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Simultaneous localization and mapping (SLAM) are fundamental elements for many emerging technologies, such as autonomous driving and augmented reality. For this paper, to get more information, we developed an improved monocular visual SLAM system by using omnidirectional cameras. Our method extends the ORB-SLAM framework with the enhanced unified camera model as a projection function, which can be applied to catadioptric systems and wide-angle fisheye cameras with 195 degrees field-of-view. The proposed system can use the full area of the images even with strong distortion. For omnidirectional cameras, a map initialization method is proposed. We analytically derive the Jacobian matrices of the reprojection errors with respect to the camera pose and 3D position of points. The proposed SLAM has been extensively tested in real-world datasets. The results show positioning error is less than 0.1% in a small indoor environment and is less than 1.5% in a large environment. The results demonstrate that our method is real-time, and increases its accuracy and robustness over the normal systems based on the pinhole model.

Passive-wireless surface-acoustic-wave (SAW) micro-pressure sensors are suitable for extreme scenarios where wired sensors are not applicable. However, as the measured pressure decreases, conventional SAW micro-pressure sensors struggle to meet expected performance due to insufficient sensitivity. This article proposes a a method of using an A2-mode Lamb SAW sensor and introduces an inertial structure in the form of a cantilever beam to enhance sensitivity. An MEMS-compatible manufacturing process was employed to create a multi-layer structure of SiO2, AlN, and SOI for the SAW micro-pressure sensor. To investigate the operational performance of the SAW micro-pressure sensor, a micro-pressure testing system was established. The experimental results demonstrate that the sensor exhibits high sensitivity to micro-pressure, validating the effectiveness of the proposed design.

Metal-organic frameworks (MOFs) with porosity and functional adjustability have great potential for the removal of organic dyes in the wastewater. Herein, an anionic porous metal-organic framework (MOFs) [Me2NH2]2In2[(TATAB)4(DMF)4]·(DMF)4(H2O)4 (HDU-1) was synthesized, which is constructed from a [In(OOC)4]− cluster and a nitrogen-rich linker H3TATAB (4,4′,4″-s-triazine-1,3,5-triyltri-p-aminobenzoic acid). The negatively charged [In(OOC)4]− cluster and uncoordinated –COOH on the linker result in one unit cell of HDU-1 having 8 negative sites. The zeta potential of -20.8 mV dispersed in pure water also shows that HDU-1 possesses negatively charged surface potential. The high electronegativity, water stability, and porosity of HDU-1 can facilitate the ion-exchange and Coulombic interaction. As expected, the HDU-1 exhibits high selectivity and removal rates towards trace cationic dyes with suitable size, such as methylene blue (MB) (96%), Brilliant green (BG) (99.3%), and Victoria blue B (VB) (93.6%).

The Middle Route of the South to North Water Diversion Project (MRP) and its water source, the Danjiangkou Reservoir (DJK), play a pivotal role in mitigating the chronic water scarcity challenges faced by northern China. Eukaryotic plankton are widespread in aquatic ecosystems, which are crucial for the water quality stability of DJK and MRP, yet comparative studies on their contemporaneous dynamics and assembly processes are scarce. In this study, amplicon sequencing was used to investigate the eukaryotic plankton communities. The results revealed that the similarity in community composition of DJK is significantly higher than that of MRP, exhibiting distance-decay patterns. Environmental heterogeneity exhibits significant differences between DJK and MRP, and it significantly influences community composition and alpha diversity. Additionally, the assembly processes of eukaryotic plankton in both DJK and MRP are predominantly influenced by stochastic processes. However, in comparison to DJK, deterministic processes have a more pronounced impact on MRP, accounting for 39.29% and 1.82%, respectively. The variations in total nitrogen (TN), chlorophy II a (Chl. a ), and conductivity (Spc) have led to a transition in the assembly of eukaryotic phytoplankton communities in MRP from a stochastic process to a deterministic process. This study extends insights into the dynamics and assembly processes of eukaryotic plankton communities in the large, engineered drinking water diversion project and its water source, which is also useful for the management and regulation of the DJK and MRP.

To reduce the impact of acoustic interference in a microelectromechanical system (MEMS) gyroscope and to improve the reliability of output data, a filtering algorithm based on orthogonal demodulation is proposed. According to the working principle and failure mechanism of a MEMS gyroscope, the sound and angular velocity frequencies are not identical, which lead to a different frequency signal output of the original single-channel demodulation scheme. Therefore, a Q channel demodulation filtering process was added to the origin single-channel demodulation scheme. For the Q channel demodulated signal, a Hilbert transform was used to compensate for the 90 degree phase shift. The IQ dual-channel difference can remove the acoustic interference signal. The simulation results indicate that the scheme can effectively suppress the acoustic interference signal and it can eliminate more than 95% of the impact of sound waves. We assembled the acoustic interference experimental platform, collected the driving and sensing data, and verified the denoising performance with our algorithm, which eliminated more than 70% of the noise signal. The simulation and experimental results demonstrate that the scheme can eliminate acoustic interference signal without destroying angular velocity signal.

In this letter, we propose an indoor visible light positioning technique using a Modified Momentum Back-Propagation (MMBP) algorithm based on received signal strength (RSS) with sparse training data set. Unlike other neural network algorithms that require a large number of training data points to locate accurately, we have realized high-precision positioning for 100 test points with only 20 training points in a 1.8 m × 1.8 m × 2.1 m localization area. In order to verify the adaptability of the MMBP algorithm, we experimentally demonstrate two different training data acquisition methods adopting either even or arbitrary training sets. In addition, we also demonstrate the positioning accuracy of the traditional RSS algorithm. Experimental results show that the average localization accuracy optimized by our proposed algorithm is only 1.88 cm for the arbitrary set and 1.99 cm for the even set, while the average positioning error of the traditional RSS algorithm reaches 14.34 cm. Comparison indicates that the positioning accuracy of our proposed algorithm is 7.6 times higher. Results also show that the performance of our system is higher than some previous reports based on RSS and RSS fingerprint databases using complex machine learning algorithms trained by a large amount of training points.

