Indoor localization is an actively researched field due to there not being a universal solution found yet. Applications of such systems include but are not limited to indoor wayfinding and automated tour guides. In previous years multiple solutions were proposed. This work looks into the performance of an indoor location sensing system in the presence of background music and tries to improve the accuracy in such a scenario. To achieve that a denoising autoencoder is proposed as a preprocessing step aiming to remove the noise from the fingerprints used for localization. In the end, it is shown that the use of such a technique introduces a tradeoff between an accuracy drop in quiet environments but an accuracy increase in environments with music.

Indoor localization is an important field of research for advancing robotics and providing more accurate estimations of indoor locations for users. There are many indoor localization algorithm implementations, but many of them underperform under certain environmental changes or restrictions. This research will present a way of combining already existing indoor localization techniques to more accurately deduce the user’s location within a building. An experiment was conducted within campus building Pulse, where multiple fingerprints of locations where gathered, and then used to train and test the combined classification models. By fusing active acoustic location sensing and WiFi localization using weighted averaging, ensemble stacking, and 2-step localization, the combination of classifiers was able to outperform individual classifiers by up to 5% of localization accuracy. Additionally, 2-step localization and weighted averaging methods did not add any performance overhead.

Multiactivity analysis investigates one's coordination of actions within a social context, such as gestures and speech, usually using video recordings of the social activity, to further understand the rules of human behaviour. This paper focuses specifically on the coordination between speaking and drinking activities within a social setting, and explores the possibility of automatically identifying these events using audio captured from a drinking glass. As social interactions occur in vastly different contexts, this paper also investigates the effect that background noise might have on the accuracy of identifying these events. Different parameters and audio features were compared. Linear classification models LR and SVM with a linear kernel were able to achieve 100% accuracy for all sample lengths between 2 and 8 seconds using the first 20 PCA components from 60 audio features. The best performing feature in identifying speaking and drinking events was MFCCs, achieving an F1 score of 99.4% on average across models with a training sample length of 3 seconds. Background noise had different effects on classification accuracy depending on the type, with music lowering the F1 score to 74.3%, noisy room audio to 64.7%, and podcast audio simulating the presence of other speakers to 59.6% using MFCCs and a 3-second sample length

In this paper, we investigate the use of heightmap-based models to predict deformations in granular materials during teleoperation tasks. Our primary objective is to provide real-time cutting feedback and contact predictions for haptic bilateral teleoperation applications, thereby enhancing operator control in remote manipulation scenarios. We developed a simulation model that utilizes heightmaps to represent deformable materials and implemented a cutting algorithm to simulate complex interactions with granular soils. Through a series of experiments, we demonstrated that higher resolution heightmaps significantly improve the accuracy and stability of cutting feedback. Our findings suggest that this approach is a viable method for keeping track of changes in a deformable object's shape and can be further expanded to include a wider range of teleoperation tasks, potentially improving the safety and effectiveness of remote operations in hazardous environments.

In this paper, we investigate the use of heightmap-based models to predict deformations in granular materials during teleoperation tasks. Our primary objective is to provide real-time cutting feedback and contact predictions for haptic bilateral teleoperation applications, thereby enhancing operator control in remote manipulation scenarios. We developed a simulation model that utilizes heightmaps to represent deformable materials and implemented a cutting algorithm to simulate complex interactions with granular soils. Through a series of experiments, we demonstrated that higher resolution heightmaps significantly improve the accuracy and stability of cutting feedback. Our findings suggest that this approach is a viable method for keeping track of changes in a deformable object's shape and can be further expanded to include a wider range of teleoperation tasks, potentially improving the safety and effectiveness of remote operations in hazardous environments.

In this paper, we investigate the use of heightmap-based models to predict deformations in granular materials during teleoperation tasks. Our primary objective is to provide real-time cutting feedback and contact predictions for haptic bilateral teleoperation applications, thereby enhancing operator control in remote manipulation scenarios. We developed a simulation model that utilizes heightmaps to represent deformable materials and implemented a cutting algorithm to simulate complex interactions with granular soils. Through a series of experiments, we demonstrated that higher resolution heightmaps significantly improve the accuracy and stability of cutting feedback. Our findings suggest that this approach is a viable method for keeping track of changes in a deformable object's shape and can be further expanded to include a wider range of teleoperation tasks, potentially improving the safety and effectiveness of remote operations in hazardous environments.

