Abstract:A terahertz parametric radiation source based on Stimulated Polariton Scattering(SPS) is an optically coherent terahertz source, characterized by high coherence, frequency tunability, and operation at room temperature. The basic principle of terahertz parametric radiation based on SPS, commonly used nonlinear crystals, and coupling techniques are firstly introduced. Then typical techniques and research achievements in recent years, both domestical and international, are summarized regarding gain enhancement and output performance improvement. Additionally, the progress in the application of terahertz radiation sources in material concentration detection is reviewed. Finally, the key technical issues and development trends of terahertz parametric radiation sources are analyzed.

Abstract:In recent years, terahertz photonics of gases (such as air plasma) has developed rapidly, but there are relatively few research reports on the generation of terahertz waves by liquids, especially liquid water, which exhibits significant absorption performance in the terahertz frequency range, leading researchers to believe that liquids cannot be sources of terahertz waves. Recently, experiments have confirmed that broadband terahertz waves can be generated by exciting liquids with femtosecond lasers, and liquids have unique characteristics as terahertz wave radiation sources. The density of liquids is close to that of solids, and they perform excellently in interactions with laser pulses, with effects far exceeding those of gas sources. The fluidity of gases and liquids ensures that each laser pulse can interact with a new target area, greatly avoiding damage or degradation of the medium, a function that is difficult to achieve with solid materials. It is these unique characteristics that make gases and liquids show great potential in the research of high-energy-density plasmas and the development of next-generation terahertz wave sources. This article reviews the research progress of gases and liquids as broadband terahertz sources and compares various methods of generating terahertz waves using gases and liquids. Terahertz gas and liquid photonics reveal the potential for developing new types of terahertz wave sources and open up new research directions for studying the interactions between lasers and liquids.

Abstract:A high-Q all-silicon structured metasurface is proposed, consisting of two layers of silicon square pillars. By adjusting the lateral offset distance between the upper and lower layers in the x -direction, the out-of plane σz symmetry is broken, allowing for the free control of perfect Bound states In the Continuum(BIC) states, polarization-sensitive quasi-BIC states, and Unidirectional Guided Resonance(UGR) states at different lattice vectors kx. Notably, since the proposed metasurface operates in momentum space, there are no strict spatial position restrictions for the incident light in real space. Simulation results indicate that after breaking the structural geometric symmetry and the symmetry of the external incident angle, quasi-BIC can achieve a Q value of the order from 10³ to 10⁴. The energy leakage of the generated quasi-BIC can be explained by the incomplete destructive interference of magnetic dipoles. By changing the lateral spacing between the upper and lower layers, unidirectional radiation with a maximum unidirectional radiation efficiency of 97.5% can be achieved at off-Γ point. This work demonstrates significant application potential in the fields of biochemical sensing, high-performance communication, and efficient grating coupling.

Abstract:Thermal stability is a key indicator for polymeric materials. Using Terahertz Time-Domain Spectroscopy(THz-TDS) technology combined with a temperature control device, two types of thermoplastic polyurethane elastomers(TPU) solid samples are tested. At room temperature, there are differences in the terahertz absorption coefficients and refractive index values of different types of TPU. During the process of heating from 20 ℃ to 160 ℃, as the temperature increases, the terahertz absorption coefficient gradually increases, while the refractive index decreases. The turning point of the linear fit corresponds to the reported Vicat transition temperature of the material, and the sample with higher thermal stability maintains its optical constants more stable after heating and cooling. The research results indicate that terahertz spectroscopy technology can provide a new approach for detecting the thermal stability of polymeric materials.

