Printed electronics (PE) enable lightweight, flexible, and low-cost devices for the Internet of Things (IoT) and wearable applications. Compared to conventional silicon-based electronics, PE trades peak performance for advantages in cost efficiency, mechanical flexibility, and large-area fabrication. However, its manufacturing processes remain unreliable and are prone to structural defects and variation due to inherent limited control in additive manufacturing. Printed neuromorphic circuits (pNCs) leverage the benefits of PE for on-demand analog edge computation in target applications but remain vulnerable to such defects. Diagnostic testing is therefore essential not only for detection but also for localizing faults to specific subcircuits and regions in the layout, a step critical for guiding yield improvement and reducing the cost of downstream inspection. We propose a diagnostic test pattern generation (DTPG) framework for fault localization in pNCs under black-box access. While ATPG is typically formulated as an optimization problem for fault detection, our approach extends this formulation by explicitly optimizing for fault distinguishability. On ten UCI datasets, the framework achieves up to 20.7% higher diagnostic coverage with a reduction of up to 3.6 times the number of undetectable subcircuits than detection-only test sets, while constraining the number of patterns to reduce storage overhead. These results demonstrate effective fault localization and establish a foundation for finer-grained, component-level diagnosis in future work.

With the rising demand for ultra-low-cost and flexible electronics in applications like smart packaging and wearable health monitoring, printed electronics provide an affordable, adaptable, and customizable alternative to conventional silicon. However, these systems often rely on printed batteries or energy harvesters with limited power capacity, making strict power budgets critical. Printed neuromorphic circuits (pNCs) are promising for their analog signal processing, reduced circuit complexity, and energy efficiency in low-power environments. Nonetheless, maintaining robust performance under strict power constraints remains challenging, necessitating advanced optimization techniques. In this work, we propose an augmented Lagrangian approach to enforce task-specific power constraints in pNCs, validated across 13 benchmark datasets. Our method preserves accuracy within strict power budgets while achieving Pareto-optimal power-accuracy trade-offs in a single training run. In contrast, the penalty-based method, which serves as the baseline, requires up to 150 runs per dataset to generate the Pareto front. For low-power scenarios ($\approx 20 \%$ of the original power), our method demonstrates a $52 \times$ improvement in accuracy-to-power ratio over the baseline. At higher power budgets $(\approx 80 \%$ of the original power), it achieves a $59 \times$ improvement, maintaining competitive performance. Experimental results demonstrate that our approach achieves $\mathbf{8 1. 8 2 \%}$ accuracy with p-tanh activation function (AF) at high power budgets and excels with p-Clipped_ReLU AF under low power constraints. This highlights the computational efficiency and effectiveness of our approach for power-constrained circuit design.

The rise of the Internet of Things demands flexible, biocompatible, and cost-effective devices. Printed electronics provide a solution through low-cost and on-demand additive manufacturing on flexible substrates, making them ideal for IoT applications. However, variations in additive manufacturing processes pose challenges for reliable circuit fabrication. Adapting neuromorphic computing to printed electronics could address these issues. Printed neuromorphic circuits offer robust computational capabilities for near-sensor processing in IoT. One limitation of existing printed neuromorphic circuits is their inability to process temporal sensory inputs. To address this, integrating temporal components in printed neuromorphic circuit architectures enables the effective processing of time-series sensory data. Printed neuromorphic circuits face challenges from manufacturing variations such as ink dispersion, sensor noise, and temporal fluctuations, especially when processing temporal data and using time-dependent components like capacitors. To mitigate these challenges, we propose robustness-aware temporal processing neuromorphic circuits with low-pass second-order learnable filters (SO-LF). This approach integrates variation awareness by considering the variation potential of component values during training and using data augmentation to enhance adaptability against physical and sensor data variations. Simulations on 15 benchmark time-series datasets show our circuit effectively handles noisy temporal information under 10% process variations, achieving an average accuracy and power improvement of ≈24.7% and ≈91% respectively compared to models lacking variation with ≈1.9×more devices.

