Motion Generation Research Articles

A Robust Generator–Harvester for Independent Sensor Systems

The research is centered on energy production and harvesting to facilitate the transformation of electrical energy with energy-independent sensor systems, using powering devices in the expected power range of P = 10–10,000 W. A model application case for a harvester is the conversion of energy stored in the compressed gas during expansion; such gas embodies the energy stored in scenarios such as braking a car using an auxiliary pump. Similar systems find use in sensing various quantities in the transport sector (bridge structures, infrastructural components, cars, and other objects). The proposed theoretical harvester models describing the transformation of linear motion energy into electricity provide relevant support for the experiments. In the given context, the results obtained in the designing and construction of a robust motion generator with a primarily linear geometry-based system technology are presented, too. The expected output of electrical power of an N-segment harvester within the tested type is variable, and the design exploits the rectilinear motion generated by an engine using compressed air, a small fuel system, and similar options to obtain an expected/adjustable N-segment power in the range of Psm = 10–500 W. The fundamental structure of the generator core has been continuously numerically modeled, and an experimental setup has been developed to analyze the specific parts and variations in order to validate the concept and to achieve the most suitable parameters with the selected construction materials (a power yield increase of up to 2000 times). A scaled-down version of the model principle was tested in the experiments, and the parameters and results were compared with the predicted theoretical analyses. Generally, the conceptual layout of an enhanced magnetic circuit layout transforming motion energy into electricity was presented and verified.

Model Bangkitan dan Tarikan Pergerakan, di Jalan Veteran Lamongan Karena Perkembangan Kampus Unisla

Unisla has experienced an increase in the number of lecturers and students successively from year to year. In addition, lecture buildings and educational facilities also continue to improve over time. The purpose of this study is to determine the model of attraction and generation of movement in the Unisla campus area, and to determine the factors that influence the attraction and generation of movement that will occur due to the development of the Unisla campus. The method in this study uses the method of regression analysis, slovin and SPSS. Based on the analysis, the results of the generation model and the attraction of movement in the Unisla campus area can be explained by the equation Y = 556.448 + -0.963X, with an R2 value close to one of 0.593, indicating a good fit between the model and the data. The factor that has a significant influence on the generation and attraction of movement in the Unisla campus area is the mode of transportation chosen by the subject. Motorcycles have an effect of 90.26% on motion generation, whereas in terms of movement attraction, motorcycles also have an influence of 91.91%. In this case it can be interpreted that what has a big influence on the generation and attraction of movement in the Unisla campus area is motorbikes.

Feasibility of Phase Velocity Imaging Using Multi Frequency Oscillation-Shear Wave Elastography.

To assess viscoelasticity, a pathologically relevant biomarker, shear wave elastography (SWE) generally uses phase velocity (PV) dispersion relationship generated via pulsed acoustic radiation force (ARF) excitation pulse. In this study, a multi-frequency oscillation (MFO)- excitation pulse with higher weight to higher frequencies is proposed to generate PV images via the generation of motion with energy concentrated at the target frequencies in contrast to the broadband frequency motion generated in pulsed SWE (PSWE). The feasibility of MFO-SWE to generate PV images at 100 to 1000 Hz in steps of 100 Hz was investigated by imaging 6 and 70 kPa inclusions with 6.5 and 10.4 mm diameter and ex vivo bovine liver with and without the presence of an aberration layer and chicken muscle ex vivo, and 4T1 mouse breast tumor, in vivo with comparisons to PSWE. MFO-SWE-derived CNR was statistically higher than PSWE for 6 kPa (both with and without aberration) and 70 kPa (with aberration) inclusions and derived SNR of the liver was statistically higher than PSWE at higher frequency (600-1000 Hz). Quantitatively, at 600-1000 Hz, MFO-SWE improved CNR of inclusions (without and with) aberration on an average by (8.2 and 156)% and of the tumor by 122%, respectively, and improved SNR of the liver (without and with) aberration by (20.2 and 51.5)% and of chicken muscle by 72%, respectively compared to the PSWE. These results indicate the advantages of MFO-SWE to improve PV estimation at higher frequencies which could improve viscoelasticity quantification and feature delineation.

