Search Results (22)

Accurate heading direction tracking is essential for immersive VR/AR, spatial audio rendering, and robotic navigation. Existing IMU-based methods suffer from drift and vibration artifacts, vision-based approaches require LoS and raise privacy concerns, and RF techniques often need dedicated infrastructure. We propose UDirEar, a COTS UWB device-based system that estimates user heading using solely high-level UWB information like distance and unit direction. By initializing an EKF with each user’s constant interaural distance, UDirEar compensates for the earbuds’ roto-translational motion without additional sensors. We evaluate UDirEar on a step-motor-driven dummy head against an IMU-only baseline (MAE 30.8°), examining robustness across dummy head–initiator distances, elapsed time, EKF calibration conditions, and NLoS scenarios. UDirEar achieves a mean absolute error of 3.84° and maintains stable performance under all tested conditions. Full article

Wearable laser Doppler devices are susceptible to complex noise interferences, such as Gaussian white noise, baseline drift, and motion artifacts, with motion artifacts significantly impacting clinical diagnostic accuracy. Addressing the limitations of existing denoising methods—including traditional adaptive filtering that relies on prior noise information, modal decomposition techniques that depend on empirical parameter optimization and are prone to modal aliasing, wavelet threshold functions that struggle to balance signal preservation with smoothness, and the high computational complexity of deep learning approaches—this paper proposes an ISSA-VMD-AWPTD denoising algorithm. This innovative approach integrates an improved sparrow search algorithm (ISSA), variational mode decomposition (VMD), and adaptive wavelet packet threshold denoising (AWPTD). The ISSA is enhanced through cubic chaotic mapping, butterfly optimization, and sine–cosine search strategies, targeting the minimization of the envelope entropy of modal components for adaptive optimization of VMD’s decomposition levels and penalty factors. A correlation coefficient-based selection mechanism is employed to separate target and mixed modes effectively, allowing for the efficient removal of noise components. Additionally, an exponential adaptive threshold function is introduced, combining wavelet packet node energy proportion analysis to achieve efficient signal reconstruction. By leveraging the rapid convergence property of ISSA (completing parameter optimization within five iterations), the computational load of traditional VMD is reduced while maintaining the denoising accuracy. Experimental results demonstrate that for a 200 Hz test signal, the proposed algorithm achieves a signal-to-noise ratio (SNR) of 24.47 dB, an improvement of 18.8% over the VMD method (20.63 dB), and a root-mean-square-error (RMSE) of 0.0023, a reduction of 69.3% compared to the VMD method (0.0075). The processing results for measured human blood flow signals achieve an SNR of 24.11 dB, a RMSE of 0.0023, and a correlation coefficient (R) of 0.92, all outperforming other algorithms, such as VMD and WPTD. This study effectively addresses issues related to parameter sensitivity and incomplete noise separation in traditional methods, providing a high-precision and low-complexity real-time signal processing solution for wearable devices. However, the parameter optimization still needs improvement when dealing with large datasets. Full article

As AI-enabled embedded systems such as smart TVs and edge devices demand efficient video processing, Versatile Video Coding (VVC/H.266) becomes essential for bandwidth-constrained Multimedia Internet of Things (M-IoT) applications. However, its block-based coding often introduces compression artifacts. While CNN-based methods effectively reduce these artifacts, maintaining robust performance across varying quantization parameters (QPs) remains challenging. Recent QP-adaptive designs like QA-Filter show promise but are still limited. This paper proposes DRIFT, a QP-adaptive in-loop filtering network for VVC. DRIFT combines a lightweight frequency fusion CNN (LFFCNN) for local enhancement and a Swin Transformer-based global skip connection for capturing long-range dependencies. LFFCNN leverages octave convolution and introduces a novel residual block (FFRB) that integrates multiscale extraction, QP adaptivity, frequency fusion, and spatial-channel attention. A QP estimator (QPE) is further introduced to mitigate double enhancement in inter-coded frames. Experimental results demonstrate that DRIFT achieves BD rate reductions of 6.56% (intra) and 4.83% (inter), with an up to 10.90% gain on the BasketballDrill sequence. Additionally, LFFCNN reduces the model size by 32% while slightly improving the coding performance over QA-Filter. Full article