Abstract:By revealing the THz spectral characteristics of glioma and contralateral normal brain tissue, and analyzing the spectral differences in tumor space, this study provides theoretical support for non-invasive tumor diagnosis. Using an orthotopic U87 glioma cell tumor-bearing mouse model, terahertz spectroscopy is employed to characterize the absorption properties of the glioma lesion area and the contralateral brain tissue. Single-factor Analysis Of Variance(ANOVA) and Tukey's Honestly Significant Difference(HSD) post hoc test are adopted to assess the significant differences in spectral absorption between different layers of the tumor. Immunofluorescence results show differences in cell proliferation ability and vascular density in the glioma lesion area. THz spectral analysis indicates that above 2 THz, the absorption coefficient of the tumor area is significantly higher than that of normal brain tissue, especially with the peripheral surrounding area (L(6-7)) having a higher absorption coefficient than the tumor enhancement area (L(1-2)). ANOVA analysis confirms that the spectral absorption differences between different layers of the tumor are statistically significant(p<0.05), and Tukey's HSD test further confirms the specific differences between each layer within the tumor. Homogeneity of variance test shows significant heterogeneity within the tumor layers, while the normal brain tissue area exhibits more consistent spectral characteristics. The study demonstrates that terahertz spectroscopy can effectively identify the internal heterogeneity of glioma, especially the absorption differences between the lesion center and the infiltration area, providing important evidence for noninvasive tumor diagnosis and showcasing its application potential.

Abstract:Precise diagnosis and personalized treatment of neurological diseases are crucial for improving patient outcomes. Terahertz(THz) metamaterials, due to their unique spectral properties, have become essential tools for studying different functional areas of brain tissue. THz metamaterials are employed to detect brain tissue sections, with a focus on analyzing key functional areas such as the amygdala, motor cortex, auditory cortex, hippocampus, hypothalamus, and thalamus. By measuring the resonant frequencies and amplitude changes in each area, the ability of THz metamaterials to identify different brain regions is verified. The resonant frequencies and amplitudes in each brain functional area have undergone significant changes. Among them, the hippocampus shows the largest change in resonance peak amplitude(ΔA), increasing from 7.62% to 20.35%. The motor cortex, auditory cortex, and amygdala show significant resonance frequency shifts, with a shift amount(Δf) reaching (369±4.4) GHz, while the hypothalamus shows a shift of 23.77 GHz. These differences are closely related to the biophysical properties of each brain area. The study indicates that THz metamaterials can effectively distinguish the spectral characteristics of brain functional areas.

Abstract:In response to the difficulties in setting the peak detection threshold for signal acquisition and the decrease in acquisition accuracy of satellite navigation receivers in dynamic scenarios or scenarios with weak navigation signal strength, a satellite navigation signal acquisition peak detection method based on improved Support Vector Machine(SVM) is proposed. This method first reduces the dimensionality of sample features through Principal Component Analysis(PCA), then classifies the acquisition correlation results of satellite navigation signals, and finally determines whether the navigation signal is successfully acquired by judging whether there is a peak in the correlation results. Simulation results show that, compared with existing traditional threshold setting methods, standard SVM methods, and logistic regression classification learning methods, the detection method proposed in this paper has the advantages of low false alarm rate and high true alarm rate, and the acquisition success rate is also better than existing methods.

Abstract:In the rapid development of wireless communication technology, Quadrature Amplitude Modulation(QAM) has become a key modulation technique in the fields of satellite communication and mobile communication. The research on reducing the Bit Error Rate(BER) which is a core indicator for evaluating the reliability of wireless communication systems is particularly important. To optimize QAM technology and reduce BER, an improved method of QAM is introduced. The core of this method lies in transforming the two orthogonal carrier signals in traditional QAM technology into three pseudo-orthogonal carrier signals within the same frequency band. After modulation, these three carrier signals are superimposed with a digital signal. Through this design, the amount of data carried by each signal is reduced, thereby maximizing the minimum distance between any two points in the three-dimensional space constellation diagram. This change not only enhances the noise tolerance but also effectively reduces the system's BER. To verify the effectiveness of this improved method, it is simulated and compared with Phase-Shift Keying(PSK) and traditional QAM. The simulation results show that the proposed three-channel QAM method is consistent with the expected performance, verifying its feasibility and advantages in practical applications.

