Static Segments Research Articles

Visual Exploration of Cycling Semantics with GPS Trajectory Data

Cycling—as a sustainable and convenient exercise and travel mode—has become increasingly popular in modern cities. In recent years, with the proliferation of sport apps and GPS mobile devices in daily life, the accumulated cycling trajectories have opened up valuable opportunities to explore the underlying cycling semantics to enable a better cycling experience. In this paper, based on large-scale GPS trajectories and road network data, we mainly explore cycling semantics from two perspectives. On one hand, from the perspective of the cyclists, trajectories could tell their frequently visited sequences of streets, thus potentially revealing their hidden cycling themes, i.e., cyclist behavior semantics. On the other hand, from the perspective of the road segments, trajectories could show the cyclists’ fine-grained moving features along roads, thus probably uncovering the moving semantics on roads. However, the extraction and understanding of such cycling semantics are nontrivial, since most of the trajectories are raw data and it is also difficult to aggregate the dynamic moving features from trajectories into static road segments. To this end, we establish a new visual analytic system called VizCycSemantics for pervasive computing, in which a topic model (i.e., LDA) is used to extract the topics of cyclist behavior semantics and moving semantics on roads, and a clustering method (i.e., k-means ++) is used to further capture the groups of similar cyclists and road segments within the city; finally, multiple interactive visual interfaces are implemented to facilitate the interpretation for analysts. We conduct extensive case studies in the city of Beijing to demonstrate the effectiveness and practicability of our system and also obtain various insightful findings and pieces of advice.

Energy-Optimal Control for Autonomous Underwater Vehicles Using Economic Model Predictive Control

Improving endurance is important for autonomous underwater vehicles (AUVs) as it affects the operational cost and application range of the vehicle. In this article, we propose an economic model predictive control (EMPC)-based controller to reduce the control energy of AUVs while performing waypoint tracking. The proposed EMPC controller optimizes stage costs capturing the control energy consumed within the prediction horizon and a terminal cost approximating the energy-to-go, the energy required to reach the desired waypoint from the end of the prediction horizon. To approximate the energy-to-go, we partition it into the dynamic and static segments based on the operational characteristics of the optimal vehicle maneuver obtained from off-line trajectory optimization using direct collocation (DC). To account for the disturbances caused by ocean currents, we adopt the energy-to-go to a virtual Earth-fixed frame that transforms the drift in the vehicle location to the drift in the desired waypoint. Theoretical and numerical analyses of the approximated energy-to-go reveal that the proposed controller can balance the tradeoffs among energy components spent for vehicle surge, heave, and yaw controls in consideration of vehicle dynamics. Simulations under different flow conditions are conducted to compare the proposed approach with DC and a line-of-sight (LOS) guidance-based approach that optimizes vehicle surge speed for energy minimization. Through simulations, it is shown that the proposed approach achieves near-optimal performance as DC and outperforms the LOS-based approach.

Steganography algorithm for reversible data hiding using LSB and reversible image transformation

We present another reversible (lossless) information stowing away (inserting) strategy that takes into consideration careful recuperation of the first host signal after the implanted data is separated. The information installing approach is proposed as a speculation of the notable LSB (least significant cycle) update, which includes extra working focuses the limit contortion bend. Compacting portions of the sign that are helpless against installing spillage and dispersing these packed subtleties as a feature of the inserted payload considers lossless recovery of the first. The pressure effectiveness and in this manner the lossless information implanting capacity of a forecast based restrictive entropy coder that utilizes static segments of the host as side-data improves.

A periodic iterative learning scheme for finite-iteration tracking of discrete networks based on FlexRay communication protocol

In this study, the finite-iteration tracking of discrete networks is analyzed by designing a new kind of periodic iterative learning control (ILC) strategy. In the proposed ILC scheme, the FlexRay communication protocol with both static and dynamic segments is introduced in the design. The design has the advantage to reduce the communication channel bandwidth load and hence improve the performance of finite-iteration tracking. This paper provides the first integrating approach to iterative learning design based on the dynamic capability of FlexRay communication channels, and can be generalized to other learning process in network control design. Simulation results are shown to clarify the effectiveness of the obtained criteria, and demonstrate that the proposed periodic ILC scheme performs better than the traditional ILC schemes.

