Today a lot of companies that produces processing machines is competing to make their product more efficient than their competitors in order to improve sales. This paper is focused on processing machines that measure weight and uses mechanical force to rotate goods to increase surface contact with a processing agent. This study will investigate combinations of algorithms that is most suitable for extracting a gradient from a noisy load signal containing a periodic disturbance. A better extraction of the gradient, which is the flowrate of the processing agent, would allow the processing machine to make better predictions for its process control. 

This study applies quantitative research methods through simulations and one experiment in order to get an understanding of each algorithm’s behavior. In each test they are tested according to certain criteria, these are phase-lag, similarity, accuracy and precision of both load and gradient. 

The results of this study suggest three different algorithms that are likely to perform well in the mentioned criteria. Kalman CV is the best choice for gradient extraction on one second intervals when tracking a periodic signal is not necessary. Kalman CV-Kalman PLI is the best choice for both load and gradient extraction when tracking a periodic signal is needed for process control. It is also the best choice for extracting a gradient on sample-to-sample intervals overall. Finally, Chebyshev is the best choice for load extraction if tracking a periodic signal is not desired. 

Since this paper could only determine which algorithm is more likely to perform better, it can only partially answer its research questions. Despite only a partial answer, this paper concludes that its findings could still be used by companies to find suitable algorithms for their processing machines or be used by those new to the field to get an introduction to the presented algorithms.

Because of the overlapping of the spectra of different metabolites and the interference of the baseline mainly from broad resonances of macromolecule and lipids, it is difficult to achieve the quantification of spectra of different metabolites which is important for both research and clinical applications of Magnetic Resonance Spectroscopy (MRS). In this paper, a novel MRS quantification method based on frequency a priori knowledge is proposed. Firstly, a wavelet filter is used to remove the broad components of an observed spectrum in which baseline and the relatively broad components of metabolite spectrum are included. Secondly, a linear nonnegative pursuit algorithm based on regularized FOCUSS (Focal Underdetermined System Solver) algorithm is used to decompose the filtered spectra in a dictionary which is based on a set of Lorentzian and Gaussian functions corresponding to spectrum models. Benefitting from the a priori knowledge of the peak frequency of each metabolite, the filtered metabolite spectrum can be sparsely represented with these basis functions and the spectra of different metabolites are relevant to certain basis functions. Therefore, with the corresponding relation between the basis functions and spectrum models and the estimated decomposition coefficients, a mixed spectrum without baseline can be reconstructed and spectra of different metabolites can be quantified at the same time. The accuracy and the robustness of MRS quantification are improved by the proposed method, from simulation data, compared with commonly used MRS quantification methods. Quantification on in vivo brain spectra is also demonstrated.

In this paper, a sparse representation method is proposed for magnetic resonance spectroscopy (MRS) quantification. An observed MR spectrum is composed of a set of metabolic spectra of interest, a baseline and a noise. To separate the spectra of interest, the a priori knowledge about these spectra, such as signal models, the peak frequencies, and linewidth ranges of different resonances, is first integrated to construct a dictionary. The separation of the spectra of interest is then performed by using a pursuit algorithm to find their sparse representations with respect to the dictionary. For the challenging baseline problem, a wavelet filter is proposed to filter the smooth and broad components of both the observed spectra and the basis functions in the dictionary. The computation of sparse representation can then be carried out by using the remaining data. Simulation results show the good performance of this wavelet filtering-based strategy in separating the overlapping components between the baselines and the spectra of interest, when no appropriate model function for the baseline is available. Quantifications of in vivo brain MR spectra from tumor patients in different stages of progression demonstrate the effectiveness of the proposed method.