Autonomous scanning motion generation adapted to individual differences in abdominal shape for robotic fetal ultrasound

The shortage of obstetricians and gynecologists is increasing in developed countries; therefore, there is a need to improve prenatal care procedures. To automate fetal ultrasound (US) imaging, this paper presents a deep neural network (DNN) model that generates US scan motions for a robot. Additionally, the robot can adapt to the abdominal surface even if the abdominal shape is not precisely known. The latent space of the DNN model is designed to represent the abdominal-shape information. The DNN model can predict the proper trajectory by mapping the height and width of the abdomen to the latent space. Moreover, the robot can detect any deviations from the correct trajectory and return to the right position. For validation, we performed a US scan using the proposed model on tissue-mimicked phantoms that were not used for training. Subsequently, we evaluated the number of dots that were wiped off by the robot from the phantom surface. Overall, the number of dots removed accounted for 91.7% of the total dots. The results demonstrated the feasibility of using the DNN model for motion generation. Hence, the proposed system has the potential to automate fetal US scans according to the individual differences in the abdominal shape.

A review of motion generation technology

Nowadays, deep learning and neural network-related research play a very important role in the widely use of artificial intelligence -related technologies, Among them, the hot development in the direction of generative adversarial networks (GAN) has given birth to many generation-related techniques. For example, MoCoGAN is based on the implementation principle of GAN, which enables video generation of different actions of the same character or the same action of different characters, through the method that decompose video into actions and content. This paper introduces the history and principles of MoCoGAN, starting from the prospect of using MoCoGAN in artificial intelligence (AI) industry and the technical challenges that need to be overcome in the future application of action generation. Besides, this paper also discusses the two main issues of how to improve the quality of video generation using MoCoGAN and the input conditions that are the most central problem to GAN networks. By summarizing the optimization solutions of other researchers in these two areas in recent years, this paper searches the core problems need to be solved and propose a broad prospect for future video generation techniques that can be implemented by using MoCoGAN in human-computer interaction (HCI) area.

DInSAR Multi-Temporal Analysis for the Characterization of Ground Deformations Related to Tectonic Processes in the Region of Bucaramanga, Colombia

The analysis of the degree of surface deformation can be a relevant aspect in the study of surface stability conditions, as it provides added value in the construction of risk management plans. This analysis provides the opportunity to establish the behaviors of the internal dynamics of the earth and its effects on the surface as a prediction tool for possible future effects. To this end, this study was approached through the analysis of Synthetic Aperture Radar (SAR) images using the Differential Interferometry (DInSAR) technique, which, in turn, is supported by the Small Baseline Subset (SBAS) technique to take advantage of the orbital separation of the Sentinel-1 satellite images in ascending and descending trajectory between the years 2014 and 2021. As a result, a time series was obtained in which there is a maximum uplift of 117.5 mm (LOS-ascending) or 49.3 mm (LOS-descending) and a maximum subsidence of −86.2 mm (LOS-ascending) or −71.5 mm (LOS-descending), with an oscillating behavior. These deformation conditions are largely associated with the kinematics of the Bucaramanga Fault, but a recurrent action of deep seismic activity from the Bucaramanga Seismic Nest was also observed, generating a surface deformation of ±20 mm for the period evaluated. These deformations have a certain degree of impact on the generation of mass movements, evaluated by the correlation with the LOS-descending images. However, their action is more focused as an inherent factor of great weight, which makes it possible to respond to early care and allows real-time follow-up, giving positive feedback to the system.

The Duckbot: A system for automated imaging and manipulation of duckweed.

Laboratory automation can boost precision and reproducibility of science workflows. However, current laboratory automation systems are difficult to modify for custom applications. Automating new experiment workflows therefore requires development of one-off research platforms, a process which requires significant time, resources, and experience. In this work, we investigate systems to lower the threshold to automation for plant biologists. Our approach establishes a direct connection with a generic motion platform to support experiment development and execution from a computational notebook environment. Specifically, we investigate the use of the open-source tool-changing motion platform Jubilee controlled using Jupyter notebooks. We present the Duckbot, a machine customized for automating laboratory research workflows with duckweed, a common multicellular plant. The Duckbot comprises (1) a set of end-effectors relevant for plant biology, (2) software modules which provide flexible control of these tools, and (3) computational notebooks which make use of these tools to automate duckweed experiments. We demonstrate the Duckbot's functionality by automating a particular laboratory research workflow, namely, duckweed growth assays. The Duckbot supports setting up sample plates with duckweed and growth media, gathering image data, and conducting relevant data analysis. We discuss the opportunities and limitations for developing custom laboratory automation with this platform and provide instructions on usage and customization.

Neuronal tuning to threat exposure remains stable in the mouse prefrontal cortex over multiple days.

