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A Moon-based spectral radiometer (MBSR) can continuously measure the Earth’s outward total reflection spectral radiative flux across different phases, offering valuable insights into climate studies, the search for extraterrestrial life, and the quest for habitable planets. Establishing the spectral radiation measurement model and analyzing stray radiation of MBSR are crucial for parameterizing the instrument. By utilizing a Sun-Earth-Moon geometric model, the Earth’s reflection spectral irradiance and stray radiation spectral irradiance models at the instrument’s pupil are established, enabling the evaluation of spectral radiation under various observation phases. The measurement conditions of MBSR are analyzed, and the key parameters of the instrument’s input dynamic range and external stray radiation suppression requirements are proposed. The results show that the spectral irradiance range of the Earth reflection spectrum at the entrance pupil of the instrument in the measurement band of 300-2500 nm is 0- $2.5^{\ast } 10^{-4}$ W/m2 /nm, and the external stray light inhibition ratio should be less than $10^{-9}$ . The development of a Moon-based Earth observation model and the analysis of external stray radiation play a pivotal role in supporting the design and advancement of the MBSR.

Reconfigurable intelligent surface (RIS) has received remarkable attention as a promising solution to enhance the capacity and coverage of wireless cellular networks. In this paper, we evaluate the performance of RIS-assisted communication systems in the presence of phase noise by exploring different copulas. In particular, we first analyze the cumulative distribution function and the probability density function of the signal-to-noise-ratio under phase noise in the presence of both direct and cascaded channel links. Then, we propose a copula-based solution to effectively model the non-linear dependencies among signal components induced by phase noise, and derive several exact closed-form expressions for outage probability in reconfigurable intelligent surfaces-assisted networks. In our method of analysis, we consider various copula families, including Archimedean copulas such as Farlie-Gumbel-Morgenstern, Frank, and Clayton, along with Elliptical copulas such as Gaussian and Student-T. Finally, numerical results are shown to confirm the validity of the closed-form expressions.

In this paper, we develop a unified theoretical framework for analyzing the outage performance of reconfigurable intelligent surfaces (RISs)-assisted communication systems over generalized fading channels and in the presence of phase noise. Fox’s H function theory is then utilized to derive the outage probability for various channel fading and phase noise distributions in closed-form. We further conduct an asymptotic outage analysis to obtain insightful findings. In particular, we present the maximum diversity order achievable over such channels and demonstrate the performance variation in comparison to conventional Rayleigh channels. Then, based on upper bounds and lower bounds, we propose a design criteria for RISs to achieve the maximum diversity order in the presence of phase noise. More specifically, we show that if the absolute difference between pairs of phase errors is less than $\pi /2$ , RIS-assisted communications achieve the full diversity order over independent fading channels, even in the presence of phase noise. The theoretical frameworks and findings are validated with the aid of Monte Carlo simulations.

A natural choice to model strong temporal dynamics of speech is the recurrent neural network (RNN) since it can exploit the sequential information from consecutive acoustic frames and generalizes the model well to unseen speakers. Besides, the convolutional neural network (CNN) can automatically extract sophisticated speech features that can maximize the performance of a model. In this paper, we propose a hybrid neural network model integrating a new low-complexity fully-convolutional CNN and a long short-term memory (LSTM) network, a variation of RNN, to estimate a phase-sensitive mask for speech enhancement. The model is designed to take full advantages of the temporal dependencies and spectral correlations present in the input speech signal while keeping the model complexity low. Also, an attention technique is embedded to recalibrate the useful CNN-extracted features adaptively. Furthermore, a grouping strategy is employed to reduce the LSTM complexity while keeping the performance almost unchanged. Through extensive comparative experiments, we show that the proposed model significantly outperforms some known neural network-based speech enhancement methods in the presence of highly non-stationary noises, while it exhibits a relatively small number of model parameters compared to some commonly employed DNN-based methods.

Tuberculosis (TB) is a communicable disease that is one of the top 10 causes of death worldwide according to the World Health Organization. Hence, Early detection of Tuberculosis is an important task to save millions of lives from this life threatening disease. For diagnosing TB from chest X-Ray, different handcrafted features were utilized previously and they provided high accuracy even in a small dataset. However, at present, deep learning (DL) gains popularity in many computer vision tasks because of their better performance in comparison to the traditional manual feature extraction based machine learning approaches and Tuberculosis detection task is not an exception. Considering all these facts, a cascaded ensembling method is proposed that combines both the hand-engineered and the deep learning-based features for the Tuberculosis detection task. To make the proposed model more generalized, rotation-invariant augmentation techniques are introduced which is found very effective in this task. By using the proposed method, outstanding performance is achieved through extensive simulation on two benchmark datasets (99.7% and 98.4% accuracy on Shenzhen and Montgomery County datasets respectively) that verifies the effectiveness of the method.

