Tight Fusion of Events and Inertial Measurements for Direct Velocity Estimation

Traditional visual-inertial state estimation targets absolute camera poses and spatial landmark locations while first-order kinematics are typically resolved as an implicitly estimated sub-state. However, this poses a risk in velocity-based control scenarios, as the quality of the estimation of kinematics depends on the stability of absolute camera and landmark coordinates estimation. To address this issue, we propose a novel solution to tight visual-inertial fusion directly at the level of first-order kinematics by employing a dynamic vision sensor instead of a normal camera. More specifically, we leverage trifocal tensor geometry to establish an incidence relation that directly depends on events and camera velocity, and demonstrate how velocity estimates in highly dynamic situations can be obtained over short time intervals. Noise and outliers are dealt with using a nested two-layer RANSAC scheme. Additionally, smooth velocity signals are obtained from a tight fusion with pre-integrated inertial signals using a sliding window optimizer. Experiments on both simulated and real data demonstrate that the proposed tight event-inertial fusion leads to continuous and reliable velocity estimation in highly dynamic scenarios independently of absolute coordinates. Furthermore, in extreme cases, it achieves more stable and more accurate estimation of kinematics than traditional, point-position-based visual-inertial odometry.Comment: Accepted by IEEE Transactions on Robotics (T-RO

Accelerating Globally Optimal Consensus Maximization in Geometric Vision

Branch-and-bound-based consensus maximization stands out due to its important ability of retrieving the globally optimal solution to outlier-affected geometric problems. However, while the discovery of such solutions caries high scientific value, its application in practical scenarios is often prohibited by its computational complexity growing exponentially as a function of the dimensionality of the problem at hand. In this work, we convey a novel, general technique that allows us to branch over an $n-1$ dimensional space for an n-dimensional problem. The remaining degree of freedom can be solved globally optimally within each bound calculation by applying the efficient interval stabbing technique. While each individual bound derivation is harder to compute owing to the additional need for solving a sorting problem, the reduced number of intervals and tighter bounds in practice lead to a significant reduction in the overall number of required iterations. Besides an abstract introduction of the approach, we present applications to three fundamental geometric computer vision problems: camera resectioning, relative camera pose estimation, and point set registration. Through our exhaustive tests, we demonstrate significant speed-up factors at times exceeding two orders of magnitude, thereby increasing the viability of globally optimal consensus maximizers in online application scenarios

Event-Based Visual Odometry on Non-Holonomic Ground Vehicles

Despite the promise of superior performance under challenging conditions, event-based motion estimation remains a hard problem owing to the difficulty of extracting and tracking stable features from event streams. In order to robustify the estimation, it is generally believed that fusion with other sensors is a requirement. In this work, we demonstrate reliable, purely event-based visual odometry on planar ground vehicles by employing the constrained non-holonomic motion model of Ackermann steering platforms. We extend single feature n-linearities for regular frame-based cameras to the case of quasi time-continuous event-tracks, and achieve a polynomial form via variable degree Taylor expansions. Robust averaging over multiple event tracks is simply achieved via histogram voting. As demonstrated on both simulated and real data, our algorithm achieves accurate and robust estimates of the vehicle's instantaneous rotational velocity, and thus results that are comparable to the delta rotations obtained by frame-based sensors under normal conditions. We furthermore significantly outperform the more traditional alternatives in challenging illumination scenarios. The code is available at \url{https://github.com/gowanting/NHEVO}.Comment: Accepted by 3DV 202

Near-Field Channel Estimation for Extremely Large-Scale Terahertz Communications

Future Terahertz communications exhibit significant potential in accommodating ultra-high-rate services. Employing extremely large-scale array antennas is a key approach to realize this potential, as they can harness substantial beamforming gains to overcome the severe path loss and leverage the electromagnetic advantages in the near field. This paper proposes novel estimation methods designed to enhance efficiency in Terahertz widely-spaced multi-subarray (WSMS) systems. Initially, we introduce three sparse channel representation methods: polar-domain representation (PD-R), multi-angular-domain representation (MAD-R), and two-dimensional polar-angular-domain representation (2D-PAD-R). Each method is meticulously developed for near-field WSMS channels, capitalizing on their sparsity characteristics. Building on this, we propose four estimation frameworks using the sparse recovery theory: polar-domain estimation (PD-E), multi-angular-domain estimation (MAD-E), two-stage polar-angular-domain estimation (TS-PAD-E), and two-dimensional polar-angular-domain estimation (2D-PAD-E). Particularly, 2D-PAD-E, integrating a 2D dictionary process, and TS-PAD-E, with its sequential approach to angle and distance estimation, stand out as particularly effective for near-field angle-distance estimation, enabling decoupled calculation of these parameters. Overall, these frameworks provide versatile and efficient solutions for WSMS channel estimation, balancing low complexity with high-performance outcomes. Additionally, they represent a fresh perspective on near-field signal processing

