Early and precise accident anticipation is critical for preventing road traffic incidents in advanced traffic systems. This paper presents a Multi-modal Architecture with Spatio-Temporal-Text Adaptation (MASTTA), featuring a Visual Encoder and a Text Encoder within a streamlined end-to-end framework for traffic accident anticipation. Both encoders leverage the CLIP model, pre-trained on large-scale text-image pairs, to utilize visual and textual information effectively. MASTTA captures complex traffic patterns and relationships by fine-tuning only the adapters, reducing retraining demands. In the Visual Encoder, spatio-temporal adaptation is achieved through a novel Temporal Adapter, a novel Spatial Adapter, and an MLP Adapter. The Temporal Adapter enhances temporal consistency in accident-prone areas, while the Spatial Adapter captures spatio-temporal interactions among visual cues. The Text Encoder, equipped with a Text Adapter and an MLP Adapter, aligns latent textual and visual features in a joint embedding space, refining semantic representation. This synergy of text and visual adapters enables MASTTA to model complex spatial interactions across long-range temporal context, improving accident anticipation. We validate MASTTA on DAD and CCD datasets, demonstrating significant improvements in both the earliness and correctness compared to state-of-the-art methods.

Anticipating traffic accidents, which involves predicting potential traffic accidents in advance, is crucial for autonomous vehicles. In this study, we introduce a novel approach that utilises Spatial and Temporal Adapters, specifically designed for image-to-video adaptation through parameter-efficient transfer learning (PEFTL) in the context of traffic accident anticipation. To fully leverage the knowledge from a pretrained CLIP Vision Transformer (CLIP-ViT), the proposed architecture incorporates lightweight Dilated Neighbourhood Attention (DNA) within Adapters. Furthermore, DNA is integrated with a cross-attention mechanism in the Temporal Adapter to capture long-range temporal dependencies. The combination of these Adapters significantly enhances spatio-temporal adaptation, addressing the limitations of existing methods in accurately identifying accident-prone areas while achieving the earliness of accident anticipation in an end-to-end manner. Extensive experiments conducted on two widespread benchmark datasets, DAD and CCD, demonstrate notable performance improvements compared to state-of-the-art works.

This paper extends the structural graph pooling scheme for video-based person re-identification (re-ID). A temporal-aware feature extractor first employs the short-term temporal correlation of the fine-grained feature maps to generate a set of multi-scale part-based CNN features. Subsequently, a spatio-temporal graph is constructed for these multi-scale part-based features. A structural graph pooling scheme is then used to extract graph features for video re-ID. Specifically, the structured graph pooling is formulated as a node clustering problem based on the structural relationships of the multi-scale part features, addressed by a novel self-attention intensified conditional random field (CRF). Different from the original structural graph pooling approach, CRF is integrated with self-attention to leverage the strength of both schemes to provide long-term structural dependencies. Thereby, it can deal with the deficiency of the existing graph-based approaches on video re-ID in learning the diverse temporal dependency of the multi-scale part features. This enables similar body part information corresponding to the person of interest to be aggregated to diminish the adverse effect of redundant and background information. Simulations on two benchmark datasets showcase the effectiveness of the new method.

This paper proposes a novel architecture for spatial-temporal action localization in videos. The new architecture first employs a two-stream 3D convolutional neural network (3D-CNN) to provide initial action detection. Next, a new Hierarchical Self-Attention Network (HiSAN), the core of this architecture, is developed to learn the spatial-temporal relationships of key actors. Spatial Gaussian priors (SGP) are also imbued to the bidirectional self-attention to enhance HiSAN in modelling the relationships of neighboring actors. Such a combination of 3D-CNN and SGP augmented HiSAN allows us to effectively extract both of the spatial context information and the long-term temporal dependency to improve action localization accuracy. Afterwards, a new fusion strategy is employed, which first re-scores the bounding boxes to settle the inconsistent detection scores caused by background clutter or occlusion, and then aggregates the motion and appearance information from the two-stream network with the motion saliency to alleviate the impact of camera movement. Finally, a tube association network based on the self-similarity of the actors’ appearance and spatial information across frames is addressed to efficaciously construct the action tubes. Simulations on four widespread datasets reveal the efficacy of the new approach.

This paper proposes a novel weakly supervised approach for anomaly detection, which begins with a relation-aware feature extractor to capture the multi-scale convolutional neural network (CNN) features from a video. Afterwards, self-attention is integrated with conditional random fields (CRFs), the core of the network, to make use of the ability of self-attention in capturing the short-range correlations of the features and the ability of CRFs in learning the inter-dependencies of these features. Such a framework can learn not only the spatio-temporal interactions among the actors which are important for detecting complex movements, but also their short- and long-term dependencies across frames. Also, to deal with both local and non-local relationships of the features, a new variant of self-attention is developed by taking into consideration a set of cliques with different temporal localities. Moreover, a contrastive multi-instance learning scheme is considered to broaden the gap between the normal and abnormal instances, resulting in more accurate abnormal discrimination. Simulations reveal that the new method provides superior performance to the state-of-the-art works on the widespread UCF-Crime and Shang-haiTech datasets.

This paper presents a new relational network for group activity recognition. The essence of the network is to integrate conditional random fields (CRFs) with self-attention to infer the temporal dependencies and spatial relationships of the actors. This combination can take advantage of the capability of CRFs in modelling the actors' features that depend on each other and the capability of self-attention in learning the temporal evolution and spatial relational contexts of every actor in videos. Additionally, there are two distinct facets of our CRF and self-attention. First, the pairwise energy of the new CRF relies on both of the temporal self-attention and spatial self-attention, which apply the self-attention mechanism to the features in time and space, respectively. Second, to address both local and non-local relationships in group activities, the spatial self-attention takes account of a collection of cliques with different scales of spatial locality. The associated mean-field inference thereafter can thus be reformulated as a self-attention network to generate the relational contexts of the actors and their individual action labels. Lastly, a bidirectional universal transformer encoder (UTE) is utilized to aggregate the forward and backward temporal context information, scene information and relational contexts for group activity recognition. A new loss function is also employed, consisting of not only the cost for the classification of individual actions and group activities, but also a contrastive loss to address the miscellaneous relational contexts between actors. Simulations show that the new approach can surpass previous works on four commonly used datasets.

