a septic rat. The CRRMI has excellent performance with spectral and spatial resolutions of 0.1 nm and 2 um, respectively. It is found for the first time that the Raman signal related to the mitochondrial dysfunction in sepsis is abnormally large only locally at many points with some random spatial distribution. Our CRRMI can detect the mitochondrial redox state through the skin of a naturally living rat even without the removal of hairs, and overcomes some issues that a pointwise measurement method of Raman signalsmay encounter when monitoring mitochondrial dysfunction of a sepsis rat, such as the fluorescence of hairs, hitting the points without mitochondrial redox metabolic disorder, etc.The present Raman imager can be used for giving an early warning for sepsis. It provides a new method for noninvasive monitoring of mitochondrial redox status in sepsis.
A fundamental building block in characterizing and tackling scientific and industrial questions boils down to the ability of quickly solving mathematical equations. However, with the ever-growing volume of information and unsustainable integration growth in electronic processors, a radically new modality for solving equations is highly imminent. Here, we introduce an electromagnetic counterpart to solve multivariable complex equations, where two metamaterialkernels are connected in series to form a closed-loop electromagnetic system. Complex-valued information is carried by electromagnetic fields, and the equation solution for arbitrary input signals can be recursively attained after a number of feedbacks. As an illustration, we present the capability of such system in solving eight complex equations, and inversely design two 4 × 4 metamaterialkernels by topology optimization, whose average element error is reduced to smaller than 10-4. Having accomplished all unknown coefficients with high fidelity, our work represents a conspicuous apparatus for a myriad of enticing applications in ultra-compact signal processing and neuromorphic computing.
Exact solutions to Maxwell equations with topological charge based on a modification to Brittingham's single cycle pulses are analyzed demonstrating that they have finite values of energy, momentum and angular momentum. Moreover, the ratio of angular momentum to energy is bounded due to the dependence of the mean frequency on topological charge. We have also analyzed the skyrmionic texture of the electric and magnetic fields showing that it is possible to obtain skyrmionic numbers higher than one for the magnetic field by means of a superposition of pulses with different topological charges and null skyrmionic number.
Photonic integrated circuits (PICs) and microwave integrated circuits (MICs) have been widely studied, but both of them face the challenge of miniaturization. On one hand, the construction of photonic elements requires spaces proportional to wavelength, and on the other hand, electromagnetic compatibility issues make it challenging to reach high-density layouts for MICs. In this paper, we review the research advances of miniaturized PICs and MICs based on surface plasmon polaritons (SPPs). By introducing SPPs, miniaturized photonic elements at subwavelength scales are realized on PICs, which can be used for highly integrated interconnects, biosensors, and visible light wireless communications. For MICs, since the metals behave as perfect conductors rather than plasmonic materials at microwave frequencies, plasmonic metamaterials are proposed to support spoof SPPs. Spoof SPPs possess similar characteristics to SPPs and can be used to realize high-density channels on MICs. Moreover, combining the latest theoretical research on SPPs, future tendencies of SPP-based MICs are discussed as well, including further miniaturization, digitization, and systematization.
In this work, a high-gain and wideband microstrip-fed slot antenna is proposed and investigated, which is composed of an anisotropic metasurface (AMS) and an aperture coupled structure. The proposed microstrip antenna with four resonances can be obtained by merging the AMS with an anomalous inverted π-slot feed structure in a low profile (1.07λ0×1.07λ0×0.06λ0). The simulated results indicate that the proposed microstrip antenna can achieve a wide impedance bandwidth of 56.1% from 3.32 to 5.91 GHz, which is verified by experiment. In addition, the measured results show that the peak gain of the proposed microstrip-fed slot antenna is 10.7 dBi at 5.3 GHz, and the relative bandwidth of 3-dBi gain is 42.2% from 3.85 to 5.91 GHz. Compared with previous works, the proposed design has a lower profile while achieving a much wider operating bandwidth, where the four controllable resonance modes offer more possibilities for band expansion. This work shows potential application in integration with high data rate systems.
Metamaterials offer a chance to design films that could achieve optical differentiation due to their special properties. Layered film would be the simplest case considering the easy-fabrication and compactness. Instead of performing the optical differentiation at the Fourier plane, Green-function based multi-layers are used to achieve optical differentiation. In this work, epsilon-near-zero (ENZ) material is utilized to realize the optical differentiation owning to the special optical properties that the reflection increases with the increase of incident angle, which fits the characteristics of optical differentiation. In addition, deep learning is also used in this work to simplify the design of ENZ layers to achieve the optical differentiation, and further realize the optical edge detection. Simulations based on the Fresnel diffraction are carried out to verify that our films designed by this method could realize the optical detection under different cases.