Sparse Representations for Object- and Ego-Motion Estimations in Dynamic Scenes.

Disentangling the sources of visual motion in a dynamic scene during self-movement or ego motion is important for autonomous navigation and tracking. In the dynamic image segments of a video frame containing independently moving objects, optic flow relative to the next frame is the sum of the motion fields generated due to camera and object motion. The traditional ego-motion estimation methods assume the scene to be static, and the recent deep learning-based methods do not separate pixel velocities into object- and ego-motion components. We propose a learning-based approach to predict both ego-motion parameters and object-motion field (OMF) from image sequences using a convolutional autoencoder while being robust to variations due to the unconstrained scene depth. This is achieved by: 1) training with continuous ego-motion constraints that allow solving for ego-motion parameters independently of depth and 2) learning a sparsely activated overcomplete ego-motion field (EMF) basis set, which eliminates the irrelevant components in both static and dynamic segments for the task of ego-motion estimation. In order to learn the EMF basis set, we propose a new differentiable sparsity penalty function that approximates the number of nonzero activations in the bottleneck layer of the autoencoder and enforces sparsity more effectively than L1- and L2-norm-based penalties. Unlike the existing direct ego-motion estimation methods, the predicted global EMF can be used to extract OMF directly by comparing it against the optic flow. Compared with the state-of-the-art baselines, the proposed model performs favorably on pixelwise object- and ego-motion estimation tasks when evaluated on real and synthetic data sets of dynamic scenes.

Efficiency of a novel non-monotonic segmented leaf sequence delivery of Varian MLC for non-split IMRT fields.

Development of bidirectional non-monotonic segmented leaf sequence (NSLS) MLC delivery technique compatible with Varian MLC for non-split IMRT fields reducing total monitor units (TotalMU) and the number of segments (NS) simultaneously and assessment of its efficiency using a plan scoring index (PSI). The optimal fluence of IMRT plans of ten patients of lung carcinoma, calculated using Eclipse TPS version 11.0 (Varian Medical Systems, Palo Alto, CA, USA), was used to generate the segmented MLC fields using our newly developed equally spaced (ES) reducing level and NSLS algorithms in MATLAB® version 2011b for 6-10 intensity levels. These MLC fields were imported into the plans with the same field setup and the final dose was recalculated. The results were compared with those of commercially available multiple static segments (MSS) leaf motion calculation (LMC) algorithm and few previously published algorithms. Plan scoring index (PSI) and degree of modulation (DoM) was calculated to compare the quality of different plans for the same patient. The average differences in TotalMU and NS with respect to MSS algorithm are -3.80% and -14.28% for the NSLS algorithm, respectively. The calculated average PSI and DoM is 0.75, 2.51 and 0.91, 2.41 for the MSS and NSLS algorithms, respectively. IMRT plans generated using the NSLS algorithm resulted in the best PSI, DoM values among all the leaf sequencing algorithms. Our proposed NSLS algorithm allows bidirectional delivery in Varian medical linear accelerator which is not commercially available. NSLS algorithm is efficient in reducing the TotalMU and NS with equivalent plan quality as that of MSS.

An efficient formulation for optimization of FlexRay frame scheduling

FlexRay is a communication bus for automotive applications, designed to ensure high data rates, fault tolerance, and operates on a time cycle, split in static and dynamic segments for event-triggered and time-triggered communications. Therefore, it becomes suitable for hard real-time and safety-critical applications. Furthermore, it enables cyclic and acyclic communication in the static and dynamic segments according to their specifications, ensuring synchronized, error-free communication between transmitters and receivers. One challenge for applying this protocol, however, is the complexity of the task of message scheduling, for which some techniques have already been developed and we explored to show our method. Here, we carried out the analysis for a specific application that deploys the FlexRay bus, through a heuristic method with the support of a computer software to search for the best combination of message scheduling of the entire system. The effectiveness of the proposed approach is demonstrated by a case study performed within the automotive domain.

