A verifiable solution to the MEG inverse problem

Magnetoencephalography (MEG) is a non-invasive brain imaging technique with the potential for very high temporal and spatial resolution of neuronal activity. The main stumbling block for the technique has been that the estimation of a neuronal current distribution, based on sensor data outside the head, is an inverse problem with an infinity of possible solutions. Many inversion techniques exist, all using different a-priori assumptions in order to reduce the number of possible solutions. Although all techniques can be thoroughly tested in simulation, implicit in the simulations are the experimenter's own assumptions about realistic brain function. To date, the only way to test the validity of inversions based on real MEG data has been through direct surgical validation, or through comparison with invasive primate data. In this work, we constructed a null hypothesis that the reconstruction of neuronal activity contains no information on the distribution of the cortical grey matter. To test this, we repeatedly compared rotated sections of grey matter with a beamformer estimate of neuronal activity to generate a distribution of mutual information values. The significance of the comparison between the un-rotated anatomical information and the electrical estimate was subsequently assessed against this distribution. We found that there was significant (P < 0.05) anatomical information contained in the beamformer images across a number of frequency bands. Based on the limited data presented here, we can say that the assumptions behind the beamformer algorithm are not unreasonable for the visual-motor task investigated.

Fibre Bragg grating sensors for distributive tactile sensing

Distributive tactile sensing is a method of tactile sensing in which a small number of sensors monitors the behaviour of a flexible substrate which is in contact with the object being sensed. This paper describes the first use of fibre Bragg grating sensors in such a system. Two systems are presented: the first is a one-dimensional metal strip with an array of four sensors, which is capable of detecting the magnitude and position of a contacting load. This system is favourably compared experimentally with a similar system using resistive strain gauges. The second system is a two-dimensional steel plate with nine sensors which is able to distinguish the position and shape of a contacting load, or the positions of two loads simultaneously. This system is compared with a similar system using 16 infrared displacement sensors. Each system uses neural networks to process the sensor data to give information concerning the type of contact. Issues and limitations of the systems are discussed, along with proposed solutions to some of the difficulties.

The application of methods of uncertain reasoning to the biological classification of river water quality

This thesis presents an investigation into the application of methods of uncertain reasoning to the biological classification of river water quality. Existing biological methods for reporting river water quality are critically evaluated, and the adoption of a discrete biological classification scheme advocated. Reasoning methods for managing uncertainty are explained, in which the Bayesian and Dempster-Shafer calculi are cited as primary numerical schemes. Elicitation of qualitative knowledge on benthic invertebrates is described. The specificity of benthic response to changes in water quality leads to the adoption of a sensor model of data interpretation, in which a reference set of taxa provide probabilistic support for the biological classes. The significance of sensor states, including that of absence, is shown. Novel techniques of directly eliciting the required uncertainty measures are presented. Bayesian and Dempster-Shafer calculi were used to combine the evidence provided by the sensors. The performance of these automatic classifiers was compared with the expert's own discrete classification of sampled sites. Variations of sensor data weighting, combination order and belief representation were examined for their effect on classification performance. The behaviour of the calculi under evidential conflict and alternative combination rules was investigated. Small variations in evidential weight and the inclusion of evidence from sensors absent from a sample improved classification performance of Bayesian belief and support for singleton hypotheses. For simple support, inclusion of absent evidence decreased classification rate. The performance of Dempster-Shafer classification using consonant belief functions was comparable to Bayesian and singleton belief. Recommendations are made for further work in biological classification using uncertain reasoning methods, including the combination of multiple-expert opinion, the use of Bayesian networks, and the integration of classification software within a decision support system for water quality assessment.

Distributive tactile sensing using fibre Bragg grating sensors

Two distributive tactile sensing systems are presented, based on fibre Bragg grating sensors. The first is a onedimensional metal strip with an array of 4 sensors, which is capable of detecting the magnitude and position of a contacting load. This system is compared experimentally with a similar system using resistive strain gauges. The second is a two-dimensional steel plate with 9 sensors which is able to distinguish the position and shape of a contacting load. This system is compared with a similar system using 16 infrared displacement sensors. Each system uses neural networks to process the sensor data to give information concerning the type of contact.

Fibre Bragg grating sensors for distributive tactile sensing

Distributive tactile sensing is a method of tactile sensing in which a small number of sensors monitors the behaviour of a flexible substrate which is in contact with the object being sensed. This paper describes the first use of fibre Bragg grating sensors in such a system. Two systems are presented: the first is a one-dimensional metal strip with an array of four sensors, which is capable of detecting the magnitude and position of a contacting load. This system is favourably compared experimentally with a similar system using resistive strain gauges. The second system is a two-dimensional steel plate with nine sensors which is able to distinguish the position and shape of a contacting load, or the positions of two loads simultaneously. This system is compared with a similar system using 16 infrared displacement sensors. Each system uses neural networks to process the sensor data to give information concerning the type of contact. Issues and limitations of the systems are discussed, along with proposed solutions to some of the difficulties. © 2007 IOP Publishing Ltd.