Presents corrections to funding agency information for the above named paper.

This paper presents an effective two-stream convolutional neural network (CNN)-based approach to detect multiple spatial-temporal action tubes in videos. A novel video localization refinement (VLR) scheme is first addressed to iteratively rectify the potentially inaccurate bounding boxes by exploiting the temporal consistency between adjacent frames. Then, to provide more faithful detection scores, a new fusion strategy is considered, which combines not only the appearance and the flow information of the two-stream networks but also the motion saliency, the latter of which is included to address the small camera motion. In addition, an efficient multiple path search (MPS) algorithm is developed to simultaneously identify multiple paths in a single run. In the forward message passing of MPS, each node stores information of a prescribed number of connections based on the accumulated scores determined in the previous stages. A backward path tracing is invoked afterward to find all multiple paths at the same time by fully reusing the information generated in the forward pass without repeating the search process. Thus, the complexity incurred can be reduced. The simulation results show that, together with VLR and the new fusion scheme, the proposed MPS, in general, can provide superior performance compared with the state-of-the-art works on four public datasets.

This paper presents a novel Hierarchical Self-Attention Network (HISAN) to generate spatial-temporal tubes for action localization in videos. The essence of HISAN is to combine the two-stream convolutional neural network (CNN) with hierarchical bidirectional self-attention mechanism, which comprises of two levels of bidirectional self-attention to efficaciously capture both of the long-term temporal dependency information and spatial context information to render more precise action localization. Also, a sequence rescoring (SR) algorithm is employed to resolve the dilemma of inconsistent detection scores incurred by occlusion or background clutter. Moreover, a new fusion scheme is invoked, which integrates not only the appearance and motion information from the two-stream network, but also the motion saliency to mitigate the effect of camera motion. Simulations reveal that the new approach achieves competitive performance as the state-of-the-art works in terms of action localization and recognition accuracy on the widespread UCF101-24 and J-HMDB datasets.

This letter presents a novel three-stream network for action recognition in extreme low resolution (LR) videos. In contrast to the existing networks, the new network uses the trajectory-spatial network, which is robust against visual distortion, instead of the pose information to complement the two-stream network. Also, the new three-stream network is combined with the inflated 3D ConvNet (I3D) model pre-trained on kinetics to produce more discriminative spatio-temporal features in blurred LR videos. Moreover, a bidirectional self-attention network is aggregated with the three-stream network to further manifest various temporal dependence among the spatio-temporal features. A new fusion strategy is devised as well to integrate the information from the three different modalities. Simulations show that the new architecture outperforms the main state-of-the-art extreme LR action recognition methods on the HMDB-51 and IXMAS datasets.

This paper presents a convolutional neural network (CNN)-based approach for first-person action recognition with a combination of temporal pooling and the Hilbert–Huang transform (HHT). The new approach first adaptively performs temporal sub-action localization, treats each channel of the extracted trajectory pooled CNN features as a time series, and summarizes the temporal dynamic information in each sub-action by temporal pooling. The temporal evolution across sub-actions is then modeled by rank pooling. Thereafter, to account for the highly dynamic scene changes in first-person videos, the HHT is employed to decompose the ranked pooling features into finite and often few data-dependent functions, called intrinsic mode functions (IMFs), through empirical mode decomposition. Hilbert spectral analysis is then applied to each IMF component, and four salient descriptors are scrutinized and aggregated into the final video descriptor. Such a framework cannot only precisely acquire both long- and short-term tendencies, but also address the cumbersome significant camera motion in first-person videos to render better accuracy. Furthermore, it works well for complex actions for limited training samples. Simulations show that the proposed approach outperforms the main state-of-the-art methods when applied to four publicly available first-person video datasets.

To lessen the redundancy of convolutional kernels, this paper proposes a new convolutional structure, i.e., weighted kernel sharing convolution (WKSC), which gathers the inputs with the same kernel, so the inputs in each group can share the same convolutional kernel. Also, an extra weighting is imposed for each input channel before the sharing process to manifest its diversity. As a consequence, the number of kernels can be greatly reduced, leading to a reduction of model parameters and the speedup of inference. Moreover, WKSC can be combined with other existing compression models such as depthwise separable convolutions, resulting in a more compressed architecture. Extensive experiments on CIFAR-100 and ImageNet classification demonstrate the effectiveness of the new approach in both computation cost and the parameters required compared with the state-of-the-art works.

To facilitate object localization, the existing convolutional neural network (CNN)-based object detection often requires an object proposal method, which, however, may produce inaccurate region proposals and thus impact the performance. To overcome this setback, this paper presents a novel iterative localization refinement method which, undertaken at a mid-layer of a CNN architecture, progressively refines a subset of region proposals in order to match as much ground-truth as possible. In each iteration, the refinement task is cast into a probabilistic framework based on an ingeniously devised probability function. To expedite the computation of the probability function, a divide-and-conquer paradigm is developed by the theorem of total probability. Moreover, an approximate variant based on a refined sampling strategy is also addressed to further reduce the complexity. The proposed ILR method is not only data-driven and free of learning, but it can also be incorporated with many existing CNN-based object detection algorithms, such as Faster R-CNN to enhance the detection accuracy without changing their configurations. Simulations show that the proposed method can improve the main state-of-the-art works on the PASCAL VOC 2007, 2012 and Youtube-Objects data sets.

