Oxidative stress (OS) plays a pivotal role in cancer development and progression, influencing critical processes such as cancer microenvironment modulation, immune cell dysfunction, and therapeutic resistance. While current methods for quantifying OS primarily focus on individual molecular markers or isolated pathways, they often fail to capture the complex interactions between oxidative and reductive pathways that govern cellular redox balance. In this study, we present OxStressNet, a novel computational framework designed to quantify OS levels in diverse biological contexts using only transcriptomic data. OxStressNet integrates gene-rank based enrichment analyses with a deep learning-based neural network to provide a holistic estimation of OS. First, gene set variation analysis (GSVA) is used to derive the initial activity levels for three curated gene sets representing reactive oxygen species (ROS) production, antioxidant responses, and net oxidative stress. Then, a customized neural network is employed to model the complex relationships between oxidative and reductive activity levels, learning to predict the final OS level under the constraint that OS arises from the imbalance between ROS generation and antioxidant capacity. Our approach provides a scalable and accurate method for estimating OS levels across various biological conditions, including cancer. The predictive capabilities of OxStressNet enable a range of applications, including suggestion of therapeutic strategies targeting at oxidative stress pathways and offering new insights into disease progression and potential treatment options. The code is available at https://github.com/XiaoJ327/OxStressNet.

In this paper, we consider the detection of range-spread targets with unknown steering vectors for radar systems. Based on the Rao test and Wald test, we proposed two novel adaptive detectors of range-spread targets with unknown steering vectors. And we exploit the persymmetric property of the noise covariance matrix, which enables the two proposed detectors robust in the situation of limited training data. Moreover, we exploit a series of equivalent transformations to transform the test statistics into the real domain to prove the CFAR property concisely. Finally, the Monte Carlo simulations verify the effective detection performance of the proposed detectors. We found that the novel detectors perform well in a vast number of situations.

This paper deeply explores the detection of a signal belonging to a union of subspace (UoS) in the presence of interference subspace. We propose three detectors based on the generalized likelihood ratio test (GLRT), Rao criterion, and Wald criterion using the presymmetry structure of the received data. By taking advantage of the data’s presymmetric structure, we can detect the signal more effectively. Experimental results show that the proposed detectors perform better than the traditional detectors in the presence of interference subspace, especially when the training data is insufficient. In general, this paper provides an effective method for solving the problem of signal detection with interference subspace.

This paper deals with the target detection problem in nonzero-mean compound Gaussian (CG) sea clutter with the generalized inverse Gaussian (GIG) texture. With the improvement of radar resolution, the CG distribution is adopted to model the sea clutter. Then, considering the characteristics of real sea clutter, the CG model with the GIG texture is applied. Furthermore, sea clutter signals are assumed to be nonzero-mean. A novel adaptive two-step maximum a posteriori (MAP) generalized likelihood ratio test (GLRT) detection algorithm is proposed. Firstly, the test statistic of the proposed detector with known GIG texture, mean vector (MV), and covariance matrix (CM) is derived. Secondly, replacing with the estimates of GIG texture, MV, and CM, the adaptive detector can be acquired. The numerical results indicate the performance of the proposed detector.

This letter deals with the target detection problem for polarimetric marine radar. The sea clutter is modelled as the compound Gaussian (CG) distribution with lognormal texture. We propose three detectors based on the two-step generalized likelihood ratio test (GLRT), the complex value Rao, and Wald tests by utilizing the persymmetric properties of the polarimetric speckle covariance matrix (CM). The lognormal texture component and the speckle CM are estimated by the maximuma posteriori(MAP) criterion and the polarimetric persymmetric fixed-point estimator, respectively. In addition, we provide proof of the constant false alarm rate (CFAR) properties of the designed polarimetric detectors. Moreover, we evaluate the detection performance of the proposed detectors in the simulated data and the measured sea clutter data, and the simulation results show the proposed detector outperforms the competitors more than 1dB in different situations.