Distributive tactile sensing using fibre bragg grating sensors

Two distributive tactile sensing systems are presented, based on fibre Bragg grating sensors. The first is a one-dimensional metal strip with an array of 4 sensors, which is capable of detecting the magnitude and position of a contacting load. This system is compared experimentally with a similar system using resistive strain gauges. The second is a two-dimensional steel plate with 9 sensors which is able to distinguish the position and shape of a contacting load. This system is compared with a similar system using 16 infrared displacement sensors. Each system uses neural networks to process the sensor data to give information concerning the type of contact.

Wearable sensors in Huntington disease:a pilot study

Background: The Unified Huntington’s Disease Rating Scale (UHDRS) is the principal means of assessing motor impairment in Huntington disease but is subjective and generally limited to in-clinic assessments. Objective: To evaluate the feasibility and ability of wearable sensors to measure motor impairment in individuals with Huntington disease in the clinic and at home. Methods: Participants with Huntington disease and controls were asked to wear five accelerometer-based sensors attached to the chest and each limb for standardized, in-clinic assessments and for one day at home. A secondchest sensor was worn for six additional days at home. Gait measures were compared between controls, participants with Huntington disease, and participants with Huntington disease grouped by UHDRS total motor score using Cohen’s d values. Results: Fifteen individuals with Huntington disease and five controls completed the study. Sensor data were successfully captured from 18 of the 20 participants at home. In the clinic, the standard deviation of step time (timebetween consecutive steps) was increased in Huntington disease (p<0.0001; Cohen’s d=2.61) compared to controls. At home with additional observations, significant differences were observed in seven additional gait measures. The gait of individuals with higher total motor scores (50 or more) differed significantly from those with lower total motor scores (below 50) on multiple measures at home. Conclusions: In this pilot study, the use of wearable sensors in clinic and at home was feasible and demonstrated gait differences between controls, participants with Huntington disease, and participants with Huntington diseasegrouped by motor impairment.

Documentation of glaciers and mountain chains in the Nordvestfjord of Scorsby Sound (East Greenland, July 2016) by landscape photography during Maria S. Merian cruise MSM56

From the 12th until the 17th of July 2016, research vessel Maria S. Merian entered the Nordvestfjord of Scorsby Sound (East Greenland) as part of research cruise MSM56, "Ecological chemistry in Arctic fjords". A large variety of chemical and biological parameters of fjord and meltwater were measured during this cruise to characterize biogeochemical fluxes in arctic fjords. The photo documentation described here was a side project. It was started when we were close to the Daugaard-Jensen glacier at the end of the Nordvestfjord and realized that not many people have seen this area before and photos available for scientists are probably rare. These pictures shall help to document climate and landscape changes in a remote area of East Greenland. Pictures were taken with a Panasonic Lumix G6 equipped with either a 14-42 or 45-150 objective (zoom factor available in jpg metadata). Polarizer filters were used on both objectives. The time between taking the pictures and writing down the coordinates was maximally one minute but usually shorter. The uncertainty in position is therefore small as we were steaming slowly most of the time the pictures were taken (i.e. below 5 knots). I assume the uncertainty is in most cases below 200 m radius of the noted position. I did not check the direction I directed the camera to with a compass at the beginning. Hence, the direction that was noted is an approximation based on the navigation map and the positioning of the ship. The uncertainty was probably around +/- 40° but initially (pictures 1-17) perhaps even higher as this documentation was a spontaneous idea and it took some time to get the orientation right. It should be easy, however, to find the location of the mountains and glaciers when being on the respective positions because the mountains have a quite characteristic shape. In a later stage of this documentation, I took pictures from the bridge and used the gyros to approximate the direction the camera was pointed at. Here the uncertainty was much lower (i.e. +/- 20° or better). Directions approximated with the help of gyros have degree values in the overview table. The ship data provided in the MSM56 cruise report will contain all kinds of sensor data from Maria S. Merian sensor setup. This data can also be used to further constrain the position the pictures were taken because the exact time a photo was shot is noted in the metadata of the .jpg photo file. The shipboard clock was set on UTC. It was 57 minutes and 45 seconds behind the time in the camera. For example 12:57:45 on the camera was 12:00:00 UTC on the ship. All pictures provided here can be used for scientific purposes. In case of usage in presentations etc. please acknowledge RV Maria S. Merian (MSM56) and Lennart T. Bach as author. Please inform me and ask for reprint permission in case you want to use the pictures for scientific publications. I would like to thank all participants and the crew of Maria S. Merian Cruise 56 (MSM56, Ecological chemistry in Arctic fjords).

