Development of an Encrypted Wireless System for Body Sensor Network Applications

Wireless body area networks (WBAN), also called wireless body sensor networks (WBSN), consist of a collection of wireless sensor nodes used to monitor and assess various human physiological conditions, which can then be used by healthcare professionals to help them make important healthcare decisions. They can be used to prevent disease, help diagnosis a disease, or manage the symptoms of a disease. An extremely important aspect of WBAN is security to protect a patient\u27s healthcare information, as a hacker could potentially cause fatal harm. Current security measures are implemented in software at the MAC layer and higher, not in the physical layer. Previous research demonstrated a chaotic encryption cipher to add a layer of security in the physical layer. This cipher exploits different properties of the Lorenz chaotic system to encrypt and decrypt digital data. Decryption involved synchronizing two chaotic signals to recover original data by sharing a state between the transmitter and receiver. In this thesis, we further develop the encryption system by implementing wireless capabilities. We use two approaches: the first by using commercially available wireless microcontrollers that communicate using Bluetooth Low Energy, and the second by the design and fabrication of a dual-band low noise amplifier (LNA) that can be used in a receiver for WBANs collecting data from implantable and on-the-body sensors. For the first approach, a custom Bluetooth Low Energy profile was created for streaming the analog encrypted signal, and signal processing was done at the receiver side. For the second approach, the LNA operates at the Medical Implant Communication System (MICS) band and the 915 MHz Industrial, Scientific, and Medical (ISM) band simultaneously through dual-band input and output matching networks

Design and implementation of a multi-modal sensor with on-chip security

With the advancement of technology, wearable devices for fitness tracking, patient monitoring, diagnosis, and disease prevention are finding ways to be woven into modern world reality. CMOS sensors are known to be compact, with low power consumption, making them an inseparable part of wireless medical applications and Internet of Things (IoT). Digital/semi-digital output, by the translation of transmitting data into the frequency domain, takes advantages of both the analog and digital world. However, one of the most critical measures of communication, security, is ignored and not considered for fabrication of an integrated chip. With the advancement of Moore\u27s law and the possibility of having a higher number of transistors and more complex circuits, the feasibility of having on-chip security measures is drawing more attention. One of the fundamental means of secure communication is real-time encryption. Encryption/ciphering occurs when we encode a signal or data, and prevents unauthorized parties from reading or understanding this information. Encryption is the process of transmitting sensitive data securely and with privacy. This measure of security is essential since in biomedical devices, the attacker/hacker can endanger users of IoT or wearable sensors (e.g. attacks at implanted biosensors can cause fatal harm to the user). This work develops 1) A low power and compact multi-modal sensor that can measure temperature and impedance with a quasi-digital output and 2) a low power on-chip signal cipher for real-time data transfer

Low power CMOS vision sensor for foreground segmentation

This thesis focuses on the design of a top-ranked algorithm for background subtraction, the Pixel Adaptive Based Segmenter (PBAS), for its mapping onto a CMOS vision sensor on the focal plane processing. The redesign of PBAS into its hardware oriented version, HO-PBAS, has led to a less number of memories per pixel, along with a simpler overall model, yet, resulting in an acceptable loss of accuracy with respect to its counterpart on CPU. This thesis features two CMOS vision sensors. The first one, HOPBAS1K, has laid out a 24 x 56 pixel array onto a miniasic chip in standard 180 nm CMOS technology. The second one, HOPBAS10K, features an array of 98 x 98 pixels in standard 180 nm CMOS technology too. The second chip fixes some issues found in the first chip, and provides good hardware and background performance metrics

Chaotification as a Means of Broadband Vibration Energy Harvesting with Piezoelectric Materials

