Indiana University-Purdue University Indianapolis (IUPUI) / Humans can visually see things and can differentiate objects easily but for computers, it is not that easy. Computer Vision is an interdisciplinary field that allows computers to comprehend, from digital videos and images, and differentiate objects. With the Introduction to CNNs/DNNs, computer vision is tremendously used in applications like ADAS, robotics and autonomous systems, etc. This thesis aims to propose an architecture, RMNv2, that is well suited for computer vision applications such as ADAS, etc. RMNv2 is inspired by its original architecture Mobilenet V2. It is a modified version of Mobilenet V2. It includes changes like disabling downsample layers, Heterogeneous kernel-based convolutions, mish activation, and auto augmentation. The proposed model is trained from scratch in the CIFAR10 dataset and produced an accuracy of 92.4% with a total number of parameters of 1.06M. The results indicate that the proposed model has a model size of 4.3MB which is like a 52.2% decrease from its original implementation. Due to its less size and competitive accuracy the proposed model can be easily deployed in resource-constrained devices like mobile and embedded devices for applications like ADAS etc. Further, the proposed model is also implemented in real-time embedded devices like NXP Bluebox 2.0 and NXP i.MX RT1060 for image classification tasks.

Today there exist a myriad of different types of access control systems that use a smart card or mobile device as a key. The mobile device enabled smart locks, as they are often referred to, operate using either WiFi or Bluetooth. This thesis has explored the use of a third emerging wireless technology called Near Field Communication (NFC). NFC technology is a relatively new technology that is on the rise and is included in almost every new mobile device. Using a NFC enabled mobile device, a highly secure access control system was developed on a Raspberry Pi Linux platform. Several different authentication protocols, mobile operating systems and NFC modes of operation where analyzed and evaluated, to ensure that the system was as secure as possible. Eventually the system was implemented using the Secure Remote Password authentication protocol on top of a NFC card emulation scheme with the client application running on the Android operating system. The final system was a secure and responsive system that would be easy to deploy in many different situations. This project shows that NFC enables a mobile device to act as akey in a secure access control system and as the user base for NFC grows larger sowill the likelihood that we will come to see more of these types of systems. / Idag finns det flera olika typer av inpasserings system som använder någon form av ”smart card” eller mobil enhet som nyckel. De smarta låsen, som det oftast kallas, som använder sig av en mobile enhet, använder antingen Wi-­‐‑Fi eller Bluetooth för att kommunicera med inpasserings systemet. I den här uppsatsen kommer en relativt ny teknologi som kalls Near Field Communication (NFC) att utforskas. Användandet av NFC är på uppgång och det finns inkluderat i nästan varje ny mobil enhet som släpps på marknaden idag. Ett inpasserings system med hög säkerhet utvecklades genom att använda en mobile enhet med NFC kapabilitet tillsammans med en Raspberry Pi Linux plattform. Flera olika typer av autentiserings protokoll, mobila operativsystem och NFC användnings moder, analyserades och utvärderades för att säkerställa att systemet var så säkert som möjligt. Tillslut valdes ett autentiserings protokoll vid namn, Secure Remote Password (SRP), som integrerades ovanpå ett kort emulerings NFC ramverk som finns tillgängligt i Android operativsystemet. Det slutgiltiga systemet har hög säkerhet och är snabbt och responsivt och kan användas i flera olika situationer. NFC tillåter en mobile enhet att agera nyckel i ett inpasseringssystem och användandet kommer bara öka med den expanderande användare basen.

This master’s thesis deals with the draft and design of HID periphery Touchpad. The Kinetis microcontroller produced by NXP, TSI periphery and embedded library NXP Touch are used for the design. The resulting device is connected as USB HID type mouse or trackpad. Hardware part of the project is implemented by whole MCU Kinetis Family on PCBs designed by author, one part also uses evaluation kits NXP Freedom KL25z and KE15z.

The master thesis deals with the development of buck-boost DC-DC converter which supplies matrix beam module. The design is focused on testing two-phase boost converter and three channel buck converter manufactured by NXP Semiconductors. Part of the design is implementation of microcontroller for converter control and communication with computer. Part of the thesis is also to design user interface on Windows platform for easy system configuration. Next thing the thesis deals with is designing load for DC-DC converter that is variable and can make different current consumption. One part of this thesis is focused to achieve the lowest conducted emissions and to maximize conducted immunity. Part of this project is production of a prototype and prototype testing.

The theme of this thesis is the design and optimization of algorithms for controlling stepper motors with feedback by measuring the BEMF. The emphasis was on self-management stepper motor current depending on the load. To verify the algorithm was designed and built sample devices with integrated circuit NCV70514 from ON Semiconductor. The result is a sample device, the control program and algorithm to control a stepper motor.

This paper discusses the possibilities of realization of VGA grabber for FITkit. Text is focused on software and hardware implementation possibilities. The first part introduces the reader to the theory of the issue. Next, the paper proposes several options of VGA grabber implementation and brief evaluation of alternatives. The second part describes a chosen architecture of VGA grabber of the featured options and includes a brief summary of the findings of the processor LPC4370 from NXP and USB Video Class UVC, on which the resulting architecture is based. The conclusion includes a brief summary.

