Trajectory Modifications Research Articles

Towards effective, robust and utility-preserving watermarking of GPS trajectories

Personal GPS trajectory is essential for businesses and emerging data markets due to its relevance in various data-driven methods, including traffic forecasting, accident prediction, and profiling driving behavior. Watermarking is a method that facilitates verification of data ownership and authenticity by embedding provenance information into the data. Whereas watermarking is commonly adopted in the image and audio domains, only a few initial watermarking methods exist for GPS trajectory data. GPS trajectory watermarking is particularly challenging due to the spatio-temporal data properties and easiness of data modification. As a result, existing watermarking methods often embed only minimal provenance information, lack robustness, and can fail to preserve data utility for downstream applications. In this work, we propose W-Trace - a novel, effective, robust, and utility-preserving GPS trajectory watermarking method. W-Trace transforms a GPS trajectory into a complex domain and applies the Fourier transformation to decompose the trajectory into the frequency representation. W-Trace embeds watermarks in the frequency representation and verifies them in a spatiotemporally-aware procedure. We demonstrate the effectiveness, robustness, and utility of the proposed W-Trace approach in realistic settings using real-world GPS trajectory datasets. In contrast to the baselines, the proposed W-Trace approach is robust to a wide range of trajectory modifications while preserving the GPS trajectory characteristics required for the downstream applications.

RE-Trace : Re-identification of Modified GPS Trajectories

GPS trajectories are a critical asset for building spatio-temporal predictive models in urban regions in the context of road safety monitoring, traffic management, and mobility services. Currently, reliable and efficient data misuse detection methods for such personal, spatio-temporal data, particularly in data breach cases, are missing. This article addresses an essential aspect of data misuse detection, namely, the re-identification of leaked and potentially modified GPS trajectories. We present RE-Trace —a contrastive learning-based model that facilitates reliable and efficient re-identification of GPS trajectories and resists specific trajectory transformation attacks aimed to obscure a trajectory’s origin. RE-Trace utilizes contrastive learning with a transformer-based trajectory encoder to create trajectory representations, robust to various trajectory modifications. We present a comprehensive threat model for GPS trajectory modifications and demonstrate the effectiveness and efficiency of the RE-Trace re-identification approach on three real-world datasets. Our evaluation results demonstrate that RE-Trace significantly outperforms state-of-the-art baselines on all datasets and identifies modified GPS trajectories effectively and efficiently.

Effect of surgical modification of deep brain stimulation lead trajectories on radiofrequency heating during MRI at 3T: from phantom experiments to clinical implementation.

Radiofrequency (RF) tissue heating around deep brain stimulation (DBS) leads is a well-known safety risk during MRI, resulting in strict imaging guidelines and limited allowable protocols. The implanted lead's trajectory and orientation with respect to the MRI electric fields contribute to variations in the magnitude of RF heating across patients. Currently, there are no surgical requirements for implanting the extracranial portion of the DBS lead, resulting in substantial variations in clinical lead trajectories and consequently RF heating. Recent studies have shown that incorporating concentric loops in the extracranial lead trajectory can reduce RF heating. However, optimal positioning of the loops and the quantitative benefit of trajectory modification in terms of added safety margins during MRI remain unknown. In this study, the authors systematically evaluated the characteristics of DBS lead trajectories that minimize RF heating during 3T MRI to develop the best surgical practices for safe access to postoperative MRI, and they present the first surgical implementation of these modified trajectories. The authors performed experiments to assess the maximum temperature increase of 244 distinct lead trajectories. They investigated the effect of the position, number, and size of the concentric loops on the skull. Experiments were performed in an anthropomorphic phantom implanted with a commercial DBS system, and RF exposure was generated by applying a high specific absorption rate sequence (B1+rms = 2.7 µT). The authors conducted test-retest experiments to assess the reliability of measurements. Additionally, they evaluated the effect of imaging landmarks and perturbations to the DBS device configuration on the efficacy of low-heating trajectories. Finally, two neurosurgeons implanted the recommended modified trajectories in patients, and the authors characterized their RF heating in comparison with unmodified trajectories. The maximum temperature increase ranged from 0.09°C to 7.34°C. The authors found that increasing the number of loops and positioning them closer to the surgical burr hole, particularly for the contralateral lead, substantially reduced RF heating. These trajectory modifications were easily incorporated during the surgical procedure and resulted in a threefold reduction in RF heating. Surgically modifying the extracranial portion of the DBS lead trajectory can substantially reduce RF heating during 3T MRI. The authors' results indicate that simple adjustments to the lead's configuration, such as small, concentric loops near the burr hole, can be readily adopted during DBS lead implantation to improve patient safety during MRI.

