The diploma thesis focuses on the implementation of production management tools in the form of modern ICT technologies. The final solution, divided into software and hardware part, assists the company in applying the principles of lean manufacturing and the theory of constraints. The work includes a proposal that helps to increase enterprise productivity and eliminates excess inventory in the organization.

Health technology or e-Health is one of the most rapidly growing areas in healthcare today and it has been an important requirement as a new concept of healthcare industry. Since global society has been changed to aging society and the healthcare cost has been increasing in the 21st century since 2007. As the total number of people aged 65 or older is expected to increase from 12% to 22% in 2050 which is double the rate, and at the same time there is a decrease in in-fertility rates and increase in life expectancy due to the increase in life quality, there is a need to investigate the needs and requirements of an intelligent embedded sensor systems in health applications, and to develop a new communication protocol or set of protocols that can be used to send data collected from a hub within a house, home-care or a complex and send it securely and reliably to a central database where the gathered data can be monitored by a medical professional to make decisions for further interventions. The employed communication protocol should also be able to securely transmit confidential parameters from the hospital network to a central server outside of the hospital network. The final protocol must be inline with the regulations of the EU. This thesis is done in collaboration with Tjeders AB, Stille AB, and Embedded Sensor Systems for Health Plus (ESS-H+) research profile at Mälardalen University. In this thesis, different communication protocols such as IPSec and TLS and algorithms such as AES and RSA are examined and based on the requirements provided by the companies certain of these protocols and algorithms will be used in the final implementation. Different performance metrics such as overhead, round trip delay and throughput will be measured for the chosen communication protocols and recommendations will be given on which of the protocols and algorithms needs to be used to obtain an optimized, secure and reliable network.

Wireless personal area networks have emerged as an important communication infrastructure in areas such as at-home healthcare and home automation, independent living and assistive technology, as well as sports and wellness. Wireless personal area networks, including body sensor networks, are becoming more mature and are considered to be a realistic alternative as communication infrastructure for demanding services. However, to transmit data from e.g., an ECG in wireless networks is also a challenge, especially if multiple sensors compete for access. Contention-based networks offer simplicity and utilization advantages, but the drawback is lack of predictable performance. Recipients of data sent in wireless sensor networks need to know whether they can trust the information or not. Performance measurements, monitoring and control is of crucial importance for medical and healthcare applications in wireless sensor networks. This thesis focuses on development, prototype implementation and evaluation of a performance management system with performance and admission control for wireless sensor networks. Furthermore, an implementation of a new method to compensate for clock drift between multiple wireless sensor nodes is also shown. Errors in time synchronization between nodes in Bluetooth networks, resulting in inadequate data fusion, are also analysed. / <p>QC 20120529</p>

Body sensor networking is a rapidly growing technology. Today wearable sensors are used to measure and monitor e.g. pulse, temperature, skin conductance, heart activity, and movement (through GPS or inertial measurement units). Mobile phones can act as coordinating nodes in wireless personal area networks used in home automation, healthcare, sport and wellness e.g. to measure pulse and distance. Integration of data from multiple sources sensors (data fusion) means that data from each sensor node needs to be associated with data from other sensor nodes sampled at approximately the same time. Accurate methods for time synchronization are therefore a necessary prerequisite for reliable data fusion. This thesis studies time synchronization problems in Bluetooth piconets between multiple wireless sensor nodes connected to a mobile phone that acts as coordinating node. Three different algorithms to enable correct data fusion have been developed, implemented and evaluated. The first is a single clock solution that synchronizes multiple wireless sensor nodes based solely on the mobile phone’s clock. The other two algorithms synchronize the clocks in sensor nodes to the clock in the coordinating node. / <p>QC 20130605</p>