Abstract:A 32-way wideband and high-efficiency power combiner operating at the W-band is designed. The component employs a hybrid E-H plane T-junction to enhance the compactness of the power combiner. To extend the combining bandwidth, ridge waveguide transmission lines are introduced, along with multi-section impedance matching steps and gradual transitions. This structure features compactness, wideband matching, and high efficiency. To validate the proposed method, a 32-way back-to-back power combiner was fabricated and tested. Experimental results show that the structure achieves a return loss better than 15 dB, an insertion loss below 1.05 dB, a relative bandwidth of 37.8%, and a combining efficiency exceeding 78.4% across the W-band. These results confirm the effectiveness of the proposed design method, demonstrating significant application potential for multi-channel power combining scenarios.

Abstract:This paper presents a compact strip-type soft surface structure, designed by introducing an aperture ring onto the foundation of the classical strip-type soft surface. Through guided wave transmission simulations, the capability of the soft surface to suppress surface waves is assessed. By analyzing the S-parameters and surface currents before and after incorporating the aperture ring, it is demonstrated that the designed soft surface in this study exhibits superior suppression capability against surface waves. Furthermore, simulations indicate the potential for electromagnetic transmission suppression at lower frequency bands, achieving structural miniaturization. When applied between microstrip antennas, the proposed soft surface can effectively suppress coupling between them. The novel miniaturized soft surface, featuring an added aperture ring on the basis of the classical strip-type soft surface, is highly symmetrical in structure with a low profile. This structure reduces the coupling between two microstrip antennas by more than 7 dB within the operating bandwidth, effectively suppressing the propagation of surface waves between the microstrip antennas and achieving decoupling effects.

Abstract:In response to the therapeutic challenges of Acute Ischemic Stroke(AIS), a wireless power supply system for sphenopalatine ganglion electrical stimulation has been designed to address the limitations of traditional treatment. By employing near-field magnetic resonance coupling technology and an S-S topology with constant current characteristics, a stable current supply is provided. To ensure stable transmission at a constant frequency, the coil coupling is controlled near the critical coupling point to suppress the frequency splitting effect. The system uses a receiving coil with a diameter of 10 mm, achieving a Power Transfer Efficiency(PTE) of 25% in air and 12% in biological tissue, and maintaining over 90% of the alignment efficiency when the receiving coil deviates from the center of the transmitting coil by 5 mm, demonstrating good misalignment tolerance. This innovative solution is expected to meet the power supply needs of sphenopalatine ganglion electrical stimulation instruments, offering new possibilities for the treatment of AIS.

Abstract:A low-profile, compact dual-band rectifier circuit is proposed, which can be used in scenarios such as radio frequency energy harvesting and microwave power transmission. The proposed rectifier circuit does not employ an impedance matching network, and achieves dual-band characteristics using only two microstrip transmission lines: the first microstrip transmission line connects the Schottky diode(HSMS-2850) with the load and filter at the rear end of the rectifier circuit; the second microstrip transmission line is a half-wavelength microstrip transmission line, adding a second frequency band to the designed rectifier circuit. This design approach not only reduces the overall size of the rectifier circuit(0.14λ₀×0.11λ₀, where λ₀ is the wavelength corresponding to the lowest frequency), but also minimizes the losses introduced by additional matching structures. After theoretical analysis, simulation, and fabrication, the measured results of the rectifier circuit are essentially consistent with the simulated results. When the input power is 0 dBm, the operating frequency bands of the rectifier circuit are 1.44~1.66 GHz(14.2%) and 3.35~3.54 GHz(5.5%), with maximum rectification efficiencies achievable within the bands being 73.7% and 69.5%, respectively.

