A system design for space-based space surveillance

This paper presents the capabilities of a Space-Based Space Surveillance (SBSS) demonstration mission for Space Surveillance and Tracking (SST) based on a micro- satellite platform. The results have been produced in the frame of ESA’s "As sessment Study for Space Based Space Surveillance Demonstration Mission (Phase A) " performed by the Airbus DS consortium. Space Surveillance and Tracking is part of Space Situational Awareness (SSA) and covers the detection, tracking and cataloguing of spa ce debris and satellites. Derived SST services comprise a catalogue of these man-made objects, collision warning, detection and characterisation of in-orbit fragmentations, sub-catalogue debris characterisation, etc. The assessment of SBSS in an SST system architecture has shown that both an operational SBSS and also already a well - designed space-based demonstrator can provide substantial performance in terms of surveillance and tracking of beyond - LEO objects. Especially the early deployment of a demonstrator, possible by using standard equipment, could boost initial operating capability and create a self-maintained object catalogue. Unlike classical technology demonstration missions, the primary goal is the demonstration and optimisation of the functional elements in a complex end-to-end chain (mission planning, observation strategies, data acquisition, processing and fusion, etc.) until the final products can be offered to the users. The presented SBSS system concept takes the ESA SST System Requirements (derived within the ESA SSA Preparatory Program) into account and aims at fulfilling some of the SST core requirements in a stand-alone manner. The evaluation of the concept has shown that an according solution can be implemented with low technological effort and risk. The paper presents details of the system concept, candidate micro - satellite platforms, the observation strategy and the results of performance simulations for GEO coverage and cataloguing accuracy

Evaluation of Swiss slaughterhouse data for integration in a syndromic surveillance system

BACKGROUND: We evaluated Swiss slaughterhouse data for integration in a national syndromic surveillance system for the early detection of emerging diseases in production animals. We analysed meat inspection data for cattle, pigs and small ruminants slaughtered between 2007 and 2012 (including emergency slaughters of sick/injured animals); investigating patterns in the number of animals slaughtered and condemned; the reasons invoked for whole carcass condemnations; reporting biases and regional effects. RESULTS: Whole carcass condemnation rates were fairly uniform (1-2‰) over time and between the different types of production animals. Condemnation rates were much higher and less uniform following emergency slaughters. The number of condemnations peaked in December for both cattle and pigs, a time when individuals of lower quality are sent to slaughter when hay and food are limited and when certain diseases are more prevalent. Each type of production animal was associated with a different profile of condemnation reasons. The most commonly reported one was "severe lesions" for cattle, "abscesses" for pigs and "pronounced weight loss" for small ruminants. These reasons could constitute valuable syndromic indicators as they are unspecific clinical manifestations of a large range of animal diseases (as well as potential indicators of animal welfare). Differences were detected in the rate of carcass condemnation between cantons and between large and small slaughterhouses. A large percentage (>60% for all three animal categories) of slaughterhouses operating never reported a condemnation between 2007 and 2012, a potential indicator of widespread non-reporting bias in our database. CONCLUSIONS: The current system offers simultaneous coverage of cattle, pigs and small ruminants for the whole of Switzerland; and traceability of each condemnation to its farm of origin. The number of condemnations was significantly linked to the number of slaughters, meaning that the former should be always be offset by the later in analyses. Because this denominator is only communicated at the end of the month, condemnations may currently only be monitored on a monthly basis. Coupled with the lack of timeliness (30-60 days delay between condemnation and notification), this limits the use of the data for early-detection.

