Rapporten beskriver processutvecklingen av integration inom smart-enheter och gör jämförelse med de traditionella datormodeller som exempelvis TensorFlow. Maskininlärning är ett område som för närvarande observeras av många människor på grund av dess anmärkningsvärda önskan inom olika intelligenta områden som datorseende, naturlig språkbehandling, föreslagna datorsystem, etcetera. Problem med utdata och tidsserier. Resursbegränsningen av smart-enheter gör det svårt att uppfatta olika aktiviteter som helt annorlunda än en stor process. En rekommenderad användarvänlig process för att utföra design, utveckling, utvärdering och leverans av maskininlärning-modeller för resurs begränsade inbäddade enheter. TensorFlow och TensorFlow Lite valdes ut för att utföra examensarbetet som tillhandahåller arbetsflödet för en maskininlärning-modell designad för en dator, bärbar dator eller en komprimerad och optimerad version av samma modell integrerad på en enda enhet har begränsade resurser. Modellerna jämförs och resultaten erhålls. Resultatet det visar sig att TensorFlow Lite-modellen är extremt starkt integrerad med maskininlärning i inbyggda enheter Lagringen av den utvecklade modell filen, tid det tog för att förutsäga värdet jämfördes. Resultaten visade att TensorFlow Lite-modellen var korrekt i jämförelse med basmodellen, då storleken på TensorFlow Lite var 60 % mindre än basstorleken och responstid för TensorFlow Lite-modellen var 70 % mindre än basmodellen. Detta visar att det finns en möjlighet att integrera maskininlärning i enheter med den process som föreslås i avhandlingen. Slutligen är modellen gjord på en Android-smartphone, dess praktiska funktionalitet och genomförbarhet har visat. Ramverket har ett unikt och pålitligt tillvägagångssätt, vilket ger flexibilitet samtidigt som det klarar utmaningen att integrera i Android-enheter. / The thesis describes development of Machine learning (ML) integration procedure in smartphone and provides a comparison with the traditional computer models like TensorFlow. Machine learning is a field that promotes a lot of observation in the current era due to its notable desire in various Intelligent applications such as computer vision, natural language processing, recommendation systems, and time series problems. The limitation of resources on a smartphone makes it challenging to apprehend varied completely different activities with high precision. A user-friendly procedure is proposed to perform the designing, development, evaluation and deployment of Machine learning models for embedded devices with limited resources. TensorFlow (TF) and TensorFlow lite (TF Lite) were selected to perform the task. The thesis provides the procedure of a base Machine learning model designed for a computer, laptop or a machine to a compressed and optimized version of the same model for integration on a device with limited resources. The models were compared, and results were obtained. It was found that the TensorFlow lite model is extremely favorable for Machine learning integration in embedded devices. The storage of the developed model file and the time taken for the prediction of the value was compared. The results showed that the TensorFlow lite model was as accurate as the basic model, the size of the TensorFlow lite model was 60% less than the size of the base model and the response time of the TensorFlow lite model was 70% less than the base model. This showed that the Machine learning integration to the embedded devices is promising with the procedure proposed in the thesis. Finally, the model was deployed in the android smart phone and its practicality and feasibility of use was showed. The framework adopts a unique and reliable approach that provides flexibility while passing the challenge of Machine learning integrated in the android device.

Edge computing is the idea of moving computations away from the cloud andinstead perform them at the edge of the network. The benefits of edge computing arereduced latency, increased integrity, and less strain on networks. Edge AI is the practiceof deploying machine learning algorithms to perform computations on the edge.In this project, a pre-trained model YAMNet is retrained and used to perform audioclassification in real-time to detect gunshots, glass shattering, and speech. The modelis deployed onto the edge device both as a full TensorFlow model and as TensorFlowLite models. Comparing results of accuracy, inference time, and memory allocationfor full TensorFlow and TensorFlow Lite models with and without optimization. Resultsfrom this research were that it was a valid option to use both TensorFlow andTensorFlow Lite but there was a lot of performance to gain by using TensorFlow Litewith little downside.