Adaptive monopulse algorithm always faces low angle measurement performance in multi-interference environments with low signal-to-noise ratio. To clarify the impact factors, we analyze the performance of monopulse ratio estimation and further derive the Cramer-Rao bound (CRB) for angle estimation. Based on the analysis, a constraint-relaxed adaptive monopulse (RCAM) optimization problem is proposed, and the closed-form solution of the problem is obtained by alternative-direction-method-of-multipliers (ADMM) with fast convergence. Numerical simulations have been provided to demonstrate the effectiveness of the proposed algorithm in improving beamforming and angle estimation.

This paper addresses the problem of subspace detection in the compound Gaussian sea clutter with lognormal texture and structured interference. We proposed three novel subspace detectors by two-step maximum a posteriori (MAP) generalized likelihood ratio test (GLRT), the Rao test, and the Wald test. In the first step, we assume the texture component and speckle covariance matrix (CM) are known, and we derive the test statistics of the proposed detectors. Then, in the second step, we substitute the estimated texture component and speckle CM to obtain the adaptive detectors. Further, we exploit the persymmetric property of the speckle CM to improve the detection performance of the proposed detectors. Moreover, we prove the constant false alarm rate (CFAR) properties of the novel subspace detectors with respect to the speckle covariance matrix and the scale parameter of the texture component of the non-Gaussian sea clutter. Besides, we verify the detection performance of the proposed subspace detectors by numerical experiments in both simulated and measured sea clutter. The simulation results show that the novel subspace detectors perform better than the comparison detectors in the case of limited training data, mismatched signals, and structured interference.

This paper proposes a reduced-dimension polarization-space-time adaptive processing (RD-PSTAP) algorithm for airborne conformal array (CFA) radar to achieve better clutter suppression performance at lower computational complexities. Firstly, we take polarization information into signal modeling. Subsequently, the dimension reduction matrix is derived based on the generalized sidelobe cancellation (GSC) structure, which incorporates the polarization information of the target and clutter into constructing the main channel and auxiliary channels, respectively. Simulation results demonstrate the effectiveness of the proposed algorithm compared with the conventional RD algorithm.

This paper deals with the target detection problem in nonzero-mean compound Gaussian sea clutter with inverse Gamma texture. Considering the improvement of radar resolution and the time-varying characteristics of real sea clutter, the compound Gaussian distribution with the inverse Gamma texture is adopted to model the sea clutter. Moreover, the mean of sea clutter signals is assumed to be nonzero and unknown. The novel adaptive detector based on the two-step maximum a posteriori (MAP) generalized likelihood ratio test (GLRT) is proposed. Firstly, we derive the test statistic of the proposed detector under the condition that the inverse Gamma texture, the mean vector (MV), and the covariance matrix (CM) are assumed to be known. Secondly, the adaptive detector can be obtained by replacing them with their estimates. Simulation experiments are conducted using the synthetic nonzero-mean compound Gaussian sea clutter data. The numerical results indicate the performance of the proposed detector.

In the paper, we propose a new direction-of-arrival (DOA) estimation algorithm with low complexity. With the assumption of the independent noise, we analyze the sparse structure of the noise covariance matrix. Then, we address the sparse recovery optimization problem by using the co-sparse characteristic of signal and noise. Numerical simulation results verify the efficiency and advantage of the proposed method compared with some other existing sparse methods.

The novel near-field sources localization methods based on sparse reconstruction are presented to estimate direction-of-arrival (DOA) and range efficiently. A special covariance matrix is first constructed, which decouples the DOA and range, and then we can obtain DOA estimations by solving the MUSIC-like weighting sparse minimization problem with l2-norm. After obtaining the estimated DOAs, the related range parameters can be obtained by the same sparse reconstruction method as the DOA estimate. Furthermore, an improved weighting sparse optimization problem based on l1-norm is proposed to enhance the estimator's robustness. Finally, several simulations illustrate the advantages of the proposed algorithms by comparison.

