Search Results (39)

Commercial wearable systems for surface electromyography (sEMG) acquisition often trade bandwidth, synchronization, and battery life for miniaturization, and their proprietary designs inhibit reproducibility and cost-effective customization. To address these limitations, we developed MOT, a fully wireless, multichannel platform built from commodity components that can be replicated in academic laboratories. Each sensor node integrates an AD8232 analog front-end configured for 19–690 Hz bandwidth (59 dB mid-band gain) with a 12-bit successive approximation ADC sampling at 1 kS/s. Packets of 120 samples are broadcast via the low-latency ESP-NOW 2.45 GHz protocol to a central hub, which timestamps and streams data to a host PC over USB-UART. Bench tests confirmed the analog response and showed mains interference at least 40 dB below voluntary contraction levels; the cumulative packet loss remained below 0.5% for six simultaneous channels at 100 m line-of-sight, with end-to-end latency under 3 ms. A 180 mAh Li-ion cell was used to power each node for 1.8 h of continuous operation at 100 mA average draw, and the complete sensor, including enclosure, was found to weigh 22 g. MOT reduced a 60 Hz artifact magnitude by up to 22 dB while preserving signal bandwidth. The hardware, therefore, provides a compact and economical solution for biomechanics, rehabilitation, and human–machine interface research that demands mobile, high-fidelity sEMG acquisition. Full article

Background and Objectives: Sarcopenia is exceedingly frequent in end-stage kidney disease (ESKD) patients on dialysis, including those undergoing peritoneal dialysis (PD), and is of multifactorial origin. MOTS-c is a mitochondrial-derived peptide that promotes muscle growth whose levels are unbalanced in ESKD. In this study, we evaluated MOTS-c balance and its relationship with sarcopenia risk in an ESKD-PD cohort. Materials and Methods: MOTS-c was measured in serum, urine, and dialysate samples of 32 chronic PD patients. Patients were thus screened for sarcopenia risk by the SARC-F tool, anthropometric measurements, and physical performance tests. Results: PD patients with a very high sarcopenia risk (SARC-F ≥ 2) had significantly lower serum (sMOTS-c) and higher dialysate (dMOTS-c) levels, suggesting an increased peritoneal clearance of this substance (d/s MOTS-c). sMOTS-c levels were directly correlated with muscle performance in physical tests, while an opposite relationship was found with dMOTS-c and d/sMOTS-c. ROC analyses demonstrated the diagnostic potential of MOTS-c, particularly in combination with physical and anthropometric assessments, to identify PD patients at very high risk of sarcopenia. Conclusions: Chronic PD may negatively affect MOTS-c balance, which, in turn, may contribute to enhanced sarcopenia risk. Larger studies are needed to confirm these observations and to validate the potential utility of this substance as a biomarker for improving sarcopenia risk stratification in PD patients. Full article

Kalman filter (KF)-based methods for 3D multi-object tracking (MOT) in autonomous driving often face challenges when detections are missed due to occlusions, sensor noise, or objects moving out of view. This leads to data association failures and cumulative errors in the update stage, as traditional Kalman filters rely on linear state estimates that can drift significantly without measurement updates. To address this issue, we propose an enhanced Kalman filter with dummy nodes and prediction confidence (KDPBTracker) to improve tracking continuity and robustness in these challenging scenarios. First, we designed dummy nodes to act as pseudo-observations generated from past and nearby frame detections in cases of missed detection, allowing for stable associations within the data association matrix when real detections were temporarily unavailable. To address the uncertainty in these dummy nodes, we then proposed a prediction confidence score to reflect their reliability in data association. Additionally, we modified a constant acceleration motion model combined with position-based heading estimation to better control high-dimensional numerical fluctuations in the covariance matrix, enhancing the robustness of the filtering process, especially in highly dynamic scenarios. We further designed bipartite graph data association to refine Kalman filter updates by integrating geometric and motion information weighted by the prediction confidence of the dummy nodes. Finally, we designed a confidence-based retention track management module to dynamically manage track continuity and deletion based on temporal and reliability thresholds, improving tracking accuracy in complex environments. Our method achieves state-of-the-art performance on the nuScenes validation set, improving AMOTA by 1.8% over the baseline CenterPoint. Evaluation on the nuScenes dataset demonstrates that KDPBTracker significantly improves tracking accuracy, reduces ID switches, and enhances overall tracking continuity under challenging conditions. Full article

