Wireless and Remote Sensing MATLAB Projects 2019

Experiences with the Extensible Sensing System ESS using MATLAB

The Extensible Sensing System (ESS) has been in use for several years in a variety of sensor network deployments. It is a key component of a collection of tools that together are a nearly complete, end-to-end, sensor-to-user facility for deploying and managing a sensor network. This paper provides the context and architectural overview of ESS, along with selected deployment details and a series of lessons learned. Lesson areas include connectivity, interactivity, energy vs. robustness, vertical integration, and real-time visibility. The current version of ESS reflects changes from these lessons; further, new tools are in development that complement ESS. ABSTRACT The Extensible Sensing System (ESS) has been in use for several years in a variety of sensor network deployments. It is a key component of a collection of tools that together are a nearly complete, end-to-end, sensor-to-user facility for deploying and managing a sensor network. This paper provides the context and architectural overview of ESS, along with selected deployment details and a series of lessons learned. Lesson areas include connectivity, interactivity, energy vs. robustness, vertical integra-tion, and real-time visibility. The current version of ESS reflects changes from these lessons; further, new tools are in development that complement ESS.


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A Minimally Invasive 64-Channel Wireless μECoG Implant using MATLAB

Emerging applications in brain-machine interface systems require high-resolution, chronic multisite cortical recordings, which cannot be obtained with existing technologies due to high power consumption, high invasiveness, or inability to transmit data wirelessly. In this paper, we describe a microsystem based on electrocorticography (ECoG) that overcomes these difficulties, enabling chronic recording and wireless transmission of neural signals from the surface of the cerebral cortex. The device is comprised of a highly flexible, high-density, polymer-based 64-channel electrode array and a flexible antenna, bonded to 2.4 mm × 2.4 mm CMOS integrated circuit (IC) that performs 64-channel acquisition, wireless power and data transmission. The IC digitizes the signal from each electrode at 1 kS/s with 1.2 μV input referred noise, and transmits the serialized data using a 1 Mb/s backscattering modulator. A dual-mode power-receiving rectifier reduces data-dependent supply ripple, enabling the integration of small decoupling capacitors on chip and eliminating the need for external components. Design techniques in the wireless and baseband circuits result in over 16× reduction in die area with a simultaneous 3× improvement in power efficiency over the state of the art. The IC consumes 225 μW and can be powered by an external reader transmitting 12 mW at 300 MHz, which is over 3× lower than IEEE and FCC regulations.


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Automatic Aligning Free Space Communication Platform using MATLAB

Recent developments in ultra-dense optical components coupled with micro-scale electro-mechanical systems have led to the ability to create a new type of optical device: the free-space self-aligning optical transmitter/receiver. With automatic search and alignment, high-speed data or power transfer, and low-power operation, this new tool is poised to become an important tool in many systems. Utilizing MEMS technology and high speed electronic control and sensing, one half of this device allows for independent beam control, automatic beam scanning and steering, as well as incoming beam detection and positioning. Pairing this host module up with a client module outfitted with a new high-speed optical modulator (also developed at UCSD) and a classic corner cube reflector, yields a unique blend of technologies that allows for secure high-speed point-to-point communications or even remote sensing and power transfer systems.


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Mobility in Wireless Sensor Networks using MATLAB

Technological advances as well as the advent of 4G communications and of pervasive and ubiquitous computing have fostered a renewed interest in multi-hop (ad hoc) communications. In particular, the interest is in self-organizing wireless multi-hop networks composed of a possibly very large number of nodes. These nodes can be either static or mobile, and are usually constrained as for the most critical resources, such as power and computation capabilities.


