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Depression is a mental disorder characterized by aberrant exploratory behavior. Environmental factors, such as chronic stress, are commonly used to induce depression-like behavior in rodent models. The medial prefrontal cortex (mPFC) and the basolateral amygdala (BLA) are crucial sites in subjects with chronic stress-induced depression. The transmission of amplitude information from the mPFC to the BLA was abated during exploratory behavior in depressive rats; however, the nature of the phase interaction between these two sites remains unknown. We used chronic unpredictable mild stress (CUMS) to model depression in rats and acquired local field potentials (LFPs) via multiple electrodes implanted in the mPFC and the BLA while rats (both the control and CUMS groups, respectively) were allowed to explore freely in an open field. The weighted phase lag index (WPLI) within the mPFC and the BLA and phase transfer entropy (PTE) from the mPFC to BLA were computed for two groups of rats (control and CUMS rats) to quantify the phase information transmission. Rats subjected to CUMS showed a decrease in exploratory behavior. The WPLI within the mPFC and the BLA showed strikingly higher phase synchrony at theta frequencies (4-12 Hz) than other frequency bands during exploratory behavior in both the control and CUMS groups. The results of theta PTE from the mPFC to BLA showed that PTE was significantly decreased in the CUMS group compared with the control group. These findings demonstrated that attenuated phase information transfer might restrain exploratory behavior in CUMS rats.

Light field imaging plays an increasingly important role in the field of three-dimensional (3D) reconstruction because of its ability to quickly obtain four-dimensional information (angle and space) of the scene. In this paper, a 3D reconstruction method of light field based on phase similarity is proposed to increase the accuracy of depth estimation and the scope of applicability of epipolar plane image (EPI). The calibration method of the light field camera was used to obtain the relationship between disparity and depth, and the projector calibration was removed to make the experimental procedure more flexible. Then, the disparity estimation algorithm based on phase similarity was designed to effectively improve the reliability and accuracy of disparity calculation, in which the phase information was used instead of the structure tensor, and the morphological processing method was used to denoise and optimize the disparity map. Finally, 3D reconstruction of the light field was realized by combining disparity information with the calibrated relationship. The experimental results showed that the reconstruction standard deviation of the two objects was 0.3179 mm and 0.3865 mm compared with the ground truth of the measured objects, respectively. Compared with the traditional EPI method, our method can not only make EPI perform well in a single scene or blurred texture situations but also maintain good reconstruction accuracy.

The ultrafast dynamic evolution of an atomic system under medium-strength laser fields is studied by performing transient absorption measurement. An analytical model developed from perturbation theory with a modified transition dipole moment is presented to explain the spectral features of the multilevel system. By fitting the measured absorption spectra to the model, the system's dynamic evolution is quantified by different amplitude and phase modulation factors in the pump-probe and probe-pump scenarios. This study provides a way to understand laser-matter interaction in the transition area between the strong-field and weak-field regimes.

With increasing needs for high-bitrate, ultra-reliability, spectral efficiency, power efficiency, and reducing latency in the wireless network, global studies on the sixth generation of this network began in 2020. In this paper, we will look at intelligent reconfigurable surface structure and its application in new promising physical layer technologies, such as terahertz communications and UM-MIMO systems, to support very high-bitrate and superior network capacity in the 6G wireless communications. However, terahertz communications and UM-MIMO systems are the primary research points and confront many challenges for practical implementation. They require many RF chains and create problems in terms of cost and hardware complexity which RIS can simplify hardware and reduce cost. Therefore, we will present different modeling of wireless communication systems based on RIS for different phase information. Simulation results obtained by examining SNR performance and the error probability that shows the improvement of the received signal quality. According to results, RIS-based wireless communications can become an optimized model for future wireless communication systems.

Shouted and normal speech classification plays an important role in many speech-related applications. The existing works are often based on magnitude-based features and ignore phase-based features, which are directly related to magnitude information. In this paper, the importance of phase-based features is explored for the detection of shouted speech. The novel contributions of this work are as follows. (1) Three phase-based features, namely, relative phase (RP), linear prediction analysis estimated speech-based RP (LPAES-RP) and linear prediction residual-based RP (LPR-RP) features, are explored for shouted and normal speech classification. (2) We propose a new RP feature, called the glottal source-based RP (GRP) feature. The main idea of the proposed GRP feature is to exploit the difference between RP and LPAES-RP features to detect shouted speech. (3) A score combination of phase- and magnitude-based features is also employed to further improve the classification performance. The proposed feature and combination are evaluated using the shouted normal electroglottograph speech (SNE-Speech) corpus. The experimental findings show that the RP, LPAES-RP, and LPR-RP features provide promising results for the detection of shouted speech. We also find that the proposed GRP feature can provide better results than those of the standard mel-frequency cepstral coefficient (MFCC) feature. Moreover, compared to using individual features, the score combination of the MFCC and RP/LPAES-RP/LPR-RP/GRP features yields an improved detection performance. Performance analysis under noisy environments shows that the score combination of the MFCC and the RP/LPAES-RP/LPR-RP features gives more robust classification. These outcomes show the importance of RP features in distinguishing shouted speech from normal speech.

