CNN Features off-the-shelf: an Astounding Baseline for Recognition

Recent results indicate that the generic descriptors extracted from the convolutional neural networks are very powerful. This paper adds to the mounting evidence that this is indeed the case. We report on a series of experiments conducted for different recognition tasks using the publicly available code and model of the \overfeat network which was trained to perform object classification on ILSVRC13. We use features extracted from the \overfeat network as a generic image representation to tackle the diverse range of recognition tasks of object image classification, scene recognition, fine grained recognition, attribute detection and image retrieval applied to a diverse set of datasets. We selected these tasks and datasets as they gradually move further away from the original task and data the \overfeat network was trained to solve. Astonishingly, we report consistent superior results compared to the highly tuned state-of-the-art systems in all the visual classification tasks on various datasets. For instance retrieval it consistently outperforms low memory footprint methods except for sculptures dataset. The results are achieved using a linear SVM classifier (or $L2$ distance in case of retrieval) applied to a feature representation of size 4096 extracted from a layer in the net. The representations are further modified using simple augmentation techniques e.g. jittering. The results strongly suggest that features obtained from deep learning with convolutional nets should be the primary candidate in most visual recognition tasks.Comment: version 3 revisions: 1)Added results using feature processing and data augmentation 2)Referring to most recent efforts of using CNN for different visual recognition tasks 3) updated text/captio

A Human Motor Control Framework based on Muscle Synergies

In spite of the complexities of the human musculoskeletal system, the central nervous system has the ability to orchestrate difficult motor tasks. Many researchers have tried to understand how the human nervous system works. Yet, our knowledge about the integration of sensory information and motor control is incomplete. This thesis presents a mathematical motor control framework that is developed to give the scientific community a biologically-plausible feedback controller for fast and efficient control of musculoskeletal systems. This motor control framework can be applied to musculoskeletal systems of various complexities, which makes it a viable tool for many predictive musculoskeletal simulations, assistive device design and control, and general motor control studies. The most important feature of this real-time motor control framework is its emphasis on the intended task. In this framework, a task is distinguished by the kinematic variables that need to be controlled. For example, in a reaching task, the task variables are the position of the hand (individual joint angles are irrelevant to the reaching task). Consequently, the task space is defined as the subspace that is formed by all the controlled variables. This motor control framework employs a hierarchical structure to speed up the calculations while maintaining high control efficiency. In this framework, there is a high-level controller, which deals with path planning and error compensation in the task space. The output of this task space controller is the acceleration vector in the task space, which needs to be fulfilled by muscle activities. The fast and efficient transformation of the task space accelerations to muscle activities in real-time is a main contribution of this research. Instead of using optimization to solve for the muscle activations (the usual practice in the past), this acceleration-to-activation (A2A) mapping uses muscle synergies to keep the computations simple enough to be real-time implementable. This A2A mapping takes advantage of the known effect of muscle synergies in the task space, thereby reducing the optimization problem to a vector decomposition problem. To make the result of the A2A mapping more efficient, the novel concept of posture-dependent synergies is introduced. The validity of the assumptions and the performance of the motor control framework are assessed using experimental trials. The experimental results show that the motor control framework can reconstruct the measured muscle activities only using the task-related kinematic/dynamic information. The application of the motor control framework to feedback motion control of musculoskeletal systems is also presented in this thesis. The framework is applied to musculoskeletal systems of various complexities (up to four-degree-of-freedom systems with 15 muscles) to show its effectiveness and generalizability to different dimensions. The control of functional electrical stimulation (FES) is another important application of my motor control framework. In FES, the muscles are activated by external electrical pulses to generate force, and consequently motion in paralysed limbs. There exists no feedback FES controller of upper extremity movements in the literature. The proposed motor control model is the first feedback FES controller that can be used for the control of reaching movements to arbitrary targets. Experimental results show that the motor control model is fast enough and accurate enough to be used as a practical motion controller for FES systems. Using such a biologically-plausible motor control model, it is possible to control the motion of a patient's arm (for example a stroke survivor) in a natural way, to accelerate recovery and improve the patient's quality of life