Accurate multi-object tracking (MOT) is essential for autonomous vehicles, enabling them to perceive and interact with dynamic environments effectively. Single-modality 3D MOT algorithms often face limitations due to sensor constraints, resulting in unreliable tracking. Recent multi-modal approaches have improved performance but rely heavily on complex, deep-learning-based fusion techniques. In this work, we present CLF-BEVSORT, a camera-LiDAR fusion model operating in the bird’s eye view (BEV) space using the SORT tracking framework. The proposed method introduces a novel association strategy that incorporates structural similarity into the cost function, enabling effective data fusion between 2D camera detections and 3D LiDAR detections for robust track recovery during short occlusions by leveraging LiDAR depth. Evaluated on the KITTI dataset, CLF-BEVSORT achieves state-of-the-art performance with a HOTA score of 77.26% for the Car class, surpassing StrongFusionMOT and DeepFusionMOT by 2.13%, with high precision (85.13%) and recall (80.45%). For the Pedestrian class, it achieves a HOTA score of 46.03%, outperforming Be-Track and StrongFusionMOT by (6.16%). Additionally, CLF-BEVSORT reduces identity switches (IDSW) by over 45% for cars compared to baselines AB3DMOT and BEVSORT, demonstrating robust, consistent tracking and setting a new benchmark for 3DMOT in autonomous driving. Full article

With the current demand for automation in the agro-food industry, accurately detecting and localizing relevant objects in 3D is essential for successful robotic operations. However, this is a challenge due the presence of occlusions. Multi-view perception approaches allow robots to overcome occlusions, but a tracking component is needed to associate the objects detected by the robot over multiple viewpoints. Multi-object tracking (MOT) algorithms can be categorized between two-stage and single-stage methods. Two-stage methods tend to be simpler to adapt and implement to custom applications, while single-stage methods present a more complex end-to-end tracking method that can yield better results in occluded situations at the cost of more training data. The potential advantages of single-stage methods over two-stage methods depend on the complexity of the sequence of viewpoints that a robot needs to process. In this work, we compare a 3D two-stage MOT algorithm, 3D-SORT, against a 3D single-stage MOT algorithm, MOT-DETR, in three different types of sequences with varying levels of complexity. The sequences represent simpler and more complex motions that a robot arm can perform in a tomato greenhouse. Our experiments in a tomato greenhouse show that the single-stage algorithm consistently yields better tracking accuracy, especially in the more challenging sequences where objects are fully occluded or non-visible during several viewpoints. Full article

This work presents an autonomous vision-based mobile target tracking and following system designed for unmanned aerial vehicles (UAVs) leveraging multi-target information. It explores the research gap in applying the most recent multi-object tracking (MOT) methods in target following scenarios over traditional single-object tracking (SOT) algorithms. The system integrates the real-time object detection model, You Only Look Once (YOLO)v8, with the MOT algorithms BoT-SORT and ByteTrack, extracting multi-target information. It leverages this information to improve redetection capabilities, addressing target misidentifications (ID changes), and partial and full occlusions in dynamic environments. A depth sensing module is incorporated to enhance distance estimation when feasible. A 3D flight control system is proposed for target following, capable of reacting to changes in target speed and direction while maintaining line-of-sight. The system is initially tested in simulation and then deployed in real-world scenarios. Results show precise target tracking and following, resilient to partial and full occlusions in dynamic environments, effectively distinguishing the followed target from bystanders. A comparison between the BoT-SORT and ByteTrack trackers reveals a trade-off between computational efficiency and tracking precision. In overcoming the presented challenges, this work enables new practical applications in the field of vision-based target following from UAVs leveraging multi-target information. Full article