This research paper presents a robust image registration algorithm tailored for the accurate estimation of image displacements. Image registration is a fundamental task in computer vision and image processing, with applications ranging from medical imaging to motion tracking in surveillance systems. The algorithm’s efficacy is explored through a series of experiments conducted on image pairs, both in scenarios without noise and those affected by additive noise. The algorithm’s core methodology involves a combination of techniques, including Fourier transforms, phase correlation, and subpixel estimation. By leveraging these techniques, the algorithm can simultaneously compute both the integer and subpixel components of image displacement. This capability is particularly valuable in scenarios demanding precise alignment and motion analysis. In the experiments, the algorithm’s performance is assessed using the Mean Estimation Error (MEE), which quantifies the accuracy of displacement estimation. The results reveal that the algorithm consistently achieves high precision and accuracy, even in the presence of uniform white noise with a mean of 25 and standard deviation of 15. This robustness to noise underscores its suitability for real-world applications where images are often affected by various sources of interference. The comparative analysis between noise-free and noisy scenarios demonstrates the algorithm’s resilience to adverse conditions, making it a versatile tool for image registration tasks in practical environments. Its potential applications encompass computer vision, medical imaging, security and surveillance, and high-precision image processing. The robustness of the algorithm to noise and sub-pixel accuracy makes it an asset for a wide range of applications, promising enhanced capabilities in image alignment and motion analysis.

Three-dimensional inverse scattering problems in the frequency domain are considered in the case when only the modulus of the scattered field is given, while the phase is unknown. Uniqueness theorems are proved.

Super-resolution technology is important not only in bio-related fields but also in nanotechnology, particularly in the semiconductor industry, where fine patterning is required and super-resolution is essential. However, observing microstructures beyond the diffraction limit proposed by Abbe and Rayleigh is considered impossible because of diffraction in traditional optical microscopy observation techniques. However, in recent years, it has been possible to observe microstructures beyond the Rayleigh criterion by analysing the phase distribution of light. This study investigated the physical reasons why phase analysis makes this new analysis technique possible using simulations. The results confirmed that the phase component of the zero-order diffracted light reflected from the microstructure and able to pass through the lens system contained phase information related to the shape of the measured object. Analysis of this information demonstrates the possibility of realising super-resolution based on speckle interferometry.

With the popularization of positioning technology, more and more industries have begun to pay attention to the application and demand of location information, and almost all industries can benefit from low-cost and high-precision location information. This paper introduces a novel three-dimensional (3D) low-cost, high-precision target perception algorithm that utilizes a Radio Frequency Identification (RFID) mobile reader and double tags. Initially, the Received Signal Strength (RSS) is employed to estimate the approximate position of the target along the length direction of the shelf. Additionally, double tags are affixed to the target, enabling the perception of its approximate height and depth through phase information measurements. Subsequently, the obtained rough position serves as an initial value for calibration using the proposed algorithm, allowing for the refinement of the target’s length information relative to the shelf. Simulation results demonstrate the exceptional accuracy of the proposed method in perceiving the 3D position information of the target, achieving centimeter-level sensing accuracy.

A complex-valued data-based model with pth order autoregressive errors and general real/imaginary error covariance structure is proposed as an alternative to the commonly used magnitude-only data-based autoregressive model for fMRI time series. Likelihood-ratio-test-based activation statistics are derived for both models and compared for experimental and simulated data. For a dataset from a right-hand finger-tapping experiment, the activation map obtained using complex-valued modeling more clearly identifies the primary activation region (left functional central sulcus) than the magnitude-only model. Such improved accuracy in mapping the left functional central sulcus has important implications in neurosurgical planning for tumor and epilepsy patients. Additionally, we develop magnitude and phase detrending procedures for complex-valued time series and examine the effect of spatial smoothing. These methods improve the power of complex-valued data-based activation statistics. Our results advocate for the use of the complex-valued data and the modeling of its dependence structures as a more efficient and reliable tool in fMRI experiments over the current practice of using only magnitude-valued datasets.