In this paper, we investigate the use of heightmap-based models to predict deformations in granular materials during teleoperation tasks. Our primary objective is to provide real-time cutting feedback and contact predictions for haptic bilateral teleoperation applications, thereby enhancing operator control in remote manipulation scenarios. We developed a simulation model that utilizes heightmaps to represent deformable materials and implemented a cutting algorithm to simulate complex interactions with granular soils. Through a series of experiments, we demonstrated that higher resolution heightmaps significantly improve the accuracy and stability of cutting feedback. Our findings suggest that this approach is a viable method for keeping track of changes in a deformable object's shape and can be further expanded to include a wider range of teleoperation tasks, potentially improving the safety and effectiveness of remote operations in hazardous environments.

This research investigates the detection of gestures using a torso-worn accelerometer sensor. Using the Conflab dataset, we focus on gestures during conversations in mingling scenarios. Due to significant variability in gesture styles among individuals, traditional methods face challenges in building personalized models. Our experiments demonstrate that Transductive Parameter Transfer (TPT), an adaptive transfer learning method, can more effectively model these individual differences in gesturing. To gain insights into individual expressiveness, we classify gestures into three classes: 'no gesture,' 'normal,' and 'large' gestures. TPT performed an average AUC score of 0.84 in binary classification and 0.77 in multiclass classification. These findings highlight the potential of using a single torso-worn accelerometer to understand social behavior in naturalistic settings.

This research investigates the detection of gestures using a torso-worn accelerometer sensor. Using the Conflab dataset, we focus on gestures during conversations in mingling scenarios. Due to significant variability in gesture styles among individuals, traditional methods face challenges in building personalized models. Our experiments demonstrate that Transductive Parameter Transfer (TPT), an adaptive transfer learning method, can more effectively model these individual differences in gesturing. To gain insights into individual expressiveness, we classify gestures into three classes: 'no gesture,' 'normal,' and 'large' gestures. TPT performed an average AUC score of 0.84 in binary classification and 0.77 in multiclass classification. These findings highlight the potential of using a single torso-worn accelerometer to understand social behavior in naturalistic settings.

This research investigates the detection of gestures using a torso-worn accelerometer sensor. Using the Conflab dataset, we focus on gestures during conversations in mingling scenarios. Due to significant variability in gesture styles among individuals, traditional methods face challenges in building personalized models. Our experiments demonstrate that Transductive Parameter Transfer (TPT), an adaptive transfer learning method, can more effectively model these individual differences in gesturing. To gain insights into individual expressiveness, we classify gestures into three classes: 'no gesture,' 'normal,' and 'large' gestures. TPT performed an average AUC score of 0.84 in binary classification and 0.77 in multiclass classification. These findings highlight the potential of using a single torso-worn accelerometer to understand social behavior in naturalistic settings.

This paper presents a novel approach to simulating liquid interactions for haptic bilateral teleoperation without complex fluid dynamics simulations. We propose a model combining a simplified drag equation with a "position tail" mechanism to approximate force feedback for viscous and turbulent liquids. The model was evaluated through theoretical analysis, manual testing, and a user study. Results of the latter demonstrate the ability to convincingly simulate different liquid types, with participants correctly identifying viscous and turbulent liquid types. The study also revealed that visual feedback delays up to 125 ms did not significantly impact user experience. While promising, the system remains sensitive to noisy input data. This research contributes to the development of efficient and believable haptic simulations for teleoperation, paving the way for future developments.

Recently non-image-based data capturing methods such as sensors like RF, ultrasonic or radars, Wi-Fi, Bluetooth, etc for People Counting (PC) applications have gained momentum as an alternative to camera-based systems due to the preference for privacy preservation. Among them mm-wave radars are a strong choice for data capture since they consume less power, they are robust to the extremes of weather and environment. Further, they record data in the form of a collection of points making them computationally lightweight. However, such type of data becomes a challenge when used outdoors since those environments are complex. It becomes extremely difficult to separately identify individuals from the collection of points. This makes PC outcomes inaccurate when using radar data. Therefore, in our project, we aimed to address these limitations of using mm-wave radar data. We treated groups of people as a unit, distinguishing them based on the number of people in the group. In this way, we were able to count the number of people by detecting the group size. To achieve this, we processed the raw radar output data made available by the AMS Institute into a dataset to suit our chosen deep learning model. Through a literature survey, we found that computer vision algorithms based on Deep Neural Networks (DNN), detect objects while maintaining a balance between speed and accuracy. Moreover, DNNs are capable of extracting features more effectively than traditional Machine Learning models. We selected the YOLOv8 model by Ultralytics after concluding our literature survey, as the most suitable model for our research problem. However, the selected model accepts three-channel images and videos to detect objects. Therefore, we customized the YOLOv8 model as well as formatted our radar data into a four-channel tensor input which would be acceptable by the model. We were successful in detecting groups of up to four people outdoors with a detection accuracy of 79.21%.