Fast and accurate pose estimation is essential for the local motion control of robots such as drones. At present, camera-based motion capture (Mocap) systems are mostly used by robots. However, this kind of Mocap system is easily affected by light noise and camera occlusion, and the cost of common commercial Mocap systems is high. To address these challenges, we propose Easy Rocap, a low-cost, open-source robot motion capture system, which can quickly and robustly capture the accurate position and orientation of the robot. Firstly, based on training a real-time object detector, an object-filtering algorithm using class and confidence is designed to eliminate false detections. Secondly, multiple-object tracking (MOT) is applied to maintain the continuity of the trajectories, and the epipolar constraint is applied to multi-view correspondences. Finally, the calibrated multi-view cameras are used to calculate the 3D coordinates of the markers and effectively estimate the 3D pose of the target robot. Our system takes in real-time multi-camera data streams, making it easy to integrate into the robot system. In the simulation scenario experiment, the average position estimation error of the method is less than 0.008 m, and the average orientation error is less than 0.65 degrees. In the real scenario experiment, we compared the localization results of our method with the advanced LiDAR-Inertial Simultaneous Localization and Mapping (SLAM) algorithm. According to the experimental results, SLAM generates drifts during turns, while our method can overcome the drifts and accumulated errors of SLAM, making the trajectory more stable and accurate. In addition, the pose estimation speed of our system can reach 30 Hz. Full article

Multiple object tracking (MOT) is a fundamental task in vision, but MOT techniques for plenoptic video are scarce. Almost all 2D MOT algorithms that show high performance mostly use the detection-based method which has the disadvantage of operating only for a specific object. To enable tracking of arbitrary desired objects, this paper introduces a groundbreaking detection-free tracking method for MOT in plenoptic videos. The proposed method deviates from traditional detection-based tracking methods, emphasizing the challenges of tracking targets with occlusions. The paper presents specialized algorithms that exploit the multifocal information of plenoptic video, including the focal range restriction and dynamic focal range adjustment schemes to secure robustness for occluded object tracking. To the improvement of the spatial searching capability, the anchor ensemble and the dynamic change of spatial search region algorithms are also proposed. Additionally, in terms of MOT, to reduce the computation time involved, the motion-adaptive time scheduling technique is proposed, which improves computation speed while guaranteeing a certain level of accuracy. Experimental results show a significant improvement in tracking performance, with a $77\%$ success rate based on intersection over union for occluded targets in plenoptic videos, marking a substantial advancement in the field of plenoptic object tracking. Full article

The Kazakhstan-Siberia Network for Spring Wheat Improvement (KASIB) was established in 2000, forming a collaboration between breeding and research programs through biannual yield trials. A core set of 142 genotypes from 15 breeding programs was selected, genotyped for 81 DNA functional markers and phenotyped for 10 agronomic traits at three sites in Kazakhstan (Karabalyk, Shortandy and Shagalaly) and one site in Russia (Omsk) in 2020–2022. The study aim was to identify markers demonstrating significant effects on agronomic traits. The average grain yield of individual trials varied from 118 to 569 g/m². Grain yield was positively associated with the number of days to heading, plant height, number of grains per spike and 1000-kernel weight. Eight DNA markers demonstrated significant effects. The spring-type allele of the Vrn-A1 gene accelerated heading by two days (5.6%) and was present in 80% of the germplasm. The winter allele of the Vrn-A1 gene significantly increased grain yield by 2.7%. The late allele of the earliness marker per se, TaMOT1-D1, delayed development by 1.9% and increased yield by 4.5%. Translocation of 1B.1R was present in 21.8% of the material, which resulted in a 6.2% yield advantage compared to 1B.1B germplasm and a reduction in stem rust severity from 27.6 to 6.6%. The favorable allele of TaGS-D1 increased both kernel weight and yield by 2–3%. Four markers identified in ICARDA germplasm, ISBW2-GY (Kukri_c3243_1065, 3B), ISBW3-BM (TA004946-0577, 1B), ISBW10-SM2 (BS00076246_51, 5A), ISBW11-GY (wsnp_Ex_c12812_20324622, 4A), showed an improved yield in this study of 3–4%. The study recommends simultaneous validation and use of selected markers in KASIB’s network. Full article