Range-based Weighted-likelihood Particle Filter for RSS-based Indoor Tracking

Attractive business cases in various application fields contribute to the sustained long-term interest in indoor localization and tracking by the research community. Location tracking is generally treated as a dynamic state estimation problem, consisting of two steps: (i) location estimation through measurement, and (ii) location prediction. For the estimation step, one of the most efficient and low-cost solutions is Received Signal Strength (RSS)-based ranging. However, various challenges - unrealistic propagation model, non-line of sight (NLOS), and multipath propagation - are yet to be addressed. Particle filters are a popular choice for dealing with the inherent non-linearities in both location measurements and motion dynamics. While such filters have been successfully applied to accurate, time-based ranging measurements, dealing with the more error-prone RSS based ranging is still challenging. In this work, we address the above issues with a novel, weighted likelihood, bootstrap particle filter for tracking via RSS-based ranging. Our filter weights the individual likelihoods from different anchor nodes exponentially, according to the ranging estimation. We also employ an improved propagation model for more accurate RSS-based ranging, which we suggested in recent work. We implemented and tested our algorithm in a passive localization system with IEEE 802.15.4 signals, showing that our proposed solution largely outperforms a traditional bootstrap particle filter.

All-sky Meteor Orbit System AMOS and preliminary analysis of three unusual meteor showers

All-sky Meteor Orbit System (AMOS) is a semi-autonomous video observatory for detection of transient events on the sky, mostly the meteors. Its hardware and software development and permanent placement on several locations in Slovakia allowed the establishment of Slovak Video Meteor Network (SVMN) monitoring meteor activity above the Central Europe. The data reduction, orbital determination and additional results from AMOS cameras–the SVMN database– as well as from observational expeditions on Canary Islands and in Canada provided dynamical and physical data for better understanding of mutual connections between parent bodies of asteroids and comets and their meteoroid streams. We present preliminary results on exceptional and rare meteor streams such as September ε Perseids (SPE) originated from unknown long periodic comet on a retrograde orbit, suspected asteroidal meteor stream of April α Comae Berenicids (ACO) in the orbit of meteorites Příbram and Neuschwanstein and newly observed meteor stream Camelopardalids (CAM) originated from Jupiter family comet 209P/Linear.

Fine-grained Indoor Tracking by Fusing Inertial Sensor and Physical Layer Information in WLANs

Indoor positioning has become an emerging research area because of huge commercial demands for location-based services in indoor environments. Channel State Information (CSI) as a fine-grained physical layer information has been recently proposed to achieve high positioning accuracy by using range-based methods, e.g., trilateration. In this work, we propose to fuse the CSI-based ranges and velocity estimated from inertial sensors by an enhanced particle filter to achieve highly accurate tracking. The algorithm relies on some enhanced ranging methods and further mitigates the remaining ranging errors by a weighting technique. Additionally, we provide an efficient method to estimate the velocity based on inertial sensors. The algorithms are designed in a network-based system, which uses rather cheap commercial devices as anchor nodes. We evaluate our system in a complex environment along three different moving paths. Our proposed tracking method can achieve 1:3m for mean accuracy and 2:2m for 90% accuracy, which is more accurate and stable than pedestrian dead reckoning and range-based positioning.

A clinically applicable laser-based image-guided system for laparoscopic liver procedures

PURPOSE Laser range scanners (LRS) allow performing a surface scan without physical contact with the organ, yielding higher registration accuracy for image-guided surgery (IGS) systems. However, the use of LRS-based registration in laparoscopic liver surgery is still limited because current solutions are composed of expensive and bulky equipment which can hardly be integrated in a surgical scenario. METHODS In this work, we present a novel LRS-based IGS system for laparoscopic liver procedures. A triangulation process is formulated to compute the 3D coordinates of laser points by using the existing IGS system tracking devices. This allows the use of a compact and cost-effective LRS and therefore facilitates the integration into the laparoscopic setup. The 3D laser points are then reconstructed into a surface to register to the preoperative liver model using a multi-level registration process. RESULTS Experimental results show that the proposed system provides submillimeter scanning precision and accuracy comparable to those reported in the literature. Further quantitative analysis shows that the proposed system is able to achieve a patient-to-image registration accuracy, described as target registration error, of [Formula: see text]. CONCLUSIONS We believe that the presented approach will lead to a faster integration of LRS-based registration techniques in the surgical environment. Further studies will focus on optimizing scanning time and on the respiratory motion compensation.