EMG-based online classification of gestures with recurrent neural networks

Online gesture classification can rely on unsupervised segmentation in order to divide the data stream into static and dynamic segments for individual classification. However, this process requires motion detection calibration and adds complexity to the classification, thus becoming an additional failure point. An alternative is the sequential (dynamic) classification of the data stream. In this study we propose the use of recurrent neural networks (RNNs) to improve the online classification of hand gestures with Electromyography (EMG) signals acquired from the forearm muscles. The proposed methodology was evaluated on the UC2018 DualMyo and the NinaPro DB5 data set. The performance of a Feed-Forward Neural Network (FFNN), a Recurrent Neural Network (RNN), a Long Short-Term Memory network (LSTM) and a Gated Recurrent Unit (GRU) are compared and discussed. Additionally, an alternative performance index, the gesture detection accuracy, is proposed to evaluate the performance of the model during online classification. It is demonstrated that the static model (FFNN) and the dynamic models (LSTM, RNN and GRU) achieve similar accuracy for both data sets, i.e., about 95% for the DualMyo and about 91% for the NinaPro DB5. Although both models had similar accuracies, the dynamic models (LSTM and GRU) have a third of the parameters, presenting smaller training and inference times. + + Long Short-Term Memory (LSTM).

Temporal Resolution Needed for Auditory Communication: Measurement With Mosaic Speech.

Temporal resolution needed for Japanese speech communication was measured. A new experimental paradigm that can reflect the spectro-temporal resolution necessary for healthy listeners to perceive speech is introduced. As a first step, we report listeners' intelligibility scores of Japanese speech with a systematically degraded temporal resolution, so-called “mosaic speech”: speech mosaicized in the coordinates of time and frequency. The results of two experiments show that mosaic speech cut into short static segments was almost perfectly intelligible with a temporal resolution of 40 ms or finer. Intelligibility dropped for a temporal resolution of 80 ms, but was still around 50%-correct level. The data are in line with previous results showing that speech signals separated into short temporal segments of <100 ms can be remarkably robust in terms of linguistic-content perception against drastic manipulations in each segment, such as partial signal omission or temporal reversal. The human perceptual system thus can extract meaning from unexpectedly rough temporal information in speech. The process resembles that of the visual system stringing together static movie frames of ~40 ms into vivid motion.

Segmented Tube Reactors (STR): A Simple Tool To Screen Multiple Reactions in Parallel in Batch Mode within a Single Tube

A simple tool to perform multiple reactions in parallel in batch mode in static segments within a single tube is described. The Segmented Tube Reactor (STR) involves using syringe pumps to load a section of Teflon tubing with solutions of reaction components while forming a preprogrammed gradient of one of the components (e.g., equivalents, concentration) along its length. Simultaneously, a chemically inert spacer is loaded to break the gradient into discrete static segments. Ten or more distinct sets of conditions can be quickly set up in a single experiment. Although the STR is set up in flow mode, reactions occur in batch mode within the tubes, usually by heating the STR for a desired duration. Segments are then individually sampled and analyzed to identify the conditions that provide the best performance. The technology is translatable: the STR was employed to screen for preferred reaction stoichiometries that were then duplicated on larger scale in batch mode in traditional lab equipment.