Sleep apnea, a severe sleep disorder, is a clinically complicated disease that requires timely diagnosis for proper treatment. In this paper, an automated deep learning-based approach is proposed for the detection of sleep apnea frames from electroencephalogram (EEG) signals. Unlike conventional methods of direct feature extraction from EEG signals, the variational mode decomposition (VMD) algorithm is utilized in the proposed method to decompose the EEG signals into a number of modes. Use of such decomposed EEG signals for feature extraction offers efficient processing of the variations introduced in the frequency spectrum during apnea events irrespective of particular patients. Afterward, a fully convolutional neural network (FCNN) is proposed to separately extract the temporal features from each VMD mode in parallel while maintaining their temporal dependencies. The FCNN block utilizes causal dilated convolutions with increasing dilation rates along with multiple kernel operations in convolutions. Subsequently, for further exploration of the inter-modal temporal variations, these extracted features from different EEG-modes are jointly optimized with a stack of bi-directional long short term memory (LSTM) layers. Hence, the trained and optimized network is capable of generating predictions of apnea frames during the evaluation phase. Contrary to other studies, this study is carried out in a subject independent manner where separate subjects are considered for training and testing. Additionally, a semi-supervised approach is explored where for facilitating better classification performance on a subject’s frames, a small portion of the patient’s data is included in training to leverage insight regarding the possible environmental variations. Extensive experimentations on three publicly available datasets provide average accuracy of 93.22%, 93.25% and 89.41% in the subject-independent cross-validation scheme.

In this paper, we investigate the uplink transmission in a satellite-aerial-terrestrial network (SATN), where an aerial platform acts as an amplify-and-forward relay assisting the communication between multiple users and satellite. The users send messages to the aerial relay via radio frequency (RF) links, which are then forwarded to the satellite through a free-space optical (FSO) link. By assuming that the angle-of-arrival based imperfect channel state information of each user is known at the aerial platform, we propose a beamforming scheme to maximize the minimum average signal-to-interference-plus-noise ratio of the users. Due to the mathematical intractability, we design an iterative algorithm to obtain the optimal beamforming vector for the RF link. Furthermore, by considering that the FSO link experiences the Málaga fading with non-zero boresight pointing error and the RF links follow Nakagami-$m$ fading, we derive an analytical expression for the outage probability of the considered SATN. Finally, computer simulation is conducted to validate our theoretical analysis. It is shown that the proposed algorithm can improve the system performance and robustness compared to existing works.

In this article, we propose a hybrid beamforming design for multiuser mmWave massive MIMO systems. We adopt a two-stage approach for designing the analog and digital beamforming separately. The analog beamforming design is based on a constrained low-rank channel decomposition and aims to simultaneously harvest the array gain and reduce the intra- and inter-user interference. The digital beamforming design is conducted by using the regularized channel diagonalization method, which provides a better trade-off between multiuser interference suppression and transmit diversity, thus attaining a better performance in low-SNR scenarios or when communicating to many users or through many data streams. We validate the effectiveness of the proposed design through numerical simulations, which have shown that our design outperforms several other hybrid beamforming designs in the literature.

The progression of a disease associates with changes in genomic activity, but it remains a challenge to screen genetic biomarkers for clinical applications. The disease progression, in dynamic network methods (DNM), can be analogous to an animated film composed of discrete frames, where each frame represents a temporary state of the time-varying gene-gene interaction network. The major shortage therein is that the transition between two neighboring temporary states was beyond investigation. Here, we develop an updated computational methodology named after VD-analysis. Because single-gene biomarkers were not approved capable of representing a complex biological process, we firstly introduce V-structure — a gene module composed of three genes and two interactions among them — and define it as unit module. We then identify the perturbed pathways that mark the disease progression, followed with the V-structures identified which drive the pathway perturbations. Such driver V-structures can be taken as eligible biomarkers for clinical applications. To test the feasibility of this method, we apply it to a time course dataset of gene expression related to mouse type-II diabetes (T2D). Result indicates that the whole process of T2D is exactly divided into 3 stages and that the driver V-structures inferred for each stage are qualified biomarkers. In summary, our method contributes to the description of dynamic disease progression and the V-structure biomarkers facilitate the treatments of disease.

Adaptive beamforming techniques are widely known for their capability of leveraging the performance of antenna arrays. The effectiveness of such techniques typically increases as the number of antennas grows. In contrast, computational and hardware costs very often limit the deployment of beamforming in large-scale arrays. To circumvent this problem, antenna selection strategies have been developed aiming to maintain much of the performance gain obtained by using a large array while keeping computational and hardware costs at acceptable levels. In this context, the present paper is dedicated to the development of two new adaptive algorithms for solving the problem of joint antenna selection and beamforming for uplink reception in mobile communication systems. Both algorithms are based on an alternating optimization strategy and are designed to operate with a limited number of radio-frequency chains. The main difference between the proposed algorithms is that the first is formulated by considering the minimum mean-square error (MMSE) criterion, while the second is based on the minimum-variance distortionless-response (MVDR) approach. The numerical simulation results confirm the effectiveness of the proposed algorithms.