Background Major depressive disorder (MDD) and bipolar disorder (BD) are psychiatric disorders that seriously impact physical and mental health. They are increasingly prevalent among children and adolescents, and the absence of objective physiological indicators makes diagnosis difficult. However, existing studies have primarily focused on adults, and few practical diagnostic tools have been developed and clinically deployed. Therefore, we investigated the voice features of children and adolescents and proposed a low-complexity automatic detection method for early recognition and self-screening. Methods A reading paradigm with 7 segments of text is applied for voice data collection. After dividing the recording, a well-developed feature set is extracted, and the double feature selection method is proposed to select the most effective features. Finally, traditional classification models are applied to reduce complexity. Results The energy, spectral slope, amplitude spectrum, and RASTA-style filtered auditory spectrum of voice are effective features. Results show that 92.4% and 95.6% for voice and subject accuracy are achieved in the ternary classification of 50 BD, 50 MDD, and 50 healthy controls (HC). Besides the satisfactory accuracies, the robustness to recording devices and environments is validated. Conclusions Voice features are potential biomarkers for diagnosing psychiatric disorders in children and adolescents. Based on optimized feature selection algorithms, traditional classifiers can achieve accurate and robust classification of BD, MDD, and HC with a small number of interpretable features, providing a feasible tool for auxiliary diagnosis.

In order to quantitatively study the interfered output of the accelerometer under an acoustic injection attack, a mathematical model for fitting and predicting the accelerometer output was proposed. With ADXL103 as an example, an acoustic injection attack experiment with amplitude sweeping and frequency sweeping was performed. In the mathematical model, the R-squared coefficient was R2 = 0.9990 in the acoustic injection attack experiment with amplitude sweeping, and R2 = 0.9888 with frequency sweeping. Based on the mathematical model, the dual frequency acoustic injection attack mode was proposed. The difference frequency signal caused by the nonlinear effect was not filtered by the low-pass filter. At a 115 dB sound pressure level, the maximum acceleration bias of the output was 4.4 m/s2 and the maximum amplitude of fluctuation was 4.97 m/s2. Two kinds of methods of prevention against acoustic injection attack were proposed, including changing the damping ratio of the accelerometer and adding a preposition low-pass filter.

Introduction The aim of this study was to identify the heterogeneous classes of CD4/CD8 ratio trajectory and their impacts on prognosis in people living with HIV (PLHIV) during long-term antiretroviral therapy. Methods A retrospective cohort study of PLHIV receiving ART treatment was conducted in the Eighth Affiliated Hospital of Guangzhou Medical University, and the latent growth mixture model (LGMM) was used to identify the trajectories of longitudinal changes in the CD4/CD8 ratio between February 10, 2004, and January 31, 2019. Cox proportional hazard model and proportional subdistribution hazard model were conducted to explore the impact of CD4/CD8 ratio trajectory on prognosis. Results Three heterogeneous trajectories were identified: the baseline-low-level slow increase class (Class 1: 57.45%), the baseline-moderate-level rapid increase class (Class 2: 34.29%), and the baseline-high-level sharp increase class (Class 3: 8.26%). Cox proportional hazard model showed that CD4/CD8 ratio trajectory was associated with all-cause mortality among PLHIV, with adjusted hazard ratios (aHR) (95% confidence interval [CI]) for Class 2, and Class 3 being 0.53 (0.38–0.75), and 0.35 (0.14–0.86) respectively, compared with Class 1. The result of the proportional subdistribution hazard model showed that the CD4/CD8 ratio trajectory was associated with the risk of AIDS-related and non-AIDS-related mortality. Conclusions Our study demonstrated that there were three different trajectories of CD4/CD8 ratio during long-term ART. Personalized interventions and treatment plans can be developed based on individual changes in CD4/CD8 ratio, which is important for improving the survival of the PLHIV and reducing disease burden.

Objective Major depressive disorder (MDD) and bipolar disorder (BD) are serious chronic disabling mental and emotional disorders, with symptoms that often manifest atypically in children and adolescents, making diagnosis difficult without objective physiological indicators. Therefore, we aimed to objectively identify MDD and BD in children and adolescents by exploring their voiceprint features. Methods This study included a total of 150 participants, with 50 MDD patients, 50 BD patients, and 50 healthy controls aged between 6 and 16 years. After collecting voiceprint data, chi-square test was used to screen and extract voiceprint features specific to emotional disorders in children and adolescents. Then, selected characteristic voiceprint features were used to establish training and testing datasets with the ratio of 7:3. The performances of various machine learning and deep learning algorithms were compared using the training dataset, and the optimal algorithm was selected to classify the testing dataset and calculate the sensitivity, specificity, accuracy, and ROC curve. Results The three groups showed differences in clustering centers for various voice features such as root mean square energy, power spectral slope, low-frequency percentile energy level, high-frequency spectral slope, spectral harmonic gain, and audio signal energy level. The model of linear SVM showed the best performance in the training dataset, achieving a total accuracy of 95.6% in classifying the three groups in the testing dataset, with sensitivity of 93.3% for MDD, 100% for BD, specificity of 93.3%, AUC of 1 for BD, and AUC of 0.967 for MDD. Conclusion By exploring the characteristics of voice features in children and adolescents, machine learning can effectively differentiate between MDD and BD in a population, and voice features hold promise as an objective physiological indicator for the auxiliary diagnosis of mood disorder in clinical practice.