This paper deals with the weak target detection and tracking for airborne radar in compound Gaussian with inverse Gaussian texture (IGCG) distribution sea clutter. Combine with the characteristics of IGCG distribution clutter and the airborne radar, a track-before-detect algorithm based on dynamic programming (DP-TBD) is proposed in this paper. In this algorithm, the multi-frame test statistic based on generalized likelihood ratio test (GLRT) under the track-before-detect (TB-D) framework is derived, and then the dynamic programming (DP) algorithm is used to give the specific realization method for detecting and tracking target in the range-azimuth-doppler domain. Compared with the traditional algorithms, simulation results show that the proposed algorithm can effectively improve the detection and tracking performance for airborne radar in IGCG distribution clutter.

If the pointing directions of antenna elements are inconsis-tent, the received signal of each sensor will vary due to the polarization effect, and this will influence the target detection performance. To analyze this problem, we establish an irreg-ular array structure and give a signal model for this structure. Then we propose a polarimetric detector based on two-step GLRT in Gaussian clutter background. By conducting ex-periments based on simulated clutter data, we prove that the proposed fully adaptive detector has constant false alarm rate (CFAR) property, and the detector has a better performance for low-speed target detection compared with regular array structures.

In this paper, we consider the detection problem with the polarization diversity technique in the compound Gaussian sea clutter with Gamma texture. We derive the polarimetric detector based on the maximum a posterior (MAP) and two-step generalized likelihood ratio test (GLRT). In the first step, we assume that the polarimetric clutter covariance matrix (PCCM) and the Gamma textures are known, and we develop the test statistics of the proposed detector. In the second step, we obtain the adaptive polarimetric detector by using the fixed point covariance estimator (FPCE) to estimate the PCCM and the MAP method to estimate the Gamma textures. Then we prove the constant false alarm rate (CFAR) property of the proposed adaptive polarimetric detector. Finally, we take advantage of the simulated data and the real sea clutter data to evaluate the performance of the proposed polarimetric detector.

In this paper, we address the polarization diversity detection problem in the non-Gaussian sea clutter environment. Considering the heterogeneous characteristic of the sea clutter, we model it as the compound Gaussian distribution. Based on the two-step generalized likelihood ratio test (GLRT), Rao test, and Wald test, we propose three persymmetric and polarimetric detectors. In detail, we first assume that the polarimetric clutter covariance matrix (PCCM) is known, and we develop three test statistics of the proposed detectors. In addition, we use persymmetric property to estimate the PCCM, and obtain the adaptive persymmetric detectors by inserting the estimate of PCCM into first-step test statistics. The experiments are conducted by using the simulated data and the real sea clutter data to verify the performance of the proposed persymmetric polarimetric detectors.

This paper addresses the adaptive persymmetric detection of range-spread targets problem in compound Gaussian sea clut-ter with Gamma texture. A new compound Gaussian detector based on two-step maximum a posteriori (MAP) generalized likelihood ratio test (GLRT) is developed. Specifically, the first step is to acquire the test statistic of GLRT on the as-sumption that the texture components and the clutter covari-ance matrix (CCM) are known. The second step is to employ the MAP approach to acquire the estimates of texture components and persymmetric method to obtain the estimate of CCM. Remarkably, the novel detector ensures the constant false alarm rate (CFAR) property with respect to the covari-ance matrix structure. The effectiveness of the proposed de-tector is verified by using simulated and real sea clutter data.

This paper considers the adaptive detection problem of range-spread targets in Gaussian environment. Model the Gaussian disturbance with unknown covariance matrix as an auto-regressive (AR) process with known AR order but unknown parameters. In order to solve this problem, we propose a novel generalised likelihood ratio test (GLRT)-based detector, which estimates the unknown parameters with secondary data support by maximum likelihood estimate (MLE). The simulation experiments are conducted by comparing the proposed detector with the traditional detectors on both synthetic data and real sea clutter data. And simulation results demonstrate the effectiveness of the proposed detector.