Human activity data is typically represented as time series data, and RNNs, often with LSTM cells, are commonly used for recognition in this field. However, RNNs and LSTM-RNNs are often too resource-intensive for real-time applications on resource constrained devices, making them unsuitable. This thesis project is carried out at Wrlds AB, Stockholm. At Wrlds, all machine learning is run in the cloud, but they have been attempting to run their AI algorithms on their embedded devices. The main task of this project was to investigate alternative network structures to minimize the size of the networks to be used on human activity data. This thesis investigates the use of Fast GRNN, a deep learning algorithm developed by Microsoft researchers, to classify human activity on resource-constrained devices. The FastGRNN algorithm was compared to state-of-the-art RNNs, LSTM, GRU, and Simple RNN in terms of accuracy, classification time, memory usage, and energy consumption. This research is limited to implementing the FastRNN algorithm on Nordic SoCs using their SDK and TensorFlow Lite Micro. The result of this thesis shows that the proposed network has similar performance as LSTM networks in terms of accuracy while being both considerably smaller and faster, making it a promising solution for human activity recognition on embedded devices with limited computational resources and merits further investigation. / Rörelse igenkännings analys är oftast representerat av tidsseriedata där ett RNN modell meden LSTM arkitektur är oftast den självklara vägen att ta. Dock så är denna arkitektur väldigt resurskrävande för applikationer i realtid och gör att det uppstår problem med resursbegränsad hårdvara. Detta examensarbete är utfört i samarbete med Wrlds Technologies AB. På Wrlds så körs deras maskin inlärningsmodeller på molnet och lokalt på mobiltelefoner. Wrlds har nu påbörjat en resa för att kunna köra modeller direkt på små inbyggda system. Examensarbete kommer att utvärdera en FastGRNN som är en NN-arkitektur utvecklad av Microsoft i syfte att användas på resurs begränsad hårdvara. FastGRNN algoritmen jämfördes med andra högkvalitativa arkitekturer som RNNs, LSTM, GRU och en simpel RNN. Träffsäkerhet, klassifikationstid, minnesanvändning samt energikonsumtion användes för att jämföra dom olika varianterna. Detta arbete kommer bara att utvärdera en FastGRNN algoritm på en Nordic SoCs och kommer att användas deras SDK samt Tensorflow Lite Micro. Resultatet från detta examensarbete visar att det utvärderade nätverket har liknande prestanda som ett LSTM nätverk men också att nätverket är betydligt mindre i storlek och därmed snabbare. Detta betyder att ett FastGRNN visar lovande resultat för användningen av rörelseigenkänning på inbyggda system med begränsad prestanda kapacitet.

Human key point tracking such as face detection and recognition has become an increasingly popular research topic. It is a platform independent functionality and already being implemented on a wide range of platforms. Android is one such platform that runs on mobile phones and top of many edge devices such as car devices and smart home appliances. In the current times, AI and ML related applications are slightly moving into those edge devices due to various reasons such as security and low latency. The hardware enhancements are also backing this trend that happened over the last few years. Many solutions and algorithms have been proposed in this context, and various frameworks and models have also been developed. Even though there are different models available, they tend to deliver varying results in terms of performance. Evaluating these different alternatives to find an optimized solution is a problem worth addressing. In this thesis project, several selected face detection and recognition models have been implemented in an Android device, and their performance been evaluated. Google ML Kit showed the best results among the face detection methods since it took only around 68 milliseconds on average to detect a face. Out of the three face recognition algorithms evaluated, FaceNet was the most accurate as it showed an accuracy above 95% for most cases. Meanwhile, MobileFaceNet was the fastest algorithm, and it took only around 90 milliseconds on average to produce and output. Eventually, a face recognition application was also developed using the best performing models selected from the experiment.

The thesis focuses on modern methods of traffic sign detection and traffic lights detection directly in traffic and with use of back analysis. The main subject is convolutional neural networks (CNN). The solution is using convolutional neural networks of YOLO type. The main goal of this thesis is to achieve the greatest possible optimization of speed and accuracy of models. Examines suitable datasets. A number of datasets are used for training and testing. These are composed of real and synthetic data sets. For training and testing, the data were preprocessed using the Yolo mark tool. The training of the model was carried out at a computer center belonging to the virtual organization MetaCentrum VO. Due to the quantifiable evaluation of the detector quality, a program was created statistically and graphically showing its success with use of ROC curve and evaluation protocol COCO. In this thesis I created a model that achieved a success average rate of up to 81 %. The thesis shows the best choice of threshold across versions, sizes and IoU. Extension for mobile phones in TensorFlow Lite and Flutter have also been created.