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Impact of a large distribution network on radiation characteristics of planar spiral antenna arrays
(2023)
Designing antenna arrays with a central feed point has gained ground in the antenna technique. This approach, which is usually applied because of manufacturing costs, is difficult to achieve and leads to a large feeding network. The impact of which is numerically investigated in the present work. Upon comparing three different antennas, it is shown that the enlargement of the feed strongly affects the antenna's overall dimensions and the antenna's radiation characteristics. The antenna with the plug-in solution is not only small in size but also performs better compared to antennas with a central feed point. Considering the high effort in designing the feed network with a central point and the influence of the resulting enlarged network on the dimensions and radiation characteristics of the antenna, the cost saving in production can be put into perspective.
Advancing mental health diagnostics: AI-based method for depression detection in patient interviews
(2023)
In this paper, we present a novel artificial intelligence (AI) application for depression detection, using advanced transformer networks to analyse clinical interviews. By incorporating simulated data to enhance traditional datasets, we overcome limitations in data protection and privacy, consequently improving the model’s performance. Our methodology employs BERT-based models, GPT-3.5, and ChatGPT-4, demonstrating state-of-the-art results in detecting depression from linguistic patterns and contextual information that significantly outperform previous approaches. Utilising the DAIC-WOZ and Extended-DAIC datasets, our study showcases the potential of the proposed application in revolutionising mental health care through early depression detection and intervention. Empirical results from various experiments highlight the efficacy of our approach and its suitability for real-world implementation. Furthermore, we acknowledge the ethical, legal, and social implications of AI in mental health diagnostics. Ultimately, our study underscores the transformative potential of AI in mental health diagnostics, paving the way for innovative solutions that can facilitate early intervention and improve patient outcomes.
Analog integrated circuit sizing still relies heavily on human expert knowledge as previous automation approaches have not found wide-spread acceptance in industry. One strand, the optimization-based automation, is often discarded due to inflated constraining setups, infeasible results or excessive run times. To address these deficits, this work proposes a alternative optimization flow featuring a designer’s intuition for feasible design spaces through integration of expert knowledge based on the gm/ID-method. Moreover, the extensive run times of simulation-based optimization flows are overcome by incorporating computationally efficient machine learning methods. Neural network surrogate models predicting eleven performance parameters increase the evaluation speed by 3 400× on average compared to a simulator. Additionally, they enable the use of optimization algorithms dependent on automatic differentiation, that would otherwise be unavailable in this field. First, an up to 4× more efficient way for sampling training data based on the aforementioned space is detailed. After presenting the architecture and training effort regarding the surrogate models, they are employed as part of the objective function for sizing three operational amplifiers with three different optimization algorithms. Additionally, the benefits of using the gm/ID-method become evident when considering technology migration, as previously found solutions may be reused for other technologies.
Most Question-answering (QA) systems rely on training data to reach their optimal performance. However, acquiring training data for supervised systems is both time-consuming and resource-intensive. To address this, in this paper, we propose TFCSG, an unsupervised similar question retrieval approach that leverages pre-trained language models and multi-task learning. Firstly, topic keywords in question sentences are extracted sequentially based on a latent topic-filtering algorithm to construct unsupervised training corpus data. Then, the multi-task learning method is used to build the question retrieval model. There are three tasks designed. The first is a short sentence contrastive learning task. The second is the question sentence and its corresponding topic sequence similarity judgment task. The third is using question sentences to generate their corresponding topic sequence task. The three tasks are used to train the language model in parallel. Finally, similar questions are obtained by calculating the cosine similarity between sentence vectors. The comparison experiment on public question datasets that TFCSG outperforms the comparative unsupervised baseline method. And there is no need for manual marking, which greatly saves human resources.