In this paper, a new speech enhancement method is proposed based on dictionary learning and low-rank matrix decomposition with the objective to improve speech quality and intelligibility. In both the learning stage and the enhancement stage of the proposed method, a new noise model is employed to capture the noise characteristics with a combination of a low-rank matrix and an overcomplete noise dictionary. In the learning stage, an unsupervised dictionary learning algorithm is first proposed based on this new noise model to train a noise dictionary with low mutual coherence to the clean speech. Then, a supervised low-rank matrix decomposition algorithm is proposed to extract the low-rank component of noise using the clean speech, noise, and noisy speech training data sets. The estimated low-rank matrix is clustered through the K-means method to generate a codebook which provides useful information for the enhancement stage. Finally, in the enhancement stage, by using the well-trained dictionaries and the reference codebook from the learning stage, a decomposition algorithm for the low-rank matrix and sparse component is developed to effectively estimate the clean speech from the noisy speech. The experimental results show that our proposed method achieves better enhancement performance than some state-of-the-art reference methods in terms of four objective performance evaluation measures especially in low signal-to-noise ratio adverse environments.

To treat the diseases or injuries of the joints, bones, muscles, and spine in both adult and pediatric imaging the musculoskeletal radiographs bring a significant depth of expertise. Abnormality detection in the musculoskeletal study is backbreaking as more than 1.7 billion people are affected by musculoskeletal condition (BMU, 2017). Hence if we want to create enough opportunity to treat a maximum amount of patients, machine learning and deep learning can play a crucial role. CNN is an excellent deep learning method for image classification and other computer vision tasks. But CNN has exhibited some serious limitations when the images are rotated and deformed. Hence capsule network architecture is introduced in this paper for musculoskeletal radiographs abnormality detection and this capsnet architecture has shown very promising features that can help to vanquish the limitations of CNN. In addition, this capsule network has scored 10% higher kappa score than 169 layer densenet using less training data in the case of musculoskeletal radiographs abnormality detection. This feature of capsule network can help to use deep learning in such cases where an aggregate of a large amount of data is not possible. For image quality investigation, blind image spatial quality evaluator (BRISQUE) and naturalness image quality evaluator (NIQE) scores are measured and it is found that when the pixel size of the resized images are more close to the pixel size of the original images, we get a better approximation. Hence in the case of musculoskeletal radiographs abnormality detection, our method outperforms state-of-the-art method using a less amount of training data.

The wireless communications is studied for the intelligent connected vehicles (ICV) networks by supporting heterogeneous access with direct access of one vehicle yet the dual-hop access of another vehicle. The dual-hop access is enhanced by one dedicated vehicle as relaying vehicle with multiple antennas. The goal in this paper is to joint design of both receive beamforming and transmit beamforming at the relaying multi-antenna vehicle to suppress the interference between the two wireless receive vehicles yet match the dual-hop wireless channel as much as possible. The energy efficiency is optimized as the objective function under the constraint of the limited power over every beamforming vector. Due to the difficulty of the optimization, the principle of the signal to leakage plus noise ratio (SLNR) is introduced to the studied ICV networks and thus to obtain the analytical expression of the beamforming vectors. With the derived closed form over all the beamforming vectors, an iterative algorithm is developed for jointly optimizing all the beamforming vectors to maximize the energy efficiency of the whole system, which is guaranteed to be convergent to one local optimum. Numerical simulations show the good performances of the proposed method in ICV networks.

We report on comprehensive research regarding the potential of phase-conjugate wave (PCW) generation via backward degenerate four-wave mixing process as a pulse cleaner in femtosecond high-power laser systems. We analyzed the performance of this technique by investigating the contrast enhancement capability, gain bandwidth, and energy conversion efficiency. All of these parameters are important in pulse cleaning. The numerical calculations verified that a considerably large efficiency bandwidth product and an excellent contrast enhancement capability can be realized by finding a balance between each physical effect. The simulation results based on this study provide guidance for the application of this technique in laser systems with different bandwidths. The method, which is based on PCW generation, provides new insights for femtosecond petawatt laser systems as pulse cleaners.