Unconscious physiological response of healthy volunteers to dynamic respiration-synchronized couch motion

BackgroundIntrafractional motion can be a substantial uncertainty in precision radiotherapy. Conventionally, the target volume is expanded to account for the motion. Couch-tracking is an alternative, where the patient is moved to compensate for the tumor motion. However, the couch motion may influence the patient’s stress and respiration behavior decreasing the couch-tracking effectiveness.MethodsIn total, 100 volunteers were positioned supine on a robotic couch, which moved dynamically and respiration synchronized. During the measurement, the skin conductivity, the heartrate, and the gaze location were measured indicating the volunteer’s stress. Volunteers rated the subjective motion sickness using a questionnaire. The measurement alternated between static and tracking segments (three cycles), each 1 min long.ResultsThe respiration amplitude showed no significant difference between tracking and static segments, but decreased significantly from the first to the last tracking segment (p < 0.0001). The respiration frequency differed significantly between tracking and static segments (p < 0.0001), but not between the first and the last tracking segment. The physiological parameters and the questionnaire showed mild signals of stress and motion sickness.ConclusionGenerally, people tolerated the couch motions. The interaction between couch motion and the patient’s breathing pattern should be considered for a clinical implementation.Trial registrationThe study was registered at ClinicalTrials.gov (NCT02820532) and the Swiss national clinical trials portal (SNCTP000001878) on June 20, 2016.

Unsupervised Gesture Segmentation by Motion Detection of a Real-Time Data Stream

Continuous and real-time gesture spotting is a key factor in the development of novel human–machine interaction modalities. Gesture recognition can be greatly improved with previous reliable segmentation. This paper introduces a new unsupervised threshold-based hand/arm gesture segmentation method to accurately divide continuous data streams into dynamic and static segments from unsegmented and unbounded input data. This segmentation may reduce the number of wrongly classified gestures in real-world conditions. The proposed approach identifies sudden inversions of movement direction, which are a cause of oversegmentation (excessive segmentation). This is achieved by the analysis of velocities and accelerations numerically derived from positional data. A genetic algorithm is used to compute feasible thresholds from calibration data. Experimental tests with three different subjects demonstrated an average oversegmentation error of 2.70% in a benchmark for motion segmentation with a feasible sliding window size.

Space-Time Tree Ensemble for Action Recognition and Localization

Human actions are, inherently, structured patterns of body movements. We explore ensembles of hierarchical spatio-temporal trees, discovered directly from training data, to model these structures for action recognition and spatial localization. Discovery of frequent and discriminative tree structures is challenging due to the exponential search space, particularly if one allows partial matching. We address this by first building a concise action word vocabulary via discriminative clustering of the hierarchical space-time segments, which is a two-level video representation that captures both static and non-static relevant space-time segments of the video. Using this vocabulary we then utilize tree mining with subsequent tree clustering and ranking to select a compact set of discriminative tree patterns. Our experiments show that these tree patterns, alone, or in combination with shorter patterns (action words and pairwise patterns) achieve promising performance on three challenging datasets: UCF Sports, HighFive and Hollywood3D. Moreover, we perform cross-dataset validation, using trees learned on HighFive to recognize the same actions in Hollywood3D, and using trees learned on UCF-Sports to recognize and localize the similar actions in JHMDB. The results demonstrate the potential for cross-dataset generalization of the trees our approach discovers.

OPTIMUM TREATMENT MODE APPLIED TO POST-OPERATIVE CERVICAL CANCER FOR 5F-IMRT PLAN BASED ON FOUR VARIABLES IN VARIAN ECLIPSE TPS