Distributed Ledger Technologies for the energy sector: facilitating interoperability analysis
(2023)
The use of distributed data storage and management structures, such as Distributed Ledger Technologies (DLT), in the energy sector has gained great interest in recent times. This opens up new possibilities in e.g. microgrid management, aggregation of distributed resources, peer-to- peer trading, integration of electromobility or proof-of-origin strategies. However, in order to benefit from those new possibilities, new challenges have to be overcome. This work focuses on one of these challenges, which is the need to ensure interoperability when integrating DLT-enabled devices in energy use cases. Firstly, the use of DLTs in the energy sector will be analyzed and the main use cases will be presented. Then, a classification of DLT-Energy use cases will be proposed. Secondly, the need for a common reference architecture framework to analyze those use cases with a focus on interoperability will be discussed and the current activities in research and standardization in this field will be presented. Finally, a new common reference architecture framework based on current activities in standardization will be presented.
Modern wide bandgap power devices promise higher power conversion performance if the device can be operated reliably. As switching speed increases, the effects of parasitic ringing become more prominent, causing potentially damaging overvoltages during device turn-off. Estimating the expected additional voltage caused by such ringing enables more reliable designs. In this paper, we present an analytical expression to calculate the expected overvoltage caused by parasitic ringing based on parasitic element values and operating point parameters. Simulations and measurements confirm that the expression can be used to find the smallest rise time of the switches’ drain-source voltage for minimum overvoltage. The given expression also allows the prediction of the trade off overvoltage amplitude in case of faster required rise times.
We present the results of an extensive characterization of the performance and stability of a third-order continuous-time delta-sigma modulator with active coefficient error compensation. Using our previously published coefficient tuning technique, process variation induced R-C time-constant (TC) errors in the forward signal path can be compensated indirectly using continuously tunable DACs in the feedback path. To validate our technique experimentally with a range of real TC variations, we designed a modulator with discretely configurable integration capacitor arrays in a 0.35-μm CMOS process. We configured the capacitors of the fabricated device for a range of total TC variations from -28.4 % to +19.3 % and measured the signal-to-noise ratio (SNR) as a function of the input amplitude before and after compensating the variations electrically using the feedback DACs. The results show that our tuning technique is capable of restoring the desired nominal modulator performance over the entire parameter variation range, including the system’s nominal maximum stable amplitude (MSA).
In this paper, it aims to model wind speed time series at multiple sites. The five-parameter Johnson distribution is deployed to relate the wind speed at each site to a Gaussian time series, and the resultant m-dimensional Gaussian stochastic vector process Z(t) is employed to model the temporal-spatial correlation of wind speeds at m different sites. In general, it is computationally tedious to obtain the autocorrelation functions (ACFs) and cross-correlation functions (CCFs) of Z(t), which are different to those of wind speed times series. In order to circumvent this correlation distortion problem, the rank ACF and rank CCF are introduced to characterize the temporal-spatial correlation of wind speeds, whereby the ACFs and CCFs of Z(t) can be analytically obtained. Then, Fourier transformation is implemented to establish the cross-spectral density matrix of Z(t), and an analytical approach is proposed to generate samples of wind speeds at m different sites. Finally, simulation experiments are performed to check the proposed methods, and the results verify that the five-parameter Johnson distribution can accurately match distribution functions of wind speeds, and the spectral representation method can well reproduce the temporal-spatial correlation of wind speeds.
This article proposes several modified quasi Z-source dc/dc boost converters. These can achieve soft-switching by using a clamp-switch network comprised of an active switch and a diode in parallel with a capacitor connected across one of the inductors of the Z-source network. In this way, ringing at the transistor switching node is mitigated, and the voltage at the turn-on of the transistor is reduced. Even a zero voltage switching (ZVS) of the main transistor is possible if the capacitor in the clamp-switch network is adequately chosen. The proposed circuit structure and operating mode are described and validated through simulations and measurements on a low-power prototype.