The neural implicit SLAM system performs excellently in static environments, offering higher-quality rendering and scene reconstruction capabilities compared to traditional dense SLAM. However, in dynamic real-world scenes, these systems often experience tracking drift and mapping errors. To address these problems, we suggest DIN-SLAM, a dynamic scene neural implicit SLAM system based on optical flow and depth gradient verification. DIN-SLAM combines depth gradients, optical flow, and motion consistency to achieve robust filtering of dynamic pixels, while optimizing dynamic feature points through optical flow registration to enhance tracking accuracy. The system also introduces a dynamic scene optimization strategy that utilizes photometric consistency loss, depth gradient loss, motion consistency constraints, and edge matching constraints to improve geometric consistency and reconstruction performance in dynamic environments. To reduce the interference of dynamic objects on scene reconstruction and eliminate artifacts in scene updates, we propose a targeted rendering and ray sampling strategy based on local feature counts, effectively mitigating the impact of dynamic object occlusions on reconstruction. Our method supports multiple sensor inputs, including pure RGB and RGB-D. The experimental results demonstrate that our approach consistently outperforms state-of-the-art baseline methods, achieving an 83.4% improvement in Absolute Trajectory Error Root Mean Square Error (ATE RMSE), a 91.7% enhancement in Peak Signal-to-Noise Ratio (PSNR), and the elimination of artifacts caused by dynamic interference. These enhancements significantly boost the performance of tracking and mapping in dynamic scenes. Full article

The fusion of LiDAR and photogrammetry point clouds is a necessary advancement in 3D-modeling, enabling more comprehensive and accurate representations of physical environments. The main contribution of this paper is the development of an innovative fusion system that combines classical algorithms, such as Structure from Motion (SfM), with advanced machine learning techniques, like Coherent Point Drift (CPD) and Feature-Metric Registration (FMR), to improve point cloud alignment and fusion. Experimental results, using a custom dataset of real-world scenes, demonstrate that the hybrid fusion method achieves an average error of less than 5% in the measurements of small reconstructed objects, with large objects showing less than 2% deviation from real sizes. The fusion process significantly improved structural continuity, reducing artifacts like edge misalignments. The k-nearest neighbors (kNN) analysis showed high reconstruction accuracy for the hybrid approach, demonstrating that the hybrid fusion system, particularly when combining machine learning-based refinement with traditional alignment methods, provides a notable advancement in both geometric accuracy and computational efficiency for real-time 3D-modeling applications. Full article

In a biological signal analysis system, signals of the same type may exhibit significant variations in their feature waveforms. Biological signals are typically weak, which increases the complexity of their analysis. Furthermore, clinical biomedical signals are susceptible to various interferences from the human body itself, including muscle movements, respiration, and heartbeat. These interference factors further escalate the complexity and difficulty of signal analysis. Therefore, precise and targeted preprocessing is often required before analyzing these clinical biomedical signals to enhance the accuracy and reliability of subsequent feature extraction and classification. Here, we have established an effective and practical algorithm model that integrates preprocessing with the initial localization of target feature waveforms, achieving the following four objectives: 1. Determining the periodic positions of target feature waveforms. 2. Preserving the original amplitude and shape of target feature waveforms while eliminating negative interference. 3. Reducing or eliminating interference from other feature waveforms in the input signal. 4. Decreasing noise in the input signal, such as baseline drift, powerline interference, and muscle artifacts commonly found in biological signals. We have validated the algorithm on clinical electrocardiogram (ECG) data and the authoritative MIT-BIH open-source ECG database demonstrating its effectiveness and reliability. Full article

Synchronous monitoring electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) have received significant attention in brain science research for their provision of more information on neuro-loop interactions. There is a need for an integrated hybrid EEG-fNIRS patch to synchronously monitor surface EEG and deep brain fNIRS signals. Here, we developed a hybrid EEG-fNIRS patch capable of acquiring high-quality, co-located EEG and fNIRS signals. This patch is wearable and provides easy cognition and emotion detection, while reducing the spatial interference and signal crosstalk by integration, which leads to high spatial–temporal correspondence and signal quality. The modular design of the EEG-fNIRS acquisition unit and optimized mechanical design enables the patch to obtain EEG and fNIRS signals at the same location and eliminates spatial interference. The EEG pre-amplifier on the electrode side effectively improves the acquisition of weak EEG signals and significantly reduces input noise to 0.9 μV_rms, amplitude distortion to less than 2%, and frequency distortion to less than 1%. Detrending, motion correction algorithms, and band-pass filtering were used to remove physiological noise, baseline drift, and motion artifacts from the fNIRS signal. A high fNIRS source switching frequency configuration above 100 Hz improves crosstalk suppression between fNIRS and EEG signals. The Stroop task was carried out to verify its performance; the patch can acquire event-related potentials and hemodynamic information associated with cognition in the prefrontal area. Full article