Abstract:In order to solve the problem of significant errors in the classic Distance Vector-Hop(DV-Hop) positioning algorithm, an improved DV-Hop extension algorithm based on multi-communication radius correction for calculating unknown node positions is proposed. By grading and refining the number of hops between multiple communication radii and neighboring/beacon nodes in Wireless Sensor Network(WSN), the precise number of hops for mobile Internet of Things(IoT) sensing positioning is determined, which corrects the irregular multi-level communication radii of the network topology. The research results show that under different communication radii, the positioning error of this algorithm is reduced by about 36.78%, 10.63% and 21.15% compared to that of traditional DV-Hop, Improved Salp Swarm Algorithm DV-Hop(ISSA_DV-Hop) and Differential Evolution DV-Hop(DEDV-Hop) algorithms, respectively; under different numbers of beacon nodes, the positioning error of this algorithm is reduced by an average of about 33.17%, 15.36%, and 21.07% compared to that of the three algorithms mentioned above. This indicates that the DV-Hop correction algorithm can improve the positioning accuracy of mobile IoT sensing, reduce data errors without adding hardware, and ensure that the average hop distance of unknown nodes in WSN is more in line with the actual DV-Hop positioning algorithm and network sensing requirements.

Abstract:The implementation scheme of Convolutional Neural Network(CNN) based on Von Neumann architecture is difficult to meet the requirements of high performance and low power consumption. Therefore, a CNN accelerator based on storage-computing integrated architecture is designed. By using the circuit structure of Resistive Random Access Memory(RRAM) to realize the storage-computing integrated architecture, and using efficient data input pipeline and CNN processing unit to process large-scale image data, high-performance digital image recognition is realized. The simulation results show that the CNN accelerator has faster computing capability and its clock frequency can reach 100 MHz; in addition, the area of the structure is 300 742 μm², which is 56.6% of that of the conventional design method. The acceleration module designed in this paper greatly improves the speed and decreases the energy consumption of CNN accelerator. It shows guiding significance for the design of high performance neural network accelerator.

Abstract:Affected by the complexity of the power Internet of Things(IoT) and the stealth of terminal vulnerabilities, the traditional vulnerability correlation mining methods currently in use exhibit local biases in correlation feature parameters. This leads to insufficient overall mining scale and low global optimization efficiency of the algorithms, which severely impacts the normal operation of power IoT terminals. To address the aforementioned issues, starting from the structural characteristics of IoT, a black-box genetic algorithm is introduced. By completing the global parameter reconstruction and optimization of the overall mining method through four parts: power IoT terminal status perception, terminal vulnerability correlation mining rule generation, introduction of black-box genetic algorithm parameters, and terminal vulnerability correlation mining, the accuracy and scale of mining are enhanced. Simulation tests indicate that the mining curve values of the proposed method are relatively large, and the mean deviation index difference is 0.1. This demonstrates that the black-box genetic algorithm has high feasibility and effectiveness in the mining of security vulnerabilities in power IoT terminals, and the mining stability is sufficient to meet the current terminal vulnerability mining task requirements.

Abstract:In response to the current issue of low prediction performance in the remaining service life of electric vehicle lithium batteries, a hybrid deep learning model for predicting the remaining service life of electric vehicle lithium batteries is proposed. The model employs Empirical Mode Decomposition(EMD) to decompose battery data, forming high-frequency and low-frequency components of the battery capacity sequence. It utilizes Multilayer Long Short-Term Memory(MLSTM) and Elman neural networks to learn high-frequency and low-frequency battery capacity characteristics, extracting high-level representations of battery capacity. It combines high-frequency and low-frequency prediction results through stacking rules to achieve high-precision prediction of the battery's remaining service life. Experimental results show that the loss generated by the proposed hybrid deep learning detection model in the training set is approximately 7.87%. Compared with Support Vector Machine(SVM), Logistic Regression(LR), Recurrent Neural Network(RNN), and LSTM models, the proposed hybrid deep learning model demonstrates superior comprehensive performance indicators, with an Mean Absolute Percentage Error(MAPE) of only 1.438%. The experiments validate the effectiveness and practicality of the proposed model.