Computing advances and component miniaturization in circuits coupled with stagnating battery technology have fueled growth in the development of high efficiency energy harvesters. Vibration-to-electricity energy harvesting techniques have been investigated extensively for use in sensors embedded in structures or in hard-to-reach locations like turbomachinery, surgical implants, and GPS animal trackers. Piezoelectric materials are commonly used in harvesters as they possess the ability to convert strain energy directly into electrical energy and can work concurrently as actuators for damping applications. The prototypical harvesting system places two piezoelectric patches on both sides of the location of maximum strain on a cantilever beam. While efficient around resonance, performance drops dramatically should the driving frequency drift away from the beam\u27s fundamental frequency. To date, researchers have worked to improve harvesting capability by modifying material properties, using alternative geometries, creating more efficient harvesting circuits, and inducing nonlinearities. These techniques have partially mitigated the resonance excitation dependence for vibration-based harvesting, but much work remains. In this dissertation, an induced nonlinearity destabilizes a central equilibrium point, resulting in a bistable potential function governing the cantilever beam system. Depending on the environment, multiple stable solutions are possible and can coexist. Typically, researchers neglect chaos and assume that with enough energy in the ambient environment, large displacement trajectories can exist uniquely. When subjected to disturbances a system can fall to coexistent lower energy solutions including aperiodic, chaotic oscillations. Treating chaotic motion as a desirable behavior of the system allows frequency content away from resonance to produce motion about a theoretically infinite number of unstable periodic orbits that can be stabilized through control. The extreme sensitivity to initial conditions exhibited by chaotic systems paired with a pole placement control strategy pioneered by Ott, Grebogi, and Yorke permits small perturbations to an accessible system parameter to alter the system response dramatically. Periodic perturbation of the system trajectories in the vicinity of isolated unstable orbit points can therefore stabilize low-energy chaotic oscillations onto larger trajectory orbits more suitable for energy harvesting. The periodic perturbation-based control method rids the need of a system model. It only requires discrete displacement, velocity, or voltage time series data of the chaotic system driven by harmonic excitation. While the analysis techniques are not fundamentally limited to harmonic excitation, this condition permits the use of standard discrete mapping techniques to isolate periodic orbits of interest. Local linear model fits characterize the orbit and admit the necessary control perturbation calculations from the time series data. This work discusses the feasibility of such a method for vibration energy harvesting, displays stable solutions under various control algorithms, and implements a hybrid bench-top experiment using MATLAB and LabVIEW FPGA. In conclusion, this work discusses the limitations for wide-scale use and addresses areas of further work; both with respect to chaotic energy harvesting and parallel advances required within the field as a whole

Complex dynamics of a microwave time-delayed feedback loop

The subject of this thesis is deterministic behaviors generated from a microwave time-delayed feedback loop. Time-delayed feedback systems are especially interesting because of the rich variety of dynamical behaviors that they can support. While ordinary differential equations must be of at least third-order to produce chaos, even a simple first-order nonlinear delay differential equation can produce higher-dimensional chaotic dynamics. The system reported in the thesis is governed by a very simple nonlinear delay differential equation. The experimental implementation uses both microwave and digital components to achieve the nonlinearity and time-delayed feedback, respectively. When a sinusoidal nonlinearity is incorporated, the dynamical behaviors range from fixed-point to periodic to chaotic depending on the feedback strength. The microwave frequency modulated chaotic signal generated by this system offers advantages in range and velocity sensing applications. When the sinusoidal nonlinearity is replaced by a binary nonlinearity, the system exhibits a complex periodic attractor with no fixed-point solution. Although there are many classic electronic circuits that produce chaotic behavior, microwave sources of chaos are especially relevant in communication and sensing applications where the signal must be transmitted between locations. The system also can exhibit random walk behavior when being operated in a higher feedback strength regime. Depending on the feedback strength values, the random behaviors can have properties of a regular or fractional Brownian motion. By unidirectional coupling two systems in the baseband, envelope synchronization between two deterministic Brownian motions can be achieved

A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning

Reservoir computing (RC), first applied to temporal signal processing, is a recurrent neural network in which neurons are randomly connected. Once initialized, the connection strengths remain unchanged. Such a simple structure turns RC into a non-linear dynamical system that maps low-dimensional inputs into a high-dimensional space. The model's rich dynamics, linear separability, and memory capacity then enable a simple linear readout to generate adequate responses for various applications. RC spans areas far beyond machine learning, since it has been shown that the complex dynamics can be realized in various physical hardware implementations and biological devices. This yields greater flexibility and shorter computation time. Moreover, the neuronal responses triggered by the model's dynamics shed light on understanding brain mechanisms that also exploit similar dynamical processes. While the literature on RC is vast and fragmented, here we conduct a unified review of RC's recent developments from machine learning to physics, biology, and neuroscience. We first review the early RC models, and then survey the state-of-the-art models and their applications. We further introduce studies on modeling the brain's mechanisms by RC. Finally, we offer new perspectives on RC development, including reservoir design, coding frameworks unification, physical RC implementations, and interaction between RC, cognitive neuroscience and evolution.Comment: 51 pages, 19 figures, IEEE Acces