This paper presents an efficient convolutional neural network (CNN)-based multiple path search (MPS) algorithm to detect multiple spatial-temporal action tubes in videos. With the pass information and the accumulated scores generated by forward message passing, the new algorithm reuses these information to simultaneously find multiple paths in backward path tracing without repeating the search process. Moreover, to rectify the potentially inaccurate bounding boxes, we also propose a video localization refinement scheme to further boost the detection accuracy. Simulations show that the proposed algorithm provides competing performance compared with the main state-of-the-art works on the widespread UCF-101 dataset with yet lower complexity.

This paper presents a new approach for action recognition in the first-person videos which aggregates both of the short- and long-term trends based on the coefficients of the Hilbert-Huang transform (HHT), a renowned time-frequency analysis tool. In contrast to previous works like Pooled Time Series (PoT), the new scheme can extract the salient features of activities based on the non-stationary HHT analysis, which consists of empirical mode decomposition and Hilbert spectral analysis, and can be incorporated with the convolutional neural network (CNN) features such as trajectory pooled CNN features to achieve superior detection accuracy. Conducted simulations show that the proposed method outperforms the main state-of-the-art works on two widespread public first-person datasets.

Accurate region proposals are of importance to facilitate object localization in the existing convolutional neural network (CNN)-based object detection methods. This paper presents a novel iterative localization refinement (ILR) method which, undertaken at a mid-layer of a CNN architecture, iteratively refines region proposals in order to match as much ground-truth as possible. The search for the desired bounding box in each iteration is first formulated as a statistical hypothesis testing problem and then solved by a divide-and-conquer paradigm. The proposed ILR is not only data-driven, free of learning, but also compatible with a variety of CNNs. Furthermore, to reduce complexity, an approximate variant based on a refined sampling strategy using linear interpolation is addressed. Simulations show that the proposed method improves the main state-of-the-art works on the PASCAL VOC 2007 dataset.

This letter addresses a maximum likelihood (ML) algorithm for multidimensional (m-D) harmonic retrieval (MHR) problems. The new algorithm iteratively estimates the parameters in a rough to fine manner, intervened with filtering processes to separate the signals into appropriate groups. To facilitate implementations of the ML estimation, a Monte Carlo method, importance sampling (IS), and the theorem of Pincus are utilized to determine the ML estimates. Moreover, the pairing of the estimated parameters is automatically achieved without extra overhead. Conducted simulations demonstrate that the new algorithm outperforms the main state-of-the-art works and can achieve the Cramer-Rao lower bound (CRLB) even in low signal-to-noise ratio (SNR) scenarios.

This paper presents a hierarchical framework for detecting local and global anomalies via hierarchical feature representation and Gaussian process regression. While local anomaly is typically detected as a 3D pattern matching problem, we are more interested in global anomaly that involves multiple normal events interacting in an unusual manner such as car accident. To simultaneously detect local and global anomalies, we formulate the extraction of normal interactions from training video as the problem of efficiently finding the frequent geometric relations of the nearby sparse spatio-temporal interest points. A codebook of interaction templates is then constructed and modeled using Gaussian process regression. A novel inference method for computing the likelihood of an observed interaction is also proposed. As such, our model is robust to slight topological deformations and can handle the noise and data unbalance problems in the training data. Simulations show that our system outperforms the main state-of-the-art methods on this topic and achieves at least 80% detection rates based on three challenging datasets.

This paper presents a hierarchical framework for detecting local and global anomalies via hierarchical feature representation and Gaussian process regression (GPR) which is fully non-parametric and robust to the noisy training data, and supports sparse features. While most research on anomaly detection has focused more on detecting local anomalies, we are more interested in global anomalies that involve multiple normal events interacting in an unusual manner, such as car accidents. To simultaneously detect local and global anomalies, we cast the extraction of normal interactions from the training videos as a problem of finding the frequent geometric relations of the nearby sparse spatio-temporal interest points (STIPs). A codebook of interaction templates is then constructed and modeled using the GPR, based on which a novel inference method for computing the likelihood of an observed interaction is also developed. Thereafter, these local likelihood scores are integrated into globally consistent anomaly masks, from which anomalies can be succinctly identified. To the best of our knowledge, it is the first time GPR is employed to model the relationship of the nearby STIPs for anomaly detection. Simulations based on four widespread datasets show that the new method outperforms the main state-of-the-art methods with lower computational burden.

This study addresses an efficient algorithm for joint estimation of the two‐dimensional (2D) angles and polarisations of the signals impinging on a planar array of co‐centred crossed dipole pairs. Based on a novel multi‐layered signal decomposition scheme, the proposed algorithm estimates 2D angles and polarisations alternatively in a coarse‐to‐fine manner by progressively decomposing the signals into finer groups using an angle‐ or polarisation‐based beamforming process. Such a decomposition not only partitions the signals with close parameters into separate groups, but it also reduces the power of the noise, both of which can in turn enhance the estimation accuracy. Moreover, the data are properly stacked in each stage by exploiting the intrinsic data structures so that only the 1D estimation of signal parameters via rotational invariance techniques algorithms are required, thus entailing lower computational load. Conducted simulations show that the performance of the proposed algorithm is close to that of previous works, but its computational complexity is substantially lower and is free of pairing of the estimated parameters with the multi‐layered parameter estimation scheme.

This paper presents a low-complexity one-dimensional (1-D) Unitary Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT)-based algorithm for multidimensional harmonic retrieval (MHR) problems based on an HIerarchical Signal Separation (HISS) technique, which interleaves the parameter estimation and filtering processes. The filtering process not only progressively partitions the signals with close parameters into separate groups, but also reduces the power of the additive noise, both of which entail higher parameter estimation accuracy. The pairing of the estimated parameters is also automatically achieved. Simulations show that the new algorithm provides satisfactory performance compared with previous works but with drastically reduced computations.

