As a low-rank regularization technique, the tensor nuclear norm (TNN) has been extensively employed for hyperspectral anomaly detection (HAD). However, traditional TNN and its variants often suffer from rank estimation bias and are difficult to effectively capture the spectral-spatial correlation of complex backgrounds. In addition, the extracted sparse component is often submerged in the background, resulting in abnormal targets being insignificant. To overcome these problems, this letter proposes an enhanced low-rank and joint saliency prior (ELRJSP) method for HAD. Specifically, within the framework of tensor singular value decomposition (t-SVD), we propose an improved weighted tensor nuclear norm (IWTNN), defined as a weighted combination of the weighted tensor nuclear norm (WTNN) and the weighted nuclear norm of the core matrix. This novel norm effectively integrates information from both the original background tensor and its core tensor, thereby leveraging spectral-spatial structural characteristics more comprehensively and leading to enhanced accuracy in background estimation. Furthermore, in order to accurately detect and highlight abnormal targets, under the constraint of the tensor ℓF,1-norm, a visual saliency sparse weight tensor to constrain the abnormal tensor is designed by combining the visual saliency weight graph and the sparse optimized weight graph and extending them to the tensor domain. Comparative experiments on three real hyperspectral datasets demonstrate that the developed ELRJSP algorithm outperforms several advanced algorithms.

This paper studies enhanced physical-layer key generation (PKG) for multiuser orthogonal frequency division multiple access (OFDMA) networks. In practical OFDMA systems, our key observation is that there are frequency correlations between different subcarriers which potentially lead to compromised randomness of the generated keys as well as reduced sum secret key rate. Motivated by this, we show that subcarrier allocation plays a key role in enhancing the PKG performance in OFMDA networks. We prove that when a single user terminal selects a finite number of subcarriers for key generation, adopting uniformly spaced subcarriers is the optimal solution as it leads to higher secret key rates and better randomness. Moreover, we derive a closed-form expression for the sum secret key rate and introduce a low-complexity near-optimal algorithm that can achieve an appropriate subcarrier allocation policy in a timely manner. Simulation results show that our proposed near-optimal algorithm exhibits significant advantages in maximizing the sum secret key rate and improving key randomness compared with existing subcarrier allocation algorithms.

Sketches are useful for network measurement thanks to their low resource overheads and theoretically bounded accuracy. However, their network-wide deployment suffers from the trade-off between optimality and scalability: (1) Most solutions rely on mixed integer linear programming (MILP) solvers to provide the optimal decisions. But they are time-consuming and can hardly scale to large-scale deployment scenarios. (2) While heuristics achieve scalability, they deteriorate resource and performance overheads. We propose Eagle, a framework that achieves scalable and near-optimal network-wide sketch deployment. Our key idea is to decompose network-wide sketch deployment into sub-problems. Such decomposition allows Eagle to (1) simultaneously optimize switch resource consumption and end-to-end performance (retaining optimality), and (2) incorporate time-saving techniques into sub-problem solving (achieving scalability). Compared to existing solutions, Eagle improves scalability by up to 255× with negligible loss of optimality. It has also saved administrators in a production network days of efforts and reduced the operation time from O(hour) to O(second).

Models based on low-rank and sparse decomposition (LRaSD) have been rapidly developed in the hyperspectral anomaly detection (HAD) task. However, traditional LRaSD models usually impose multiple complex regularizers on the background components, which inevitably increases the computational cost and fails to maximize the effectiveness between them. In addition, regularizers for abnormal components mostly penalize each pixel with the same intensity. To tackle these challenges, we propose a model based on enhanced 3-D total variation (TV) and sparse reweighted regularization, referred to as E-3DTVSR. Specifically, an enhanced 3-D TV (E-3DTV) regularization is adopted to simultaneously characterize the low-rank and piecewise smoothness of the background. Since E-3DTV applies sparse regularization to subspace base maps of gradient maps along all bands of a hyperspectral image (HSI), rather than the gradient maps themselves, this effectively removes noise and improves the detection efficiency of the model. Meanwhile, in order to enhance the sparsity of abnormal targets and distinguish sparse nonabnormal pixels, combined with the log-sum function and the reweighted$\ell _{1}$minimization strategy, a sparse reweighted regularization is designed to adaptively assign weight to each target. Experiments demonstrate that E-3DTVSR reaches the area under the ROC curve (AUC) scores of 99.86%, 99.59%, and 99.72% on three public HSI datasets, respectively, outperforming the current advanced approaches.