This study is about replacing an old bluetooth module with a new variant, namely the BM83 which is an integrated circuit that contains an internal DSP circuit, profiles and important functions such as noise reduction, acoustic echo removal and EQ filtering. The purpose of this study is to replace the old module with the BM83, as well as explore the internal DSP circuit in the BM83. The goal is to develop the audio system with a high performance bluetooth and explore the BM83's built-in DSP functions and its profiles to optimize sound quality. This study takes different methods and performs different experiments to test the ability of BM83 to replace RN52 in a DSP platform. In addition, it was proposed that an audiosystem should be managed in an operating system with the idea of optimizing data transfer by using some of new operating system mechanism methods. In this study, the differences between BM83 and RN52 were discussed in terms of their profiles, built-in CODEC and DSP algorithm.This study provides how SPP, A2DP and HFP can be used together when the BM83 is controlled by a DSP platform. In addition, it was tested how A2DP and HFP have a great importance in providing events that a DSP platform can decide on and how it chooses its source depending on these events. In addition, the data communication between the BM83 and an STM32F7 was tested via the SPP protocol, with the idea that any data would be sent and received via SPP without loss in data transmission. In conclusion, it was discussed what type of data communication is used between BM83 and an MCU in comparison between RN52 and how the built-in DSP circuit and itsprofiles are important for future research. This project was funded by the Hearing Research Foundation in project FA21-0017 and was carried out under the supervision of Amin Saremi. / Denna studie handlar om att ersätta en gammal bluetooth modul med en ny variant, nämligen BM83 som är en integrerad krets som innehåller en interna DSP krets, profiler och viktiga funktioner som brusreducering, akustisk ekoborttagning och EQ-filtrering. Syftet med denna studie är att ersätta den gamla modulen med BM83, samt utforska den interna DSP kretsen i BM83. Målet är att utveckla ljudsystemet med en högprestanda bluetooth och utforska BM83:s inbyggda DSP-funktioner och dess profilerför att optimera ljudkvalitet. Denna studie tar olika metoder och utför olika experiment för att testa BM83:s förmåga att ersätta RN52 i en DSP plattform. Dessutom föreslogs om att hur ett ljudsystem skulle hanteras i ett operativsystem med tanken på att optimera dataöverföringen. I denna studie diskuterades skillnaderna mellan BM83 och RN52 när det gäller deras profiler, inbyggda CODEC- och DSP-algoritmen. I denna studie belystes hur SPP, A2DP och HFP användes tillsammans när BM83 styrdes av en DSP plattform. Dessutom testades hur A2DP och HFP har en stor betydelse för att tillhandahålla händelser som kan en DSP plattform bestämma sig av och hur den väljer sin källa beroende på dessa händelser. Dessutom testades datakommunikationen mellan BM83 och en STM32F7 via SPP protokollet, med tanken på att valfri data skulle skickas och tas emot via SPP utan förlust i dataöverföring. Avslutningsvis diskuterades vilken typ av datakommunikation används mellan BM83 ochen MCU i jämförelse mellan RN52 och hur den inbyggda DSP kretsen och dess profiler är viktiga för framtida forskningar. Detta projekt finansierades av Hörselforskningsfonden i projekt FA21-0017 och utfördes under uppsikt av Amin Saremi.

The emergence of Bluetooth Low Energy in combination with optimized coders has made it possible to transfer digital audio at very low bitrates, paving the way for small devices with longlasting batteries. The aim of this study is to compare the audio codecs LC3 and aptX, as well as peoples’ attitude towards audio quality in different contexts. Two open source implementations of the codecs are evaluated in terms of time for execution. Furthermore, the perceived audio quality of low bitrates are subjectively compared in a listening test in combination with a questionnaire regarding peoples’ attitude towards audio quality. The results show that LC3 is capable of delivering satisfying audio quality at very low bitrates, whilst also outperforming aptX. It will be interesting to see how LC3 will affect transmission latency, battery life and overall QoS once it is established in everyday products