This letter addresses the persymmetric adaptive detection problem of range-spread targets in compound Gaussian sea clutter. Based on the generalized likelihood ratio test (GLRT), Rao test, and Wald test, three novel compound Gaussian detectors are developed. Specifically, the sea clutter is modeled as compound Gaussian with inverse Gaussian distribution, and the detectors are derived by the two-step maximuma posterior(MAP) procedures. In the first step, we assume the clutter covariance matrix (CCM) and the inverse Gaussian texture are known and derive the proposed detectors’ test statistics. In the second step, we use the persymmetric property and MAP criterion to estimate the CCM and inverse Gaussian texture parameters. Then the proposed detectors are proved to be constant false alarm rate (CFAR) detectors with respect to the real CCM. The simulation experiments are conducted by comparing the proposed detectors with their counterparts on both synthetic data and real sea clutter data. The numerical results indicate that the novel detectors exhibit better detection performance than their competitors.

This letter deals with the adaptive detection problem in compound Gaussian sea clutter with inverse Gaussian texture when the mismatched signal occurs. In order to reject the mismatched signal, we introduce a fictitious signal to the null hypothesis based on the adaptive beamformer orthogonal rejection test (ABORT). We adopt the two-step generalized likelihood ratio test (GLRT) criterion and develop two selective detectors, i.e., the ABORT-like compound Gaussian with inverse Gaussian (A-IGCG) detector and ABORT-like maximuma posteriori(MAP) compound Gaussian with inverse Gaussian (AM-IGCG) detector. Both the proposed detectors are proved to be constant false alarm rate (CFAR) detectors. The several experiments that compare the proposed detectors with other selective detectors and compound Gaussian detectors are conducted by using two types of real sea data, i.e., IPIX 1998 and 2019 real sea data. The numerical results indicate that the novel detectors exhibit significant tradeoffs between performances of detection and selectivity property.

This letter addresses the polarimetric target detection problem in compound Gaussian sea clutter. In view of the heavy-tailed characteristic of sea clutter, we model the sea clutter as compound Gaussian distribution with inverse Gaussian texture (IGCG). We propose three polarimetric compound Gaussian detectors based on the two-step Rao test, the Wald test, and the generalized likelihood ratio test (GLRT). Specifically, we assume that the polarimetric clutter covariance matrix (PCCM) and the inverse Gaussian textures are known in the first step, and the test statistics of the proposed polarimetric detectors are derived. In the second step, we use the training data to estimate PCCM and maximuma posteriori(MAP) to estimate the inverse Gaussian textures, and we obtain the fully adaptive detectors. Then, we give proof of the constant false alarm rate (CFAR) properties of the proposed detectors. We validate the performance of the proposed polarimetric detectors by conducting experiments based on simulated and real sea clutter data. Finally, the numerical results indicate that the proposed detectors exhibit better detection performance than their competitors and are robust when the mismatched signal occurs.

This letter presents the derivation of the generalized likelihood ratio test (GLRT)-based polarimetric detector in the non-Gaussian clutter. We model the non-Gaussian sea clutter as compound-Gaussian distribution with inverse-Gaussian texture (IG-CG), which has better goodness-of-fit for the high-resolution sea clutter. Based on the two-step GLRT criterion, we develop the test statistic of the proposed detector by assuming the texture component is known in the first step. In the second step, with the texture of the secondary data estimated as the power of the clutter, we insert the maximuma posterioriestimate (MAPE) of the texture of the primary data into the test statistic to achieve the fully adaptive detection. Furthermore, the model-based polarimetric detector, which exploits the independence between the co-polarized and the cross-polarized component, is derived. Finally, we conduct experiments with simulated clutter and real sea clutter to evaluate the performance of the proposed detector. The simulation results verify that the proposed detectors have better detection performance and the MAPE under different hypotheses leads to different levels of robustness to the mismatched signal.