Verification of an active time constant tuning technique for continuous-time delta-sigma modulators
(2022)
In this work we present a technique to compensate the effects of R-C / g m -C time-constant (TC) errors due to process variation in continuous-time delta-sigma modulators. Local TC error compensation factors are shifted around in the modulator loop to positions where they can be implemented efficiently with finely tunable circuit structures, such as current-steering digital-to-analog converters (DAC). We apply our technique to a third-order, single-bit, low-pass continuous-time delta-sigma modulator in cascaded integrator feedback structure, implemented in a 0.35-μm CMOS process. A tuning scheme for the reference currents of the feedback DACs is derived as a function of the individual TC errors and verified by circuit simulations. We confirm the tuning technique experimentally on the fabricated circuit over a TC parameter variation range of ±20%. Stable modulator operation is achieved for all parameter sets. The measured performances satisfy the expectations from our theoretical calculations and circuit-level simulations.
A single-phase fixed-frequency operated power factor correction circuit with reduced switching losses is proposed. The circuit uses the combination of a boost converter with an added clamp-switch, a pulse wave shaping circuit, and a standard control IC to discharge the transistor's output capacitance prior to its turn-on. In this way, a very low-complexity control circuit implementation to reduce switching losses or even achieve complete zero-voltage switching without additional sensors is possible. Moreover, this operation method is achieved at a constant switching frequency, possibly simplifying the design of the EMI filter and the converter's inductor. Experimental test results for a 100 W prototype converter are presented to validate the feasibility of the proposed operating method and corresponding circuit structure.
On the influence of ground and substrate on the radiation characteristics of planar spiral antennas
(2022)
The unidirectional radiation of spiral antennas mounted on a substrate requires the presence of a ground plane. In this work, we successively illustrate the impact of dielectric material and ground plane on the key metrics of a planar equiangular spiral antenna (PESA). For this purpose, a PESA mounted on several substrates with different dielectric properties and thicknesses is modeled and simulated. We introduce the tertiary current flowing on spiral arms when backed by a ground plane.
This paper presents a compact four-arm spiral antenna, which may be used in direction-finding applications but also mobile communication systems. The antenna is fed sequentially at its outside-ends using a sequential phase network embedded in grounded multilayer dielectric media. Sequential rotation is applied to generate the axial mode M1 but also the conical mode M2 in the same frequency band. The antenna exhibits good radiation characteristics in the frequency band of interest.
The majority of people in sub-Saharan Africa (SSA) rely on so-called “paratransit” for their mobility needs. The term refers to a large informal transport sector that runs independent of government, of which 83% comprises minibus taxis (MBT). MBT technology is often old and contribute significantly to climate change with their high carbon dioxide (CO2) emissions. Issues related to sustainability and climate change are becoming more important world-wide and hardly any attention is given to MBTs. Converting the MBTs from internal combustion engines (ICEs) to electric motors could be a possible solution. The existing power grid in SSA is largely based on fossil power plants and is unstable. This can be seen by frequent local power blackouts. To avoid further strain on the existing power grid, it would therefore make sense to charge the electric minibus taxis (eMBTs) through a grid consisting of renewable energies. A mobility map is created via simulations with collected data points of the MBTs. By using this mobility map, the energy demand of the eMBTs is calculated. Furthermore, a region-specific photovoltaic (PV) and wind simulation can be realised based on existing weather data, and a tool to size the supply system to charge the eMBTs is developed after all data has been collected. With the help of this work, it can be determined to what extent renewable energies such as PV and wind power can be used to support the transition from ICEs to electric engines in the MBT sector.
In this work, a brushless, harmonic-excited wound-rotor synchronous machine without any auxiliary windings which can provide full torque at startup is investigated experimentally. The excitation power is transferred inductively by superimposing an additional harmonic field of different pole-pair number on top of the airgap field. This is achieved by feeding the parallel paths of the stator and rotor winding separately. A prototype for the harmonic-excited synchronous machine has been constructed and experimental results are presented to verify the concept. The main loss contributors are identified and the importance of considering core losses under harmonic excitation is discussed. A general analytical model for harmonic excited synchronous machines is proposed which enables a quick estimation of the iron core flux densities and the core losses generated by the additional harmonic currents.