In this paper we propose a novel subspace decomposition-based detection scheme with the assistance of soft interference cancellation in the uplink of interleaved orthogonal frequency division multiple access (OFDMA) systems. By utilizing the inherent data structure, the interference is first separated with the desired symbol and then further decomposed into the one caused by the residues of decision errors and the other one by the undetected symbols in the successive interference cancellation (SIC) process.With such an ingenious interference decomposition along with the soft processing scheme, the new receiver can render more thorough interference cancellation, which in turn entails enhanced system performance. Moreover, for practical implementations, a low-complexity version, which only deal with the principal components of inter-carrier interference (ICI), is also addressed. Conducted simulations show that the developed receiver and its low-complexity implementation can provide superior performance compared with pervious works and is resilient to the presence of carrier-frequency offsets (CFOs). The low complexity implementation, in particular, requires substantially lower computational overhead with only slight performance loss.

Abstract This paper presents a new space–time two‐stage receiver with the assistance of soft information for the Alamouti space–time block code (STBC) and spatially multiplexing (SM) combined multiple‐input multiple‐output (MIMO) systems, which possess both the advantages of high diversity gain and high data rates to entail the next generation wireless communication systems. The first stage of the receiver, utilizing the inherent structure of the STBC, consists of a bank of soft generalized sidelobe canceller (GSC)‐based detectors, each for every STBC block, and intends to yield a more precise initial estimate of the transmitted symbols. In the second stage, the groupwise detection is conducted successively by using the matched filters (MFs) to simultaneously detect the two consecutive symbols in one STBC block with the removal of the soft interferences in between. Since the interferences have been faithfully reproduced and thoroughly annihilated, the new receiver can yield accurate symbol detection even with simple MFs. Moreover, some extreme cases regarding the soft information employed in the new receiver and its extension to the multiuser (MU) MIMO downlink are addressed as well. Conducted simulations show that the developed receiver, with modest computational load, can provide superior performance compared with pervious works, especially in the MU MIMO downlink. Copyright © 2010 John Wiley & Sons, Ltd.

This study presents a two-stage power allocation for the amplify-and-forward (AF) cooperative networks with distributed generalised ABBA (GABBA) space–time codes. The new power allocation scheme first determines the transmit power between the source node and the relay nodes by maximising the instantaneous rate, and thereafter optimises the power distribution among the relay nodes via the water-filling. Also, a maximum-likelihood detection, which makes use of the encoding structure of the distributed GABBA space–time codes, is addressed to alleviate the computational overhead. Moreover, a performance analysis including the array gain and the diversity gain is also scrutinised to provide further insights into the cooperative networks considered, where the destination is equipped with multiple antennas. Conducted simulations show that the GABBA coded AF cooperative networks incorporated with the proposed two-stage power allocation can attain close or even superior performance compared with previous works but with substantially reduced computational complexity.

This study presents some joint source and relay power allocation algorithms in the amplify-and-forward (AF) relay networks using the efficacious geometric programming (GP). According to the constraints, approximate expressions of the received signal-to-noise ratio are first obtained. Thereafter, the problems are cast into appropriate GP forms according to the constraints. The power allocated to the source(s) and to the relays is then determined iteratively via the single condensation method. The new GP approach is shown to be applicable to a variety of constraints by using all of the relays for assistance and is amenable to asymmetric channels in both the single-user and multi-user AF relay networks. Conducted simulations show that the proposed power allocation schemes can attain superior performance compared with the previous works under various constraints in miscellaneous scenarios.

This study presents a simple and efficient genetic algorithm-assisted approach for joint quantised precoding and transmit antenna selection based on the criterion of maximum capacity. The objective is to alleviate the effect of multi-user interference and to reduce hardware costs, such as the cost of radio frequency chains associated with antennas in the downlink of multi-input multi-output systems with limited feedback. To avoid the enormous search effort required by existing approaches, the authors propose a novel variant of the conventional genetic algorithm, called the hybrid genetic algorithm, in which each chromosome is divided into a bit string for precoding vector selection and an integer string for transmit antenna selection. In addition, new crossover and mutation operations are employed to accommodate these new chromosomes. The results of simulations show that the performance of the proposed approach is close to that of the exhaustive search method, but its computational complexity is substantially lower.

This paper presents a novel video-based human fall detection system that can detect a human fall in real-time with a high detection rate. This fall detection system is based on an ingenious combination of skeleton feature and human shape variation, which can efficiently distinguish “fall-down” activities from “fall-like” ones. The experimental results indicate that the proposed human fall detection system can achieve a high detection rate and low false alarm rate.

This paper presents a two-stage power allocation for the distributed GABBA space-time coded amplify-and-forward (AF) cooperative networks . The new power allocation scheme first determines the transmit power between the source node and the relay nodes by maximizing the instantaneous rate, and thereafter optimizes the power distribution among the relay nodes via the watering filling. Moreover, a maximum-likelihood (ML) detection, which makes use of the encoding structure of the GABBA code, is addressed to alleviate the computational overhead. Conducted simulations show that the GABBA coded AF cooperative networks incorporated with the proposed twostage power allocation can attain close performance as the opportunistic relaying reported in the literature but with substantially reduced computational complexity.

To alleviate the interference while lowering the hardware cost such as the RF chains associated with antennas in the downlink of multiuser multi-input multi-output (MIMO) systems with limited feedback, this paper presents a simple, yet effective approach for joint precoding and transmit antenna selection with the assistance of the genetic algorithm (GA). To overcome the enormous amount of search called for, a novel variant of the conventional GA is addressed, where each chromosome consists of a bit string for the precoding vector selection and an integer string for the transmit antenna selection. A new crossover operation and a new mutation operation are also considered for the new chromosome. Conducted simulations show that the new approach yields close performance as the exhaustive search approach but with substantially reduced computational complexity.