In recent years, tensor representation (TR) based hyperspectral anomaly detection approaches have attracted more and more attention. However, two urgent issues still need to be addressed: 1) existing tensor decomposition approaches for hyperspectral anomaly detection (HAD) cannot make full use of the spectral-spatial correlation of background components in hyperspectral images (HSIs); 2) most approaches based on TR overlook the piecewise-smooth of background components that exist simultaneously in the spectral and spatial domains. To this end, with the aid of an advanced multi-scale entanglement renormalization ansatz (MERA) tensor network, this paper proposes an algorithm based on MERA decomposition and enhanced total variation regularization (MERAETV) for HAD. Specifically, MERA decomposes the background tensor by contracting a top-level factor with the remaining semi-orthogonal and orthogonal factors. Due to the intricate interplay between semi-orthogonal (low-rank) and orthogonal factors, low-rank MERA approximation exhibits a robust representational capacity that effectively captures the spectral-spatial correlation of the background component. Meanwhile, an enhanced total variation (ETV) regularization is devised to capture the inherent piecewise-smooth of the background component in both spectral and spatial domains. Furthermore, our algorithm incorporates group sparsity constraint and Gaussian noise term to enhance the discrimination between anomalies and background. Finally, a highly efficient update scheme based on the alternating direction method of multipliers (ADMM) is designed. A large number of experiments on one synthetic and seven real HSIs demonstrate the superiority of our proposed approach.

Network administrators usually realize network functions in data plane programs. They employ the network-wide program deployment that decomposes input programs into match-action tables (MATs) while deploying each MAT on a specific switch. Since MATs may be deployed on different switches, existing solutions propose the inter-switch coordination that uses the per-packet header space to deliver crucial packet processing information among switches. However, such coordination incurs non-trivial per-packet byte overhead, leading to end-to-end performance degradation. We propose, a framework that aims to minimize the per-packet byte overhead. The key idea is to formulate network-wide program deployment as a mixed-integer programming (MIP) problem with the objective of minimizing the per-packet byte overhead. Also, offers a greedy-based heuristic that solves the problem in a near-optimal and timely manner. We have implemented on Tofino switches. Compared to existing frameworks, decreases the per-packet byte overhead by 156 bytes while preserving end-to-end performance in terms of flow completion time and goodput.

Offloading network functions (NFs) to data plane switches brings remarkable performance benefits. In such offloading, NFs are required to process all the flows of interest (i.e., full coverage) to preserve the quality of services. However, existing solutions fail to guarantee full coverage for NFs. Thus, NFs may miss some essential flows, leading to accuracy drops. In this paper, we propose Halia, a framework that makes NF offloading decisions while ensuring full coverage for NFs. Specifically, Halia formulates the problem of NF offloading as an optimization problem. It encodes the requirement of full coverage as a constraint. Thus, its decisions activate enough NF instances in the substrate network to achieve full coverage.. We have implemented Halia and conducted experiments under multiple realistic network topologies to evaluate Halia. The experimental results indicate that compared to existing solutions, Halia achieves full coverage and high scalability in large-scale networks.

Some existing anomaly detection methods convert a 3-D hyperspectral data cube into a 2-D matrix, which inevitably destroys the spatial-spectral structure information of the hyperspectral data, resulting in the degradation of detection performance. In this paper, we propose a tensor low-rank sparse representation learning (TLRAD) method for hyperspectral anomaly detection (HAD), which can effectively maintain the spatial-spectral structure of raw hyperspectral data. Specifically, based on tensor low-rank representation (TLRR) learning, both low-rank constraints and sparsity constraints are simultaneously imposed on the coefficient tensor to capture the global and local spatial-spectral structure information of hyperspectral image (HSI), respectively. For anomaly tensor, the tensor ℓ21-norm is applied to encourage the group sparsity of anomalous pixels. Furthermore, the tensor robust principal component analysis (TRPCA) approach is utilized to construct a robust background dictionary tensor. Experimental results gained employing two real hyperspectral datasets prove the superiority of the proposed approach compared to some state-of-the-art algorithms.