Recurrent neural network variants based model for Cassini-Huygens spacecraft trajectory modifications recognition

Over the last 13.7 years period of the Cassini mission, amendments to the spacecraft’s flight path were needed. This research is being carried out as there is a limited number of studies that use a temporal discrimination analysis to handle raw data. More complex inspection and analysis of the collected broad trajectory dataset is necessary to classify orbital events in the signal travel period (approximately 88 minutes on the Earth-Cassini travel channel length). This paper provides an innovative, in-depth learning method to identify offline modifications in the Cassini spacecraft trajectory. The models are based on variants of Recurrent Neural Networks (RNNs: Gated Recurrent Unit (GRU)/ Long Short-Term Memory (LSTM)/ Bidirectional Long Short-Term Memory (BiLSTM)) to derive valuable data and learn the inner data structure of the time sequence, along with the penetration of long-term and short-term phase-dependencies of the RNNs layers. To validate our models, we used a variety of statistical approaches in our analysis. A considerable number of tests have been carried out, and the findings obtained have shown that the GRU and LSTM give a substantial boost to increasing the efficiency of the detection mechanism. The proposed model would consolidate potential exploration in outer space exploration to accommodate massive databases, search for correlations, and recognize complex events and outliers with an accuracy that exceeds 99 %. This method can be utilized for similar detection processes within the future outer space expedition. The results show that binary classifications of Matthews Correlation Coefficient (MCC) are more accurate than F_1 score.

CAE: Contextual auto-encoder for multivariate time-series anomaly detection in air transportation

The Automatic Dependent Surveillance-Broadcast protocol is one of the latest compulsory advances in air surveillance. While it supports the tracking of the ever-growing number of aircraft in the air, it also introduces cybersecurity issues that must be mitigated e.g., false data injection attacks where an attacker emits fake surveillance information. The recent data sources and tools available to obtain flight tracking records allow the researchers to create datasets and develop Machine Learning models capable of detecting such anomalies in En-Route trajectories. In this context, we propose a novel multivariate anomaly detection model called Contextual Auto-Encoder (CAE). It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase (e.g. climbing, cruising or descending) during its training. To illustrate the CAE’s efficiency, an evaluation dataset was created using real-life anomalies as well as realistically crafted trajectory modifications, with which the CAE as well as three anomaly detection models from the literature were evaluated. Results show that the CAE achieves better results in both accuracy and speed of detection. The dataset, the models implementations and the evaluation results are available in an online repository, thereby enabling replicability and facilitating future experiments.

Near-Real-Time IDS for the U.S. FAA’s NextGen ADS-B

Modern-day aircraft are flying computer networks, vulnerable to ground station flooding, ghost aircraft injection or flooding, aircraft disappearance, virtual trajectory modifications or false alarm attacks, and aircraft spoofing. This work lays out a data mining process, in the context of big data, to determine flight patterns, including patterns for possible attacks, in the U.S. National Air Space (NAS). Flights outside the flight patterns are possible attacks. For this study, OpenSky was used as the data source of Automatic Dependent Surveillance-Broadcast (ADS-B) messages, NiFi was used for data management, Elasticsearch was used as the log analyzer, Kibana was used to visualize the data for feature selection, and Support Vector Machine (SVM) was used for classification. This research provides a solution for attack mitigation by packaging a machine learning algorithm, SVM, into an intrusion detection system and calculating the feasibility of processing US ADS-B messages in near real time. Results of this work show that ADS-B network attacks can be detected using network attack signatures, and volume and velocity calculations show that ADS-B messages are processable at the scale of the U.S. Next Generation (NextGen) Air Traffic Systems using commodity hardware, facilitating real time attack detection. Precision and recall close to 80% were obtained using SVM.