In this work, a comparison between different brushless harmonic-excited wound-rotor synchronous machines is performed. The general idea of all topologies is the elimination of the slip rings and auxiliary windings by using the already existing stator and rotor winding for field excitation. This is achieved by injecting a harmonic airgap field with the help of power electronics. This harmonic field does not interact with the fundamental field, it just transfers the excitation power across the airgap. Alternative methods with varying number of phases, different pole-pair combinations, and winding layouts are covered and compared with a detailed Finite-Element-parameterized model. Parasitic effects due to saturation and coupling between the harmonic and main windings are considered.
This paper presents a toolbox in Matlab/Octave for procedural design of analog integrated circuits. The toolbox contains all native functions required by analog designers (namely, schematic-generation, simulation setup and execution, integrated look-up tables and functions for design space exploration) to capture an entire design strategy in an executable script. This script - which we call an Expert Design Plan (EDP) - is capable of executing an analog circuit design fully automatically. The toolbox is integrated in an existing design flow. A bandgap reference voltage circuit is designed with this tool in less than 15 min.
This article illustrates a method for sensorless control of a switched reluctance motor. The detection of the time instants for switching between the working phases is determined based on the evaluation of the switching frequency of the hysteresis current controllers for appropriately selected sensing phases. This enables a simple and cost efficient implementation. The method is compared with a pulse injection method in terms of efficiency and resolution.
We propose a novel technique to compensate the effects of R-C / gm-C time-constant (TC) errors due to process variation in continuous-time delta-sigma modulators. Local TC error compensation factors are shifted around in the modulator loop to positions where they can be implemented efficiently with tunable circuit structures, such as current-steering digital-to-analog converters (DAC). This approach constitutes an alternative or supplement to existing compensation techniques, including capacitor or gm tuning. We apply the proposed technique to a third-order, single-bit, low-pass continuous-time delta-sigma modulator in cascaded integrator feedback structure. A feedback path tuning scheme is derived analytically and confirmed numerically using behavioral simulations. The modulator circuit was implemented in a 0.35-μm CMOS process using an active feedback coefficient tuning structure based on current-steering DACs. Post-layout simulations show that with this tuning structure, constant performance and stable operation can be obtained over a wide range of TC variation.
This paper presents a permanent magnet tubular linear generator system for powering passive sensors using vertical vibration harvesting energy. The system consists of a permanent magnet tubular linear vibration generator and electric circuits. By using the design of mechanical resonant movers, the generator is capable of converting low frequencies small amplitude vertical vibration energy into more regular sinusoidal electrical energy. The distribution of the magnetic field and electromotive force are calculated by Finite Element Analysis. The characteristics of the linear vibration generator system are observed. The experimental results show the generator can produce about 0.4W~1.6W electrical power when the vibration source's amplitude is fixed on 2mm and the frequencies are between 13Hz and 22Hz.
This contribution presents a three-phase power stage for motor control with continuous output voltages using wide bandgap semiconductors and an asynchronous delta-sigma based switching signal generation. The focus of the paper is on an active damping approach for the LC output filter based on inductor current feedback.
This paper illustrates the implementation of series connected hardware modules as part of a scalable and modular power electronics device, which is ideally suited in the field of electric vehicles using wide bandgap semiconductor devices. The main benefit of the modular concept is that different current or voltage requirements can be satisfied based on the appropriate series or parallel connection of single modules. The particular design is based on the fact that the single modules generate a continuous and specified output voltage from a given dc voltage. The current work focuses on a brief classification of this work in different series connected concepts of power converters and in particular on an active damping approach for the series connected LC output filters based on inductor current feedback.