Purpose: This study aimed to determine the dosimetric effect on the target volume, organs at risk (OARs) and normal tissues based on the different choice for four types of mechanical variables, i.e., treatment position, dose calculation algorithm, mulitleaf collimator (MLC) motion mode and X-ray energy; and to investigate the optimum treatment mode applied to post-operative cervical cancer for 5-field intensity-modulated radiation therapy (5F-IMRT) technique. Methods: The dosimetric difference on the target volume and OARs under the influence of four types of variables were initially compared by changing one variable at a time. Then, based on the above compared results, we compared the dosimetric difference on planning target volume (PTV) and OARs between group A composed of the superior four variables and group B composed of the relatively inferior four variables. The dosimetric parameters included dose distribution of the target volume, OARs and normal tissues, conformal index (CI), homogeneity index (HI), monitor units (MU) and beam-on time ([Formula: see text]. The independent and paired t-tests were used for statistical analysis, and the threshold for statistical significance was [Formula: see text]. Results: Compared with the supine position, the maximum dose of PTV ([Formula: see text]), the maximum dose of small intestine ([Formula: see text]) and [Formula: see text] of bladder ([Formula: see text] were all lower in prone position. In contrast with the pencil beam convolution (PBC), CI of PTV (CI[Formula: see text]) was larger while HI of PTV (HI[Formula: see text]) was lower, both [Formula: see text] and the maximum dose of rectum ([Formula: see text]) were lower using anisotropic analytical algorithm (AAA). Moreover, the same results were obtained using sliding window (SW) compared with multiple static segments (MSS). The mean dose of PTV ([Formula: see text] and CI[Formula: see text] was larger while the maximum dose of the spinal cord ([Formula: see text]), [Formula: see text] and the maximum dose of femoral heads were lower with 15 MV X-rays compared with 6 MV X-rays. In comparison with group B comprising the supine position, PBC, MSS and 6 MV X-rays, [Formula: see text] and HI[Formula: see text] decreased 1.4% and 53.4% respectively, CI[Formula: see text] increased 5.8% medially, while [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text] all decreased in group A comprising of prone position, AAA, SW and 15 MV X-rays. Conclusion: The treatment mode composed of prone position, AAA algorithm, SW and 15 MV X-rays is chosen for the post-operative cervical cancer of 5F-IMRT technique, which is more capable of meeting the target volume constraints and maximal protection of OARs.

Primo software as a tool for Monte Carlo simulations of intensity modulated radiotherapy: A feasibility study

Introduction Intensity Modulated Radiation Therapy (IMRT) allows creating complex dose distributions. The Monte Carlo (MC) is considered the gold standard method to perform dose calculations. A recent new MC user-friendly interface named PRIMO was developed targeting External Radiotherapy environment. Nevertheless, advanced features such as IMRT are not yet introduced. Purpose To assess the capability of PRIMO to simulate IMRT procedures. Materials and methods Two Radiotherapy units, both using Varian 2300CD LINAC head were simulated. While one uses Millennium120, the other has 120HD as Multi Leaf Collimator (MLC). Static simulations were performed to validate LINAC heads and MLC models. Homemade software was developed to configure PRIMO to reproduce MLC modulation as planned by the Treatment Planning System and create dose distribution images at specific planes from PRIMO output files. For each unit, a dynamic MLC delivery was split into a number of static segments and simulated on solid water phantom. Gafchromic films were used to measure the actual dose and make comparisons with the simulations. Assessments were performed using the 2D Gamma analysis. Results More than 95% of Gamma points Conclusion A workflow was found to drive PRIMO to simulate IMRT procedures. The results of simulations agree with the experimental measurements, indicating PRIMO software as a potential tool for clinical implementation of MC simulations of IMRT technique. Disclosure The author has no relevant financial or nonfinancial relationships to disclose.

Configuration of the PRIMO software to simulate intensity modulated radiation therapy through dynalog file