SYLVIUS: A multimodal and multidisciplinary platform for epilepsy surgery

Background and objectiveWe present SYLVIUS, a software platform intended to facilitate and improve the complex workflow required to diagnose and surgically treat drug-resistant epilepsies. In complex epilepsies, additional invasive information from exploration with stereoencephalography (SEEG) with deep electrodes may be needed, for which the input from different diagnostic methods and clinicians from several specialties is required to ensure diagnostic efficacy and surgical safety. We aim to provide a software platform with optimal data flow among the different stages of epilepsy surgery to provide smooth and integrated decision making. MethodsThe SYLVIUS platform provides a clinical workflow designed to ensure seamless and safe patient data sharing across specialities. It integrates tools for stereo visualization, data registration, transfer of electrode plans referred to distinct datasets, automated postoperative contact segmentation, and novel DWI tractography analysis. Nineteen cases were retrospectively evaluated to track modifications from an initial plan to obtain a final surgical plan, using SYLVIUS. ResultsThe software was used to modify trajectories in all 19 consulted cases, which were then imported into the robotic system for the surgical intervention. When available, SYLVIUS provided extra multimodal information, which resulted in a greater number of trajectory modifications. ConclusionsThe architecture presented in this paper streamlines epilepsy surgery allowing clinicians to have a digital clinical tool that allows recording of the different stages of the procedure, in a common multimodal 2D/3D setting for participation of different clinicians in defining and validating surgical plans for SEEG cases.

Deep Learning-Based Approach for Detecting Trajectory Modifications of Cassini-Huygens Spacecraft

There were necessary trajectory modifications of Cassini spacecraft during its last 14 years movement cycle of the interplanetary research project. In the scale 1.3 hour of signal propagation time and 1.4-billion-kilometer size of Earth-Cassini channel, complex event detection in the orbit modifications requires special investigation and analysis of the collected big data. The technologies for space exploration warrant a high standard of nuanced and detailed research. The Cassini mission has accumulated quite huge volumes of science records. This generated a curiosity derives mainly from a need to use machine learning to analyze deep space missions. For energy saving considerations, the communication between the Earth and Cassini was executed in non-periodic mode. This paper provides a sophisticated in-depth learning approach for detecting Cassini spacecraft trajectory modifications in post-processing mode. The proposed model utilizes the ability of Long Short Term Memory (LSTM) neural networks for drawing out useful data and learning the time series inner data pattern, along with the forcefulness of LSTM layers for distinguishing dependencies among the long-short term. Our research study exploited the statistical rates, Matthews correlation coefficient, and F1 score to evaluate our models. We carried out multiple tests and evaluated the provided approach against several advanced models. The preparatory analysis showed that exploiting the LSTM layer provides a notable boost in rising the detection process performance. The proposed model achieved a number of 232 trajectory modification detections with 99.98% accuracy among the last 13.35 years of the Cassini spacecraft life.

A mixed integer linear programming model for multi-sector planning using speed and heading changes

The Multi-Sector Planning (MSP) concept, adopted in both the SESAR and NextGen projects, promotes the control of aircraft and resolution of conflicts over a medium time horizon to reduce and balance controller workload. In the context of MSP, we propose a first formulation of the complexity resolution problem that allows trajectory modifications using both speed and heading changes assuming exact knowledge of aircraft positions. This model is also the first to have the capacity to ensure workload balancing among sectors in a Multi-Sector Area (MSA). The number of crossing conflicts in a sector is used as a measure of controller workload. This problem is formulated as a mixed integer linear programming model that allows obtaining optimal solutions. This model ensures neighbor trajectory recovery and minimal delays. This model was tested on a set of conflict detection and resolution benchmark test problems with up to 300 simultaneous conflicts. Conflict-free solutions were obtained in less than 1.4 s. The model was also tested on several distinct sets of randomly generated problems with an MSA of four sectors and up to 150 aircraft. The number of crossing conflicts was reduced by more than 99% with a computation time smaller than four seconds. It was found that it is beneficial to allow the use of both speed and heading changes in high traffic situations. It was also found that considering workload balancing allows the minimization of the total workload in the MSA while preventing overloading some sectors.

