Spike processing model of the brain

The timing of a spike within a specific time period is used to identify a place in space (input terminal) and/or sense changes in energy or position in the environment, and is used to determine the motion of an actuator or the activation of a place in space (output terminal). The timing of a spike is specified by a sensor or a time delay memory cell that is preset (predetermined) or set through experience (empirical). Time delay memory cells are arranged in decoding networks that activate specific output terminals based upon the timing of incoming spike trains, or arranged in encoding networks that generate spike trains from activated input terminals. These spike trains form semi-axes that can transmit large quantities of information in one direction through a single conductor, and are essential in the transmission of information from peripheral neurons to and from the brain through the spinal chord

Application of inertial instruments for DSN antenna pointing and tracking

The feasibility of using inertial instruments to determine the pointing attitude of the NASA Deep Space Network antennas is examined. The objective is to obtain 1 mdeg pointing knowledge in both blind pointing and tracking modes to facilitate operation of the Deep Space Network 70 m antennas at 32 GHz. A measurement system employing accelerometers, an inclinometer, and optical gyroscopes is proposed. The initial pointing attitude is established by determining the direction of the local gravity vector using the accelerometers and the inclinometer, and the Earth's spin axis using the gyroscopes. Pointing during long-term tracking is maintained by integrating the gyroscope rates and augmenting these measurements with knowledge of the local gravity vector. A minimum-variance estimator is used to combine measurements to obtain the antenna pointing attitude. A key feature of the algorithm is its ability to recalibrate accelerometer parameters during operation. A survey of available inertial instrument technologies is also given

Glove for Augmented and Virtual Reality: Glove for Augmented and Virtual Reality

This work is focused on developing and prototyping a haptic feedback system glove, which will be able to enable interaction of real arm movements with 3D computer models. This glove will be developed using only IMU for human hand data gathering. This approach should increase accuracy of device and give additional flexibility in interaction with different object. For this purpose, IMU should be tested and calibrated using complementary filters. The design and implementation of hardware and software as well as proof-of-concept experiments are presented

Real-Time Indoor Localization using Visual and Inertial Odometry

This project encompassed the design of a mobile, real-time localization device for use in an indoor environment. A system was designed and constructed using visual and inertial odometry methods to meet the project requirements. Stereoscopic image features were detected through a C++ Sobel filter implementation and matched. An inertial measurement unit (IMU) provided raw acceleration and rotation coordinates which were transformed into a global frame of reference. A Kalman filter produced motion approximations from the input data and transmitted the Kalman position state coordinates via a radio transceiver to a remote base station. This station used a graphical user interface to map the incoming coordinates

Space shuttle avionics system

The Space Shuttle avionics system, which was conceived in the early 1970's and became operational in the 1980's represents a significant advancement of avionics system technology in the areas of systems and redundacy management, digital data base technology, flight software, flight control integration, digital fly-by-wire technology, crew display interface, and operational concepts. The origins and the evolution of the system are traced; the requirements, the constraints, and other factors which led to the final configuration are outlined; and the functional operation of the system is described. An overall system block diagram is included

An Evaluation of the Suitability of Commercially Available Sensors for Use in a Virtual Reality Prosthetic Arm Motion Tracking Device

The loss of a hand or arm is a devastating life event that results in many months of healing and challenging rehabilitation. Technology has allowed the development of an electronic replacement for a lost limb but similar advancements in therapy have not occurred. The situation is made more challenging because people with amputations often do not live near specialized rehabilitation centres. As a result, delays in therapy can worsen common complications like nerve pain and joint stiffness. For children born without a limb, poor compliance with the use of their prosthesis leads to delays in therapy and may affect their development. In many parts of the world, amputation rehabilitation does not exist. Fortunately, we live in an age where advances in technology and engineering can help solve these problems. Virtual reality creates a simulated world or environment through computer animation much like what is seen in modern video games. An experienced team of rehabilitation doctors, therapists, engineers and computer scientists are required to realize a system such as this. A person with an amputation will be taught to control objects in the virtual world by wearing a modified electronic prosthesis. Using computers, it will be possible to analyze his or her movements within the virtual world and improve the wearer's skills. The goals of this system include making the system portable and internet compatible so that people living in remote areas can also receive therapy. The novel approach of using virtual reality to rehabilitate people with upper limb amputations will help them return to normal activities by providing modern and appropriate rehabilitation, reducing medical complications, improving motivation (via gaming modules), advancing health care technology and reducing health care costs. The use of virtual reality technology in the field of amputee rehabilitation is in its earliest stages of development world wide. A virtual environment (VE) will facilitate the early rehabilitation of a patient before they are clinically ready to be fitted with an actual prosthesis. In order to create a successful virtual reality rehabilitation system such as this, an accurate method of tracking the arm in real-time is necessary. A linear displacement sensor and a microelectromechanical system (MEMS) inertial measurement unit (IMU) were used to create a device for capturing the motion of a user's movement with the intent that the data provided by the device be used along with a VE as a virtual rehabilitation tool for new upper extremity amputation patients. This thesis focuses on the design and testing of this motion capture device in order to determine the suitability of current commercially available sensing components as used in this system. Success will be defined by the delivery of accurate position and orientation data from the device so that that data can be used in a virtual environment. Test results show that with current MEMS sensors, the error introduced by double integrating acceleration data is too significant to make an IMU an acceptable choice for position tracking. However, the device designed here has proven to be an excellent cable emulator, and would be well suited if used as an orientation tracker

Robust Activity Recognition for Aging Society

Human activity recognition (HAR) is widely applied to many industrial applications. In the context of Industry 4.0, driven by the same demand of machines\u27 self-organizing ability, HAR can also be adopted in elderly healthcare. However, HAR should be adaptive to the application scenarios in elderly healthcare. In this paper, we propose a nonintrusive activity recognition method that can be applied to long-term and unobtrusive monitoring for elderlies. The method is robust to obstruction and nontarget object interference. Skeleton sequence is estimated from RGB images. Based on two activity continuity metrics, an interframe matching algorithm is proposed to filter nontarget objects. In order to make full use of spatial-temporal information, we propose a novel activity encoding method based on the interframe joints distances. A convolutional neural network is used to learn the distinguishing features automatically. A specific data augmentation method is designed to avoid the overfitting problem on small-scale datasets. The experiments are performed on two public activity datasets and a newly released noisy activity dataset (NAD). The NAD contains obstruction, nontarget object interference. The experimental results show that the proposed method achieves the state-of-the-art performance while only using one ordinary camera. The proposed method is robust to a realistic environment

A prospective geoinformatic approach to indoor navigation for Unmanned Air System (UAS) by use of quick response (QR) codes

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesThis research study explores a navigation system for autonomous indoor flight of Unmanned Aircraft Systems (UAS) dead reckoning with Inertial Navigation System (INS) and the use of low cost artificial landmarks, Quick Response (QR) codes placed on the floor and allows for fully autonomous flight with all computation done onboard UAS on embedded hardware. We provide a detailed description of all system components and application. Additionally, we show how the system is integrated with a commercial UAS and provide results of experimental autonomous flight tests. To our knowledge, this system is one of the first to allow for complete closed-loop control and goal-driven navigation of a UAS in an indoor setting without requiring connection to any external infrastructures