Intense threat elicits action in the form of active and passive coping. The medial prefrontal cortex (mPFC) executes top-level control over the selection of threat coping strategies, but the dynamics of mPFC activity upon continuing threat encounters remain unexplored. Here, we used 1-photon calcium imaging in mice to probe the activity of prefrontal pyramidal cells during repeated exposure to intense threat in a tail suspension (TS) paradigm. A subset of prefrontal neurons displayed selective activation during TS, which was stably maintained over days. During threat, neurons showed specific tuning to active or passive coping. These responses were unrelated to general motion tuning and persisted over days. Moreover, the neural manifold traversed by low-dimensional population activity remained stable over subsequent days of TS exposure and was preserved across individuals. These data thus reveal a specific, temporally, and interindividually conserved repertoire of prefrontal tuning to behavioral responses under threat.

The preoptic area and dorsal habenula jointly support homeostatic navigation in larval zebrafish

Animals must maintain physiological processes within an optimal temperature range despite changes in their environment. Through behavioral assays, whole-brain functional imaging, and neural ablations, we show that larval zebrafish, an ectothermic vertebrate, achieves thermoregulation through homeostatic navigation-non-directional and directional movements toward the temperature closest to its physiological setpoint. A brain-wide circuit encompassing several brain regions enables this behavior. We identified the preoptic area of the hypothalamus (PoA) as a key brain structure in triggering non-directional reorientation when thermal conditions are worsening. This result shows an evolutionary conserved role of the PoA as principal thermoregulator of the brain also in ectotherms. We further show that the habenula (Hb)-interpeduncular nucleus (IPN) circuit retains a short-term memory of the sensory history to support the generation of coherent directed movements even in the absence of continuous sensory cues. We finally provide evidence that this circuit may not be exclusive for temperature but may convey a more abstract representation of relative valence of physiologically meaningful stimuli regardless of their specific identity to enable homeostatic navigation.

A novel teacher–student hierarchical approach for learning primitive information

In this study, we focus on the challenge of robot motion generation, which is critical for improving the ability of robots to perform downstream tasks. To this end, inspired by the idea of variational inference, we propose a novel teacher–student hierarchical approach for learning continuous primitive spaces to generate robot motions. We first learn a variational auto-encoder (VAE) based on Gated Recurrent Unit (GRU) from demonstrations, the encoder is used as a teacher model to encode trajectories to primitive information, and the decoder aims to decode latent information to joint trajectories. To learn a primitive action space for the high-level model, we propose a novel learning method to train a student model. We utilize the trained teacher to encode trajectories to labels, and use the Kullback–Leibler (KL) divergence and the reconstruction errors between the label and the predicted primitive information as the loss to train a student model. We then utilize trained student models to guide high-level policy learning. We evaluate our approach on three different robot datasets. Experiments demonstrate that our method obtains the ability to generate new motion and discover common information across tasks, it also can be used to accelerate downstream task learning.

Wand-Based Calibration of Unsynchronized Multiple Cameras for 3D Localization.

Three-dimensional (3D) localization plays an important role in visual sensor networks. However, the frame rate and flexibility of the existing vision-based localization systems are limited by using synchronized multiple cameras. For such a purpose, this paper focuses on developing an indoor 3D localization system based on unsynchronized multiple cameras. First of all, we propose a calibration method for unsynchronized perspective/fish-eye cameras based on timestamp matching and pixel fitting by using a wand under general motions. With the multi-camera calibration result, we then designed a localization method for the unsynchronized multi-camera system based on the extended Kalman filter (EKF). Finally, extensive experiments were conducted to demonstrate the effectiveness of the established 3D localization system. The obtained results provided valuable insights into the camera calibration and 3D localization of unsynchronized multiple cameras in visual sensor networks.

Regional Differences in Skeletal Muscle O2 Dynamics During Body-Weight Resistance Exercise with Slow Movement and Tonic Force Generation.

The aim of this study was to investigate the skeletal muscle O2 dynamics during body-weight resistance exercise with slow movement and tonic force generation. Thirteen untrained, healthy, young males performed Bulgarian split squats and push-ups until volitional failure. Relative changes from rest in oxygenated haemoglobin (oxy-Hb) and deoxygenated haemoglobin (deoxy-Hb) concentrations were continuously monitored at the vastus lateralis (VL) and rectus femoris (RF) muscles during squats, and pectoralis major (PM) and triceps brachii (TB) muscles during push-ups using spatial resolved near-infrared spectroscopy oximetry. During the squat exercise, deoxy-Hb continuously increased at RF until failure (10.8±7.0μmol/L), while at VL, deoxy-Hb was relatively maintained during 25-100% of the number of maximum repetitions (18.0±7.4μmol/L at volitional failure). During the push-up exercise, a significant increase in deoxy-Hb was observed during exercise from rest at PM and TB. We found a significant increase in oxy-Hb during exercise at PM (28.1±15.8μmol/L at volitional failure), while at TB, no significant difference was observed from rest (-2.7±13.7μmol/L at volitional failure). Our findings suggest that the deoxygenation patterns during body-weight squat exercise were heterogeneous within the quadriceps muscles. Moreover, differences in O2 dynamics between the trunk and extremity muscles may be partly explained by convective O2 supply during resistance exercise. However, future studies are needed due to inter-individual differences in skills for body-weight resistance exercise.