The design process for a single phase, smart, universal charger for light electric vehicles, is presented. With a step up, power factor correction circuit, followed by a phase shifted, full bridge converter, with synchronous rectification on the secondary side. Due to the resistor-capacitor-diode snubber on the secondary side, the current peak at the start of power transfer, leads to false triggering during light load control with peak current mode control. The solution developed for light loads, is to change from peak current control to voltage control. This is achieved by limiting the maximum phase shift, instead of changing the reference value. For the power factor correction stage, measured and calculated efficiencies are compared as a function of the output power. The voltage and current waveforms are shown for the power factor correction circuit, and for the phase shifted bridge, the measured current waveform is compared with simulation.
In this work, a brushless, harmonic-excited wound-rotor synchronous machine is investigated which utilizes special stator and rotor windings. The windings magnetically decouple the fundamental torque-producing field from the harmonic field required for the inductive power transfer to the field coil. In contrast to conventional harmonic-excited synchronous machines, the whole winding is utilized for both torque production and harmonic excitation such that no additional copper for auxiliary windings is needed. Different rotor topologies using rotating power electronic components are investigated and their efficiencies have been compared based on Finite-Element calculation and circuit analysis.
Energy efficient electric control of drives is more and more important for electric mobility and manufacturing industries. Online dynamic optimization of induction machines is challenging due to the computational complexity involved and the variable power losses during dynamic operation of induction machines. This paper proposes a simple technique for sub-optimal online loss optimization using rotor flux linkage templates for energy efficient dynamic operation of induction machines. Such a rotor flux linkage template is given by a rotor flux linkage trajectory which is optimal for a specific scenario. This template is calculated in an offline optimization process. For a specific scenario during real time operation the rotor flux linkage is calculated by appropriately scaling the given template.
Steady state efficiency optimization techniques for induction motors are state of the art and various methods have already been developed. This paper provides new insights in the efficiency optimized operation in dynamic regime. The paper proposes an anticipative flux modification in order to decrease losses during torque and speed transients. These trajectories are analyzed based on a numerical study for different motors. Measurement results for one motor are given as well.
We propose a method for recognizing dynamic gestures using a 3D sensor. New aspects of the developed system include problem-adapted data conversion and compression as well as automatic detection of different variants of the same gesture via clustering with a suitable metric inspired by Jaccard metric. The combination of Hidden Markov Models and clustering leads to robust detection of different executions based on a small set of training data. We achieved an increase of 5% recognition rate compared to regular Hidden Markov Models. The system has been used for human-machine interaction and might serve as an assistive system in physiotherapy and neurological or orthopedic diagnosis.
In this paper, we address the novel EDP (Expert Design Plan) principle for procedural design automation of analog integrated circuits, which captures the knowledge-based design strategy of human circuit designers in an executable script, making it reusable. We present the EDP Player, which enables the creation and execution of EDPs for arbitrary circuits in the Cadence® Virtuoso® Design Environment. The tool provides a generic version of an instruction set, called EDPL (EDPLanguage), enabling emulation of a typical manual analog sizing flow. To automate the design of a Miller Operational Amplifier and to create variants of a Smart Power IC, several EDPs were implemented using this tool. Employing these EDPs leads to a strong reduction of design time without compromising design quality or reliability.
This paper presents an approach for the implementation of a modular and scalable power electronics device for controlling electric drives in the field of electric vehicles using wide bandgap semiconductor devices. The main idea is to achieve the required output currents or voltages by connecting adequately designed hardware modules in parallel or in series. This particular design is based on the fact that the single modules generate a continuous and specified output voltage from a given dc voltage, e.g. an intermediate circuit or battery voltage. The main benefit is, that different current or voltage requirements can be satisfied based on a single module thus decreasing development and production costs. The current paper focuses on the connection in parallel of such modules. A control architecture is illustrated and a first proof of concept is given.
This paper discusses the optimal control problem for increasing the energy efficiency of induction machines in dynamic operation including field weakening regime. In an offline procedure optimal current and flux trajectories are determined such that the copper losses are minimized during transient operations. These trajectories are useful for a subsequent online implementation.