This paper proposes a frequency-domain Magnetic Resonance (MR) spectral processing method based on sparse representation for accurate quantification of prostate spectra. Generally, an observed prostate spectrum can be considered as a mixture of resonances of interest, a baseline and noise. As the resonances of interest often overlap and the baseline is unknown, their separation and quantification can be difficult. In the proposed method, based on the commonly used signal model of prostate spectra and some a priori knowledge of nonlinear model parameters, a dictionary is constructed which can sparsely represent the resonances of interest as well as the baseline in an input spectrum. The estimation of the resonances of interest is achieved by finding their sparse representations with respect this dictionary. A linear pursuit algorithm based on regularized FOCUSS (Focal Underdetermined System Solver) algorithm is proposed to estimate these sparse representations. The robustness and accuracy of prostate spectrum quantification of the proposed method are improved compared with two classical spectral processing methods: model-based time domain fitting and frequency-domain analysis based on peak integration when tested on simulation data. Quantification on in vivo prostate spectra is also demonstrated and the results appear encouraging.

Introduction

This study aims to investigate the possibility of driving multiple NFC antennas with a single NFC-controller by using a multiplexer. An artifact was developed and experiments were conducted in order to evaluate the design choice and in addition, to observe the impact of a multiplexer in an NFC system with respect to the number of successful tag detections, bit error rate (BER), the utilization of automatic antenna tuning (AAT) and the physical distance to the NFC tag. Data collected from the experiments was analyzed, and the results were discussed. The purpose of the thesis was concretized by formulating three research questions:

[RQ1] How can the design proposed in the problem statement be implemented?

[RQ2] How does the multiplexer impact the system’s ability to detect an NFC tag and read its contents at different distances?

[RQ3] To which degree does the AAT-technology compensate for the issues caused by the multiplexer?

Method

This research has been conducted according to the design science research (DSR) methodology. DSR is a well-established methodology and suits the nature of this research, therefore it is used to answer the research questions.

Findings

The thesis demonstrates a viable solution using a multiplexer in NFC systems, with reduced detection distance as a factor to consider. The impact of AAT remains inconclusive, requiring further research.

Implications

This study shows that using a multiplexer in NFC systems can save costs and space, leading to more affordable and compact devices. It enables scalable, advanced NFC devices for diverse applications, encouraging wider adoption. However, developers must consider the impact of a multiplexer on detection distance when designing NFC-based applications

Limitations

This study has certain limitations. It focuses on a particular multiplexer and NFC antenna type, and specific testing conditions. It does not delve into the potential profitability, diverse applications, security or encryption concerns, compatibility with other devices, compliance with regulations, or hands-on verification of part connectivity.

Purpose – Examine if there is any difference in performance between the C++ framework JUCE and the domain-specific programming language FAUST to create a decision basis to facilitate choices between them when developing plugins.

Method – An experimental study where two delay-plugins with identical functionality were developed and compared in latency, CPU load and memory usage. The experiment consisted of three test cases and were performed on three different computers.

Findings – FAUST performed better than JUCE regarding latency and CPU load during the experiment. JUCE on the other hand performed better than FAUST regarding memory usage.

Implications – This study has made it easier to make a decision based on performance when choosing between JUCE and FAUST regarding development of plugins.

Limitations – Time restrictions has led to only comparing JUCE and FAUST, leaving other relevant alternatives aside. It has also led to only developing one type of plugin. The results of the study cannot be generalized or applied to other frameworks and programming languages whose purpose is to ease processing of digital signals.

This study aims at investigating a central mounted Indoor Positioning System based onBluetooth RSSI values and Kalman filtering where the Bluetooth module is mounted on arotating arm around an axle. The purpose is to investigate how the length of the arm and therotational speed influence on the possible positioning accuracy of the artefact. As requested bythe company Combitech AB, the artefact will also be evaluated whether the design is areasonable alternative to de most common Bluetooth based Indoor Positioning Systems. Thecase study is performed using design science as an artefact is developed and evaluated. Theartefact is tested in a predetermined test environment, in this case an office room. In the testenvironment, measurements will be realized on the different iterations of the artefact. Theresult of the study is the achieved positioning accuracy for the artefact in two and threedimensions. Linear equations describing the relationship between the length of the arm andthe achieved accuracy of the artefact will also be proposed. The best accuracy achieved in twodimensions was 510 centimeters. The best achieved accuracy in three dimensions was 730 centimeters. The observed relationship between the length of the arm and the uncertainty ofthe trilaterated position is that when the length of the arm increases the uncertainty decreasesin both two and three dimensions.