Facial Prior Guided Micro-Expression Generation.

This paper focuses on the facial micro-expression (FME) generation task, which has potential application in enlarging digital FME datasets, thereby alleviating the lack of training data with labels in existing micro-expression datasets. Despite obvious progress in the image animation task, FME generation remains challenging because existing image animation methods can hardly encode subtle and short-term facial motion information. To this end, we present a facial-prior-guided FME generation framework that takes advantage of facial priors for facial motion generation. Specifically, we first estimate the geometric locations of action units (AUs) with detected facial landmarks. We further calculate an adaptive weighted prior (AWP) map, which alleviates the estimation error of AUs while efficiently capturing subtle facial motion patterns. To achieve smooth and realistic synthesis results, we use our proposed facial prior module to guide motion representation and generation modules in mainstream image animation frameworks. Extensive experiments on three benchmark datasets consistently show that our proposed facial prior module can be adopted in image animation frameworks and significantly improve their performance on micro-expression generation. Moreover, we use the generation technique to enlarge existing datasets, thereby improving the performance of general action recognition backbones on the FME recognition task. Our code is available at https://github.com/sysu19351158/FPB-FOMM.

Versatile Telescopic-Wheeled-Legged Locomotion of Tachyon 3 via Full-Centroidal Nonlinear Model Predictive Control

This paper presents a nonlinear model predictive control (NMPC) toward versatile motion generation for the telescopic-wheeled-legged robot Tachyon 3, the unique hardware structure of which poses challenges in control and motion planning. We apply the full-centroidal NMPC formulation with dedicated constraints that can capture the accurate kinematics and dynamics of Tachyon 3. We have developed a control pipeline that includes an internal state integrator to apply NMPC to Tachyon 3, the actuators of which employ high-gain position-controllers. We conducted simulation and hardware experiments on the perceptive locomotion of Tachyon 3 over structured terrains and demonstrated that the proposed method can achieve smooth and dynamic motion generation under harsh physical and environmental constraints.

Assessing playability limits of bowed-string transients using experimental measurements

Understanding the dynamics of bowed-string attacks involves exploring the relationship between bow acceleration, bow force, and the generation of Helmholtz motion during transients. This study addresses the following research question: How do theoretical limits of “playability” predict these parameters? Motivated by the need for experimental evidence in this domain, we present a comprehensive investigation into bowed-string transients within the bow acceleration and bow force parameter space, known as the Guettler diagram. This study exclusively employs an experimental methodology. The setup, employing a robotic arm, permits the collection of transient data under varying bowing conditions. Analysis of the bridge force waveform allows for the extraction of pre-Helmholtz transient times. Our results reveal a triangular playable region in the Guettler diagram, consistent with theoretical predictions and previous experimental findings. However, Guettler’s analytical limits for playable regions during transients show limitations. We investigate the role of friction, a key parameter idealized in the model used for obtaining these limits. Measured friction coefficients from transients reveal discrepancies with prior experimental studies, highlighting the need for further investigations in this direction.

Reform and resurgence: The transformation of Islamic movements in the 21st century

This paper studies the development in the thought and praxis of Islamic movements that aim to revive a political system governed by Islamic law. Post-Caliphate Islamic movements - the subject of the current study - have undergone recent reforms in thought, approach, and even branding since the Arab Spring. Notwithstanding the movements? legal and theological diversity, the author argues that they share common features of reform characterized by (a) appeal to public opinion and grassroots work; and (b) willingness to work with groups outside their movement. These reforms were shaped in tandem with and in reaction to the War on Terror, the Arab Spring, and the Taliban?s victory in Afghanistan. The author argues that these changes gave rise to dynamic reform in the latest generation of Islamic movements, leading to substantive revisions in their approach (manhaj) to achieving Islamic change. Despite tightening proscription and censorship, the author argues that the fruit of these reforms has been a resurgence in the form of highly efficient and creative collaboration between movements. Examples studied include collaboration between the Muslim Brothers (MB) and Hizb ut Tahrir in the West, and between former-al-Qaeda and MB affiliates in Syria. The author estimates these strategies - following a period of political failure and internal skepticism - have precipitated a resurgence that makes the future of Islamic movements highly potent, despite resulting in a dilution of each group?s identity and possible decline in membership. As a result of this trajectory, this study argues for the need to theorize contemporary Islamic movements beyond historical categories that have fossilized in Western academia, and often do not capture the dynamic, fluidity and comprehensiveness of Islamic struggle. This study utilizes a mixed methodology, including archival analysis and intellectual genealogy.