CT-guided radiofrequency ablation of osteoid osteoma: established concepts and new ideas.

Osteoid osteoma is a painful benign bone tumour of children and young adults with characteristic clinico-radiological features depending upon the location of the lesion. Intraoperative visualisation of the nidus is difficult and therefore curative surgery is often associated with excessive bone removal, significant perioperative morbidity and potential need of bone grafting procedures. With advancement in cross-sectional imaging and radiofrequency ablation (RFA) technology, CT-guided RFA has emerged as the treatment of choice for the osteoid osteoma. This procedure involves accurate cannulation of the nidus and subsequent thermocoagulation-induced necrosis.Multidisciplinary management approach is the standard of care for patients with osteoid osteoma. Appropriate patient selection, identification of imaging pitfalls, pre-anaesthetic evaluation and a protocol-based interventional approach are the cornerstone for a favourable outcome. Comprehensive patient preparation with proper patient position and insulation is important to prevent complications. Use of spinal needle-guided placement of introducer needle, namely, "rail-road technique" is associated with fewer needle trajectory modifications, reduced radiation dose and patient morbidity and less intervention time. Certain other procedural modifications are employed in special situations, for example, intra-articular osteoid osteoma and osteoid osteoma of the subcutaneous bone in order to reduce complications. Treatment follow-up generally includes radiographic assessment and evaluation of pain score. Dynamic contrast-enhanced MRI has been recently found useful for demonstrating post-RFA healing.

Momentum predictability and heat accumulation in laser-based space debris removal

Small space debris objects of even a few centimeters can cause severe damage to satellites. Powerful lasers are often proposed for pushing small debris by laser-ablative recoil toward an orbit where atmospheric burn-up yields their remediation. We analyze whether laser-ablative momentum generation is safe and reliable concerning predictability of momentum and accumulation of heat at the target. With hydrodynamic simulations on laser ablation of aluminum as the prevalent debris material, we study laser parameter dependencies of thermomechanical coupling. The results serve as configuration for raytracing-based Monte Carlo simulations on imparted momentum and heat of randomly shaped fragments within a Gaussian laser spot. Orbit modification and heating are analyzed exemplarily under repetitive laser irradiation. Short wavelengths are advantageous, yielding momentum coupling up to ∼40 mNs / kJ, and thermal coupling can be minimized to 7% of the pulse energy using short-laser pulses. Random target orientation yields a momentum uncertainty of 86% and the thrust angle exhibits 40% scatter around 45 deg. Moreover, laser pointing errors at least redouble the uncertainty in momentum prediction. Due to heat accumulation of a few Kelvin per pulse, their number is restricted to allow for intermediate cooldown. Momentum scatter requires a sound collision analysis for conceivable trajectory modifications.

3D MicroCT spatial and temporal characterization of thoracic aorta perivascular adipose tissue and plaque volumes in the ApoE-/- mouse model

ABSTRACTPerivascular adipose tissue (PVAT) influences vascular function and pathology. We present a protocol using micro-computed tomography (microCT), a novel imaging technique typically used for hard biological tissue, to characterize the temporal and spatial development of aorta PVAT and luminal plaque soft tissue. Apolipoprotein E deficient (ApoE) and C57Bl/6J (control) mice were fed a high fat western diet up to 30 weeks. 3D microCT reconstructions were used to quantify: 1) vascular wall volume, a surrogate measure of remodeling, was greater in ApoE, 2) aorta PVAT volume was reduced in ApoE, 3) plaque volumes increased over time in ApoE, 4) plaque development co-localized with luminal ostia, origins of branching arteries, which traveled through areas of greatest PVAT volume, 5) qualitatively, the same arteries showed evidence of increased tortuosity in ApoE. This study reflects the potential of microCT analyses to assess vascular wall, PVAT and arterial trajectory modifications in relevant animal models.Abbreviations: PVAT: perivascular adipose tissue; ApoE: apolipoprotein E deficient mouse strain; Control: C57Bl/6J mouse strain; PTA: 0.3% phosphotungstic acid; microCT: micro-computed tomography; CV: cardiovascular; CVD: cardiovascular disease; IQR: interquartile range; PPARγ: peroxisome proliferator activated receptor – gamma; VV: vasa vasorum; 3D: three dimensional