Benthic organic carbon flux and oxygen penetration depth in the Southern Ocean

For the investigation of organic carbon fluxes reaching the seafloor, oxygen microprofiles were measured at 145 sites in different sub-regions of the Southern Ocean. At eleven sites, an in situ oxygen microprofiler was deployed for the measurement of oxygen profiles and the calculation of organic carbon fluxes. At four sites, both in situ and ex situ data were determined for high latitudes. Based on this dataset as well as on previous published data, a relationship was established for the estimation of fluxes derived by ex situ measured O2 profiles. The fluxes of labile organic matter range from 0.5 to 37.1 mgC m**2/day. The high values determined by in situ measurements were observed in the Polar Front region (water depth of more than 4290 m) and are comparable to organic matter fluxes observed for high-productivity, upwelling areas like off West Africa. The oxygen penetration depth, which reflects the long-term organic matter flux to the sediment, was correlated with assemblages of key diatom species. In the Scotia Sea (~3000 m water depth), oxygen penetration depths of less than 15 cm were observed, indicating high benthic organic carbon fluxes. In contrast, the oxic zone extends down to several decimeters in abyssal sediments of the Weddell Sea and the southeastern South Atlantic. The regional pattern of organic carbon fluxes derived from micro-sensor data suggest that episodic and seasonal sedimentation pulses are important for the carbon supply to the seafloor of the deep Southern Ocean.

Non-contact Pressure-based Sleep/Wake Discrimination

Poor sleep is increasingly being recognised as an important prognostic parameter of health. For those with suspected sleep disorders, patients are referred to sleep clinics which guide treatment. However, sleep clinics are not always a viable option due to their high cost, a lack of experienced practitioners, lengthy waiting lists and an unrepresentative sleeping environment. A home-based non-contact sleep/wake monitoring system may be used as a guide for treatment potentially stratifying patients by clinical need or highlighting longitudinal changes in sleep and nocturnal patterns. This paper presents the evaluation of an under-mattress sleep monitoring system for non-contact sleep/wake discrimination. A large dataset of sensor data with concomitant sleep/wake state was collected from both younger and older adults participating in a circadian sleep study. A thorough training/testing/validation procedure was configured and optimised feature extraction and sleep/wake discrimination algorithms evaluated both within and across the two cohorts. An accuracy, sensitivity and specificity of 74.3%, 95.5%, and 53.2% is reported over all subjects using an external validation
dataset (71.9%, 87.9% and 56%, and 77.5%, 98% and 57% is reported for younger and older subjects respectively). These results compare favourably with similar research, however this system provides an ambient alternative suitable for long term continuous sleep monitoring, particularly amongst vulnerable populations.

Bewegungsklassifikation mithilfe mobiler Sensoren

In diesem Beitrag wird eine neue Methode zur Analyse des manuellen Kommissionierprozesses vorgestellt, mit der u. a. die Kommissionierzeitanteile automatisch erfasst werden können. Diese Methode basiert auf einer sensorgestützten Bewegungsklassifikation, wie sie bspw. im Sport oder in der Medizin Anwendung findet. Dabei werden mobile Sensoren genutzt, die fortlaufend Messwerte wie z. B. die Beschleunigung oder die Drehgeschwindigkeit des Kommissionierers aufzeichnen. Auf Basis dieser Daten können Informationen über die ausgeführten Bewegungen und insbesondere über die durchlaufenen Bewegungszustände gewonnen werden. Dieser Ansatz wird im vorliegenden Beitrag auf die Kommissionierung übertragen. Dazu werden zunächst Klassen relevanter Bewegungen identifiziert und anschließend mit Verfahren aus dem maschinellen Lernen verarbeitet. Die Klassifikation erfolgt nach dem Prinzip des überwachten Lernens. Dabei werden durchschnittliche Erkennungsraten von bis zu 78,94 Prozent erzielt.