This paper presents a general review of the results of the experimental and theoretical work carried out by our research group to study the $5P_{3/2}\to 6P_J$ electric quadrupole transition in atomic rubidium. The experiments were carried out with room-temperature atoms in an absorption cell. A steady-state population of atoms in the $5P_{3/2}$ excited state is produced by a a narrow-bandwidth preparation laser locked to the D2 transition. A second CW laser is used to produce the forbidden transition with resolution of the $6P_J$ hyperfine states of both rubidium isotopes. The process is detected by recording the 420(422) nm fluorescence that occurs when the atoms in the $6P_J$ state decay directly into the $5S$ ground state. The fluorescence spectra show a strong dependence on the relative polarization directions of the preparation laser and the beam producing the forbidden transition. This dependence is directly related to a strong anisotropy in the populations of the $5P_{3/2}$ intermediate magnetic substates, and also to the electric quadrupole selection rules over magnetic quantum numbers. A calculation based on the rate equations that includes velocity and detuning dependent transition rates is adequate to reproduce these results. The forbidden transition is also shown to be an ideal probe to measure the Autler–Townes splitting generated in the preparation of the $5P_{3/2}$ state. Examples of spectra obtained with cold atoms in a magneto-optical trap (MOT) are also presented. These spectra show the expected Autler–Townes doublet structure with asymmetric line profiles that result as a consequence of the red-detuning of the trapping laser in the MOT. Full article

LiDAR-based Multi-Object Tracking (MOT) is a critical technology employed in various autonomous systems, including self-driving vehicles and autonomous delivery robots. In this paper, a novel LiDAR-based 3D MOT approach is introduced. The proposed method was built upon the Tracking-by-Detection (TbD) paradigm and incorporated multi-level associations that exploit an object’s short-term and long-term relationships with the existing tracks. Specifically, the short-term association leverages the fact that objects do not move much between consecutive frames. In contrast, the long-term association assesses the degree to which a long-term trajectory aligns with current detections. The evaluation of the matching between the current detection and the maintained trajectory was performed using a Graph Convolutional Network (GCN). Furthermore, an inactive track was maintained to address the issue of incorrect ID switching for objects that have been occluded for an extended period. The proposed method was evaluated on the KITTI benchmark MOT tracking dataset and achieved a Higher-Order Tracking Accuracy (HOTA) of 75.65%, marking a 5.66% improvement over the benchmark method AB3DMOT, while also accomplishing the number of ID switches of 39, 74 less than AB3DMOT. These results confirmed the effectiveness of the proposed approach in diverse road environments. Full article

Computer vision has made remarkable progress in traffic surveillance, but determining whether a motor vehicle yields to pedestrians still requires considerable human effort. This study proposes an automated method for detecting whether a vehicle yields to pedestrians in intelligent transportation systems. The method employs a target-tracking algorithm that uses feature maps and license plate IDs to track the motion of relevant elements in the camera’s field of view. By analyzing the positions of motor vehicles and pedestrians over time, we predict the warning points of pedestrians and hazardous areas in front of vehicles to determine whether the vehicles yield to pedestrians. Extensive experiments are conducted on the MOT16 dataset, real traffic street scene video dataset, and a Unity3D virtual simulation scene dataset combined with SUMO, which demonstrating the superiority of this tracking algorithms. Compared to the current state-of-the-art methods, this method demonstrates significant improvements in processing speed without compromising accuracy. Specifically, this approach substantially outperforms in operational efficiency, thus catering aptly to real-time recognition requirements. This meticulous experimentation and evaluations reveal a commendable reduction in ID switches, enhancing the reliability of violation attributions to the correct vehicles. Such enhancement is crucial in practical urban settings characterized by dynamic interactions and variable conditions. This approach can be applied in various weather, time, and road conditions, achieving high predictive accuracy and interpretability in detecting vehicle–pedestrian interactions. This advanced algorithm illuminates the viable pathways for integrating technological innovation and sustainability, paving the way for more resilient and intelligent urban ecosystems. Full article