Geosynchronous Debris Conjunction Lead-Time Requirements for Autonomous Low-Thrust Disposal Guidance

Autonomous, low-thrust guidance for active disposal of geosynchronous debris, subject to collision avoidance with the local debris population, is studied. A bisection method is employed to determine trajectory modifications to avoid a conjuncting debris object by a range of distances, assuming a range of collision lead times. A parametric study is performed, in which re-orbit thrust accelerations are varied from 10−6 to 10−3 m/s 2, to demonstrate how the continuous-thrust level impacts the required lead time to achieve a desired debris miss distance. The lowest thrust levels considered show that a 6-12 hour lead time is required to achieve a 1-10 km debris separation at the predicted collision time.

Laser-based removal of irregularly shaped space debris

While the feasibility of laser space debris removal by high energy lasers has been shown in concept studies and laboratory proofs of principle, we address the question of the effectiveness and responsibility associated with this technique. The large variety of debris shapes poses a challenge for predicting amount and direction of the impulse imparted to the target. We present a numerical code that considers variation of fluence throughout the target surface with respect to the resulting local momentum coupling. Simple targets as well as an example for realistic space debris are investigated with respect to momentum generation. The predictability of the imparted momentum is analyzed in a Monte Carlo study. It was found that slight variations of the initial debris position and orientation may yield large differences of the modified trajectories. We identify highly cooperative targets, e.g., spheres, as well as targets that are strongly sensitive to orientation, e.g., plates, and exhibit a poor performance in laser debris removal. Despite limited predictability for the motion of a particular debris object, the laser-based approach appears to be suitable for space debris removal, albeit not with a deterministic but rather with a probabilistic treatment of the resulting trajectory modifications.

Hyperarticulation in Lombard speech: Global coordination of the jaw, lips and the tongue.

Over the last century, researchers have collected a considerable amount of data reflecting the properties of Lombard speech, i.e., speech in a noisy environment. The documented phenomena predominately report effects on the speech signal produced in ambient noise. In comparison, relatively little is known about the underlying articulatory patterns of Lombard speech, in particular for lingual articulation. Here the authors present an analysis of articulatory recordings of speech material in babble noise of different intensity levels and in hypoarticulated speech and report quantitative differences in relative expansion of movement of different articulatory subsystems (the jaw, the lips and the tongue) as well as in relative expansion of utterance duration. The trajectory modifications for one articulator can be relatively reliably predicted by those for another one, but subsystems differ in a degree of continuity in trajectory expansion elicited across different noise levels. Regression analysis of articulatory modifications against durational expansion shows further qualitative differences between the subsystems, namely, the jaw and the tongue. The findings are discussed in terms of possible influences of a combination of prosodic, segmental, and physiological factors. In addition, the Lombard effect is put forward as a viable methodology for eliciting global articulatory variation in a controlled manner.

Trajectory Optimization With Particle Swarm Optimization for Manipulator Motion Planning

Optimization-based methods have been recently proposed to solve motion planning problems with complex constraints. Previous methods have used optimization methods that may converge to a local minimum. In this study, particle swarm optimization (PSO) is proposed for trajectory optimization. PSO is a population-based stochastic global optimization method inspired by group behaviors in wildlife, and has the advantages of simplicity and fast convergence. Trajectory modifications are encoded in particles that are optimized with PSO. The normalized step cost (NSC) concept is used for the initialization of the particles in PSO. A method for reusing previously optimized parameters is also developed. The optimized parameters are stored together with corresponding NSC vectors, and when a constraint violation occurs, the parameters associated with an NSC vector that is similar to the query NSC vector are selected. The selected vector is used for initializing the particles. The reuse of the previously optimized parameters improves the convergence of the PSO in motion planning. The effectiveness of these methods is shown with simulations and an experiment using a three-dimensional problem with constraints. The proposed algorithm successfully optimized a trajectory while satisfying the constraints and is less likely to converge to a local minimum.