In this work design rules for a novel brushless excitation system for externally excited synchronous machines are discussed. The concept replaces slip rings with a fullbridge active rectifier and a controller mounted on the rotor. An AC signal induced from the stator is used to charge the rotor DC link. The DC current for the rotor excitation is provided from this DC link source. Finite element analysis of an existing machine is used to analyze the practicability of the excitation system.
A novel brushless excitation concept for synchronous machines with a rotating power converter is proposed in this paper. The concept does not need an auxiliary winding or any other modification to the machine structure apart from an inverter with a DC link capacitor and a controller on the rotor. The power required for the rotor excitation is provided by injecting harmonics into the stator winding. Thus, a voltage in the field coil is induced. The rotor inverter is controlled such that the alternating current charges the DC link capacitor. At the same time the inverter supplies the DC field current to the field coil. The excitation concept is first developed in theory, then presented using an analytical model and FEA, and lastly investigated with a prelimininary experimental setup.
Novel design for a coreless printed circuit board transformer realizing high bandwidth and coupling
(2019)
Rogowski coils offer galvanic isolation and can measure alternating currents with a high bandwidth. Coreless printed circuit board (PCB) transformers have been used as an alternative to limit the additional stray inductance if a Rogowski coil can not be attached to the circuit. A new PCB transformer layout is proposed to reduce cost, decrease additional stray inductance, increase the bandwidth of current measurements and simplify the integration into existing designs.
Improved inductive feed-forward for fast turn-on of power semiconductors during hard switching
(2019)
A transformer is used to increase the gate voltage during turn-on, thus reducing the necessary bias voltage of the gate driver. Counteracting the voltage dependency of the gate capacitance of high-voltage power devices, faster transitions are possible. The additional transformer only slighly increases the over-voltage during turn-off.
Nowadays, the demand for a MEMS development/design kit (MDK) is even more in focus than ever before. In order to achieve a high quality and cost effectiveness in the development process for automotive and consumer applications, an advanced design flow for the MEMS (micro electro mechanical systems) element is urgently required. In this paper, such a development methodology and flow for parasitic extraction of active semiconductor devices is presented. The methodology considers geometrical extraction and links the electrically active pn junctions to SPICE standard library models and subsequently extracts the netlist. An example for a typical pressure sensor is presented and discussed. Finally, the results of the parasitic extraction are compared with fabricated devices in terms of accuracy and capability.
Due to the lack of sophisticated component libraries for microelectromechanical systems (MEMS), highly optimized MEMS sensors are currently designed using a polygon driven design flow. The advantage of this design flow is its accurate mechanical simulation, but it lacks a method for an efficient and accurate electrostatic analysis of parasitic effects of MEMS. In order to close this gap in the polygon-driven design flow, we present a customized electrostatic analysis flow for such MEMS devices. Our flow features a 2.5D fabrication-process simulation, which simulates the three typical MEMS fabrication steps (namely deposition of materials including topography, deep reactive-ion etching, and the release etch by vapor-phase etching) very fast and on an acceptable abstraction level. Our new 2.5D fabrication-process simulation can be combined with commercial field-solvers such as they are commonly used in the design of integrated circuits. The new process simulation enables a faster but nevertheless satisfactory analysis of the electrostatic parasitic effects, and hence simplifies the electrical optimization of MEMS.
The Dual Active Bridge (DAB) is a very promising topology for future power converters. However, careless operation can lead to a DC component in the transformer current. The problem is further exacerbated when the phase shift changes during operation. This work presents a study of DC bias effects on the DAB with special regard to transient effects introduced by sudden shifts in the output load. We present a simple yet effective approach to avoid DC bias entirely.