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Synthetic Aperture Radar Imaging Simulated in MATLAB using MATLAB

This thesis further develops a method from ongoing thesis projects with the goal of generating images using synthetic aperture radar (SAR) simulations coded in MATLAB. The project is supervised by Dr. John Saghri and sponsored by Raytheon Space and Airborne Systems. SAR is a type of imaging radar in which the relative movement of the antenna with respect to the target is utilized. Through the simultaneous processing of the radar reflections over the movement of the antenna via the range Doppler algorithm (RDA), the superior resolution of a theoretical wider antenna, termed synthetic aperture, is obtained. The long term goal of this ongoing project is to develop a simulation in which realistic SAR images can be generated and used for SAR Automatic Target Recognition (ATR). Current and past Master’s theses on ATR were restricted to a small data set of Man-portable Surveillance and Target Acquisition Radar (MSTAR) images as most SAR images for military ATR are not released for public use. Also, with an in-house SAR image generation scheme the parameters of noise, target orientation, the elevation angle or look angle to the antenna from the target and other parameters can be directly controlled and modified to best serve ATR purposes or other applications such as three-dimensional SAR holography. At the start of the project in September 2007, the SAR simulation from previous Master’s theses was capable of simulating and imaging point targets in a two dimensional plane with limited mobility. The focus on improvements to this simulation through the course of this project was to improve the SAR simulation for applications to more complex two-dimensional targets and simple three-dimensional targets, such as a cube. The input to the simulation uses a selected two-dimensional, grayscale target image and generates from the input a two-dimensional target profile of reflectivity over the azimuth and range based on the intensity of the pixels in the target image. For three-dimensional simulations, multiple two-dimensional azimuth/range profiles are imported at different altitudes. The output from both the two-dimensional and three-dimensional simulations is the SAR simulated and RDA processed image of the input target profile. Future work on this ongoing project will include an algorithm to calculate line of sight limitations of point targets and processing optimization of the radar information generation implemented in the code so that more complex and realistic targets can be simulated and imaged using SAR for applications in ATR and 3D SAR holography.


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Autonomous Brain-Controlled Functional Electrical Stimulation for Grasp and Release in Complete Cervical Spinal Cord Injury using MATLAB

There are over 33,000 people in the United States living with complete tetraplegia due to traumatic spinal cord injury (SCI). These individuals rely heavily on family and caregivers as they are unable to perform many activities of daily living. People with complete tetraplegia rank restoration of hand and arm function as their highest priority, as it would offer greater independence and improved quality of life. In this study, we show that subjects with chronic (>1-year post-injury) C5/C6-level, motor-complete SCI are able to control a brain computer interface-functional electrical stimulation (BCI-FES) system to perform a hand grasp and release task. Electroencephalographic (EEG) signals were acquired using a 20-channel wireless EEG system and input to a BCI, which enabled autonomous control over FES of paralyzed muscles for hand grasp and release. A novel stimulation configuration and control paradigms were developed in order to provide reliable activation of the muscles responsible for hand movements. Input features and decoding strategies were evaluated from subjects with SCI, as well as uninjured, control subjects. After optimization of the BCI-FES system and experimental paradigm, 5 subjects with C5/C6, motor complete spinal cord injury and 5 uninjured, control subjects participated in 6 sessions of closed-loop BCI-FES. Subjects were asked to imagine opening and closing their right hand during the trials for motor imagery. Average power in 5 Hz bins (5-35 Hz) was extracted from C3, C1, Cz, C2, and C4 electrodes and input as features to a Support Vector Machine classification algorithm. When “movement intention” was classified correctly from the motor imagery period, a custom stimulation sequence was delivered to the forearm muscles via surface electrodes to enable opening and closing of the hand for grasp and release. Spinal cord injured subjects produced an average of 21.0% ± 3.9% event-related desynchronization and control subjects averaged 13.5% ± 3.2%. Average decoding accuracy was similar, at 73.3% ± 5.6% in the spinal cord injury group and 73.6% ± 3.8% in the control group. Over the course of experiments, average event-related desynchronization increased significantly in the SCI group and decoding accuracy improved. This study demonstrates that subjects with motor complete, cervical SCI were able to control a BCI-FES system with performance levels as high as healthy controls with minimal training. Non-invasive BCI-FES systems may have the potential to restore hand function in people with motor-complete SCI, which would increase independence and improve quality of life.


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