A new 12-gene diagnostic biomarker signature of melanoma revealed by integrated microarray analysis

NoGenome-wide microarray technology has facilitated the systematic discovery of diagnostic biomarkers of cancers and other pathologies. However, meta-analyses of published arrays often uncover significant inconsistencies that hinder advances in clinical practice. Here we present an integrated microarray analysis framework, based on a genome-wide relative significance (GWRS) and genome-wide global significance (GWGS) model. When applied to five microarray datasets on melanoma published between 2000 and 2011, this method revealed a new signature of 200 genes. When these were linked to so-called ‘melanoma driver’ genes involved in MAPK, Ca2+, and WNT signaling pathways we were able to produce a new 12-gene diagnostic biomarker signature for melanoma (i.e., EGFR, FGFR2, FGFR3, IL8, PTPRF, TNC, CXCL13, COL11A1, CHP2, SHC4, PPP2R2C, and WNT4). We have begun to experimentally validate a subset of these genes involved in MAPK signaling at the protein level, including CXCL13, COL11A1, PTPRF and SHC4 and found these to be over-expressed in metastatic and primary melanoma cells in vitro and in situ compared to melanocytes cultured from healthy skin epidermis and normal healthy human skin. While SHC4 has been reported previously to be associated to melanoma, this is the first time CXCL13, COL11A1, and PTPRF have been associated with melanoma on experimental validation. Our computational evaluation indicates that this 12-gene biomarker signature achieves excellent diagnostic power in distinguishing metastatic melanoma from normal skin and benign nevus. Further experimental validation of the role of these 12 genes in a new signaling network may provide new insights into the underlying biological mechanisms driving the progression of melanoma

Mutual Information-Based Integrated Sensing and Communications: A WMMSE Framework

In this letter, a weighted minimum mean square error (WMMSE) empowered integrated sensing and communication (ISAC) system is investigated. One transmitting base station and one receiving wireless access point are considered to serve multiple users a sensing target. Based on the theory of mutual-information (MI), communication MI and sensing MI rate are utilized as the performance metrics under the presence of clutters. In particular, we propose an novel MI-based WMMSE-ISAC method by developing a unique transceiver design mechanism to maximize the weighted sensing and communication sum-rate of this system. Such a maximization process is achieved by utilizing the classical method -- WMMSE, aiming to better manage the effect of sensing clutters and the interference among users. Numerical results show the effectiveness of our proposed method, and the performance trade-off between sensing and communication is also validated

Deep reinforcement learning for real-time economic energy management of microgrid system considering uncertainties

The electric power grid is changing from a traditional power system to a modern, smart, and integrated power system. Microgrids (MGs) play a vital role in combining distributed renewable energy resources (RESs) with traditional electric power systems. Intermittency, randomness, and volatility constitute the disadvantages of distributed RESs. MGs with high penetrations of renewable energy and random load demand cannot ignore these uncertainties, making it difficult to operate them effectively and economically. To realize the optimal scheduling of MGs, a real-time economic energy management strategy based on deep reinforcement learning (DRL) is proposed in this paper. Different from traditional model-based approaches, this strategy is learning based, and it has no requirements for an explicit model of uncertainty. Taking into account the uncertainties in RESs, load demand, and electricity prices, we formulate a Markov decision process for the real-time economic energy management problem of MGs. The objective is to minimize the daily operating cost of the system by scheduling controllable distributed generators and energy storage systems. In this paper, a deep deterministic policy gradient (DDPG) is introduced as a method for resolving the Markov decision process. The DDPG is a novel policy-based DRL approach with continuous state and action spaces. The DDPG is trained to learn the characteristics of uncertainties of the load, RES output, and electricity price using historical data from real power systems. The effectiveness of the proposed approach is validated through the designed simulation experiments. In the second experiment of our designed simulation, the proposed DRL method is compared to DQN, SAC, PPO, and MPC methods, and it is able to reduce the operating costs by 29.59%, 17.39%, 6.36%, and 9.55% on the June test set and 30.96%, 18.34%, 5.73%, and 10.16% on the November test set, respectively. The numerical results validate the practical value of the proposed DRL algorithm in addressing economic operation issues in MGs, as it demonstrates the algorithm’s ability to effectively leverage the energy storage system to reduce the operating costs across a range of scenarios

The association between neutrophil percentage-to-albumin ratio and cardiovascular disease: evidence from a cross-sectional study

