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The growing demand for active medical implantable devices requires data and or power links between the implant and the outside world. Every implant has to be encapsulated from the body by a specific housing and one of the most common materials used is titanium or titanium alloy. Titanium thas the necessary properties in terms of mechanical and chemical stability and biocompatibility. However, its electrical conductivity presents a challenge for the electromagnetic transmission of data and power. The proposed paper presents a fast and practical method to determine the necessary transmission parameters for titanium encapsulated implants. Therefore, the basic transformer-transmission-model is used with measured or calculated key values for the inductances. Those are then expanded with correction factors to determine the behavior with the encapsulation. The correction factors are extracted from finite element method simulations. These also enable the analysis of the magnetic field distribution inside of the housing. The simulated transmission properties are very close to the measured values. Additionally, based on lumped elements and magnetic field distribution, the influential parameters are discussed in the paper. The parameter discussion describes how to enhance the transmitted power, data-rate or distance, or to reduce the size of the necessary coils. Finally, an example application demonstrates the usage of the methods.
A platform of an electronic capsule is being developed for multi-task medical assistant application. It includes a near field telemetry unit for bidirectional communication system of 115 KHz low carrier frequency for inductive data transmission suited for human body energy transfer. The system triggers an actuator for drug delivery in various time and release forms via wireless external control, it has the ability to record temperature, measure pH of the body (additional sensors), and retrieve data to the outside. It consists of a 32bit processor, memory, external peripheries, and detection facility. The complete system is designed to fit small-size mass medical application with low power consumption, size of 7x25mm. The system is designed, simulated and emulated on FPGA. A final layout of the complete chip design is still under progress.
A new electronic capsule with bidirectional communication system is being developed for multi-task application. The capsule is designed to be a platform for medical assistant application inside the body. The designed telemetry unit is a synchronous bidirectional communication block using continuous phase DQPSK of 115 kHz low carrier frequency for inductive data transmission suited for human body energy transfer. The communication system can assist the electronic pill to trigger an actuator for drug delivery, to record temperature, or to measure pH of the body. It consists additionally to a 32bit processor, memory, external peripheries, and detection facility. The complete system is designed to fit small-size mass medical application with low power consumption, size of 7x25mm. The system is designed, simulated and emulated on FPGA. A final layout of the complete chip design is still under progress.
Remote measurement of the physiology, so-called biotelemetry, is a key technology in the modern veterinary medicine. The usage of wireless implants has less impact on the behavior of animals than manual measurement methods and cause less disturbance than wired devices. But, common biotelemetry still uses proprietary communication and power concepts focused on small systems with one animal. Therefore, the University of Applied Sciences Offenburg is developing a low-cost RFID system called muTrans1, which is able to measure ECG, pressure, temperature, oxygen saturation and activity. The muTrans uses an own RFID sensor transponder and standardized commercial components and combines them to a scalable RFID system able to build-up RFID sensor networks with a nearly unlimited size.
In this paper, a complete passive transponder device has been discussed which is meant to monitor leakage in silicone breast implants. The passive tag operates in the HF frequency range of 13.56MHz using RFID ISO 15693 standard. The complete system consists of the transponder, reader and a PC. This paper focusses on the development of such a state of the art passive RFID transponder to monitor the wellness of the silicone breast implants periodically in order to detect leakage in the same. Keyword: RFID (Radio frequency identification device), EM (Electromagnetic) field, Passive Transponder, Silicone breast implants.
In this paper, the influence of the material hardening behavior on plasticity-induced fatigue crack closure is investigated for strain-controlled loading and fully plastic, large-scale yielding conditions by means of the finite element method. The strain amplitude and the strain ratio are varied for given Ramberg–Osgood material properties representing materials with different hardening behavior. The results show a pronounced influence of the hardening behavior on crack closure, while no significant effect is found from the considered strain amplitude and strain ratio. The effect of the hardening behavior on the crack opening stress cannot be described by existing crack opening stress equations.
In this paper, the effect of the polycrystalline microstructure on crack-tip opening displacement and crack closure is investigated for microstructural short plane strain fatigue cracks using the finite-element method. To this end, cracks are introduced in synthetically generated microstructures and the grain properties are described using a single crystal plasticity model with kinematic hardening. Additionally, finite-element calculations without resolved microstructure and von Mises plasticity with kinematic hardening are performed. Fully-reversed strain-controlled cyclic loadings are considered under large-scale yielding conditions as typical for low-cycle fatigue problems. The crack opening stress and the cyclic crack-tip opening displacement are significantly influenced by the local grain structure. While the stabilized crack opening stresses obtained with the microstructure-based finite-element model are in good accordance with the von Mises plasticity results, the differences in the cyclic crack opening displacement are addressed to the asymmetric plastic strain fields in the plastic wake behind the crack-tip of the microstructure-based model. The asymmetric plastic strain fields result in discontinuous and premature contact of the crack flanks.
Short-term load forecasting (STLF) has been playing a key role in the electricity sector for several decades, due to the need for aligning energy generation with the demand and the financial risk connected with forecasting errors. Following the top-down approach, forecasts are calculated for aggregated load profiles, meaning the sum of singular loads from consumers belonging to a balancing group. Due to the emerging flexible loads, there is an increasing relevance for STLF of individual factories. These load profiles are typically more stochastic compared to aggregated ones, which imposes new requirements to forecasting methods and tools with a bottom-up approach. The increasing digitalization in industry with enhanced data availability as well as smart metering are enablers for improved load forecasts. There is a need for STLF tools processing live data with a high temporal resolution in the minute range. Furthermore, behin-the-meter (BTM) data from various sources like submetering and production planning data should be integrated in the models. In this case, STLF is becoming a big data problem so that machine learning (ML) methods are required. The research project “GaIN” investigates the improvement of the STLF quality of an energy utility using BTM data and innovative ML models. This paper describes the project scope, proposes a detailed definition for a benchmark and evaluates the readiness of existing STLF methods to fulfil the described requirements as a reviewing paper.
