Wireless electromagnetic tracking (WEMT) technique offers full six degrees-of-freedom (6-DoF) motion capture without the line-of-sight issue, making it highly suitable for wearable devices and virtual/augmented reality. However, WEMT faces a critical challenge: magnetic field sign ambiguity caused by the inability to calculate the synchronous phase difference between the transmitter and the wireless receiver, which leads to ambiguous solutions or restricts tracking to a single quadrant. Existing solutions typically rely on auxiliary hardware, such as inertial sensors or wireless communication modules, to resolve this ambiguity, increasing system complexity. To address this issue, this study proposes an auxiliary-free dynamic phase reference synthesis (DPRS) methodology based on a synthetic virtual transmitter (SVT), which dynamically generates a phase reference for each transmitting coil from receiver measurements using time-evolving sinusoidal models. Crucially, the SVT integrates real-time calibration based on dominant signal components to suppress phase drift, thereby ensuring long-term stability without wired synchronization. Numerical simulations validate the feasibility of the proposed methodology, demonstrating robustness even under varying signal-to-noise ratios. A long-term operation experiment lasting 1 hour was conducted on a self-developed prototype, demonstrating sustained performance with maximum pose errors of 0.48mm and 0.20°. Dynamic 4-quadrant experiments achieved root-mean-square errors of 6.61mm and 0.84°. The lightweight DPRS algorithm enables broader WEMT applications in human-machine interfaces and movement sciences by resolving magnetic field sign ambiguity.

In recent years, a great deal of research has focused on in developing neural network (NN)-based speech enhancement (SE) models, which have achieved promising results. However, NN-based models typically require expensive computations to achieve remarkable performance, constraining their deployment in real-world scenarios, especially when hardware resources are limited or when latency requirements are strict. To reduce this computational burden, we propose a unified residual fusion probabilistic knowledge distillation (KD) method for the SE task, in which knowledge is transferred from a deep teacher to a shallower student model. Previous KD approaches commonly focused on narrowing the output distances between teachers and students, but research on the intermediate representation of these models is lacking. In this paper, we first study the cross-layer residual feature fusion strategy, which enables the student model to distill knowledge contained in multiple teacher layers from shallow to deep. Second, a frame weighting probabilistic distillation loss is proposed to assign more emphasis to frames containing essential information and preserve pairwise probabilistic similarities in the representation space. The proposed distillation framework is applied to the dual-path dilated convolutional recurrent network (DPDCRN), which won the championship of the SE track in the L3DAS23 challenge. Extensive experiments are conducted on single-channel and multichannel SE datasets. Objective evaluations show that the proposed KD strategy outperforms other distillation methods and considerably improves the enhancement effect of the low-complexity student model (with only 17% of the teacher's parameters).

In this study, we propose a siamese recurrent residual refinement network (SR3Net) for change detection in high-resolution remote sensing images. SR3Net uses the siamese network to fully extract multi-scale features of bi-temporal images. The obtained multi-scale feature maps are input to the multi-level difference module (MDM) to generate difference feature maps. The residual refinement module (RRM) with residual refinement blocks (RRBs) learns the residual between the intermediate change map and the ground truth by alternately exploiting low-level and high-level integrated difference features. Moreover, RRBs can obtain complementary information of the intermediate predictions and add residuals to the intermediate prediction to refine the change map. Experiments on the WHU-CD dataset show that the proposed method outperforms state-of-the-art methods.

Hearing aids aims to improve speech intelligibility for hearing impaired patients to levels comparable to those for normal hearing listeners. However, the interference of environmental noises greatly increase the difficulty of hearing loss compensation. Most related research only focuses on one aspect of noise reduction and hearing loss compensation. In this letter, we propose a metric generative adversarial framework based on a frequency-time convolution recurrent network for joint noise reduction and hearing loss compensation. The audiogram is extended along the frequency axis to form embedded features. A metric discriminator is introduced and the optimization of the generator is guided by an evaluation score related to hearing loss compensation. Additional perceptual-based losses are set to stabilize optimization. Experimental results show that the proposed method can better reduce noise and compensate for hearing loss compared with other algorithms.

In this article, we introduce a new neonatal facial expression database for pain analysis. This database, called facial expression of neonatal pain (FENP), contains 11,000 neonatal facial expression images associated with 106 Chinese neonates from two children's hospitals, i.e., the Children's Hospital Affiliated to Nanjing Medical University and Second Affiliated Hospital Affiliated to Nanjing Medical University in China. The facial expression images cover four categories of facial expressions, i.e., severe pain expression, mild pain expression, crying expression and calmness expression, where each category contains 2750 neonatal facial expression images. Based on this database, we also investigate the pain facial expression recognition problem using several state-of-the-art facial expression features and expression recognition methods, such as Gabor+SVM, LBP+SVM, HOG+SVM, LBP+HOG+SVM, and several Convolutional Neural Network (CNN) methods (including AlexNet, VGGNet, GoogLeNet, ResNet and DenseNet). The experimental results indicate that the proposed neonatal pain facial expression database is very suitable for the study of both neonatal pain and facial expression recognition. Moreover, the FENP database is publicly available after signing a license agreement (the users can contact Jingjie Yan ([email protected]), Guanming Lu ([email protected])) or Xiaonan Li ([email protected]).