A fully passive RFID temperature sensor SoC with an accuracy of ±0.4 ◦C (3σ) from 0 ◦C to 125 ◦C
(2019)
This paper presents a fully passive 13.56 -MHz RFID temperature sensor system-on-chip. Its power management unit operates over a large temperature range using a zero temperature coefficient bias source. On-chip temperature sensing is accomplished with low-voltage, low-power CMOS circuitry, and time-domain signal processing. Two readout commands have been defined to study supply noise sensitivity: 1) standard readout, where just a single set of data is transferred to the reader and 2) serial readout, where several sets of data are sent one after the other to the reader. With the standard readout command, the sensor suffers from interference from the RFID command packet and outputs interference as well, while the sensor outputs no interference with the serial readout command. Measurements show that sensor resolution with serial readout is improved by a factor of approximately 16 compared to standard readout. The chip was fabricated in a standard 0.35-μm CMOS technology and chip-on-board mounted to a tuned RFID transponder coil on an aluminum core FR4 PCB substrate. Real time wireless temperature sensing has been demonstrated with a commercial HF RFID reader. With a two-point calibration, the SoC achieves a 3σ sensing accuracy of ±0.4 ◦C from 0◦C to 125 ◦C.
The complexity of supply chains increases, especially due to the geographical spread of supplier and customer networks. In the connected and automated supply chains of the industry 4.0, even more nodes are incorporated in supply chains. This paper discusses the possible improvement of process quality in the industry 4.0 through the different blockchain and distributed ledger technologies. We derived hypotheses from a literature review and asked German blockchain experts from the industry to validate and discuss the hypotheses. We find that the different blockchain technologies and consensus algorithms have different strength with regard to quality improvement. One central finding is that IOTA, developed especially for the IoT and deemed the ’next evolutionary step’ is scalable and hence may increase the process efficiency, but at the same time is more vulnerable than other blockchain implementations, which again may reduce the overall process quality.
SLAM systems are mainly applied for robot navigation while research on feasibility for motion planning with SLAM for tasks like bin-picking, is scarce. Accurate 3D reconstruction of objects and environments is important for planning motion and computing optimal gripper pose to grasp objects. In this work, we propose the methods to analyze the accuracy of a 3D environment reconstructed using a LSD-SLAM system with a monocular camera mounted onto the gripper of a collaborative robot. We discuss and propose a solution to the pose space conversion problem. Finally, we present several criteria to analyze the 3D reconstruction accuracy. These could be used as guidelines to improve the accuracy of 3D reconstructions with monocular LSD-SLAM and other SLAM based solutions.
We present a new method for detecting gait disorders according to their stadium using cluster methods for sensor data. 21 healthy and 18 Parkinson subjects performed the time up and go test. The time series were segmented into separate steps. For the analysis the horizontal acceleration measured by a mobile sensor system was considered. We used dynamic time warping and hierarchical custering to distinguish the stadiums. A specificity of 92% was achieved.
Fitting 3D Morphable Face Models (3DMM) to a 2D face image allows the separation of face shape from skin texture, as well as correction for face expression. However, the recovered 3D face representation is not readily amenable to processing by convolutional neural networks (CNN). We propose a conformal mapping from a 3D mesh to a 2D image, which makes these machine learning tools accessible by 3D face data. Experiments with a CNN based face recognition system designed using the proposed representation have been carried out to validate the advocated approach. The results obtained on standard benchmarking data sets show its promise.
This paper investigates the evaluation of dense 3D face reconstruction from a single 2D image in the wild. To this end, we organise a competition that provides a new benchmark dataset that contains 2000 2D facial images of 135 subjects as well as their 3D ground truth face scans. In contrast to previous competitions or challenges, the aim of this new benchmark dataset is to evaluate the accuracy of a 3D dense face reconstruction algorithm using real, accurate and high-resolution 3D ground truth face scans. In addition to the dataset, we provide a standard protocol as well as a Python script for the evaluation. Last, we report the results obtained by three state-of-the-art 3D face reconstruction systems on the new benchmark dataset. The competition is organised along with the 2018 13th IEEE Conference on Automatic Face & Gesture Recognition.