The programmable switch offers a limited capacity of packet header vector (PHV) words that store packet header fields and metadata fields defined by network functions. However, existing switch compilers employ inefficient strategies of encoding fields on PHV words. Their encoding wastes scarce PHV words and may result in failures when deploying network functions. In this paper, we propose Melody, a new framework that reuses PHV words for as many fields as possible to achieve resource-efficient PHV encoding. Melody offers a field analyzer and an optimization framework. The analyzer identifies which fields can reuse PHV words while preserving the original packet processing logic. The framework integrates analysis results into its encoding to offer the resource-optimal decisions. We evaluate Melody with production-scale network functions. Our results show that Melody reduces the consumption of PHV words by up to 85%.

Nowadays, some tensor-based hyperspectral anomaly detection (HAD) approaches are still insufficient in utilizing the spatial-spectral structure information of hyperspectral images (HSIs), resulting in the inability to isolate the background and abnormal targets well. In this letter, an enhanced tensor low-rank representation learning (ETLR) model is proposed for HAD. Specifically, the original 3-D hyperspectral image (HSI) data is firstly decomposed into a structural background component, an anomaly component and a noise component. Among them, with the help of multi-subspace learning technology, the structural background component is reformulated by the t-product of the background dictionary tensor and the corresponding coefficient tensor. Then, tensor nuclear norm (TNN) is adopted to preserve the global low-rank property of the background component in both spatial and spectral dimensions. For the abnormal component, an ℓ2,1,1-norm is designed to enhance the group sparsity of abnormal pixels. For the noise component, a tensorF-norm constraint is imposed to suppress the confusion of noise and anomalies. Meanwhile, a robust dictionary tensor that can adequately characterize the background is constructed by using tensor robust principal component analysis (TRPCA). Furthermore, to reduce the interference of redundant information on detection accuracy, the optimal clustering framework (OCF) method is utilized for band selection. Finally, extensive experiments on one simulated and three real HSI datasets confirm that our algorithm is superior than current HAD algorithms.

Recently, hyperspectral anomaly detection (HAD) methods based on tensor low-rank representation (TLRR) have received widespread attention. However, most of them tend to emphasize the utilization of multiple types of prior knowledge to characterize background components, while the prior information about anomaly components is limited. Additionally, the constructed background dictionary is also susceptible to noise and outliers. To address these challenges, this paper focuses on both the background and abnormal components, proposing a robust tensor low-rank sparse representation with saliency prior (RTLSR-SP) method for HAD. Specifically, for the background component described by the dictionary tensor and the corresponding coefficient tensor, tensor nuclear norm (TNN) constraint and sparsity constraint are imposed on the coefficient tensor simultaneously to capture the global and local spatial-spectral structure information of the hyperspectral image (HSI), respectively. For the anomalous component, we design a sparse saliency prior weight tensor to enhance the saliency of anomalous targets. Meanwhile, the tensor ℓF,1-norm is also integrated into the model to better separate abnormal targets from the background. Furthermore, combining tensor robust principal component analysis (TRPCA) and skinny tensor singular value decomposition (skinny t-SVD), a robust background dictionary is constructed. Finally, an efficient iterative algorithm based on the alternating direction method of multipliers (ADMM) is derived to optimize the RTLSR-SP model. Comprehensive experimental findings on one simulated dataset and six real hyperspectral datasets demonstrate the effectiveness and superiority of the proposed algorithm compared with eight state-of-the-art HAD algorithms.

In modern networks, administrators realize their desired functions such as network measurement in several data plane programs. They often employ the network-wide program deployment paradigm that decomposes input programs into match-action tables (MATs) while deploying each MAT on a specific programmable switch. Since MATs may be deployed on different switches, existing solutions propose the inter-switch coordination that uses the per-packet header space to deliver crucial packet processing information among switches. However, such coordination introduces non-trivial per-packet byte overhead, leading to significant end-to-end network performance degradation. In this paper, we propose Hermes, a program deployment framework that aims to minimize the per-packet byte overhead. The key idea of Hermes is to formulate the network-wide program deployment as a mixed-integer linear programming (MILP) problem with the objective of minimizing the per-packet byte overhead. In view of the NP hardness of the MILP problem, Hermes further offers a greedy-based heuristic that solves the problem in a near-optimal and timely manner. We have implemented Hermes on Tofino-based switches. Our experiments show that compared to existing frameworks, Hermes decreases the per-packet byte overhead by 156 bytes while preserving end-to-end performance in terms of flow completion time and goodput.