Indiana University-Purdue University Indianapolis (IUPUI) / Computer vision is a domain which deals with the goal of making technology as efficient as human vision. To achieve that goal, after decades of research, researchers have developed algorithms that are able to work efficiently on resource constrained hardware like mobile or embedded devices for computer vision applications. Due to their constant efforts, such devices have become capable for tasks like Image Classification, Object Detection, Object Recognition, Semantic Segmentation, and many other applications. Autonomous systems like self-driving cars, Drones and UAVs, are being successfully developed because of these advances in AI. Deep Learning, a part of AI, is a specific domain of Machine Learning which focuses on developing algorithms for such applications. Deep Learning deals with tasks like extracting features from raw image data, replacing pipelines of specialized models with single end-to-end models, making models usable for multiple tasks with superior performance. A major focus is on techniques to detect and extract features which provide better context for inference about an image or video stream. A deep hierarchy of rich features can be learned and automatically extracted from images, provided by the multiple deep layers of CNN models. CNNs are the backbone of Computer Vision. The reason that CNNs are the focus of attention for deep learning models is that they were specifically designed for image data. They are complicated but very effective in extracting features from an image or a video stream. After AlexNet won the ILSVRC in 2012, there was a drastic increase in research related with CNNs. Many state-of-the-art architectures like VGG Net, GoogleNet, ResNet, Inception-v4, Inception-Resnet-v2, ShuffleNet, Xception, MobileNet, MobileNetV2, SqueezeNet, SqueezeNext and many more were introduced. The trend behind the research depicts an increase in the number of layers of CNN to make them more efficient but with that, the size of the model increased as well. This problem was fixed with the advent of new algorithms which resulted in a decrease in model size. As a result, today we have CNN models, which are implemented on mobile devices. These mobile models are compact and have low latency, which in turn reduces the computational cost of the embedded system. This thesis resembles similar idea, it proposes two new CNN architectures, A-MnasNet and R-MnasNet, which have been derived from MnasNet by Design Space Exploration. These architectures outperform MnasNet in terms of model size and accuracy. They have been trained and tested on CIFAR-10 dataset. Furthermore, they were implemented on NXP Bluebox 2.0, an autonomous driving platform, for Image Classification.

This thesis is focused on smart buildings where communication and automatization technologies are deployed. It includes overview and description of frequently applied network protocols in smart homes and their side by side comparison with an emphasis on Thread networking protocol. The next chapters describe details and parameters of used development boards provided by NXP Semiconductors and their implementation in a smart home model. Subsequent chapters are focused on hardware and software components which are the basis for smart home model’s internal workings. The last chapter highlights how a physical model of a smart home was developed with suggestions for future improvements.

This master thesis describes the design of a battery-powered smart lock using IoT networks and NFC technology for user authentication. The first part of the thesis describes the individual components to be used for the device design and also deals with the design of matching circuits and the antenna for the NFC. The following part of the thesis describes the design of the hardware, particularly the design of the printed circuit board. The third part describes the firmware and the final part of the thesis is dedicated to the security analysis.

Denna rapport omfattar konstruktionen av ett fristående drivkort med tillhörande mobilapplikation för styrning och konfiguration av ett hydraulsystem. Projektets syfte var att på beställaren Bosch Rexroth ABs begäran undersöka om pump control module (PCM) - en del ur det existerande Spider 3 styrsystemet - kunde användas som en fristående modul. Hårdvara med en Bluetoothmodul skapades och kopplades mot PCM för att möjliggöra kommunikation via Bluetooth. En mobilapplikation utvecklades i Android studio för att tillåta konfiguration från en mobil enhet. Mjukvaran i PCM modifierades för att kommunicera med Bluetooth-hårdvaran över CAN och även för att tillåta analog styrning av pumpen via en strömstyrd ingång. De grundläggande kraven för systemet uppfylldes och en fungerande prototyp kunde uppvisas vid projektets slut. Prototypen visade stor potential men saknade dock viss funktionalitet som vore önskvärd hos en kommersiell produkt. / This report encompasses the development of a standalone drive card with an accompanying mobile application for control and configuration of a hydraulic system. The purpose of the project was to - according to an inquiry from the client Bosch Rexroth - investigate whether the pump control module (PCM) from the existing Spider 3 control system could be used as a standalone module. Hardware including a Bluetooth module was assembled and connected to the PCM to allow communication over Bluetooth Low Energy (BLE). A mobile application where developed in Android Studio to allow configuration from a mobile device. The software of the PCM were modified to communicate with the Bluetooth hardware over CAN and also for allowing analog control of the hydraulic pump through a current controlled input.The basic demands for the system were met and a working prototype could be demonstrated at the end of the project. The prototype did show large potential but was lacking some functionality which would be desirable for a commercial product.