Smooth pursuits are natural eye movements that occur when we track a moving target with our gaze. While they were explored as a gaze-based input method using external screens to display moving stimuli, we propose BodyPursuits, a novel HCI method that eliminates additional screens. Stimuli are generated by users tracing smooth trajectories with their hand in mid-air while fixating their gaze on their thumb. We conducted a user study to collect eye-tracking and baseline IMU data from 20 participants performing 10 BodyPursuits gestures. Based on 1800 samples and noise data, we train a TinyHAR classifier. It achieves a macro-average F1 score of 0.772. With UEQ results indicating a positive user experience and RTLX scores showcasing low subjective workload for all 10 gestures, we successfully demonstrated BodyPursuits’ potential as viable interaction method. We envision BodyPursuits could be integrated into EOG earphones to detect mid-air hand gestures without external screens or cameras.

Biologically-inspired Spiking Neural Networks (SNNs) have emerged as a promising avenue toward energy-efficient neuromorphic computing, particularly in edge applications such as soft robotics, wearable health monitors, and IoT devices. Printed Electronics (PE), offering advantages of ultra-low cost fabrication and mechanical flexibility, present a viable platform to realize such neuromorphic systems at scale. However, designing adaptable and efficient spiking circuits that meet the unique constraints of PE applications remains a challenge. To address this, we propose a novel analog spiking neuromorphic circuit with a learnable spike generator (LSG). Unlike fixed-threshold models, our generator adapts spike timing dynamics during training, enabling better task-specific performance. To optimize for ultra-low power consumption on resource-constrained platforms, we further introduce a robustness-aware training framework that further minimizes the energy consumption adaptively. Simulation results across 13 benchmarks demonstrate an average 57.6% power reduction for the LSG while improving the average classification accuracy by 8%, area and energy reduction by 89% and 28.7% respectively compared to the state-of-the-art printed analog spiking neural networks (P-SNNs).

The graph structures generated by natural or simple heuristics often fail to represent the spatial correlations influenced by complex factors. Therefore, introducing graph structure learning (GSL) can enhance the graph neural network-based spatial interpolation models. However, the input features of the GSL module are systematically unbalanced in spatial interpolation tasks. For example, many natural variables follow Gaussian- or gamma-distribution, and sensor spatial distributions are generally uneven. Thus, the GSL module must systematically incorporate corresponding solutions to avoid negatively impacting its generalization ability and degrading model performance. Our proposed model utilizes two encoders to embed feature deviations of node readings and centroid distance from preset distributions, respectively. Notably, these encoders are jointly optimized with other model components, and their generalization ability is improved through an adaptively adjustable information bottleneck. Consequently, the GSL module can offer a more robust graph structure by explicitly perceiving feature deviations in the input. Experimental results demonstrate that our model outperforms existing state-of-the-art baselines across multiple real-world datasets with diverse characteristics.

Printed electronics (PEs) is an additive fabrication technology which not only allows for a highly flexible printing of circuit patterns, but also produce soft, nontoxic, and degradable electronics at an extremely low cost. These properties make PE an enabler of new application domains, e.g., fast moving consumer goods and disposable healthcare devices. A particularly promising class of circuits in this technology is the printed analog neuromorphic circuits, offering efficient and highly tailored computational functionalities. In this work, we leverage the highly flexible fabrication process of PE to address the bottleneck of PE, i.e., the large feature sizes and low device counts. This issue is crucial, as it impairs the integration of printed circuits into target applications with limited footprint, such as smart band-aids. We propose an evolutionary algorithm (EA) to improve the circuit compactness through circuit architecture optimization. As baseline, we compare the proposed EA method with a state-of-the-art pruning method and a modified area-aware pruning method. All of them are able to optimize circuit architecture. Experimental simulation reveals that the proposed EA approach can effectively achieve compact circuits and outperform the pruning method by$3.1\times $lower area with no loss of accuracy. As a byproduct, the power is reduced by$3.0\times $, paving the way to energy-harvested printed systems.

The demand for next-generation flexible electronics in applications like smart packaging and smart bandages has driven the need for cost-effective solutions. Traditional silicon-based electronics struggle with high costs and rigidity, making them unsuitable for these emerging markets. In this regard, additive printed electronics (PE) offer a viable alternative with their flexibility and ultra-low-cost manufacturing. printed analog neuromorphic circuits (pNCs) are well-suited for these target applications, especially for classification tasks, as their low device count can efficiently meet the needs of the technology. However, low-cost additive manufacturing comes with higher defect rates, such as misprints, broken connections, and defective components, posing significant challenges to the reliability of printed circuits. This article presents a novel co-design of training algorithm and hardware for fault-tolerant pNCs using fault-aware training (FAT). The proposed method introduces a fault-tolerant version of printed nonlinear transformation circuits, combined with a bespoke training process that selects different types of printed activation functions (AFs) for different neurons to optimize both fault endurance and hardware costs. Experiments on benchmark datasets demonstrate an improvement in the accuracy of fault-tolerant (FT) pNCs from 62.1% to 79.4% under a 10% fault rate. Moreover, combining both normal and fault-tolerant versions of activation functions (AFs) using gumble-softmax distribution shows an acceptable accuracy drop with an average reduction in power and area of 54.5% and 6.54%, respectively, while reducing the training time significantly by 56.2%, compared to only using FT-AFs.