Design and Hardware-in-the-Loop Testing of Optimal Controllers for Hybrid Electric Powertrains

The main objective of this research is the development of a flexible test-bench for evaluation of hybrid electric powertrain controllers. As a case study, a real-time near-optimal powertrain controller for a series hybrid electric vehicle (HEV) has been designed and tests. The designed controller, like many other optimal controllers, is based on a simple model. This control-oriented model aims to be as simple as possible in order to minimize the controller computational effort. However, a simple model may not be able to capture the vehicle's dynamics accurately, and the designed controller may fail to deliver the anticipated behavior. Therefore, it is crucial that the controller be tested in a realistic environment. To evaluate the performance of the designed model-based controller, it is first applied to a high-fidelity series HEV model that includes physics-based component models and low-level controllers. After successfully passing this model-in-the-loop test, the controller is programmed into a rapid-prototyping controller unit for hardware-in-the-loop simulations. This type of simulation is mostly intended to consider controller computational resources, as well as the communication issues between the controller and the plant (model solver). As the battery pack is one of the most critical components in a hybrid electric powertrain, the component-in-the-loop simulation setup is used to include a physical battery in the simulations in order to further enhance simulation accuracy. Finally, the driver-in-the-loop setup enables us to receive the inputs from a human driver instead of a fixed drive cycle, which allows us to study the effects of the unpredictable driver behavior. The developed powertrain controller itself is a real-time, drive cycle-independent controller for a series HEV, and is designed using a control-oriented model and Pontryagin's Minimum Principle. Like other proposed controllers in the literature, this controller still requires some information about future driving conditions; however, the amount of information is reduced. Although the controller design procedure is based on a series HEV with NiMH battery as the electric energy storage, the same procedure can be used to obtain the supervisory controller for a series HEV with an ultra-capacitor. By testing the designed optimal controller with the prescribed simulation setups, it is shown that the controller can ensure optimal behavior of the powertrain, as the dominant system behavior is very close to what is being predicted by the control-oriented model. It is also shown that the controller is able to handle small uncertainties in the driver behavior

A model-based approach to predict muscle synergies using optimization: application to feedback control

This Document is Protected by copyright and was first published by Frontiers. All rights reserved. it is reproduced with permission.This paper presents a new model-based method to define muscle synergies. Unlike the conventional factorization approach, which extracts synergies from electromyographic data, the proposed method employs a biomechanical model and formally defines the synergies as the solution of an optimal control problem. As a result, the number of required synergies is directly related to the dimensions of the operational space. The estimated synergies are posture-dependent, which correlate well with the results of standard factorization methods. Two examples are used to showcase this method: a two-dimensional forearm model, and a three-dimensional driver arm model. It has been shown here that the synergies need to be task-specific (i.e., they are defined for the specific operational spaces: the elbow angle and the steering wheel angle in the two systems). This functional definition of synergies results in a low-dimensional control space, in which every force in the operational space is accurately created by a unique combination of synergies. As such, there is no need for extra criteria (e.g., minimizing effort) in the process of motion control. This approach is motivated by the need for fast and bio-plausible feedback control of musculoskeletal systems, and can have important implications in engineering, motor control, and biomechanics.The authors wish to thank the Natural Sciences and Engineering Research Council of Canada (NSERC) for funding this study

A battery hardware-in-the-loop setup for concurrent design and evaluation of real-time optimal HEV power management controllers

Razavian, R. S., Azad, N. L., & McPhee, J. (2013). A battery hardware-in-the-loop setup for concurrent design and evaluation of real-time optimal HEV power management controllers. International Journal of Electric and Hybrid Vehicles, 5(3), 177. Final version published by Inderscience Publishers, and available at: https://doi.org/10.1504/IJEHV.2013.057604We have developed a battery hardware-in-the-loop (HIL) setup, which can expedite the design and evaluation of power management controllers for hybrid electric vehicles (HEVs) in a novel cost- and time-effective manner. The battery dynamics have a significant effect on the HEV power management controller design; therefore, physical batteries are included in the simulation loop for greater simulation fidelity. We use Buckingham's Pi Theorem in the scaled-down battery HIL setup to reduce development and testing efforts, while maintaining the flexibility and fidelity of the control loop. In this paper, usefulness of the setup in parameter identification of a simple control-oriented battery model is shown. The model is then used in the power management controller design, and the real-time performance of the designed controller is tested with the same setup in a realistic control environment. Test results show that the designed controller can accurately capture the dynamics of the real system, from which the assumptions made in its design process can be confidently justified.Financial support for this research has been provided by the Natural Sciences and Engineering Research Council of Canada (NSERC), Toyota, and Maplesoft