Concepts of Bitemporal Database Theory and the Evolution of Web-Documents

A vast amount of temporal information is provided on the Web. Even though many facts expressed in documents are time-related, the temporal properties of Web presentations have not received much attention. In database research, temporal databases have become a mainstream topic in recent years. In Web documents, temporal data may exist as meta data in the header and as user-directed data in the body of a document. Whereas temporal data can easily be identified in the semi-structured meta data, it is more difficult to determine temporal data and its role in the body. We propose procedures for maintaining temporal integrity of Web pages and outline different approaches of applying bitemporal data concepts for Web documents. In particular, we regard desirable functionalities of Web repositories and other Web-related tools that may support the Webmasters in managing the temporal data of their Web documents. Some properties of a prototype environment are described.

Detecting single-target changes in multiple object tracking: The case of peripheral vision

In the current study it is investigated whether peripheral vision can be used to monitor multi-ple moving objects and to detect single-target changes. For this purpose, in Experiment 1, a modified MOT setup with a large projection and a constant-position centroid phase had to be checked first. Classical findings regarding the use of a virtual centroid to track multiple ob-jects and the dependency of tracking accuracy on target speed could be successfully replicat-ed. Thereafter, the main experimental variations regarding the manipulation of to-be-detected target changes could be introduced in Experiment 2. In addition to a button press used for the detection task, gaze behavior was assessed using an integrated eye-tracking system. The anal-ysis of saccadic reaction times in relation to the motor response shows that peripheral vision is naturally used to detect motion and form changes in MOT because the saccade to the target occurred after target-change offset. Furthermore, for changes of comparable task difficulties, motion changes are detected better by peripheral vision than form changes. Findings indicate that capabilities of the visual system (e.g., visual acuity) affect change detection rates and that covert-attention processes may be affected by vision-related aspects like spatial uncertainty. Moreover, it is argued that a centroid-MOT strategy might reduce the amount of saccade-related costs and that eye-tracking seems to be generally valuable to test predictions derived from theories on MOT. Finally, implications for testing covert attention in applied settings are proposed.

Comparison of whole carcass condemnation and partial carcass condemnation data for integration in a national syndromic surveillance system: The Swiss experience

We obtained partial carcass condemnation (PCC) data for cattle (2009-2010) from a Swiss slaughterhouse. Data on whole carcass condemnations (WCC) carried out at the same slaughterhouse over those years were extracted from the national database for meat inspection. We found that given the differences observed in the WCC and PCC time series, it is likely that both indicators respond to different health events in the population and that one cannot be substituted by the other. Because PCC recordings are promising for syndromic surveillance, the meat inspection database should be capable to record both WCC and PCC data in the future. However, a standardised list of reasons for PCC needs to be defined and used nationwide in all slaughterhouses.

Fine-grained indoor positioning and tracking systems

Indoor positioning has attracted considerable attention for decades due to the increasing demands for location based services. In the past years, although numerous methods have been proposed for indoor positioning, it is still challenging to find a convincing solution that combines high positioning accuracy and ease of deployment. Radio-based indoor positioning has emerged as a dominant method due to its ubiquitousness, especially for WiFi. RSSI (Received Signal Strength Indicator) has been investigated in the area of indoor positioning for decades. However, it is prone to multipath propagation and hence fingerprinting has become the most commonly used method for indoor positioning using RSSI. The drawback of fingerprinting is that it requires intensive labour efforts to calibrate the radio map prior to experiments, which makes the deployment of the positioning system very time consuming. Using time information as another way for radio-based indoor positioning is challenged by time synchronization among anchor nodes and timestamp accuracy. Besides radio-based positioning methods, intensive research has been conducted to make use of inertial sensors for indoor tracking due to the fast developments of smartphones. However, these methods are normally prone to accumulative errors and might not be available for some applications, such as passive positioning. This thesis focuses on network-based indoor positioning and tracking systems, mainly for passive positioning, which does not require the participation of targets in the positioning process. To achieve high positioning accuracy, we work on some information of radio signals from physical-layer processing, such as timestamps and channel information. The contributions in this thesis can be divided into two parts: time-based positioning and channel information based positioning. First, for time-based indoor positioning (especially for narrow-band signals), we address challenges for compensating synchronization offsets among anchor nodes, designing timestamps with high resolution, and developing accurate positioning methods. Second, we work on range-based positioning methods with channel information to passively locate and track WiFi targets. Targeting less efforts for deployment, we work on range-based methods, which require much less calibration efforts than fingerprinting. By designing some novel enhanced methods for both ranging and positioning (including trilateration for stationary targets and particle filter for mobile targets), we are able to locate WiFi targets with high accuracy solely relying on radio signals and our proposed enhanced particle filter significantly outperforms the other commonly used range-based positioning algorithms, e.g., a traditional particle filter, extended Kalman filter and trilateration algorithms. In addition to using radio signals for passive positioning, we propose a second enhanced particle filter for active positioning to fuse inertial sensor and channel information to track indoor targets, which achieves higher tracking accuracy than tracking methods solely relying on either radio signals or inertial sensors.