In this paper we propose a Multiple-SIgnal-Classification (MUSIC) based algorithm for joint direction finding and delay estimation of the impinging rays in the presence of antenna mutual coupling. We show that with the addition of auxiliary sensors on both sides of the antenna array the algorithm developed earlier can be extended to this scenario. The new algorithm also consists of three stages of the one-dimensional (1-D) MUSIC algorithms which alternatively estimate the impinging directions of arrival (DOAs) and delays in a hierarchical tree structure. Moreover, a constrained temporal filtering process or a constrained spatial beamforming process are employed to partitioned progressively the signals with either close DOAs or close delays into finer subgroups, aiming at higher estimation accuracy and lower computational load. In addition, the developed algorithm proceeds in a tree structure, so the estimated parameters are automatically paired. Conducted simulation results show that the new algorithm provides satisfactory performance but with drastically reduced computations compared with previous work in the presence of mutual coupling.

This paper presents a new space-time two-stage receiver with the assistance of soft information for the Alamouti space-time block code (STBC) and spatially multiplexing (SM) combined systems. The first stage of the receiver consists of a set of soft generalized sidelobe canceller (GSC)-based detectors, each for every STBC group and is intended to yield a more precise initial estimate of the transmitted symbols. In the second stage, the groupwise detection is conducted successively using the matched filters (MF) with the removal of the soft interferences in between. Conducted simulations show that the developed receiver, with modest computational load, can provide superior performance compared with pervious works, especially in multiple user (MU)downlink MIMO scenarios.

In this paper, we propose an efficient algorithm to jointly estimate the directions of arrival (DOAs) and carrier frequency offsets (CFOs) in interleaved orthogonal frequency division multiple access / space division multiple access (OFDMA/SDMA) uplink networks. The algorithm makes use of the signal structure by estimating the CFOs and DOAs in a hierarchical tree structure, in which two CFO estimations and one DOA estimation are employed alternatively. One special feature in the proposed algorithm is that the algorithm proceeds in a coarse-fine manner with temporal filtering or spatial beamforming being invoked between the parameter estimations to decompose the signals progressively into subgroups so as to enhance the estimation accuracy and lower the computational overhead. Simulations show that the proposed algorithm can provide satisfactory performance with increased channel capacity.

This paper presents an effective multiuser detector (MUD) for the uplink of dual-signaling multiple-input multiple-output (MIMO) code division multiple access (CDMA) systems over multipath fading channels, where the data are transmitted using either the spatially multiplexing (SM) or the space-time block code (STBC) scheme. The new MUD first separates users into two groups according to their transmission signaling schemes and then alternatively detects the users in each group with the removal of iteratively refined soft information-assisted multiple access interferences (MAI) to enhance the interference cancellation capability. Moreover, for practical low-complexity implementations, the users in each group are further partitioned into smaller subgroups to reduce the the computational load. Conducted simulations show that the proposed MUD can render superior performance compared with previous approaches, especially in highly loaded scenarios.

The high-density deployment of access points (APs) and their serious mutual interference have made both frequency acquisition and data detection even more difficult in wireless local area network (WLAN). In light of this, this paper presents an antenna-array-assisted algorithm to solve above problems in a multiuser multiple-input-multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) interference network. The algorithm begins with the estimation of the channel parameters, including the frequency offsets, delays, and angle selectivity. To make a good use of the array signal characteristics, these parameters are estimated in an frequency-angle-frequency (FAF) tree structure, in which two frequency estimations and one angle estimation are employed alternatively. One special feature in the FAF tree structure is that temporal filtering or spatial beamforming is invoked between the parameter estimations to decompose the signals so as to enhance the estimation accuracy. Thereafter, based on these parameter estimates, a data detection procedure is developed to mitigate both multiple access interference (MAI) and co-channel interference (CCI). Simulations show that the proposed algorithm can provide satisfactory performance even in networks with MAIs and CCIs sharing the same frequency band.

This letter presents an effective space-time multiuser detector (MUD) with the assistance of soft information in multipath code division multiple access (CDMA) channels. The space-time MUD considered is a simple, separable spatial-temporal filter which consists of a single spatial filter and a single temporal filter. Based on the soft-decision outputs determined in the previous iteration, the soft information is then exchanged in the alternating updates of either the spatial filters or the temporal filters. Furnished simulations show that the proposed scheme can offer substantial performance improvement compared with previous works, especially in highly loaded scenarios.

We propose a compact 3-D object representation scheme that can greatly assist the search/retrieval process in a network environment. A 3-D mesh-based object is transformed into a new coordinate frame by using the Isomap (isometric feature mapping) method. During the transformation process, not only the structure of the salient parts of an object will be kept, but also the geometrical relationships will be preserved. From the viewpoint of cognitive psychology, the data distributed on the Isomap manifold can be regarded as a set of significant features of a 3-D mesh-based object. To perform efficient matching, we project the Isomap domain 3-D object onto two different 2-D maps, and the two 2-D feature descriptors are used as the basis to measure the degree of similarity between two 3-D mesh-based objects. Experiments demonstrate that the proposed method in retrieving similar 3-D models is very effective. Most importantly, the proposed 3-D mesh retrieval scheme is still valid even if a 3-D mesh undergoes a mesh simplification process.

This paper presents an uplink retransmission control scheme to improve the system throughput based on the active-user estimation capabilities offered by Kalman filtering in a multiuser single CDMA cell. The improvement is achieved by means of selecting the best subset of users with the power- controlled opportunistic retransmission control (PORC) based on the radio resources reservation and a joint PHY-MAC opportunity function, which also takes the channel information and the waiting time into account. Simulation results show that the proposed strategy exhibits significant improvement in terms of throughput and fairness.

In order to reduce hardware cost and achieve superior performance in multi-input multi-output (MIMO) systems, this paper proposes a novel scheme for joint antenna combination and symbol detection. More specifically, the new approach simultaneously determines the transformation weighting for antenna combination to lower the RF chains called for and to design the minimum bit error rate (MBER) detector to effectively mitigate the impairment due to interference. The joint decision statistic, however, is highly nonlinear and the particle swarm optimization (PSO) algorithm is employed to reduce the computational overhead. Conducted simulation results show that the new approach yields satisfactory performance with reduced computational overhead compared with pervious works.