Electrooculography (EOG) serves as a widely employed technique for tracking saccadic eye movements in a diverse array of applications. These encompass the identification of various medical conditions and the development of interfaces facilitating human–computer interaction. Nonetheless, EOG signals are often met with skepticism due to the presence of multiple sources of noise interference. These sources include electroencephalography, electromyography linked to facial and extraocular muscle activity, electrical noise, signal artifacts, skin-electrode drifts, impedance fluctuations over time, and a host of associated challenges. Traditional methods of addressing these issues, such as bandpass filtering, have been frequently utilized to overcome these challenges but have the associated drawback of altering the inherent characteristics of EOG signals, encompassing their shape, magnitude, peak velocity, and duration, all of which are pivotal parameters in research studies. In prior work, several model-based adaptive denoising strategies have been introduced, incorporating mechanical and electrical model-based state estimators. However, these approaches are really complex and rely on brain and neural control models that have difficulty processing EOG signals in real time. In this present investigation, we introduce a real-time denoising method grounded in a constant velocity model, adopting a physics-based model-oriented approach. This approach is underpinned by the assumption that there exists a consistent rate of change in the cornea-retinal potential during saccadic movements. Empirical findings reveal that this approach remarkably preserves EOG saccade signals, resulting in a substantial enhancement of up to 29% in signal preservation during the denoising process when compared to alternative techniques, such as bandpass filters, constant acceleration models, and model-based fusion methods. Full article

The resolution of 3D structure reconstructed by laboratory nanoCT is often affected by changes in ambient temperature. Although correction methods based on projection alignment have been widely used, they are time-consuming and complex. Especially in piecewise samples (e.g., chips), the existing methods are semi-automatic because the projections lose attenuation information at some rotation angles. Herein, we propose a fast correction method that directly processes the reconstructed slices. Thus, the limitations of the existing methods are addressed. The method is named multiscale dense U-Net (MD-Unet), which is based on MIMO-Unet and achieves state-of-the-art artifacts correction performance in nanoCT. Experiments show that MD-Unet can significantly boost the correction performance (e.g., with three orders of magnitude improvement in correction speed compared with traditional methods), and MD-Unet+ improves 0.92 dB compared with MIMO-Unet in the chip dataset. Full article

A self-correction method for the drift artifacts of laboratory cone-beam nanoscale X-ray computed tomography (nano-CT) based on the trajectory of projection centroid (TPC) is proposed. This method does not require additional correction phantoms, simplifying the correction process. The whole TPC is estimated by the partial TPC in the optimal projection set. The projection drift is calculated by the measured TPC and the estimated TPC. The interval search method is used so that the proposed method can adapt to the case of a truncated projection due to drift. The fixed-angle scanning experiment of the Siemens star and the partial derivative analysis of the projection position show the necessity of correcting drift artifacts. Further, the Shepp–Logan phantoms with different drift levels are simulated. The results show that the proposed method can effectively estimate the horizontal and vertical drifts within the projection drift range of $\pm$2 mm (27 pixels) with high accuracy. Experiments were conducted on tomato seed and bamboo stick to validate the feasibility of the proposed method for samples with different textures. The correction effect on different reconstructed slices indicates that the proposed method provides performance superior to the reference scanning method (RSM) and global fitting. In addition, the proposed method requires no extra scanning, which improves the acquisition efficiency, as well as radiation utilization. Full article

Thermal drift of nano-computed tomography (CT) adversely affects the accurate reconstruction of objects. However, feature-based reference scan correction methods are sometimes unstable for images with similar texture and low contrast. In this study, based on the geometric position of features and the structural similarity (SSIM) of projections, a rough-to-refined rigid alignment method is proposed to align the projection. Using the proposed method, the thermal drift artifacts in reconstructed slices are reduced. Firstly, the initial features are obtained by speeded up robust features (SURF). Then, the outliers are roughly eliminated by the geometric position of global features. The features are refined by the SSIM between the main and reference projections. Subsequently, the SSIM between the neighborhood images of features are used to relocate the features. Finally, the new features are used to align the projections. The two-dimensional (2D) transmission imaging experiments reveal that the proposed method provides more accurate and robust results than the random sample consensus (RANSAC) and locality preserving matching (LPM) methods. For three-dimensional (3D) imaging correction, the proposed method is compared with the commonly used enhanced correlation coefficient (ECC) method and single-step discrete Fourier transform (DFT) algorithm. The results reveal that proposed method can retain the details more faithfully. Full article