Carbohydrate Estimation by a Mobile Phone-Based System Versus Self-Estimations of Individuals With Type 1 Diabetes Mellitus: A Comparative Study

Background: Diabetes mellitus is spreading throughout the world and diabetic individuals have been shown to often assess their food intake inaccurately; therefore, it is a matter of urgency to develop automated diet assessment tools. The recent availability of mobile phones with enhanced capabilities, together with the advances in computer vision, have permitted the development of image analysis apps for the automated assessment of meals. GoCARB is a mobile phone-based system designed to support individuals with type 1 diabetes during daily carbohydrate estimation. In a typical scenario, the user places a reference card next to the dish and acquires two images using a mobile phone. A series of computer vision modules detect the plate and automatically segment and recognize the different food items, while their 3D shape is reconstructed. Finally, the carbohydrate content is calculated by combining the volume of each food item with the nutritional information provided by the USDA Nutrient Database for Standard Reference. Objective: The main objective of this study is to assess the accuracy of the GoCARB prototype when used by individuals with type 1 diabetes and to compare it to their own performance in carbohydrate counting. In addition, the user experience and usability of the system is evaluated by questionnaires. Methods: The study was conducted at the Bern University Hospital, “Inselspital” (Bern, Switzerland) and involved 19 adult volunteers with type 1 diabetes, each participating once. Each study day, a total of six meals of broad diversity were taken from the hospital’s restaurant and presented to the participants. The food items were weighed on a standard balance and the true amount of carbohydrate was calculated from the USDA nutrient database. Participants were asked to count the carbohydrate content of each meal independently and then by using GoCARB. At the end of each session, a questionnaire was completed to assess the user’s experience with GoCARB. Results: The mean absolute error was 27.89 (SD 38.20) grams of carbohydrate for the estimation of participants, whereas the corresponding value for the GoCARB system was 12.28 (SD 9.56) grams of carbohydrate, which was a significantly better performance ( P=.001). In 75.4% (86/114) of the meals, the GoCARB automatic segmentation was successful and 85.1% (291/342) of individual food items were successfully recognized. Most participants found GoCARB easy to use. Conclusions: This study indicates that the system is able to estimate, on average, the carbohydrate content of meals with higher accuracy than individuals with type 1 diabetes can. The participants thought the app was useful and easy to use. GoCARB seems to be a well-accepted supportive mHealth tool for the assessment of served-on-a-plate meals.

Fine-grained Indoor Tracking by Fusing Inertial Sensor and Physical Layer Information in WLANs

Indoor positioning has become an emerging research area because of huge commercial demands for location-based services in indoor environments. Channel State Information (CSI) as fine-grained physical layer information has been recently proposed to achieve high positioning accuracy by using range based methods, e.g., trilateration. In this work, we propose to fuse the CSI-based ranging and velocity estimated from inertial sensors by an enhanced particle filter to achieve highly accurate tracking. The algorithm relies on some enhanced ranging methods and further mitigates the remaining ranging errors by a weighting technique. Additionally, we provide an efficient method to estimate the velocity based on inertial sensors. The algorithms are designed in a network-based system, which uses rather cheap commercial devices as anchor nodes. We evaluate our system in a complex environment along three different moving paths. Our proposed tracking method can achieve 1.3m for mean accuracy and 2.2m for 90% accuracy, which is more accurate and stable than pedestrian dead reckoning and range-based positioning.