This paper presents an effective multi-rate multiuser detector (MUD) for the uplink of single-input multiple- output (SIMO) multi-carrier code division multiple access (MC- CDMA) systems. The MUD considered is an iterative receiver which utilizes the soft information to refine the estimation of the interference to enhance the interference cancellation capability. More specifically, users with different transmission rates are classified into separate groups and, in each iteration, these groups of users are detected sequentially based on a set of minimum mean-squared error (MMSE) group detectors with the removal of multiple access interferences (MAI) group by group. Furthermore, the estimated interferences in each group, either from the same or the other groups, are refined successively with the assistance of the soft information in the symbol detection process. Conducted simulations show that the proposed MUD, with moderate computational overhead, can effectively suppress the MAI to render superior performance compared with previous works.

In this paper, a detailed performance analysis of the multiple signal classification (MUSIC) algorithm for non-resolvable sources with non-equal power is carried out. The signals considered consist of clusters of sources, in which the directions of arrival (DOAs) of the sources are close in each cluster. Only one of the source is of interest, while the others are treated as interferences. In this scenario, the estimation accuracy is influenced by both the finite sample effect and the perturbation caused by the interferences, the latter of which is the focus of this paper. By using the first order of the Taylor series expansion of the perturbation caused by the interferences, the bias of the DOAs are derived in a closed form. It is shown that if the closely spaced signals exist, the MUSIC algorithm become biased, and the bias depends on their power and the distance between their DOAs. Simulation results are also conducted to verify the derived analytic expressions.

This paper presents a fast algorithm for joint estimation of the azimuth and elevation angles, and frequencies of the incoming signals using a hierarchical space-time decomposition (HSTD) technique. Based on the HSTD, the proposed algorithm makes use of a sequence of one-dimensional (1-D) unitary estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithms to estimate these parameters alternatively in a hierarchical tree structure. Also, in between every other 1-D unitary ESPRIT, a temporal filtering process or a spatial beamforming process is invoked to partition the signals into finer groups to enhance the estimation accuracy and to alleviate the contaminated noise. Furthermore, the pairing of these parameters is automatically determined. Simulation results show that the new algorithm provides satisfactory performance but with drastically reduced computations compared with previous works. Simulation results show that the new algorithm provides satisfactory performance but with drastically reduced computations compared with previous works.

In this paper, we propose an automatic mesh decomposition technique based on principal component analysis (PCA) and Boolean operations. First, we calculate the normalized protrusion degree of each dual vertex on the smoothed 3-D mesh. The protrusion degree of a vertex and the vertex's 3-D coordinates form a 4-D feature vector, which we use to represent the polygon mesh. Since a 3-D object is composed of a large number of polygon meshes, we apply PCA to the set of 4-D feature vectors. We take the axes corresponding to the top three principal components as the three axes of a new coordinate system and project the set of 4-D vectors onto the system. Surprisingly, the projected data along the first axis reveals the salient structures of the 3-D object. Therefore, using the first component axis as the search basis, we can identify all the salient parts of an arbitrary 3-D object.

This paper presents an effective semiblind group multiuser detector (MUD) for uplink multiple-input multiple- output (MIMO) multi-carrier code division multiple access (MC- CDMA) systems. The MUD considered is a two-stage linear constrained minimum variance (LCMV) detector which uses heterogeneous information (both hard and soft) to enhance the interference cancellation capability. The first stage LCMV detector produces a tentative detection output, while at the second stage a bank of soft LCMV detectors successively refine the estimated interference group by group with the assistance of the heterogeneous information in the symbol detection process. In addition, to avoid the annoying ordering mechanism, a channel- strength criterion is employed to order the groups at the second stage. Conducted simulations show that the proposed MUD can drastically enhance the performance compared with previous works, especially when the hardware cost is at a premium.

This paper presents an effective space-time multiuser detection (MUD) with the assistance of soft information in multipath code division multiple access (CDMA) channels. The space-time MUD considered is a separable spatial-temporal filter which consists of a single spatial filter and temporal filter. The soft information determined from the previous iteration is then exchanged in the alternating updates of either the spatial or temporal filters. The conducted simulations show that the proposed scheme can offer substantial performance improvement compared with previous works, especially in highly loaded scenarios.

This paper presents an antenna-array-assisted approach to jointly estimate nominal directions of arrival (DOAs) and frequency offsets, and detect data in uplink multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) wireless networks. To achieve this, two MUltiple SIgnal Classification (MUSIC)-based algorithms are first addressed to jointly estimate the nominal DOAs and frequency offsets of the incoming rays. The first algorithm is an extension of the classic two-dimensional (2-D) MUSIC, which, however, calls for enormous amount of computations. To alleviate the computational overhead, the second algorithm estimates the nominal DOAs and frequency offsets in a space-frequency-space (SFS) tree structure, in which two S-MUSICs and one F-MUSIC are invoked alternatively to estimate the nominal DOAs and the frequency offsets, respectively. In between every other MUSIC, a spatial beamforming process and a temporal filtering process are employed to decouple the uplink signals from different transmitters, thus enhancing the estimation accuracy. Thereafter, based on the estimated nominal DOAs and frequency offsets, a data detection procedure is also addressed. Simulations show that both algorithms can provide satisfactory performance while the SFS MUSIC calls for substantially lower computational complexity.