CONCURRENT LOCALIZATION AND MAPPING WITH SONAR SENSORS AND CONSIDERATION OF VEHICLE MOTION

Simultaneous Localization and Mapping (SLAM) is a procedure used to determine the location of a mobile vehicle in an unknown environment, while constructing a map of the unknown environment at the same time. Mobile platforms, which make use of SLAM algorithms, have industrial applications in autonomous maintenance, such as the inspection of flaws and defects in oil pipelines and storage tanks. A typical SLAM consists of four main components, namely, experimental setup (data gathering), vehicle pose estimation, feature extraction, and filtering. Feature extraction is the process of realizing significant features from the unknown environment such as corners, edges, walls, and interior features. In this work, an original feature extraction algorithm specific to distance measurements obtained through SONAR sensor data is presented. This algorithm has been constructed by combining the SONAR Salient Feature Extraction Algorithm and the Triangulation Hough Based Fusion with point-in-polygon detection. The reconstructed maps obtained through simulations and experimental data with the fusion algorithm are compared to the maps obtained with existing feature extraction algorithms. Based on the results obtained, it is suggested that the proposed algorithm can be employed as an option for data obtained from SONAR sensors in environment, where other forms of sensing are not viable. The algorithm fusion for feature extraction requires the vehicle pose estimation as an input, which is obtained from a vehicle pose estimation model. For the vehicle pose estimation, the author uses sensor integration to estimate the pose of the mobile vehicle. Different combinations of these sensors are studied (e.g., encoder, gyroscope, or encoder and gyroscope). The different sensor fusion techniques for the pose estimation are experimentally studied and compared. The vehicle pose estimation model, which produces the least amount of error, is used to generate inputs for the feature extraction algorithm fusion. In the experimental studies, two different environmental configurations are used, one without interior features and another one with two interior features. Numerical and experimental findings are discussed. Finally, the SLAM algorithm is implemented along with the algorithms for feature extraction and vehicle pose estimation. Three different cases are experimentally studied, with the floor of the environment intentionally altered to induce slipping. Results obtained for implementations with and without SLAM are compared and discussed. The present work represents a step towards the realization of autonomous inspection platforms for performing concurrent localization and mapping in harsh environments.

Dendritic Cells for SYN Scan Detection

Artificial immune systems have previously been applied to the problem of intrusion detection. The aim of this research is to develop an intrusion detection system based on the function of Dendritic Cells (DCs). DCs are antigen presenting cells and key to the activation of the human immune system, behaviour which has been abstracted to form the Dendritic Cell Algorithm (DCA). In algorithmic terms, individual DCs perform multi-sensor data fusion, asynchronously correlating the fused data signals with a secondary data stream. Aggregate output of a population of cells is analysed and forms the basis of an anomaly detection system. In this paper the DCA is applied to the detection of outgoing port scans using TCP SYN packets. Results show that detection can be achieved with the DCA, yet some false positives can be encountered when simultaneously scanning and using other network services. Suggestions are made for using adaptive signals to alleviate this uncovered problem.

Dendritic Cells for Anomaly Detection

Artificial immune systems, more specifically the negative selection algorithm, have previously been applied to intrusion detection. The aim of this research is to develop an intrusion detection system based on a novel concept in immunology, the Danger Theory. Dendritic Cells (DCs) are antigen presenting cells and key to the activation of the human immune system. DCs perform the vital role of combining signals from the host tissue and correlate these signals with proteins known as antigens. In algorithmic terms, individual DCs perform multi-sensor data fusion based on time-windows. The whole population of DCs asynchronously correlates the fused signals with a secondary data stream. The behaviour of human DCs is abstracted to form the DC Algorithm (DCA), which is implemented using an immune inspired framework, libtissue. This system is used to detect context switching for a basic machine learning dataset and to detect outgoing portscans in real-time. Experimental results show a significant difference between an outgoing portscan and normal traffic.

Reasoning over knowledge-based generation of situations in context spaces to reduce food waste

With the ever-growing amount of connected sensors (IoT), making sense of sensed data becomes even more important. Pervasive computing is a key enabler for sustainable solutions, prominent examples are smart energy systems and decision support systems. A key feature of pervasive systems is situation awareness which allows a system to thoroughly understand its environment. It is based on external interpretation of data and thus relies on expert knowledge. Due to the distinct nature of situations in different domains and applications, the development of situation aware applications remains a complex process. This thesis is concerned with a general framework for situation awareness which simplifies the development of applications. It is based on the Situation Theory Ontology to provide a foundation for situation modelling which allows knowledge reuse. Concepts of the Situation Theory are mapped to the Context Space Theory which is used for situation reasoning. Situation Spaces in the Context Space are automatically generated with the defined knowledge. For the acquisition of sensor data, the IoT standards O-MI/O-DF are integrated into the framework. These allow a peer-to-peer data exchange between data publisher and the proposed framework and thus a platform independent subscription to sensed data. The framework is then applied for a use case to reduce food waste. The use case validates the applicability of the framework and furthermore serves as a showcase for a pervasive system contributing to the sustainability goals. Leading institutions, e.g. the United Nations, stress the need for a more resource efficient society and acknowledge the capability of ICT systems. The use case scenario is based on a smart neighbourhood in which the system recommends the most efficient use of food items through situation awareness to reduce food waste at consumption stage.