Unconscious and out of control: Subliminal priming is insensitive to observer expectations

We asked whether the influence of an invisible prime on movement is dependent on conscious movement expectations. Participants reached to a central target, which triggered a directional prime-mask arrow sequence. Participants were instructed that the visible arrows (masks) would most often signal a movement modification in a specific (biased) direction. Kinematic analyses revealed that responses to the visible mask were influenced by participants' intentional bias, as movements were fastest when the more probable mask was displayed. In addition, responses were influenced by the invisible prime without regard to its relationship to the more probable mask. Analysis of the time of initial trajectory modifications revealed that both primes influenced responses in a similar manner after accounting for participants' bias. These results imply that invisible stimuli automatically activate their associated responses and that unconscious priming of the motor system is insensitive to the conscious expectations of the participant making the pointing movements.

Solar sail elastic displacement effects on interplanetary trajectories

Space agencies are paying greater attention to solar sail technologies and missions. Actually, one of the most demanding issues when considering solar sailing is to assess the sail deformation as well as the following trajectory modifications. The main purpose of this paper is to show the order of accuracy that can be reached when coupling structural and dynamical behavior of a solar sail. Based on the application of the Finite Element Method, the deformations affecting the large structure of the sail, up to the second order of accuracy, are estimated, together with the real-time updated thrust vector according to such deformations. The new thrust vector, evaluated for an Earth–Venus mission, allows one to find a more realistic sailcraft trajectory. The results obtained show a change in the thrust's magnitude with a not negligible variation of the sailcraft trajectory with respect to the undeformed case. Another issue deserving particular attention concerns solar sail deployment. Both structural and dynamical behavior affecting a solar sail's performance will be analyzed even in the event of partial deployment. The results obtained show the importance of the right sizing of the attitude control, which may not be able to compensate such a failure and what strategies could be used to save the mission including the need for a new mission analysis.

Optimal In-Flight Trajectory Modifications for Ballistic Missiles and Free Rockets

Exoatmospheric trajectory modifications of ballistic missiles by a single velocity impulse can be optimized by appropriate selection of the points of transfer. This research deals with the optimization of theweighted cost function that includes the change of velocity needed for the maneuver and the remaining time of flight after the maneuver. Closed-form analytic expressions are developed, and the optimization is performed using exhaustive search techniques. Numerical results are presented for minimum energy, depressed, and lofted trajectories cases.

Antisaccades exhibit diminished online control relative to prosaccades

Convergent evidence suggests that stimulus-driven saccades (i.e., prosaccades) are mediated via online trajectory modifications (e.g., Gaveau et al. 2003). The goal of the present investigation was to determine whether manipulating the cognitive demands of a saccade influences the extent to which the response's trajectory is structured online. To that end, participants completed pro- and antisaccades (i.e., 180 degrees mirror-symmetrical transformation) to target stimuli that were continuously visible (Experiment 1) or occluded (Experiment 2) during the response. To index trajectory modifications, we computed the proportion of variance (R (2) values) explained by the spatial location of the eye at 10% increments of normalized movement time [i.e., 10, 20, ... 80, 90% of movement time (MT)] relative to the saccade's ultimate movement endpoint. The basis for this analysis is that between-task differences in the magnitude of R (2) values reflect differences in the use of feedback for online trajectory amendments. Results indicated that antisaccades produced larger R (2) values (from 40 to 80% of MT) as well as less accurate and more variable endpoints than their prosaccade counterparts. Such a pattern of results indicates that antisaccades were not controlled online to the same degree as prosaccades. In particular, we propose that the cognitive nature of the antisaccade task disrupts the normally online operation of saccade networks and renders a mode of control that is not optimized for feedback-based trajectory amendments.