We present a compact battery charger topology for weight and cost sensitive applications with an average output current of 9A targeted for 36V batteries commonly found in electric bicycles. Instead of using a conventional boost converter with large DC-link capacitors, we accomplish PFC-functionality by shaping the charging current into a sin²-shape. In addition, a novel control scheme without input-current sensing is introduced. A-priori knowledge is used to implement a feed-forward control in combination with a closed-loop output current control to maintain the target current. The use of a full-bridge/half bridge LLC converter enables operation in a wide input-voltage range.
A fully featured prototype has been built with a peak output power of 1050W. An average output power of 400W was measured, resulting in a power density of 1.8 kW/dm³. At 9A charging current, a power factor of 0.96 was measured and the efficiency exceeds 93% on average with passive rectification.
The impact of pulse charging has been evaluated on a 400Wh battery which was charged with the proposed converter as well as CC-CV-charging for reference. Both charging schemes show similar battery surface temperatures.
This paper presents a digitally controlled boost converter IC for high output voltage and fast transient applications. Thus, it is well applicable in automotive and industrial environments. The 3V-to-6V input voltage, 6.3V output voltage, 1A boost converter IC is fabricated in a 180nm BCD technology. Digital control enables cost savings, advanced control concepts, and it is less parameter sensitive compared to common analog control. A 90 ns latency, 6-bit delay line ADC operates with a window concept, meeting high resolution requirements, e.g. in car battery applications. An output voltage live tracking is included for extending the ADC conversion window. A charge pump DAC provides high resolution, monotonicity, and short 128 ns conversion time. Further, a standard digital PI controller is enhanced by a simple but effective ΔV/Δt-intervention control. It results in 2.8x reduced output voltage deviations in case of load steps, scaling down the output capacitor value by the same factor.
Boost converters suffer from a bandwidth limitation caused by the right-half plane zero (RHPZ), which occurs in the control-to-output transfer function. In contrast, there are many applications that require superior dynamic behavior. Further, size and cost of boost converter systems can be minimized by reduced voltage deviations and fast transient responses in case of large signal load transients. The key idea of the proposed ΔV/Δt-intervention control concept is to adapt the controller output to its new steady state value immediately after a load transient by prediction from known parameters. The concept is implemented in a digital control circuit, consisting of an ASIC in a 110 nm-technology and a Xilinx Spartan-6 field programmable gate array (FPGA). In a boost converter with 3.5V input voltage, 6.3V output voltage, 1.2A load, and 500 kHz switching frequency, the output voltage deviations are 2.8x smaller, scaling down the output capacitor value by the same factor. The recovery times are 2.4x shorter in case of large signal load transients with the proposed concept. The control is widely applicable, as it supports constant switching frequencies and allows for duty cycle and inductor current limitations. It also shows various advantages compared to conventional control and to selected adaptive control concepts.
This paper presents a wide-Vin step-down parallel-resonant converter (PRC), comprising an integrated 5-bit capacitor array and a 300-nH resonant coil, placed in parallel to a conventional buck converter. Soft-switching resonant converters are beneficial for high-Vin multi-MHz converters to reduce dominant switching losses, enabling higher switching frequencies. The output filter inductor is optimized based on an empirical study of available inductors. The study shows that faster switching significantly reduces not only the inductor value but also volume, price, and even the inductor losses. In addition, unlike conventional resonant concepts, soft-switching control as part of the proposed PRC eliminates input voltage-dependent losses over a wide operating range, resulting in 76.3% peak efficiency. At Vin = 48 V, a loss reduction of 35% is achieved compared with the conventional buck converter. Adjusting an integrated capacitor array, and selecting the number of oscillation periods, keeps the switching frequency within a narrow range. This ensures high efficiency across a wide range of Vin = 12–48 V, 100–500-mA load, and 5-V output at up to 25-MHz switching frequency. Thanks to the low output current ripple, the output capacitor can be as small
as 50 nF.