Sensing is a key basis for building an intelligent environment. Using channel state information (CSI) from the IEEE 802.11 physical layer in the wireless local access networks, the CSIbased device-free sensing technique has become very promising to the current sensing solutions because of its non-invasion of privacy, non-contact, easy deployment, and low cost. In recent years, the integrated communication and sensing (ICAS) technology has become one of the popular research topics in both wireless communications and computer areas. Given the fruitful advancements of ICAS, it is essential to review these advancements to synthesize and give previous research experiences and references to aid the development of relevant research fields and real-world applications. Motivated by this, this paper aims to provide a comprehensive survey of CSI-based sensing techniques. This study categorizes the surveyed works into model-based methods, data-based methods, and model-data hybrid-driven methods. Some important physical models and machine learning algorithms are also introduced. The sensing functions are classified into detection, estimation, and recognition according to specific application scenarios. Furthermore, future directions and challenges are discussed.
We present a low-cost and high-performance 5-bit programmable phased array antenna at Ku-band, which consists of 1-bit reconfigurable radiation structures, digital phase shifters, and coplanar waveguide feeding network. The 1-bit reconfigurable radiation structure utilizes symmetric geometries and PIN diodes to form stable 180° phase difference. The digital phase shifter provides 168.75° phase difference and together with the radiation structure form a 348.75° phase coverage. The antenna operates between 14.4 and 15.4 GHz, and the overall array contains 24×2 elements with each of them being individually addressable. By changing the states of the diodes and thus adjusting the phase coding sequences of the array, the antenna achieves 0°-60° precise beam scanning at 14.8 GHz, with the sidelobe level, cross-polarization, and gain fluctuation being less than -16 dB, -26 dB, and 2.4 dB, respectively. A prototype was fabricated to verify the design, and the measurement results agree well with simulations. Compared with traditional phased arrays composed of numerous phase shifters and T/R components, the proposed antenna features high performance, high flexibility, low profile, and low cost. The antenna provides a new and feasible solution of wavefront steering and will benefit the various application scenarios.
This study presents an effective solution on the basis of Discontinuous-Galerkin Time-Domain (DGTD) scheme for the injection of elliptically polarized plane wave through total-field/scattered-field (TF/SF) boundary. Generally, the elliptically polarized wave can be resolved into two linearly polarized waves in phase quadrature with the polarization planes at right angles to each other, but the proposed methodology is focused to utilize the principle of wave field formation to induce left-handed or right-handed elliptically polarized waves by regulating the phase and amplitude of the incident waves. The outcome of the proposed technique is achieved by deriving the EB-scheme equations and employing the explicit fourth order Runge-Kutta (RK4) time integration scheme in the DGTD methodology. An anisotropic Riemann solver and non-conformal mesh schemes are introduced for domain decomposition to allow efficient spatial discretization. Additionally, the proposed work is extended from single frequency to broadband elliptical polarized plane wave injection in the DGTD method, and the significance of this study is observed in the results. The experimental outcomes reveal that the proposed method is consistent with the analytical solution in free space and expected to provide efficient numerical solutions for analyzing scattering characteristics generated by various elliptically polarized waves.
We demonstrate the recovery of simple geometric and permittivity information of breast models in an experimental microwave breast imaging system using a synthetically trained machine learning workflow. The recovered information consists of simple models of adipose and fibroglandular regions. The machine learning model is trained on a labelled synthetic dataset constructed over a range of possible adipose and fibroglandular regions and the trained neural network predicts the geometry and average permittivty of the adipose and fibroglandular regions from calibrated experimental data. The proposed workflow is tested on two different experimental models of the human breast. The first model is comprised of two simple, symmetric phantoms representing the adipose and fibroglandular regions of the breast that match the model used to train the neural network. The second, more realistic model replaces the symmetric fibroglandular phantom with an irregularly shaped, MRI-derived fibroglandular phantom. We demonstrate the ability of the machine learning workflow to accurately recover geometry and complex valued average permittivity of the fibroglandular region for the simple case, and to predict a symmetric convex hull that is a reasonable approximation to the proportions of the MRI-derived fibroglandular phantom.