The number of devices connected to the internet is constantly increasing and therefore putting more weight on the importance of low energy usage. LoRa which is the physical part of the LoRaWAN communication protocol that this paper focuses on is popular in the area of IoT because of its low energy usage. The purpose of this paper was to find out how the energy usage differentiate between two of the most commonly used LoRa frequencies 433MHz and 868MHz.

LoRa is often used with battery driven components within IoT such as temperature or humidity sensors. It is of great importance to keep the maintenance cost for these devices to a minimum. And it is therefore important to find the most energy efficient solutions for communication between LoRa components.

To get an answer from the purpose of this paper, two questions were formulated, and they were in turn answered by using an experimental research method. For the experiment, two devices were set up using: one transmitter combined with a current sensor and one receiver, both of which were capable of using either LoRa 433MHz or 868MHz. The experiment was then conducted by transmitting 1000 packages for every DR and both of the frequencies.

The results were analysed in an empirical quantitative way which showed that the higher frequency of 868MHz consumed more energy in all of the experiments. One discovery that was quite interesting was that the difference could be mitigated by using different data rates.

The result in this paper is intended to increase the knowledge regarding LoRa and its energy usage. This papers result can be used as a reference when choosing between what frequency and data rate to use when working with LoRa.

This article describes a content-based indexing and retrieval (CBIR) system based on hierarchical binary signatures. Binary signatures are obtained through a described binarization process of classical features (color, texture and shape). The Hamming binary distance (based on binary XOR operation) is used during retrieval. This technique was tested on a real image collection containing 7200 images and on a virtual collection of one million images. Results are very good both in terms of speed and accuracy allowing real-time image retrieval in very large image collections.

This article presents a visual browsing content-based indexing and retrieval (CBIR) system for large image databases applied to a paleontology database. The studied system offers a hierarchical organization of feature vectors into signature vectors leading to a research tree so that users can explore the database visually. To build the tree, our technique consists in transforming the images using multiresolution analysis in order to extract features at multiple scales. Then a hierarchical signature vector for each scale is built using extracted features. An automatic classification of the obtained signatures is performed using the k-means algorithm. The images are grouped into clusters and for each cluster a model image is computed. This model image is inserted into a research tree proposed to users to browse the database visually. The process is reiterated and each cluster is split into sub-clusters with one model image per cluster, giving the nodes of the tree. The multiresolution approach combined with the organized signature vectors offers a coarse-to-fine research during the retrieval, process (i.e. during the progression in the research tree).

Objective. In recent years, ICA has been one of the more popular methods for extracting event-related potential (ERP) at the single-trial level. It is a blind source separation technique that allows the extraction of an ERP without making strong assumptions on the temporal and spatial characteristics of an ERP. However, the problem with traditional ICA is that the extraction is not direct and is time-consuming due to the need for source selection processing. In this paper, the application of an one-unit ICA-with-Reference (ICA-R), a constrained ICA method, is proposed.

Approach. In cases where the time-region of the desired ERP is known a priori, this time information is utilized to generate a reference signal, which is then used for guiding the one-unit ICA-R to extract the source signal of the desired ERP directly.

Main results. Our results showed that, as compared to traditional ICA, ICA-R is a more effective method for analysing ERP because it avoids manual source selection and it requires less computation thus resulting in faster ERP extraction.

Significance. In addition to that, since the method is automated, it reduces the risks of any subjective bias in the ERP analysis. It is also a potential tool for extracting the ERP in online application.