This paper investigates the dual-polarimetric target detection problem in non-Gaussian sea clutter. The compound Gaussian model with inverse Gamma texture is adopted to describe the real sea clutter of which characteristics are time-varying and heterogeneous. Three novel polarimetric detectors are proposed based on the two-step maximuma posteriori(MAP) generalized likelihood ratio test (GLRT), MAP Rao, and MAP Wald criteria. Specifically, we assume the polarimetric clutter covariance matrix (PCCM) and the inverse Gamma textures are known in the first step, and the test statistics of the proposed polarimetric detectors are derived. In the second step, we take advantage of the proposed polarimetric persymmetric property and MAP criterion to estimate the PCCM and inverse Gamma textures, respectively. The fully adaptive persymmetric polarimetric detectors are obtained by substituting the estimates into the test statistics of the proposed detectors. Then the proposed detectors are proved to have the constant false alarm rate (CFAR) properties with respect to the real PCCM. The performance assessments are evaluated by contrasting the proposed detectors with their counterparts on the simulated and measured sea clutter data. The numerical results verify the performance of the proposed persymmetric polarimetric detectors.

This paper deals with the adaptive persymmetric detection problem in compound Gaussian sea clutter with inverse Gaussian texture. We derive a novel compound Gaussian detector based on the two-step generalized likelihood ratio test (GLRT). In detail, we assume the texture component and the clutter covariance matrix are known in the first step; Then we employ the maximum a posterior (MAP) and persymmetric property method to acquire the estimates of texture component and the clutter covariance matrix, respectively. The new detector is proved to be constant false alarm rate (CFAR) detector. The experiments that compare the proposed detector with the non-persymmetric detection are conducted by the simulated data and the measured data. The results reveal that the proposed detector outperforms the non-persymmetric detector in detection performance.

This paper deals with the adaptive target detection in compound Gaussian sea clutter with inverse Gaussian texture. The detectors based on two-step Rao and Wald criteria are derived, and both of the proposed detectors possess the property of constant false alarm rate (CFAR). Specifically, we assume that the texture parameter and clutter covariance matrix are known and derive the Rao and Wald tests' test statistics in the first step. Then we utilize the maximum a posteriori (MAP) method and fixed point covariance estimator (FPCE) to estimate the parameter of texture and clutter covariance matrix, respectively. Finally, we conduct the simulation experiments by comparing with its counterpart generalized likelihood ratio test (GLRT) using simulated data and real sea clutter data, i.e., IPIX 1998. The performance assessment results indicate that the proposed Rao detector outperforms the GLRT, and the proposed Wald detector holds better selectivity than GLRT.

Cancer is a rapidly evolving disease, with complex physiological changes throughout its development. Different patients of the same cancer type may require distinct treatments depending on the level of development. Hence it is essential to identify the genes and biological processes strongly associated with cancer progression to design an effective treatment plan. Our study has found that cancer samples of the same development stage (or grade, subtype) tend to share highly unique co-expression patterns, providing considerably stronger discerning power than differential expressions for cancer staging (and grading, subtyping). Based on this, we have developed a framework for identification and analyses of genes and pathways strongly associated with a cancer's development through identification of co-expression patterns that become increasingly stronger or weaker over cancer samples from early through advanced stages. Functional analyses of such co-expressed genes reveal that (1) cell-cycle, immune response, ribosome, proteasome and oxidative phosphorylation, among others, strongly associate with cancer development, (2) the co-expression patterns among ribosome, proteasome and oxidative phosphorylation genes tend to become increasingly weaker as a cancer advances, for almost all cancer types, and (3) the co-expression patterns among cell cycle and immune response genes tend to become increasingly stronger with cancer progression. We anticipate that co-expression-based analyses like we present here will become a key technique for functional studies of cancer development and evolution.