BackgroundCardiovascular disease (CVD) is a leading cause of death and disability worldwide. Available studies suggest that inflammation and nutritional status play a key role in the development of CVD. As a new combined indicator of inflammation and nutritional status, the neutrophil percentage-to-albumin ratio (NPAR) may be important in CVD prediction.ObjectiveThis study investigated the association between NPAR and CVDs such as heart failure, coronary heart disease, angina pectoris, and stroke. It aimed to confirm the validity of NPAR as a potential biomarker of CVD using data from the National Health and Nutrition Examination Survey (NHANES).MethodsThis study used a cross-sectional study design that analyzed the neutrophil percentage, albumin levels, and CVD diagnostic information of 12,165 adults. Multifactorial logistic regression modeling was employed to explore the association between NPAR and CVDs such as heart failure, coronary heart disease, angina pectoris, and stroke, while the nonlinear relationships were examined via restricted cubic spline. In addition, subgroup analyses were performed to assess the effect of age, sex, and race on the association between NPAR and CVD.ResultsOur findings suggested that higher NPAR levels were significantly associated with an increased odds of CVD events. Specifically, each NPAR unit increase was associated with a 3% higher odds of a CVD event (OR = 1.03, 95% CI: 1.01–1.06). Individuals in the highest NPAR quartile displayed a significantly higher odds of heart failure (OR = 1.66, 95% CI: 1.18–2.34, p = 0.0035)and stroke (OR = 1.74, 95% CI: 1.28–2.36, p = 0.0004) than those in the lowest quartile. Subgroup analyses showed a more pronounced association between NPAR and CVD in women (OR = 1.04, 95% CI: 1.00–1.08, p = 0.0499), hypertensive patients (OR = 1.04, 95% CI: 1.01–1.07, p = 0.0154), and diabetic patients (OR = 1.05, 95% CI: 1.01–1.09, p = 0.0178).ConclusionThe study demonstrated that as a comprehensive indicator of inflammation and nutritional status, NPAR could effectively predict CVD occurrence. Although the clinical application value of NPAR requires further validation, it shows promise as a novel biomarker for early CVD screening and prevention

Bidirectional association between polycystic ovary syndrome and periodontal diseases

Polycystic ovary syndrome (PCOS) and periodontal disease (PDD) share common risk factors. The bidirectional interaction between PCOS and PDD has been reported, but until now, the underlying molecular mechanisms remain unclear. Endocrine disorders including hyperandrogenism (HA) and insulin resistance (IR) in PCOS disturb the oral microbial composition and increase the abundance of periodontal pathogens. Additionally, PCOS has a detrimental effect on the periodontal supportive tissues, including gingiva, periodontal ligament, and alveolar bone. Systemic low-grade inflammation status, especially obesity, persistent immune imbalance, and oxidative stress induced by PCOS exacerbate the progression of PDD. Simultaneously, PDD might increase the risk of PCOS through disturbing the gut microbiota composition and inducing low-grade inflammation and oxidative stress. In addition, genetic or epigenetic predisposition and lower socioeconomic status are the common risk factors for both diseases. In this review, we will present the latest evidence of the bidirectional association between PCOS and PDD from epidemiological, mechanistic, and interventional studies. A deep understanding on their bidirectional association will be beneficial to provide novel strategies for the treatment of PCOS and PDD

The formaldehyde stress on photosynthetic efficiency and oxidative stress response of moss Racomitrium japonicum L.

IntroductionFormaldehyde is a common gaseous pollutant emitted by buildings and decorative materials. In recent years, growing concerns have been raised regarding its harmful effects on health in indoor air. Therefore, this study aims to investigate the physiological and photosynthetic response mechanisms of Racomitrium japonicum under formaldehyde stress.MethodsR. japonicum was exposed to dynamic fumigation with formaldehyde for 7 days, with each day comprising an 8-h exposure period within a sealed container. The effects on plant structure, pigment content, photosynthetic efficiency, and reactive oxygen species (ROS) generation were assessed.Results and discussionOur findings revealed that formaldehyde stress caused structural damage, reduced pigment content, decreased photosynthetic efficiency, and increased ROS production in R. japonicum. Significantly, distinct stress-response pathways were observed at different formaldehyde concentrations. In response to low and moderate formaldehyde concentrations, R. japonicum activated its antioxidant enzyme system to mitigate ROS accumulation. In contrast, the high-concentration treatment group demonstrated suppressed antioxidant enzyme activity. In response, R. japonicum used nonphotochemical quenching and activated cyclic electron flow to mitigate severe cellular damage. This study provides an in-depth understanding of the physiological changes in R. japonicum under formaldehyde stress, elucidating its response mechanisms. The findings offer valuable insights for developing effective indoor formaldehyde monitoring and purification methods