Abstract In this paper, we present a blind and robust algorithm to jointly estimate the directions of arrival (DOAs) and propagation delays in DS/CDMA systems. The rationale of the proposed approach is a hybrid of one‐dimensional (1‐D)multiplesignalclassification (MUSIC) algorithms and constrained spatial/temporal filtering processes. As previously addressed TST MUSIC, two temporal (T)‐MUSIC and one spatial (S)‐MUSIC are employed alternatively in the proposed algorithm to estimate the group delays and the DOAs respectively. However, by utilizing the structure of the data model, no training sequences are required. Furthermore, a constrained temporal filtering process and a constrained spatial beamforming process, which minimize the filtered noise power under a set of judiciously chosen linear constraints, are now employed after the T‐MUSIC and S‐MUSIC respectively. These filtering processes aim to effectively partition the incoming rays and to be robust against the propagation errors in the tree‐structured estimation scheme so that the overall performance can be enhanced. Also, the incoming rays are grouped, isolated and estimated, and the pairing of the estimated delays and DOAs is automatically achieved without extra computational overhead. Furnished simulations show that the new approach can exhibit satisfactory performance with low complexity compared with previous works. Copyright © 2005 John Wiley & Sons, Ltd.

This paper presents a tree-structured subspace-based algorithm for joint estimation of the directions of arrival (DOAs) and frequencies of arrival (FOAs) in wireless communication systems. The proposed approach is a hybrid of one-dimensional (1-D) subspace-based algorithms and spatial/temporal filtering processes, both of which are invoked alternatively to enhance the estimation accuracy. Two temporal and one spatial 1-D subspace-based algorithms are employed alternatively to estimate the FOAs and the DOAs, respectively. Between these subspace-based algorithms, a constrained temporal filtering process and a constrained spatial beamforming process are addressed, which minimize the filtered noise power under a set of linear constraints. These filtering processes aim to effectively partition the incoming rays and to be robust against the propagation errors in the tree-structured estimation scheme so that the overall performance can be enhanced. Furthermore, the estimated FOAs and DOAs are automatically paired without extra computational overhead. Furnished simulations show that the new approach can provide comparable performance with reduced complexity compared with previous works.

This paper presents an improved tree-structured multiple signal classification (MUSIC) algorithm to jointly estimate the directions of arrival (DOA) and propagation delays in a CDMA system. The proposed algorithm makes use of one spatial (S)-MUSIC and two temporal (T)-MUSIC algorithms alternatively to estimate the group delays and the DOA, respectively. In contrast to the previous version, a constrained temporal filtering process and a constrained spatial beamforming process are addressed, which try to minimize the filtered output power under a set of judicious chosen linear constraints. Such a constrained filtering approach can effectively partition the incoming rays and suppress the propagation error in the tree-structured estimation scheme, thus in turn enhancing the overall performance. Furthermore, the pairing of the estimated DOA and delays is also automatically determined. Compared with previous works, the new approach calls for low computational complexity but exhibits superior performance, as shown in the furnished simulations.

We present a low complexity, yet high accuracy algorithm to jointly estimate the direction of arrival (DOAs) and propagation delays in the CDMA system. The proposed algorithm employs one spatial MUltiple SIgnal Classification (S-MUSIC) and two temporal (T)-MUSIC algorithms alternatively to estimate the group delays and the DOAs, respectively. Furthermore, a temporal filtering process and a spatial filtering process are also addressed to group the incoming rays so that the delays and the DOAs can be precisely estimated using the MUSIC algorithm. As such, the incoming rays are thus grouped, isolated, and estimated. Simulation results show that with such a tree-structured estimation scheme, the incoming rays can be resolved even with very close DOAs or delays.

This paper addresses a simple, yet effective, cascade of the beamforming and the multiuser detection (MUD), where the beamformer (spatial filtering) first cancels out the interferences with directions of arrivals (DOAs) different from that of the desired user and the succeeding MUD (temporal filtering) eliminates the remaining interferences whose DOAs are the same as that of the desired user. Both stages are based on constrained cyclostationarity and, thus, the proposed scheme makes use of a priori information of users and, at the same time, is free of the training sequence and near-far resistant. Furnished simulations show that the new approach yields performance close to the previously addressed jointly spatial-temporal (S-T) processing structure, but with substantially reduced computational complexity.

In this paper, we propose a novel low complexity minimum mean-squared based multiuser detection (MMSE MUD), which employs partial but essential information of the interference, in a multichannel TDMA system. The new approach begins with the determination of the effective channel length of the interference based on information theoretic criteria. We then truncate the channel taps according to the taps' power using the thus obtained effective channel length. The computational complexity of the resulting MUD is thereby reduced. Analytic studies are also carried out to provide more insight into the proposed approach. The furnished simulations show that the new MUD offers close bit-error-rate (BER) performance to the full complexity one but with substantially reduced complexity.

A multiple signal classification (MUSIC)-based approach, time-space-time MUSIC (TST-MUSIC), is proposed to jointly estimate the directions of arrival (DOAs) and the propagation delays of a wireless channel. The MUSIC for the DOA and the propagation delay estimation are referred to as the S-MUSIC and the T-MUSIC, respectively. Using the space-time characteristics of the multiray channel, the proposed algorithm in a tree structure combines the temporal filtering techniques and the spatial beamforming techniques with one S-MUSIC and two T-MUSICs. The incoming rays are thus grouped, isolated, and estimated.

A multiple signal classification (MUSIC) based approach, time-space-time MUSIC (TST-MUSIC), is proposed to jointly estimate the directions of arrival (DOAs) and the propagation delays of a wireless multipath channel. The MUSIC algorithms for the DOA and propagation delay estimation are referred to as the S-MUSIC and T-MUSIC algorithms, respectively. By using the space-time characteristics of the multipath channel, the approach combines the temporal filtering and spatial beamforming techniques along with one S-MUSIC and two T-MUSIC algorithms in a tree structure. As such, the incoming rays are grouped, isolated, then estimated in the DOA-delay domain, and the paring of the estimated DOAs and delays are automatically determined. Also, the proposed approach can resolve the incoming rays from very close DOAs or with very close delays. Furthermore, the number of antennas required can be less than that of the incoming rays. The furnished simulations justify the new algorithm.