This paper investigates the energy efficiency (EE) maximization problem with secrecy constraints for a multi-user underlay cognitive radio network (CRN), where each single-antenna cognitive user is surrounded by a multi-antenna eavesdropper user. Specifically, we aim to maximize the EE of the cognitive base station (CBS), which is defined as the ratio of the achievable rate to total power consumption, under the quality-of-service requirement of primary user, the secrecy rate (SR) constraint of CRN and the transmit power limitation of CBS. The formulated optimization problem is nonconvex and nontrivial due to the fractional form of EE and the subtractive form of SR. To solve this complex non-convex problem, we first convert the original fractional form of the EE into an equivalent subtractive form, and then adopt the difference of two-convex functions (D.C.) method to approximate the subtractive form of SR into a convex one. Finally, a convergent iterative algorithm is proposed to obtain the optimal solution of the considered EE maximization problem. Numerical results showing the effectiveness of the proposed algorithm are given.

In this paper, we investigate a covariance matrix reconstruction approach (CMRA) for direction-of-arrival (DOA) estimation in correlated/coherent sources scenario. We incorporate a spatial filtering (SF) model into our recently developed method CMRA in order to enhance its adaptation ability. In particular, a sliding window scheme is proposed to estimate the number of sources, and an iterative procedure is provided to estimate the DOAs of the signals. Since the original CMRA provides inaccurate estimate of the noise power which is undesirable during iterations, a new update rule for the noise power is proposed. Moreover, we derive a fast implementation of the SF-CMRA to accelerate the DOA estimation in each iteration. The proposed methods are suitable for both uniform and sparse linear arrays and are able to provide accurate estimates of DOAs, signal powers, and noise power. We also show that the proposed algorithmic framework can be easily extended to other gridless DOA estimation methods for accuracy improvement. Simulation results are provided to illustrate the superiority of our proposed methods.

Electroencephalogram (EEG) is getting special attention of late in the detection of sleep apnea as it is directly related to the neural activity. But apnea detection through visual monitoring of EEG signal by an expert is expensive, difficult, and susceptible to human error. To counter this problem, an automatic apnea detection scheme is proposed in this paper using a single lead EEG signal, which can differentiate apnea patients and healthy subjects and also classify apnea and non-apnea frames in the data of an apnea patient. Each sub-frame of a given frame of EEG data is first decomposed into band-limited intrinsic mode functions (BLIMFs) by using the variational mode decomposition (VMD). The advantage of using VMD is to obtain compact BLIMFs with adaptive center frequencies, which give an opportunity to capture the local information corresponding to varying neural activity. Furthermore, by extracting features from each BLIMF, a temporal within-frame feature variation pattern is obtained for each mode. We propose to fit the resulting pattern with the Rician model (RiM) and utilize the fitted model parameters as features. The use of such VMD-RiM features not only offers better feature quality but also ensures very low feature dimension. In order to evaluate the performance of the proposed method, K nearest neighbor classifier is used and various cross-validation schemes are carried out. Detailed experimentation is carried out on several apnea and healthy subjects of various apnea-hypopnea indices from three publicly available datasets and it is found that the proposed method achieves superior classification performances in comparison to those obtained by the existing methods, in terms of sensitivity, specificity, and accuracy.

In this paper, we conduct large system analysis of heterogeneous cellular networks (HCNs) which are modeled as a two-tier cellular network consisting of one macro base station (MaBS) and multiple micro base stations (MiBSs) distributed according to a Poisson point process. Interference alignment is performed via rank constrained rank minimization (RCRM) to design the transmit precoding and receive beamforming matrices of the users in HCNs for the suppression of interference signal. We then analyse the average achievable rate for both macro users (MaUEs) and micro users (MiUEs) with an objective of obtaining a closed-form expression. It is shown that the MaUE and MiUE rate expressions are functions of the transmit power, noise power, MiBS density, and the transmit distance. Extensive simulations show that the rate expressions derived through large system analysis provide accurate estimates of the average achievable rates of HCNs with different system parameters.

In this paper, we investigate a tradeoff between the secrecy rate (SR) and energy efficiency (EE) in an underlay cognitive radio network that consists of a cognitive base station (CBS), a cognitive user (CU), a primary user (PU), and multiple eavesdroppers (EDs). By using a so-called secrecy EE (SEE), which is defined as the ratio of SR to the total power consumption of CBS, as the design criterion, we formulate an SEE maximization (SEEM) problem for the CBS-CU transmission under the constraints of the transmit power of CBS, the SR of CU, and the quality-of-service (QoS) requirement of PU. Since the formulated optimization problem with a fractional objective function is non-convex and mathematically intractable, we first convert the original fractional objective function into an equivalent subtractive form, and then develop a method of combining the penalty function and the difference of two-convex functions (D.C.) approach to obtain an approximate convex problem. Based on this, an optimal beamforming (OBF) scheme is finally proposed to obtain the optimal solution. Furthermore, to reduce the computational complexity, we design a zero-forcing-based beamforming (ZFBF) scheme to achieve a sub-optimal solution to the SEEM problem. Simulation results are given to illustrate the effectiveness and advantage of the proposed SEE oriented OBF and ZFBF schemes over conventional SR maximization and EE maximization schemes.