Biologically-inspired Spiking Neural Networks have emerged as a promising avenue for energy-efficient, high-performance neuromorphic computing. With the demand for highly-customized and cost-effective solutions in emerging application domains like soft robotics, wearables, or IoT-devices, Printed Electronics has emerged as an alternative to traditional silicon technologies leveraging soft materials and flexible substrates. In this paper, we propose an energy-efficient analog printed spiking neuromorphic circuit and a corresponding learning algorithm. Simulations on 13 benchmark datasets show an average of 3.86 x power improvement with similar classification accuracy compared to previous works.

Tu Anh Dinh, Carlos Mullov, Leonard Bärmann, Zhaolin Li, Danni Liu, Simon Reiß, Jueun Lee, Nathan Lerzer, Jianfeng Gao, Fabian Peller-Konrad, Tobias Röddiger, Alexander Waibel, Tamim Asfour, Michael Beigl, Rainer Stiefelhagen, Carsten Dachsbacher, Klemens Böhm, Jan Niehues. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.

Printed Electronics (PE) is an emerging technology with flexible substrates and ultra-low-cost manufacturing, providing an appealing alternative to traditional wafer-scale silicon fabrication. With the increasing integration of various printed neural network (NN) architectures in diverse applications, the reliability of printed circuits has become a critical concern. This work provides a comprehensive analysis of the fault sensitivity on a variety of classification tasks for various digital and analog realizations of printed multilayer perceptrons (MLPs). We further evaluate different digital architectures, i.e., generic, bespoke, and approximate, to provide a comprehensive fault analysis on different benchmark datasets.

The market demand for next-generation flexible electronics is experiencing a significant upsurge, particularly in cost-sensitive consumer applications like smart packaging and smart bandages. These products are beyond the reach of traditional silicon-based electronics due to their high production cost and rigid form factor. Printed electronics (PE), with its adaptable and ultra-low-cost solutions, essentially meet the unique needs of these emerging application areas. This work presents a novel approach using an evolutionary algorithm (EA) to design highly bespoke printed analog neuromorphic circuits (pNCs) offering robustness against variability inherent in the printing process. By leveraging this algorithm and designing robust activation circuits, not only the resistances (weights) in the crossbar and parameters in the activation circuits, but also the types of nonlinear circuits (i.e., functional forms of activation functions) as well as the circuit topologies (neural architecture) can be learned to enhance the circuit robustness against printing variations. Experiments on 13 benchmark datasets demonstrate that, compared to the baseline, the proposed methodology can further outperform the normalized classification error rate by ≈ 55.38% and ≈ 25.11% under high-precision (±5%) and low-precision (±10%) printing scenarios, respectively. Moreover, the algorithm suggests the ReLU as the most robust activation function (AF) circuit family with only ≈ 21% susceptible to low precision (±10%) printing variation.

Recent trends in Wearable Human Activity Recognition (WHAR) have led to an unprecedented 42.9% increase in scholarly articles in 2022, underscoring the urgency for a comprehensive review to systematically categorize their varied research directions. Moreover, our analysis reveals that the contributions of current articles often deviate from the traditional stages of the human activity recognition pipeline, as established in prior literature. This misalignment suggests the necessity for an updated pipeline that more accurately reflects the intricacies and nuances of WHAR studies. In response, we review WHAR articles from 2021 to 2023 and introduce an innovative WHAR pipeline, emphasizing a research-focused approach. This new pipeline offers distinct advantages: it provides researchers with a clear and systematic categorization of WHAR articles, thereby enhancing understanding of the field. For practitioners, it facilitates the selection of customized methods for each stage, thereby optimizing final assembled model efficacy.