Persistent Evidence of Local Image Properties in Generic ConvNets

Supervised training of a convolutional network for object classification should make explicit any information related to the class of objects and disregard any auxiliary information associated with the capture of the image or the variation within the object class. Does this happen in practice? Although this seems to pertain to the very final layers in the network, if we look at earlier layers we find that this is not the case. Surprisingly, strong spatial information is implicit. This paper addresses this, in particular, exploiting the image representation at the first fully connected layer, i.e. the global image descriptor which has been recently shown to be most effective in a range of visual recognition tasks. We empirically demonstrate evidences for the finding in the contexts of four different tasks: 2d landmark detection, 2d object keypoints prediction, estimation of the RGB values of input image, and recovery of semantic label of each pixel. We base our investigation on a simple framework with ridge rigression commonly across these tasks, and show results which all support our insight. Such spatial information can be used for computing correspondence of landmarks to a good accuracy, but should potentially be useful for improving the training of the convolutional nets for classification purposes

Predictive Simulation of Reaching Moving Targets Using Nonlinear Model Predictive Control

This Document is Protected by copyright and was first published by Frontiers. All rights reserved. it is reproduced with permission.This article investigates the application of optimal feedback control to trajectory planning in voluntary human arm movements. A nonlinear model predictive controller (NMPC) with a finite prediction horizon was used as the optimal feedback controller to predict the hand trajectory planning and execution of planar reaching tasks. The NMPC is completely predictive, and motion tracking or electromyography data are not required to obtain the limb trajectories. To present this concept, a two degree of freedom musculoskeletal planar arm model actuated by three pairs of antagonist muscles was used to simulate the human arm dynamics. This study is based on the assumption that the nervous system minimizes the muscular effort during goal-directed movements. The effects of prediction horizon length on the trajectory, velocity profile, and muscle activities of a reaching task are presented. The NMPC predictions of the hand trajectory to reach fixed and moving targets are in good agreement with the trajectories found by dynamic optimization and those from experiments. However, the hand velocity and muscle activations predicted by NMPC did not agree as well with experiments or with those found from dynamic optimization.The authors would like to thank the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Canada Research Chairs program for financial support of this research

Design and evaluation of a real-time fuel-optimal control system for series hybrid electric vehicles

Razavian, R. S., Taghavipour, A., Azad, N. L., & McPhee, J. (2012). Design and evaluation of a real-time fuel-optimal control system for series hybrid electric vehicles. International Journal of Electric and Hybrid Vehicles, 4(3), 260. Final version published by Inderscience Publishers, and available at: https://doi.org/10.1504/IJEHV.2012.050501We propose a real-time optimal controller that will reduce fuel consumption in a series hybrid electric vehicle (HEV). This real-time drive cycle-independent controller is designed using a control-oriented model and Pontryagin's minimum principle for an off-line optimisation problem, and is shown to be optimal in real-time applications. Like other proposed controllers in the literature, this controller still requires some information about future driving conditions, but the amount of information is reduced. Although the controller design procedure explained here is based on a series HEV with NiMH battery as the electric energy storage, the same procedure can be used to find the supervisory controller for a series HEV with an ultra-capacitor. To evaluate the performance of the model-based controller, it is coupled to a high-fidelity series HEV model that includes physics-based component models and low-level controllers. The simulation results show that the simplified control-oriented model is accurate enough in predicting real vehicle behaviour, and final fuel consumption can be reduced using the model-based controller. Such a reduction in HEVs fuel consumption will significantly contribute to nationwide fuel saving.The authors would like to thank the Natural Sciences and Engineering Research Council (NSERC) of Canada, Toyota, and Maplesoft for their support of this research