We are seeing a revolution in computing and artificial intelligence; intelligent machines have become ingrained in and improved every aspect of our lives. Despite the increasing number of intelligent devices and breakthroughs in artificial intelligence, we have yet to achieve truly intelligent environments. Audio is one of the most common sensing and actuation modalities used in intelligent devices. In this thesis, we focus on how we can more robustly integrate audio intelligence into a wide array of resource-constrained platforms that enable more intelligent environments. We present systems and methods for adaptive audio filtering that enables us to more robustly embed acoustic intelligence into a wide range of real time and resource-constrained mobile, embedded, and cyber-physical systems that are adaptable to a wide range of different applications, environments, and scenarios. First, we introduce methods for embedding audio intelligence into wearables, like headsets and helmets, to improve pedestrian safety in urban environments by using sound to detect vehicles, localize vehicles, and alert pedestrians well in advance to give them enough time to avoid a collision. We create a segmented architecture and data processing pipeline that partitions computation between embedded front-end platform and the smartphone platform. The embedded front-end hardware platform consists of a microcontroller and commercial-off-the shelf (COTS) components embedded into a headset and samples audio from an array of four MEMS microphones. Our embedded front-end platform computes a series of spatiotemporal features used to localize vehicles: relative delay, relative power, and zero crossing rate. These features are computed in the embedded front-end headset platform and transmitted wirelessly to the smartphone platform because there is not enough bandwidth to transmit more than two channels of raw audio with low latency using standard wireless communication protocols, like Bluetooth Low-Energy. The smartphone platform runs machine learning algorithms to detect vehicles, localize vehicles, and alert pedestrians. To help reduce power consumption, we integrate an application specific integrated circuit into our embedded front-end platform and create a new localization algorithm called angle via polygonal regression (AvPR) that combines the physics of audio waves, the geometry of a microphone array, and a data driven training and calibration process that enables us to estimate the high resolution direction of the vehicle while being robust to noise resulting from movements in the microphone array as we walk the streets. Second, we explore the challenges in adapting our platforms for pedestrian safety to more general and noisier scenarios, namely construction worker safety sounds of nearby power tools and machinery that are orders of magnitude greater than that of a distant vehicle. We introduce an adaptive noise filtering architecture that allows workers to filter out construction tool sounds and reveal low-energy vehicle sounds to better detect them. Our architecture combines the strengths of both the physics of audio waves and data-driven methods to more robustly filter out construction sounds while being able to run on a resource-limited mobile and embedded platform. In our adaptive filtering architecture, we introduce and incorporate a data-driven filtering algorithm, called probabilistic template matching (PTM), that leverages pre-trained statistical models of construction tools to perform content-based filtering. We demonstrate improvements that our adaptive filtering architecture brings to our audio-based urban safety wearable in real construction site scenarios and against state-of-art audio filtering algorithms, while having a minimal impact on the power consumption and latency of the overall system. We also explore how these methods can be used to improve audio privacy and remove privacy-sensitive speech from applications that have no need to detect and analyze speech. Finally, we introduce a common selective audio filtering platform that builds upon our adaptive filtering architecture for a wide range of real-time mobile, embedded, and cyber-physical applications. Our architecture can account for a wide range of different sounds, model types, and signal representations by integrating an algorithm we present called content-informed beamforming (CIBF). CIBF combines traditional beamforming (spatial filtering using the physics of audio waves) with data driven machine learning sound detectors and models that developers may already create for their own applications to enhance and filter out specified sounds and noises. Alternatively, developers can also select sounds and models from a library we provide. We demonstrate how our selective filtering architecture can improve the detection of specific target sounds and filter out noises in a wide range of application scenarios. Additionally, through two case studies, we demonstrate how our selective filtering architecture can easily integrate into and improve the performance of real mobile and embedded applications over existing state-of-art solutions, while having minimal impact on latency and power consumption. Ultimately, this selective filtering architecture enables developers and engineers to more easily embed robust audio intelligence into common objects found around us and resource-constrained systems to create more intelligent environments.

The rapid development of technology in the health-care sector has led to the discovery of many new illnesses and improved treatments that were not possible earlier. However, many treatments and medicines for a specific disease often come with several side effects. The accuracy in treatments with an optimal result on specified targets is therefore desired with minimum side effects. This requires that the production and the usage processes should be precise. The scope of this study is not about the medicine production phase but rather on managing a medicine schedule. How many times a medicine should be taken in a day is strongly related to its dosage and following a precise timing plays a crucial role in the individual’s health. As a solution, a pill box based on a low power display (Electronic Paper Display, EPD) together with an embedded system has been introduced by the project owner (Victrix AB, Stockholm) .The pill box should have some different functions like alarms, data logging and wireless reporting. Different types of alarms including ringtone, vibration and voice recording/playing are required as well. To be able to trace the already planned timing for taking medicines, system will be able to save and report history of the past 100 days. Since every single idea for solving different parts of the problem should be tested in real system, a Quantitative Research based on experiments be used and the best possible solution be selected and implemented in the project. Studying technical material and also related works besides analyzing generated data after each experiment were a useful tool for the system integration in this work. As the result, a pill box based on an embedded system was designed and integrated successfully. A hardware platform, in form of a prototype system based on an ARM microcontroller and a compatible embedded software have been designed, improved and tested successfully and are available. At the end of this work, the low power E-paper display works properly, alarms can be set and activated, data can be saved and also sent wirelessly. Basically, the result of this project shows how an embedded system can be specialized and programmed to be able to interact with patients and e.g. nurses in order to make a stable and continuous connection between them. Most of determined goals have been achieved and some of them be changed and modified during the work. Also a few additional functions and improvements be suggested as future work.

The individual monitoring of cross-country skiers’ technique-related parameters is crucial to identifying possible athlete-individual deficits that need to be corrected in order to optimize the athlete’s performance in competition. To be able to record relevant biomechanical parameters during training in the field, the development of measuring systems exploiting the athlete’s full potential is the key. Known mobile monitoring systems for measuring forces on ski poles use comparably heavy uniaxial load cells mounted on the pole with a data logger also attached to the pole or carried by the athlete. Measurements that are more accurate can be acquired using wire-based systems. However, wire-based systems are highly immobile and only usable when the athletes undergo a stationary test, e.g., on a treadmill. This paper focuses on the functional design of a measuring system using specialized, miniaturized electronics for acquiring data from strain sensors. These data are then used to determine the technique-related parameters pole force and angle of bend. The functional design is also capable of transmitting the acquired data wirelessly via Bluetooth to a smartphone that runs a proprietary app. This approach is advantageous regarding mass, dynamic behavior, analyzing functionality, and signal processing compared to the state of the art.

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