This paper presents a novel narrowband adaptive beamformer with the generalized sidelobe canceller (GSC) as the underlying structure. The new beamformer employs the regular M-band wavelet filters in the design of the blocking matrix of the GSC, which, as justified analytically, can indeed block the desired signals as required, provided the wavelet filters have sufficiently high regularity. Additionally, the eigenvalue spreads of the covariance matrices of the blocking matrix outputs, as demonstrated in various scenarios, decrease, thus accelerating the convergence speed of the succeeding least mean squares (LMS) algorithm. Also, the new beamformer belongs to a specific type of partially adaptive beamformers, wherein only a portion of weights is utilized in the adaptive processing. Consequently, the computational complexity is substantially reduced as compared with previous approaches. The issues of choosing the parameters involved for superior performance are addressed as well. Simulation results are furnished to justify this new approach.

Modern subspace-based algorithms can offer high-resolution spectral estimates but with a cost of high computational complexity for the eigenvalue decomposition (EVD) involved. We propose a novel preprocessing scheme which can be used in conjunction with the subspace-based algorithms to alleviate the high computations previously required. The new scheme is to demodulate the input data first, and then takes the computationally efficient discrete-time Haar wavelet transform (HWT). Only the principle subband component (PSC) of the transformed data is kept for further processing, which not only retains the same amount of information but also possesses the same characteristic as that of the original (noiseless) harmonic data. The subspace-based algorithms are thus applicable to this new set of transformed data but with substantially reduced computational load. Some simulation results are provided to justify the proposed approach.

Modern subspace-based algorithms can offer high-resolution spectral estimates but with a cost of high computational complexity for the eigenvalue decomposition (EVD) involved. We propose a novel preprocessing scheme that can be used in conjunction with the subspace-based algorithms to alleviate the high amount of computations previously required. The main crux of the new scheme lies in a subband decomposition of the input data via the computationally efficient discrete-time Haar wavelet transform (HWT). Some simulation results are provided to justify the proposed approach.

We propose a new adaptive beamformer which uses the generalized sidelobe canceller (GSC) as the underlying structure. However, unlike the traditional adaptive beamformer, which uses the time-domain least mean squares (LMS) algorithm, the new one employs the wavelet-based LMS as the adaptive scheme for adjusting the system weights. The latter, as demonstrated in the literature, can indeed yield faster convergence rate as opposed to the time-domain counterpart. In addition, the new beamformer has also incorporated the spatial smoothing technique so that it can handle the case of coherent interference. The resulting adaptive beamformer, thus, admits efficient hardware implementations as the GSC. It also exhibits a fast tracking capability, and, at the same time, works well for both noncoherent and coherent interference. Simulation results are provided to verify this new structure.

In this paper, we present a new high resolution algorithm for the two-dimensional (2-D) harmonic retrieval problem, which, in particular, is noise insensitive in view of the fact that in many practical applications the contaminated noise may not be white noise. For this purpose, the approach is set in the context of higher-order statistics (HOS), which has demonstrated to be an effective approach under colored noise environment. The algorithm begins with the consideration of the fourth-order moments of the available 2-D data. Two auxiliary matrices, constituted by a novel stacking of the diagonal slice of the computed fourth-order moments, are then introduced and through which the two frequency components can be precisely determined, respectively, via matrix factorizations along with subspace rotational invariance (SRI) technique. Some simulation results are also provided to verify the proposed algorithm.

A novel artificial neural network (ANN) called the unitary decomposition ANN (UNIDANN), which can perform the unitary (Schur) decomposition of the synaptic weight matrix, is presented. It is shown both analytically and quantitatively that if the synaptic weight matrix is positive definite and normal, the dynamic equation involved will converge to a unitary matrix which can transform the weight matrix into an upper triangular one via the Schur decomposition. In particular, if the synaptic weight matrix is also Hermitian (symmetric for real case), the UNIDANN will perform the eigendecomposition. Compared with other existing ANNs, the proposed one possesses several attractive features such as being more versatile in the sense that it is capable of performing the Schur decomposition, has a low computation time and there is no synchronization problem due to the application of an of analog circuit structure, and a faster convergence speed. Some simulations with particular emphasis at the MUSIC bearing estimation algorithm are provided to justify the validity of the proposed ANN.

This paper is concerned with the development of new split algorithms for the design of linear least squares prediction filters with linear phase. The proposed fast algorithm, which fully expresses the inherent symmetry of the problem, requires lower computational complexity than other existing ones. Moreover, unlike other existing ones, the new recurrences involve only the order updates, which lend themselves to more efficient hardware implementations. For parallelization consideration, a new split Schur-like algorithm is also proposed to overcome the nonparallelizable inner product. Some numerical simulation results are provided to verify the proposed fast algorithms and highlight possible applications.

A zero-mean homogeneous random field is defined on a discrete polar raster. The problem is to estimate, given example values inside a disk of finite radius, the field's power spectral density using linear prediction. A generalized autocorrelation procedure that guarantees positive semidefinite covariance estimates (required for a meaningful spectral density) is given. It first interpolates the data using Gaussians, computes its Radon transform, and applies familiar one-dimensional techniques to each slice. Some numerical examples are provided to justify the validity of the proposed procedure. A correlation matching covariance extension procedure that uses the Radon transform is proposed to extend a given set of covariance lags to the entire plane, when this is possible. Circumstances for which this is impossible are discussed.>

Discrete generalized split Levinson and Schur algorithms for the two-dimensional linear least-squares prediction problem on a polar raster are derived. The algorithms compute the prediction filter for estimating a random field at the edge of a disk from noisy observations inside the disk. The covariance functions of the random field is assumed to have a Toeplitz-plus-Hankel structure for its radial part and its transverse part. This assumption can be shown to be closely related with some types of random fields, such as isotropic random fields. The algorithms generalized the split Levinson and Schur algorithms in two ways: (1) to two dimensions; and (2) to Toeplitz-plus-Hankel covariances.>