The Use of Digital Media Arts in Animation Filmmaking in the Age of Digital Intelligence

Abstract The development of digital media art has brought a broader application prospect for the production of digital animation movies. The incorporation of Digital Media Arts into Animation Filmmaking during the Age of Digital Intelligence The DTW algorithm is selected to temporally regularize and align the motion data of the animated character, and the motion model is downsized and optimized by combining with the PCA method, to obtain the motion sequence with the characteristics of the animated character itself. The multi-scale method is utilized to decompose the original motion data when combined with motion capture technology to establish a 3D motion database. Analyze the motion connection curves between different video clips and the generated animation data after and before motion modeling using the 3D motion database of broadcast gymnastics. Analyzing the drawing efficiency of virtual character models is done using an ordinary human character model with different voxel accuracy. Before the motion library modeling, the average joint error measured in degrees was 10.012°, as indicated by the data. The modeling procedure results in a reduction of the average joint error to 4.281°. The accuracy of animated character generation motion can be improved by using motion modeling. Character motion generation suggestions for animated movie production are provided in this study.

Development and Test of a Multimode Piezomotor With Active Contact Force Adjusting

Piezomotors have been widely used for motion generation with long stroke and fine resolution, where stick-slip mode features a high motion velocity but a low load capacity, inchworm mode has a high load capacity but a low motion velocity, and scan mode is able to realize super-high resolution but less travel range. Combining these motion modes to achieve a well-rounded piezomotor is challengeable but significant. In this study, a piezomotor with multiple motion modes is proposed. A compliant mechanism with a parallelogram mechanism and lever-type amplifiers is designed to realize various motion modes and adjust the contact force, which has a significant impact on motion performance. To prevent the backward motion and slippage during motion, two power-function based driving signals are designed and analyzed for stick-slip mode and inchworm mode, respectively. A prototype of the designed piezomotor was fabricated to validate the feasibility. According to the experiment results, the maximum motion velocity is 3.24 mm/s, the load capacity is 7.85 N, and the most fine displacement resolution is 10 nm. The proposed methodology is applicable to other types of multi-mode piezomotors.

An Upper Limb Exoskeleton Motion Generation Algorithm Based on Separating Shoulder and Arm Motion.

Many rehabilitation exoskeletons have been used in the field of stroke rehabilitation. Generating human-like motion is necessary for exoskeletons to help patients perform activities of daily living (ADL) while maintaining interaction quality and ergonomics. However, most of the current motion generation algorithms utilize inverse kinematics (IK) to solve the final configuration before generation, and do not consider the movement of shoulder girdle. Separately considering the shoulder girdle motion and arm motion, this paper proposes an algorithm integrated IK to generate human-like motion. The arm moves towards the target with a bell-shaped velocity in the absence of the final configuration, and the shoulder girdle maintain natural passive motion. Moreover, the generated motion can be mapped to the configuration space of exoskeletons. Compared with the experimental data collected using a motion capture system, the values of RMSE and HPDI of the generated wrist trajectory in the task space are within 0.2 and 0.17, respectively, while those of RMSE in the joint space are within 15 deg, which demonstrates the human-like nature of the generated motion.

Diverse Motion In-betweening from Sparse Keyframes with Dual Posture Stitching.

In-betweening is a technique for generating transitions given start and target character states. The majority of existing works require multiple (often ≥ 10) frames as input, which are not always available. In addition, they produce results that lack diversity, which may not fulfill artists' requirements. Addressing these gaps, our work deals with a focused yet challenging problem: generating diverse and high-quality transitions given exactly two frames (only the start and target frames). To cope with this challenging scenario, we propose a bi-directional motion generation and stitching scheme which generates forward and backward transitions from the start and target frames with two adversarial autoregressive networks, respectively, and stitches them midway between the start and target frames. In contrast to stitching at the start or target frames, where the ground truth cannot be altered, there is no strict midway ground truth. Thus, our method can capitalize on this flexibility and generate high-quality and diverse transitions simultaneously. Specifically, we employ conditional variational autoencoders (CVAEs) to implement our autoregressive networks and propose a novel stitching loss to stitch the bi-directional generated motions around the midway point.