In deep-learning-based speech enhancement (SE) systems, trained models are often used to handle unseen noise types and language environments in real-life scenarios. However, since production environments differ from training conditions, mismatch problems arise that may cause a serious decrease in the performance of an SE system. In this study, a domain adaptive method combining two adaptation strategies is proposed to improve the generalization of unlabeled noisy speech. In the proposed encoder-decoder-based SE framework, a domain discriminator and a domain confusion adaptation layer are introduced to conduct adversarial training. The model has two main innovations. First, the algorithm optimizes adversarial training by introducing a relativistic discriminator that relies on relative values by applying the difference, thus avoiding possible bias and better reflecting domain differences. Second, the multi-kernel maximum mean discrepancy (MK-MMD) between domains is taken as the regularization term of the domain adversarial loss, thereby further decreasing the edge distribution distance between domains. The proposed model improves the adaptability to unseen noises by encouraging the feature encoder to generate domain-invariant features. The model was evaluated using cross-noise and cross-language-and-noise experiments, and the results show that the proposed method provides considerable improvements over the baseline without an adaptation in the perceptual evaluation of speech quality (PESQ), the short time objective intelligibility (STOI) and the frequency-weighted signal-to-noise ratio (FWSNR).

The enrichment of the application scenarios of rotorcrafts presents new challenges for the study of their aerodynamic characteristics, such as operating above a building surface of finite size. In this paper, the ground effect is divided into infinite ground effect and finite ground effect, and three types of finite ground effects with different blocked area are studied. Through numerical simulations, the rotor thrust data and flow field figures in ground effect are obtained. Based on the rotor thrust data, mathematical models are established to describe the rotor thrust alteration caused by infinite and finite ground effect. The analysis of the flow field reveals the mechanism of the finite ground effect.

Automatic speech emotion recognition has been a research hotspot in the field of human-computer interaction over the past decade. However, due to the lack of research on the inherent temporal relationship of the speech waveform, the current recognition accuracy needs improvement. To make full use of the difference of emotional saturation between time frames, a novel method is proposed for speech recognition using frame-level speech features combined with attention-based long short-term memory (LSTM) recurrent neural networks. Frame-level speech features were extracted from waveform to replace traditional statistical features, which could preserve the timing relations in the original speech through the sequence of frames. To distinguish emotional saturation in different frames, two improvement strategies are proposed for LSTM based on the attention mechanism: first, the algorithm reduces the computational complexity by modifying the forgetting gate of traditional LSTM without sacrificing performance and second, in the final output of the LSTM, an attention mechanism is applied to both the time and the feature dimension to obtain the information related to the task, rather than using the output from the last iteration of the traditional algorithm. Extensive experiments on the CASIA, eNTERFACE, and GEMEP emotion corpora demonstrate that the performance of the proposed approach is able to outperform the state-of-the-art algorithms reported to date.

Facial landmark detection is a fundamental step for many tasks in computer vision such as expression recognition and face alignment. In this paper, we focus on the detection of landmarks under realistic scenarios that include pose, illumination and expression challenges as well as blur and low-resolution input. In our approach, an n-point shape of point-landmarks is represented as a union of simpler polygonal sub-shapes. The core idea of our method is to find the sequence of deformation parameters simultaneously for all sub-shapes that transform each point-landmark into its target landmark location. To accomplish this task, we introduce an agglomerate of fern regressors. To optimize the convergence speed and accuracy we take advantage of search localization using component-landmark detectors, multi-scale analysis and learning of point cloud dynamics. Results from extensive experiments on facial images from several challenging publicly available databases demonstrate that our method (ACFeR) can reliably detect landmarks with accuracy comparable to commercial soft ware and other state-of-the-art methods.

In this paper we analysis the speech emotions related to cognitive process. An automatic system is established for detecting speech emotions including anxiety, hesitation, confidence and joy. In order to obtain a naturalistic database we use noise to induce negative emotions, sleep deprivation is also used for this purpose. The lack of sleep is an important cause for anxiety. Annotation of emotional speech is then done manually with a self evaluation for the felt emotions in each utterance. Acoustic features are extracted both for valence dimension and arousal dimension including voice quality features. For the recognition algorithm Gaussian Mixture Model is adopted for detecting each type of emotions from neutral speech. Based on the detection of each emotion in the continuous recognition of emotion states an error correcting method is proposed. With the previous emotion states and cognitive performance the detection errors in current emotion state is corrected with empirical probability. Experimental results show that our system can detect "practical" speech emotions related to cognitive process. With the proposed error correcting method the recognition performance is improved compared to the baseline system based on Gaussian Mixture Model. We believe the detection of these "practical" emotions is important for real world applications, especially for helping people cope with negative emotions in cognitive activities.