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This paper presents an optimized algorithm for estimating static and dynamic gait parameters. We use a marker- and contact-less motion capture system that identifies 20 joints of a person walking along a corridor.
Based on the proposed gait cycle detection basic metrics as walking frequency, step/stride length, and support phases are estimated automatically. Applying a rigid body model, we are capable to calculate static and dynamic gait stability metrics. We conclude with initial results of a clinical study evaluating orthopaedic technical support.
Optimised Nutrient Recovery from Biogas Digestate by Solid/Liquid Separation and Membrane Treatment
(2019)
Anaerobic digestion products of agricultural biogas plants are characterised by high nitrogen, phosphorus, and potassium content. In three scale-up steps, a membrane based digestate treatment process of solid-liquid-separation, ultrafiltration, and reverse osmosis for nutrient recovery was investigated. Lab-scale trials delivered a very good understanding of fluid properties and subsequent ultrafiltration performance, which is the limiting process step in terms of energy demand and operation costs. In semi-technical experiments, optimisation, and design parameters were developed, which were subsequently applied to pilot-scale tests at two full-scale biogas plants. The process optimisation resulted in 50 % energy reduction of the ultrafiltration step. About 36 % of the sludge volume was recovered as dischargeable water, 20 % as solid N/P-fertiliser, and 44 % as liquid N/K-fertiliser.
We describe an automated approach, to easily track patients regaining their walking ability while recovering from neurological diseases like e.g. stroke. Based on captured gait data and objective measures derived out of it the rehabilitation process can be optimized and thus steered. In order to apply such system in clinical practice two key requirements have to be fulfilled: (i) the system needs to be applicable in terms of ease of use and performance; (ii) the derived measures need to be accurate.
The usage of high-level synthesis (HLS) tools for FPGAs has increased significantly over the last years since they matured and allow software programmers to take advantage of reconfigurable hardware technology.
Most HLS tools employ methods to optimize for loops, e. g. by unrolling or pipelining them. But there is hardly any work on the optimization of while loops. This comes at no surprise since most while loops have loop-carried dependences involving the loop condition which result in large recurrence cycles in the dataflow graphs. Therefore typical while loops cannot be parallelized or pipelined.
We propose a novel transformation which allows to optimize while loops nested within a for loop. By interchanging the two loops, it is possible to pipeline (and thereby parallelize) the inner loop, resulting in a reduced execution time. We present two case studies on different hardware platforms and show the speedup factors - compared to a host processor and to an unoptimized hardware implementation - achieved by our while loop optimization method.
The Internet of Things (IoT) relies on sensor devices to measure real-world phenomena in order to provide IoT services. The sensor readings are shared with multiple entities, such as IoT services, other IoT devices or other third parties. The collected data may be sensitive and include personal information. To protect the privacy of the users, the data needs to be protected through an encryption algorithm. For sharing cryptographic cipher-texts with a group of users Attribute-Based Encryption (ABE) is well suited, as it does not require to create group keys. However, the creation of ABE cipher-texts is slow when executed on resource constraint devices, such as IoT sensors. In this paper, we present a modification of an ABE scheme, which not only allows to encrypt data efficiently using ABE, but also reduces the size of the cipher-text, that must be transmitted by the sensor. We also show how our modification can be used to realise an instantaneous key revocation mechanism.
Biogas plants produce nutrient rich digestates as side products, which are usually used as local fertilisers. Yet the large amount and regional gradients of biogas plants in Germany necessitate management, conditioning, and transportation of digestates, in order to follow good fertilising procedure and prohibit local over-fertilisation. With a membrane-based treatment chain, i.e. centrifugation, ultrafiltration, and reverse osmosis, digestates can be separated into a solid N,P-fertiliser, a liquid N,K-fertiliser, and dischargeable water. Up to now, the high energy demand of the process chain, in particular the ultrafiltration step, limits the economical market launch of the treatment chain. A reduction of the energy demand is challenging, as digestates exhibit a high fouling potential and ultrafiltration fluxes differ considerably for digestates from different biogas plants. In a systematic screening of 28 digestate samples from agricultural biogas plants and 6 samples from bio-waste biogas plants, ultrafiltration performance could be successfully linked to the rheological properties of the digestate’s liquid phase and to its macromolecular biopolymer concentration. By modification of the fluid characteristics through enzymatic treatment, ultrafiltration performance was considerably increased by factor 2.8 on average, which equals energy savings in the ultrafiltration step of approximately 45%. Consequently, the energy demand of the total treatment chain decreases, which offers potential for further rollout of the membrane-based digestate treatment.
The present study gives an overview of recent investigations dealing with the fatigue behaviour of the tempered martensitic steel 50CrMo4 (Fe-0.5wt%C-1wt%Cr) in the HCF and VHCF regime by taking into account a variation in material strength, by modifying the heat treatment parameters. The parameters for the tempering treatment were adapted to receive two material conditions with 37HRC and 57HRC, respectively. Subsequently, fatigue specimens were machined from the heat-treated bars for fatigue tests in an ultrasonic (f=20000Hz) and a resonance (f=95Hz) fatigue testing machine under fully reversed loading (R=-1) at laboratory air atmosphere. It was found that the dominant fatigue and fracture mechanisms change with increasing material strength. For 37HRC moderate-strength specimens crack initiation was shown to occur on the specimen surface within Cr depleted bands (segregation bands) as the dominant fatigue damage mechanism. Contrary to that, only internal crack initiation at non-metallic inclusions was observed for the high strength 57HRC condition. Furthermore, the completely different crack initiation mechanisms of the two heat treatment conditions were assessed by applying the Murakami approach relating the fatigue limit with the size of non-metallic inclusions.
he development of context-aware applications is a difficult and error-prone task. The dynamics of the environmental context combined with the complexity of the applications poses a vast number of possibilities for mistakes during the creation of new applications. Therefore it is important to test applications before they are deployed in a life system. For this reason, this paper proposes a testing tool, which will allow for automatic generation of various test cases from application description documents. Semantic annotations are used to create specific test data for context-aware applications. A test case reduction methodology based on test case diversity investigations ensures scalability of the proposed automated testing approach.
Smart city applications in the Big Data era require not only techniques dedicated to dynamicity handling, but also the ability to take into account contextual information, user preferences and requirements, and real-time events to provide optimal solutions and automatic configuration for the end user. In this paper, we present a specific functionality in the design and implementation of a declarative decision support component that exploits contextual information, user preferences and requirements to automatically provide optimal configurations of smart city applications. The key property of user-centricity of our approach is achieved by enabling users to declaratively specify constraints and preferences on the solutions provided by the smart city application through the Decision Support component, and automatically map these constraints and preferences to provide optimal responses targeting user needs. We showcase the effectiveness and flexibility of our solution in two real usecase scenarios: a multimodal travel planner and a mobile parking application. All the components and algorithms described in this paper have been defined and implemented as part of the Smart City Framework CityPulse.