In response to the growing demand for sustainable transportation solutions, bike-sharing systems have gained prominence in supporting an eco-friendly means of commuting. Within this landscape, Skopei, a forward-thinking company specializing in innovative sharing propositions, has developed a smart bike lock capable of autonomous rentals and returns. This master’s thesis delves into the application of radio frequency-based distance measurements to create an indoor positioning system for the efficient management of bike storage. Specifically, the system is designed to determine whether bikes have been correctly parked at docking locations, enabling users to conclude their rentals autonomously. The architecture of this system uses a network of anchor nodes (known-location routers) that should ascertain the positions of mobile nodes (bikes) within the bicycle storage area. Notably, the solution developed in this thesis employs sophisticated distance measurement techniques, including frequency hopping and phase shift analysis. By finding the amplitude and phase shift over multiple frequencies, we can find the channel impulse response and estimate the distance using machine learning. We employ a novel Multi-layer Perceptron neural network regressor to improve the accuracy in the presence of complex environmental factors in bike storage environments. In the bike storage test case, we achieved a mean absolute error in position estimation of 1.68m compared to 3.80m of a naive approach. We improved the parking state classification from 75.99% of a naive approach to 98.09% with our machine-learning-based approach. This thesis underscores the importance of cutting-edge distance measurement methods and real world field studies in advancing indoor positioning systems, specifically for smart bike storage management. By bridging the gap between technology and sustainable transportation, this work aims to make urban bike-sharing systems more scalable, efficient, user-friendly, and environmentally conscious.

The ability to accurately determine the location within indoor settings is crucial for various applications such as indoor navigation, interactive floor plans, and room-specific services. While GPS technology has revolutionized outdoor positioning, it falls short in providing precise location information within buildings due to signal blockage. To address this limitation, specialized indoor positioning systems utilizing acoustic sensing have been explored, leveraging deep learning models. This paper presents a comparative study of passive and active acoustic sensing systems for room recognition. Passive sensing involves capturing existing background noise in a room and using it as an identifier, while active sensing emits acoustic signals and analyzes the resulting echoes. Previous research has primarily focused on active sensing, achieving high classification accuracy but facing challenges related to device orientation and the presence of multiple individuals. Moreover, the emission of high-frequency chirps used in active sensing may cause discomfort to pets. The results indicate that passive sensing achieves an accuracy of 73.7%, slightly outperforming active sensing at 63.5% in baseline conditions. However, in the presence of constant background noise, passive sensing accuracy drops to 21.7%, while active sensing exhibits better resilience with an accuracy of 59.7%. Furthermore, when the device orientation is altered by 90 degrees, active sensing results in a lower in accuracy (45.5%), while passive sensing maintains better performance at 71%. The impact of multiple individuals in the room had a relatively minor effect on passive sensing systems, achieving an accuracy of 72.2%. Active sensing was shown to be not as resilient, reaching an accuracy of 44.2%.

In the last decades our usage of the Radio Frequency (RF) spectrum has intensified a lot due to the increasing number of mobile devices. The spectrum is getting crowded and new communication alternatives might become necessary in the future. One such alternative is Visible Light Communication (VLC),which uses the visible light spectrum instead of RF. Passive VLC, in particular, combines this property with a low power usage, suited for battery powered devices. However, high data rates are lacking. This thesis proposes a multi-channel passive VLC system, based on light dispersion principles, as a novel concept to try and enhance these data rates. Results of the system so far only show low data rates however, at a maximum of 4 bits per second. The low data rate in particular is caused by choice of transmitter and receiver, thereby limiting the bandwidth and the number of channels that can be created. Yet, the concept itself is still considered a valid and valuable approach, and higher data rates can be expected from future works. In this thesis we will see exactly how we can go from light dispersion to creating a multi-channel passive VLC system. As we will see this requires an optical design, a transmitter and a receiver, all of which will be working together. Besides an evaluation of the performance it will give insight in challenges this concept faces and, also, what improvements might increase the system performance.