Introduction Varian DynaLog files contain information on the Multi Leaf Collimator (MLC) actual configuration, and gantry and collimator angles during Intensity Modulated Radiation Therapy (IMRT). The Monte Carlo (MC) simulation of an actual IMRT procedure allows studying the dosimetric effect of possible mismatches between planned and actual delivery. The PRIMO software is a MC software targeted to External Radiotherapy environment. Nevertheless, IMRT is not part of the implemented options. Purpose To configure PRIMO to simulate an actual IMRT procedure by DynaLog files manipulation. Materials and methods A Varian 2300CD LINAC associated to a 120HD MLC was simulated. Static simulations were primarily performed to verify the LINAC and MLC models against experimental data. A dynamic irradiation with MLC modulation was split into a sequence of static segments. PRIMO was configured by introducing the DynaLog files information and the procedure was simulated on a solid water phantom. Gafchromic films were used to measure actual dose distribution and to compare it with the simulations through a 2D Gamma analysis. Results More than 95% of Gamma points Conclusion A workflow was developed to configure PRIMO to simulate IMRT through the direct introduction of DynaLog files. These results represent an initial study towards a clinically useful implementation of IMRT simulation using PRIMO. Disclosure The authors have no relevant financial or nonfinancial relationships to disclose.

Emulation-based stabilization of networked control systems implemented on FlexRay

We investigate the emulation controller design approach for nonlinear networked control systems (NCS) with FlexRay. FlexRay is a deterministic communication protocol which is increasingly used in the automotive industry as it provides a high bandwidth and allows for safety critical applications. It is characterized by pre-set communication cycles that are subdivided into static and dynamic segments; the data transmissions are scheduled by different rules depending on the segment. We propose for the first time a hybrid model of NCS with FlexRay for this purpose. We show, under reasonable assumptions, that the asymptotic stability property ensured by the controller in the absence of communication constraints is preserved when the latter is implemented over FlexRay with sufficiently frequent data transmission. In particular, we assume that on each communication segment, the data transmissions are governed by uniformly globally exponentially stable protocols. This covers the case when the round-robin protocol is implemented on the static segment and the try-once-discard protocol is implemented on the dynamic segment. We provide explicit maximum allowable transmission interval bounds that guarantee stability.

Small MU Segments: Static Segments or Control Points? A Path Forward

Small MU Segments: Static Segments or Control Points? A Path Forward

Agent-based methodology for design and simulation of automotive distributed embedded systems communication protocols

Multi-agent systems MAS are becoming more and more relevant to model and simulate complex distributed systems. This increasing interest in MAS is due to their ability to model autonomy, interaction, cooperation, intelligence, reactivity, and organisational aspects in an explicit manner. In this context we present our agent-based approach for automotive distributed embedded systems modelling and simulation. As a case study, we have targeted the FlexRay protocol basic services such as the startup, the wakeup, the clock synchronisation and media access control services in the static and dynamic segments. Our proposed architecture is modelled as a multi-agent system and implemented in the JADE platform following mainly the so-called O-MaSE methodology. Using JADE, We developed an ontology that provides an automatic interpretation of frame fields.

Design and Implementation of Holistic Scheduling and Efficient Storage for FlexRay

FlexRay is a new industry standard for next-generation communication in automotives. Though there are a few recent researches on performance analysis of FlexRay, two important aspects of the FlexRay design have been overlooked. The first is a holistic integrated scheduling scheme that can handle both static and dynamic segments in a FlexRay network. The second is cost-effective and scalable performance. To address these aspects, we propose a novel holistic scheduling scheme, called HOSA, which can provide scalable performance by using flexible and ease-of-use dual channel communication in FlexRay. HOSA is built upon a novel slot pilfering technique to schedule and optimize the available slots in both static and dynamic segments. Moreover, to achieve efficient implementation, we propose approximate computation, which can efficiently support cost-effective and holistic scheduling by judiciously obtaining the tradeoff between computation complexity and available pilfered slots. HOSA hence offers the salient feature of improving bandwidth utilization. Moreover, to deliver high performance and bridge the gap between real-time communications in networks and storage management in end devices, we implement a deduplication-aware scheme in a real SSD prototype that can reduce space overhead and extend SSD lifespan (by significantly reducing duplicate write operations). The deduplication scheme meets the needs of message updates in FlexRay. Extensive experiments based on synthetic test cases and real-world case studies demonstrate the efficiency and efficacy of HOSA.