In this work, we present a battery-less wireless Micro-Electro-Mechanical (MEMS)-based respiration sensor capable of measuring the respiration profile of a human subject from up to 2 m distance from the transceiver unit for a mean excitation power of 80 µW and a measured SNR of 124.8 dB at 0.5 m measurement distance. The sensor with a footprint of ~10 cm² is designed to be inexpensive, maximize user mobility, and cater to applications where disposability is desirable to minimize the sanitation burden. The sensing system is composed of a custom UHF RFID antenna, a low-loss piezoelectric MEMS resonator with two modes within the frequency range of interest, and a base transceiver unit. The difference in temperature and moisture content of inhaled and exhaled air modulates the resonance frequency of the MEMS resonator which in turn is used to monitor respiration. To detect changes in the resonance frequency of the MEMS devices, the sensor is excited by a pulsed sinusoidal signal received through an external antenna directly coupled to the device. The signal reflected from the device through the antenna is then analyzed via Fast Fourier Transform (FFT) to extract and monitor the resonance frequency of the resonator. By tracking the resonance frequency over time, the respiration profile of a patient is tracked. A compensation method for the removal of motion-induced artifacts and drift is proposed and implemented using the difference in the resonance frequency of two resonance modes of the same resonator. Full article

From a video coding perspective, there are two challenges when performing live video distribution over error-prone networks, such as wireless networks: random access and packet loss repair. There is a scarceness of solutions that do not impact steady-state usage and users with reliable connections. The proposed solution minimizes this impact by complementing a compression-efficient video stream with a companion stream solely consisting of keyframes. Although the core idea is not new, this paper is the first work to provide restrictions and modifications necessary to make this idea work using the High-Efficiency Video Coding (H.265/HEVC) compression standard. Additionally, through thorough quantification, insight is provided on how to provide low-latency fast channel switching capabilities and error recovery at low quality impact, i.e., less than 0.94 average Video Multimethod Assessment Fusion (VMAF) score decrease. Finally, worst-case drift artifacts are described and visualized such that the reader gets an overall picture of using the keyframe insertion technique. Full article

Indoor positioning remains a challenge and, despite much research and development carried out in the last decade, there is still no standard as with the Global Navigation Satellite Systems (GNSS) outdoors. This paper presents an indoor positioning system called LOCATE-US with adjustable granularity for use with commercial mobile devices, such as smartphones or tablets. LOCATE-US is privacy-oriented and allows every device to compute its own position by fusing ultrasonic, inertial sensor measurements and map information. Ultrasonic Local Positioning Systems (U-LPS) based on encoded signals are placed in critical zones that require an accuracy below a few decimeters to correct the accumulated drift errors of the inertial measurements. These systems are well suited to work at room level as walls confine acoustic waves inside. To avoid audible artifacts, the U-LPS emission is set at 41.67 kHz, and an ultrasonic acquisition module with reduced dimensions is attached to the mobile device through the USB port to capture signals. Processing in the mobile device involves an improved Time Differences of Arrival (TDOA) estimation that is fused with the measurements from an external inertial sensor to obtain real-time location and trajectory display at a 10 Hz rate. Graph-matching has also been included, considering available prior knowledge about the navigation scenario. This kind of device is an adequate platform for Location-Based Services (LBS), enabling applications such as augmented reality, guiding applications, or people monitoring and assistance. The system architecture can easily incorporate new sensors in the future, such as UWB, RFiD or others. Full article

The analysis of $\gamma$-ray spectra can be an arduous task, especially in the case of room temperature semiconductor detectors, where several distortions and instrumental artifacts conceal the true spectral shape. We developed a genetic algorithm to perform the unfolding of $\gamma$-spectra in order to restore the true energy distribution of the incoming radiation. We successfully validated our approach on experimental spectra of four radionuclides (${}^{241}$Am, ${}^{57}$Co, ${}^{137}$Cs and ${}^{133}$Ba) acquired with two $\mathrm{CdZnTe}$-based detectors with different contact geometries (single pixel and drift strip). The unfolded spectra consist of $\delta$-like peaks in correspondence with the radiation emissions of each radioisotope. Full article