This essay sets out to explore the unexpected but amusing entanglement of three Jewish writers—Harry (“Heinrich”) Heine, Sigismund (“Sigmund”) Freud, and Jackie (“Jacques”) Derrida. You will not often find a reference to Heine in the work of Jacques Derrida, but you will find a Heine joke in Derrida’s discussion of forgiveness in Le parjure et le pardon (1998–1999), where the name Heine is invoked precisely in order to recall the scandalous automaticity, the machine-like quality of forgiveness. Beginning with Derrida’s surprising reference to the man George Eliot called a “unique German wit”, this essay will begin by arguing that there is something about Heine’s jokes, his Witze, his mots d’esprit, that not only plays up, but also paradoxically takes seriously, what Derrida, echoing Nietzsche in Of Grammatology, describes as the “play of the world.” The second part of this essay will engage Freud’s particular and quite special relation to Heine: Heine is the third most cited German writer in all of Freud’s work (after Goethe and Schiller). Neither Homer nor Sophocles is cited more often than Heine. Indeed, a bon mot from Heine is always ready-to-hand in the face of theoretical obstacles (e.g., “Observations on Transference Love”, “On Narcissism”, etc.). But perhaps nowhere is Freud’s affinity with Heine more apparent and more striking than in Jokes and Their Relation to the Unconscious (1905), where Heine’s witticisms offer the best and most canonical examples of jokes. In conclusion, this essay will argue that Heine’s wit can be read as a playbook—not only for psychoanalysis’s economic understanding of jokes, but also, more radically, for deconstruction’s thinking of play. Full article

The high cell density, immobilization and stability of biofilms are ideal characteristics for bacteria in resisting antibiotic therapy. CsgD is a transcription activating factor that regulates the synthesis of curly fimbriae and cellulose in Escherichia coli, thereby enhancing bacterial adhesion and promoting biofilm formation. To investigate the role of CsgD in biofilm formation and stress resistance in bacteria, the csgD deletion mutant ΔcsgD was successfully constructed from the engineered strain E. coli BL21(DE3) using the CRISPR/Cas9 gene-editing system. The results demonstrated that the biofilm of ΔcsgD decreased by 70.07% (p < 0.05). Additionally, the mobility and adhesion of ΔcsgD were inhibited due to the decrease in curly fimbriae and extracellular polymeric substances. Furthermore, ΔcsgD exhibited a significantly decreased resistance to acid, alkali and osmotic stress conditions (p < 0.05). RNA-Seq results revealed 491 differentially expressed genes between the parent strain and ΔcsgD, with enrichment primarily observed in metabolism-related processes as well as cell membrane structure and catalytic activity categories. Moreover, CsgD influenced the expression of biofilm and stress response genes pgaA, motB, fimA, fimC, iraP, ompA, osmC, sufE and elaB, indicating that the CsgD participated in the resistance of E. coli by regulating the expression of biofilm and stress response. In brief, the transcription factor CsgD plays a key role in the stress resistance of E. coli, and is a potential target for treating and controlling biofilm. Full article

This paper presents GAVT, a highly accurate audiovisual 3D tracking system based on particle filters and a probabilistic framework, employing a single camera and a microphone array. Our first contribution is a complex visual appearance model that accurately locates the speaker’s mouth. It transforms a Viola & Jones face detector classifier kernel into a likelihood estimator, leveraging knowledge from multiple classifiers trained for different face poses. Additionally, we propose a mechanism to handle occlusions based on the new likelihood’s dispersion. The audio localization proposal utilizes a probabilistic steered response power, representing cross-correlation functions as Gaussian mixture models. Moreover, to prevent tracker interference, we introduce a novel mechanism for associating Gaussians with speakers. The evaluation is carried out using the AV16.3 and CAV3D databases for Single- and Multiple-Object Tracking tasks (SOT and MOT, respectively). GAVT significantly improves the localization performance over audio-only and video-only modalities, with up to $50.3\%$ average relative improvement in 3D when compared with the video-only modality. When compared to the state of the art, our audiovisual system achieves up to $69.7\%$ average relative improvement for the SOT and MOT tasks in the AV16.3 dataset (2D comparison), and up to $18.1\%$ average relative improvement in the MOT task for the CAV3D dataset (3D comparison). Full article