Digital design of an EPC Gen2 controller for enhanced RFID tags

This thesis presents an improvement of the long range battery-less UHF RFID platform for sensor applications which is based on the open source Wireless Identification and Sensing Platform (WISP) project. The purpose of this work is to design a digital logic that performs the RFID EPC gen2 protocol communication, is able to acquire information by sensors and provide an accurate estimation of tag location ensuring low energy consumption. This thesis will describe the hardware architecture on which the digital logic was inserted, the Verilog code developed, the methods by which the digital logic was tested and an explorative study of chip synthesis on Cadence.

Mac protocols for linear wireless (sensor) networks

Wireless sensor networks (WSNs) consist of a large number of sensor nodes, characterized by low power constraint, limited transmission range and limited computational capabilities [1][2].The cost of these devices is constantly decreasing, making it possible to use a large number of sensor devices in a wide array of commercial, environmental, military, and healthcare fields. Some of these applications involve placing the sensors evenly spaced on a straight line for example in roads, bridges, tunnels, water catchments and water pipelines, city drainages, oil and gas pipelines etc., making a special class of these networks which we define as a Linear Wireless Network (LWN). In LWNs, data transmission happens hop by hop from the source to the destination, through a route composed of multiple relays. The peculiarity of the topology of LWNs, motivates the design of specialized protocols, taking advantage of the linearity of such networks, in order to increase reliability, communication efficiency, energy savings, network lifetime and to minimize the end-to-end delay [3]. In this thesis a novel contention based Medium Access Control (MAC) protocol called L-CSMA, specifically devised for LWNs is presented. The basic idea of L-CSMA is to assign different priorities to nodes based on their position along the line. The priority is assigned in terms of sensing duration, whereby nodes closer to the destination are assigned shorter sensing time compared to the rest of the nodes and hence higher priority. This mechanism speeds up the transmission of packets which are already in the path, making transmission flow more efficient. Using NS-3 simulator, the performance of L-CSMA in terms of packets success rate, that is, the percentage of packets that reach destination, and throughput are compared with that of IEEE 802.15.4 MAC protocol, de-facto standard for wireless sensor networks. In general, L-CSMA outperforms the IEEE 802.15.4 MAC protocol.

PEDOT:PSS thin films: Applications in Bioelectronics

Owing to their capability of merging the properties of metals and conventional polymers, Conducting Polymers (CPs) are a unique class of carbon-based materials capable of conducting electrical current. A conjugated backbone is the hallmark of CPs, which can readily undergo reversible doping to different extents, thus achieving a wide range of electrical conductivities, while maintaining mechanical flexibility, transparency and high thermal stability. Thanks to these inherent versatility and attracting properties, from their discovery CPs have experienced incessant widespread in a great plethora of research fields, ranging from energy storage to healthcare, also encouraging the spring and growth of new scientific areas with highly innovative content. Nowadays, Bioelectronics stands out as one of the most promising research fields, dealing with the mutual interplay between biology and electronics. Among CPs, the polyelectrolyte complex poly (3,4-ethylenedioxythiophene): poly (styrenesulfonate) (PEDOT:PSS), especially in the form of thin films, has been emphasized as ideal platform for bioelectronic applications. Indeed, in the last two decades PEDOT:PSS has played a key role in the sensing of bioanalytes and living cells interfacing and monitoring. In the present work, development and characterization of two kinds of PEDOT:PSS-based devices for applications in Bioelectronics are discussed in detail. In particular, a low-cost amperometric sensor for the selective detection of Dopamine in a ternary mixture was optimized, taking advantage of the electrocatalytic and antifouling properties that render PEDOT:PSS thin films appealing tools for electrochemical sensing of bioanalytes. Moreover, the potentialities of this material to interact with live cells were explored through the fabrication of a microfluidic trapping device for electrical monitoring of 3D spheroids using an impedance-based approach.

Analysis and Evaluation of Tiny Machine Learning applications

The aim of TinyML is to bring the capability of Machine Learning to ultra-low-power devices, typically under a milliwatt, and with this it breaks the traditional power barrier that prevents the widely distributed machine intelligence. TinyML allows greater reactivity and privacy by conducting inference on the computer and near-sensor while avoiding the energy cost associated with wireless communication, which is far higher at this scale than that of computing. In addition, TinyML’s efficiency makes a class of smart, battery-powered, always-on applications that can revolutionize the collection and processing of data in real time. This emerging field, which is the end of a lot of innovation, is ready to speed up its growth in the coming years. In this thesis, we deploy three model on a microcontroller. For the model, datasets are retrieved from an online repository and are preprocessed as per our requirement. The model is then trained on the split of preprocessed data at its best to get the most accuracy out of it. Later the trained model is converted to C language to make it possible to deploy on the microcontroller. Finally, we take step towards incorporating the model into the microcontroller by implementing and evaluating an interface for the user to utilize the microcontroller’s sensors. In our thesis, we will have 4 chapters. The first will give us an introduction of TinyML. The second chapter will help setup the TinyML Environment. The third chapter will be about a major use of TinyML in Wake Word Detection. The final chapter will deal with Gesture Recognition in TinyML.