This paper deals with the grating notch cancellation problem in the uniform subarray configuration of airborne radar system. Grating notches, caused by the receive beamforming at the subarray level alone, make the main lobe distorted in the presence of signal-dependent interference. A transmit prefilter is designed to zero the radar radiant energy of the grating-lobe area to eliminate the grating notches. Numerical results verify the effectiveness of the proposed method.

This paper deals with the detection problem of airborne phased array radar in known and unknown prior spectrum knowledge scenarios. In the former case, several novel knowledge-aided (KA) detectors under the generalized likelihood ratio test (GLRT) framework are proposed, e.g., two detectors based on structured clutter covariance matrix (CCM) and two step least square (TSLS) algorithm without samples. We further present another two improved KA detectors on the basis of training data. In the latter case, we develop compressive sensing (CS) detectors, e.g., Bayesian compressive sensing (BCS) detector without using samples. We further propose block sparse Bayesian compressive sensing (BSBCS) detector with training data available. Finally, we compare the several proposed detectors with each other and numerical results indicate that the proposed detectors exhibit more significant performances than the traditional detector.

H.264/AVC adopts 9 intra 4×4 prediction modes to improve the intra frame compression efficiency. This makes the intra frame coding and transcoding very complex. In this paper, a fast intra 4×4 mode decision algorithm is proposed to reduce the intra transcoding complexity. Firstly, the surrounding modes in the high bitrate and the modes of the neighboring blocks in the current bitrate are used to form the candidate modes. Secondly, the variance and mean of the intra prediction residual are used to further reduce the size of the candidate mode set. Thirdly, the intra mode context model is built from the high bitrate modes and updated by the low bitrate modes to order the candidate modes. An early skip scheme is used to terminate the mode decision process. Experimental results show that about 50% intra 4×4 modes can be saved with a penalty on coding efficiency less than 0.1dB. The transcoding performance can even gain up to 0.6 dB on some sequences.

The rho-domain rate control algorithm is very simple and effective. There is only one parameter, thetas, used in the model. thetas reflects the relationship between the rate and the quantization step. Accurately estimating the thetas value of the I (intra) frame is important for an efficient rho-domain rate control algorithm because the I frame will influence the remaining frames in the GOP (group of pictures) greatly. In this paper, we propose to estimate the thetas value based on the energy of the frame. The energy difference of the I frames is used to predict the new thetas. A linear relationship between thetas and the energy is deduced. Experimental results show that using the energy can more accurately predict the thetas value of I frame than only using the thetas value of the previous P (predict) frame.

Traditionally, rate control consists of bit allocation and QP decision on R-QP model. In bit allocation, target bits is further confined if buffer overflows. Meanwhile, rate control should also give as smooth as possible picture quality. However, there is no explicit relationship between picture quality and encoding parameters, so the coding result on picture quality is usually unpredictable and uncontrollable. On the condition of smooth picture quality, the smooth buffer occupancy is preferable certainly. In our work, we first proposed a "window model" formulating the size of window and variations of picture quality and buffer occupancy. Thus, given the constraint on picture quality and buffer occupancy, the compliant coding result about them can be expected employing window model. Second, a window-level rate distortion (RD) model inspired by the traditional rho-domain model is introduced. Lastly, the evaluation of our proposal is presented with elaborate experiments.

H.264/AVC is the latest video coding standard developed by MPEG and ITU. AVS is the latest video coding standard developed by China. The two new video coding standards will co-exist in the market in the future. So it is necessary to transcode the AVS format to the H.264/AVC format or vice versa. Because the AVS video coding standard is based on the 8times8 block and also uses the intra prediction technology, it is possible to use the AVS intra mode information to predict the H.264/AVC intra mode. In this paper, a fast intra mode decision algorithm was proposed to reduce the computation complexity for AVS to H.264/AVC intra transcoding. The evaluation results show that the proposed algorithm can save at least 50% computation with about 0.1 dB loss in PSNR