The review highlights that recent STLF investigations focus on ML methods. Especially hybrid models gain more and more importance. ML can outperform classical methods in terms of automation degree and forecasting accuracy. Nevertheless, the potential for improving forecasting accuracy by the use of ML models depends on the underlying data and the types of input variables. The described methods in the analyzed publications only partially fulfil the tool requirements for STLF on company level. There is still a need to develop suitable ML methods to integrate the expanded data base in order to improve load forecasts on company level.
Protecting software from illegal access, intentional modification or reverse engineering is an inherently difficult practical problem involving code obfuscation techniques and real-time cryptographic protection of code. In traditional systems a secure element (the "dongle") is used to protect software. However, this approach suffers from several technical and economical drawbacks such as the dongle being lost or broken.
We present a system that provides such dongles as a cloud service, and more importantly, provides the required cryptographic material to control access to software functionality in real-time.
This system is developed as part of an ongoing nationally funded research project and is now entering a first trial stage with stakeholders from different industrial sectors.
The development of secure software systems is of ever-increasing importance. While software companies often invest large amounts of resources into the upkeeping and general security properties of large-scale applications when in production, they appear to neglect utilizing threat modeling in the earlier stages of the software development lifecycle. When applied during the design phase of development, and continuously throughout development iterations, threat modeling can help to establish a "Secure by Design" approach. This approach allows issues relating to IT security to be found early during development, reducing the need for later improvement – and thus saving resources in the long term. In this paper the current state of threat modeling is investigated. This investigation drove the derivation of requirements for the development of a new threat modelling framework and tool, called OVVL. OVVL utilizes concepts of established threat modeling methodologies, as well as functionality not available in existing solutions.
The increase in households with grid connected Photovoltaic (PV) battery system poses challenge for the grid due to high PV feed-in as a result of mismatch in energy production and load demand. The purpose of this paper is to show how a Model Predictive Control (MPC) strategy could be applied to an existing grid connected household with PV battery system such that the use of battery is maximized and at the same time peaks in PV energy and load demand are reduced. The benefits of this strategy are to allow increase in PV hosting capacity and load hosting capacity of the grid without the need for external signals from the grid operator. The paper includes the optimal control problem formulation to achieve the peak shaving goals along with the experiment set up and preliminary experiment results. The goals of the experiment were to verify the hardware and software interface to implement the MPC and as well to verify the ability of the MPC to deal with the weather forecast deviation. A prediction correction has also been introduced for a short time horizon of one hour within this MPC strategy to estimate the PV output power behavior.
In rural low voltage grid networks, the use of battery in the households with a grid connected Photovoltaic (PV) system is a popular solution to shave the peak PV feed-in to the grid. For a single electricity price scenario, the existing forecast based control approaches together with a decision based control layer uses weather and load forecast data for the on–off schedule of the battery operation. These approaches do bring cost benefit from the battery usage. In this paper, the focus is to develop a Model Predictive Control (MPC) to maximize the use of the battery and shave the peaks in the PV feed-in and the load demand. The solution of the MPC allows to keep the PV feed-in and the grid consumption profile as low and as smooth as possible. The paper presents the mathematical formulation of the optimal control problem along with the cost benefit analysis . The MPC implementation scheme in the laboratory and experiment results have also been presented. The results show that the MPC is able to track the deviation in the weather forecast and operate the battery by solving the optimal control problem to handle this deviation.
In recent times, the energy consumed by buildings facilities became considerable. Efficient local energy management is vital to deal with building power demand penalties. This operation becomes complex when a hybrid energy system is included in the power system. This study proposes new energy management between photovoltaic (PV) system, Battery Energy Storage System (BESS) and the power network in a building by controlling the PV/BESS inverter. The strategy is based on explicit model predictive control (MPC) to find an optimal power flow in the building for one-day ahead. The control algorithm is based on a simple power flow equation and weather forecast. Then, a cost function is formulated and optimised using genetic algorithms-based solver. The objective is reducing the imported energy from the grid preventing the saturation and emptiness of BESS. Including other targets to the control policy as energy price dynamic and BESS degradation, MPC can optimise dramatically the efficacy of the global building power system. The strategy is implemented and tested successfully using MATLAB/SimPowerSystems software, compared to classical hysteresis management, MPC has given 10% in energy cost economy and 25% improvement in BESS lifetime.
A new, small, and optimized for low power processor core named SIRIUS has been developed, simulated, synthesized to a netlist and verified. From this netlist, containing only primitives like gates and flip-flops, a mapping to an ASIC - or FPGA technology can easily be done with existing synthesizer tools, allowing very complex SOC designs with several blocks. Emulation via FPGA can be done on already simple setups and cheap hardware because of the small core size. The performance is estimated 50 MIPS on Cyclone II FPGA and about 100 MIPS on a 0.35 CMOS 5M2P technology with 4197 primitives used for the core, including a 16 x 16 multiplier. An example design of the ASIC for an electronic ePille device currently in development is shown.