Pruning is a pivotal approach in network compression. It not only encourages lightweight deep neural networks, but also helps to mitigate overfitting. Generally, regularization is used to guide more parameters towards zero and thus reduce the overall model complexity. Unfortunately, there are two issues remaining unsolved in regularization based pruning. One is that the optimal trade-off between regularization and loss minimization, often expressed via a scaling hyperparameter, needs to be found through extensive experimentation. The other one is the importance criteria that can reflect the true relative importance. The most widely used criterion is the magnitude-based, which has been argued to be inaccurate. To these two issues, in this paper, we propose a progressive regularization scheme, in which the factor scaling the regularization term is gradually increased during training, until the target sparsity for filter pruning is reached. Compared to the previous approach, the scaling factor is no longer a hyperparameter that needs to be tuned, but is replaced with a sparsity-aware parameter that increases progressively. In this way, the value of the scaling factor can be automatically aligned with the target sparsity, avoiding the drawbacks in its tuning. Furthermore, only parameters below a minimal and learnable soft threshold are pruned, therefore, informative parameters (even with small magnitudes) are optimally preserved. Consequently, the performance loss due to pruning is mitigated. Experiments with various models and benchmark datasets prove the effectiveness of the proposed method.

Printed and flexible electronics promises smart devices for application domains, such as smart fast moving consumer goods and medical wearables, which are generally untouchable by conventional rigid silicon technologies. This is due to their remarkable properties such as flexibility, non-toxic materials, and having low-cost per area. Combined with neuromorphic computing, printed neuromorphic circuits pose an attractive solution for these application domains. Particularly, the additive printing technologies can reduce large amount of fabrication complexities and costs. On the one hand, high-throughput additive printing processes, such as roll-to-roll printing, can reduce the per-device fabrication time and cost. On the other hand, jet-printing can provide point-of-use customization at the expense of lower fabrication throughput. In this work, we propose a machine learning based design framework, that respects the objective and physical constraints of split additive manufacturing for printed neuromorphic circuits. With the proposed framework, multiple printed neural networks are trained jointly with the aim to sensibly combine multiple fabrication techniques (e.g., roll-to-roll and jet-printing). This should lead to a cost-effective fabrication of multiple different printed neuromorphic circuits and achieve high fabrication throughput, lower cost, and point-of-use customization.

With the rapid growth of the Internet of Things, smart fast-moving consumer products, and wearable devices, requirements such as flexibility, non-toxicity, and low cost are desperately required. However, these requirements are usually beyond the reach of conventional rigid silicon technologies. In this regard, printed electronics offers a promising alternative. Combined with neuromorphic computing, printed neuromorphic circuits offer not only the aforementioned properties, but also compensate for some of the weaknesses of printed electronics, such as manufacturing variations, low device count, and high latency. Generally, (printed) neuromorphic circuits express their functionality through printed resistor crossbars to emulate matrix multiplication, and nonlinear circuitry to express activation functions. The values of the former are usually learned, while the latter is designed beforehand and considered fixed in training for all tasks. The additive manufacturing feature of printed electronics allows the design of highly-bespoke designs. In the case of printed neuromorphic circuits, the circuit is optimized to a particular dataset. Moreover, we explore an approach to learn not only the values of the crossbar resistances, but also the parameterization of the nonlinear components for a bespoke implementation. While providing additional flexibility of the functionality to be expressed, this will also allow an increased robustness against printing variation. The experiments show that the accuracy and robustness of printed neuromorphic circuits can be improved by 26% and 75% respectively under 10% variation of circuit components.

There is an increasing demand for next-generation flexible electronics in emerging low-cost applications such as smart packaging and smart bandages, where conventional silicon electronics cannot enter due to cost and form factor. In these domains, ultra-low-cost, high flexibility, and customizability are required. In this regard, printed electronics emerge as a complementary solution offering the aforementioned properties. To respect the constraints in those application scenarios and equip printed devices with the fundamental capability to process information, analog printed neuromorphic circuits offer multiple advantages, including strong expressiveness, streamlined circuit primitives, and a highly efficient machine learning-based design process. In this work, we focus on designing low-power printed neuromorphic circuits at the algorithmic level. By developing accurate power models for the circuit primitives, the power consumption can be considered into the design process. Subsequently, Pareto analysis is employed to examine the relationship between accuracy and power consumption. Experimental results reveal that, with the proposed approach, 2 x reduction of the power consumption can be realized while maintaining 95 % of classification accuracy. This approach has significant implications for the future development of energy-efficient printed neuromorphic circuits and their potential applications in IoT and AI intersections.

