Optimized spectrally selective steady-state free precession sequences for cartilage imaging at ultra-high fields

OBJECT: Fat suppressed 3D steady-state free precession (SSFP) sequences are of special interest in cartilage imaging due to their short repetition time in combination with high signal-to-noise ratio. At low-to-high fields (1.5-3.0 T), spectral spatial (spsp) radio frequency (RF) pulses perform superiorly over conventional saturation of the fat signal (FATSAT pulses). However, ultra-high fields (7.0 T and more) may offer alternative fat suppression techniques as a result of the increased chemical shift. MATERIALS AND METHODS: Application of a single, frequency selective, RF pulse is compared to spsp excitation for water (or fat) selective imaging at 7.0 T. RESULTS: For SSFP, application of a single frequency selective RF pulse for selective water or fat excitation performs beneficially over the commonly applied spsp RF pulses. In addition to the overall improved fat suppression, the application of single RF pulses leads to decreased power depositions, still representing one of the major restrictions in the design and application of many pulse sequences at ultra-high fields. CONCLUSION: The ease of applicability and implementation of single frequency selective RF pulses at ultra-high-fields might be of great benefit for a vast number of applications where fat suppression is desirable or fat-water separation is needed for quantification purposes.

Sheep persistently infected with Border disease readily transmit virus to calves seronegative to BVD virus.

Bovine viral diarrhea- and Border disease viruses of sheep belong to the highly diverse genus pestivirus of the Flaviviridae. Ruminant pestiviruses may infect a wide range of domestic and wild cloven-hooved mammals (artiodactyla). Due to its economic importance, programs to eradicate bovine viral diarrhea are a high priority in the cattle industry. By contrast, Border disease is not a target of eradication, although the Border disease virus is known to be capable of also infecting cattle. In this work, we compared single dose experimental inoculation of calves with Border disease virus with co-mingling of calves with sheep persistently infected with this virus. As indicated by seroconversion, infection was achieved only in one out of seven calves with a dose of Border disease virus that was previously shown to be successful in calves inoculated with BVD virus. By contrast, all calves kept together with persistently infected sheep readily became infected with Border disease virus. The ease of viral transmission from sheep to cattle and the antigenic similarity of bovine and ovine pestiviruses may become a problem for demonstrating freedom of BVD by serology in the cattle population.

Top-Down Parsing with Parsing Contexts

The domain of context-free languages has been extensively explored and there exist numerous techniques for parsing (all or a subset of) context-free languages. Unfortunately, some programming languages are not context-free. Using standard context-free parsing techniques to parse a context-sensitive programming language poses a considerable challenge. Im- plementors of programming language parsers have adopted various techniques, such as hand-written parsers, special lex- ers, or post-processing of an ambiguous parser output to deal with that challenge. In this paper we suggest a simple extension of a top-down parser with contextual information. Contrary to the tradi- tional approach that uses only the input stream as an input to a parsing function, we use a parsing context that provides ac- cess to a stream and possibly to other context-sensitive infor- mation. At a same time we keep the context-free formalism so a grammar definition stays simple without mind-blowing context-sensitive rules. We show that our approach can be used for various purposes such as indent-sensitive parsing, a high-precision island parsing or XML (with arbitrary el- ement names) parsing. We demonstrate our solution with PetitParser, a parsing-expression grammar based, top-down, parser combinator framework written in Smalltalk.

Green Wireless-Energy Efficiency in Wireless Networks

Wireless networks have become more and more popular because of ease of installation, ease of access, and support of smart terminals and gadgets on the move. In the overall life cycle of providing green wireless technology, from production to operation and, finally, removal, this chapter focuses on the operation phase and summarizes insights in energy consumption of major technologies. The chapter also focuses on the edge of the network, comprising network access points (APs) and mobile user devices. It discusses particularities of most important wireless networking technologies: wireless access networks including 3G/LTE and wireless mesh networks (WMNs); wireless sensor networks (WSNs); and ad-hoc and opportunistic networks. Concerning energy efficiency, the chapter discusses challenges in access, wireless sensor, and ad-hoc and opportunistic networks.

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.

Sensitive Questions in Online Surveys: Experimental Results for the Randomized Response Technique (RRT) and the Unmatched Count Technique (UCT)

Gaining valid answers to so-called sensitive questions is an age-old problem in survey research. Various techniques have been developed to guarantee anonymity and minimize the respondent's feelings of jeopardy. Two such techniques are the randomized response technique (RRT) and the unmatched count technique (UCT). In this study we evaluate the effectiveness of different implementations of the RRT (using a forced-response design) in a computer-assisted setting and also compare the use of the RRT to that of the UCT. The techniques are evaluated according to various quality criteria, such as the prevalence estimates they provide, the ease of their use, and respondent trust in the techniques. Our results indicate that the RRTs are problematic with respect to several domains, such as the limited trust they inspire and non-response, and that the RRT estimates are unreliable due to a strong false "no" bias, especially for the more sensitive questions. The UCT, however, performed well compared to the RRTs on all the evaluated measures. The UCT estimates also had more face validity than the RRT estimates. We conclude that the UCT is a promising alternative to RRT in self-administered surveys and that future research should be directed towards evaluating and improving the technique.

Sensitive Questions in Online Surveys: Experimental Results for the Randomized Response Technique (RRT) and the Unmatched Count Technique (UCT)

Gaining valid answers to so-called sensitive questions is an age-old problem in survey research. Various techniques have been developed to guarantee anonymity and minimize the respondent's feelings of jeopardy. Two such techniques are the randomized response technique (RRT) and the unmatched count technique (UCT). In this study we evaluate the effectiveness of different implementations of the RRT (using a forced-response design) in a computer-assisted setting and also compare the use of the RRT to that of the UCT. The techniques are evaluated according to various quality criteria, such as the prevalence estimates they provide, the ease of their use, and respondent trust in the techniques. Our results indicate that the RRTs are problematic with respect to several domains, such as the limited trust they inspire and non-response, and that the RRT estimates are unreliable due to a strong false "no" bias, especially for the more sensitive questions. The UCT, however, performed well compared to the RRTs on all the evaluated measures. The UCT estimates also had more face validity than the RRT estimates. We conclude that the UCT is a promising alternative to RRT in self-administered surveys and that future research should be directed towards evaluating and improving the technique.

Towards moldable development tools

Developers commonly ask detailed and domain-specific questions about the software systems they are developing and maintaining. Integrated development environments (IDEs) form an essential category of tools for developing software that should support software engineering decision making. Unfortunately, rigid and generic IDEs that focus on low-level programming tasks, that promote code rather than data, and that suppress customization, offer limited support for informed decision making during software development. We propose to improve decision making within IDEs by moving from generic to context-aware IDEs through moldable tools. In this paper, we promote the idea of moldable tools, illustrate it with concrete examples, and discuss future research directions.