To this day, various approaches for providing local explanation of black box machine learning models have been introduced. Despite these efforts, existing methods suffer from deficiencies such as being difficult to comprehend, only considering one feature at a time and disregarding inter-feature dependencies, lacking meaningful values for each feature, or only highlighting features that support the model's decision. To overcome these drawbacks, this study presents a new approach to explain the predictions of any black box classifier, called Monte Carlo tree search for eXplainable Artificial Intelligence (McXai). It employs a reinforcement learning strategy and models the explanation generation as two distinct games. In the first game, the objective is to identify feature sets that support the model's decision, while in the second game, the aim is to find feature sets that lead to alternative decisions. The output is a human-friendly representation in the form of a tree structure, where each node represents a set of features to be examined, with less specific interpretations at the top of the tree. Our experiments demonstrate that the features identified by McXai are more insightful with regard to the classifications compared to traditional algorithm like LIME and Gram-cam. Furthermore, the ability to identify misleading features provides guidance towards improved robustness of the black box classifier.

User experience (UX) summarizes user perceptions and responses resulting from the interaction with a product, system, or service. The User Experience Questionnaire (UEQ) is one standardized instrument for measuring UX. With six scales, it identifies areas in which product improvements will have the highest impact. In this paper, we evaluate the reliability and validity of this questionnaire. The data of N = 1, 121 participants who interacted with one of 23 products indicated an acceptable to good reliability of all scales. The results show, however, that the scales were not independent of each other. Combining perspicuity, efficiency, and dependability to pragmatic aspects as well as novelty and stimulation to hedonic aspects of UX improved the model fit significantly. The systematic variations of product properties and correlations with the System Usability Scale (SUS) in a second experiment with N=499 participants supported the validity of these two factors. Practical implications of the results are discussed.

Printed electronic (PE) can meet the requirements of many application domains with requirements on cost, conformity, and non-toxicity which silicon-based computing systems cannot achieve. A typical computational task to be performed in many of such applications is classification. Therefore, printed Neural Networks (pNNs) have been proposed to meet these requirements. However, PE suffers from high process variations due to low resolution printing in low-cost additive manufacturing. This can severely impact the inference accuracy of pNNs. In this work, we show how a unique feature of PE, namely additive printing can be leveraged to perform in-situ tuning of pNNs to compensate accuracy losses induced by device variations. The experiments show that, even under 30 % variation of the conductances, up to 90 % of the initial accuracy can be recovered.

Printed electronics allow for ultra-low-cost circuit fabrication with unique properties such as flexibility, non-toxicity, and stretchability. Because of these advanced properties, there is a growing interest in adapting printed electronics for emerging areas such as fast-moving consumer goods and wearable technologies. In such domains, analog signal processing in or near the sensor is favorable. Printed neuromorphic circuits have been recently proposed as a solution to perform such analog processing natively. Additionally, their learning-based design process allows high efficiency of their optimization and enables them to mitigate the high process variations associated with low-cost printed processes. In this work, we address the aging of the printed components. This effect can significantly degrade the accuracy of printed neuromorphic circuits over time. For this, we develop a stochastic aging-model to describe the behavior of aged printed resistors and modify the training objective by considering the expected loss over the lifetime of the device. This approach ensures to provide acceptable accuracy over the device lifetime. Our experiments show that an overall 35.8% improvement in terms of expected accuracy over the device lifetime can be achieved using the proposed learning approach.

With ever-increasing transistor density in nanoscale-integrated circuits, the impact of process variations on circuit performance and chip yield becomes dominant. To prevent failures in the field, simulation-based circuit optimization tools are performed during design time as a countermeasure. However, the efficiency of these tools requires accurate modeling of failure probabilities. Especially, for high-sigma problems such as yield estimation of memory cells, which require very high production yield, the failure rate assessment must be highly accurate. Importance sampling (IS) methods are deployed in this context to uncover very rare failure events, which cannot be revealed by standard Monte Carlo methods. Besides a highly accurate yield prediction model, the limited time budget of the simulation-based analysis tools has to be taken into account. Especially in conjunction with yield optimization techniques, the required number of circuit simulations for the failure rate estimation has to be substantially reduced. In this article, we propose a yield optimization method, which is based on a Bayesian optimization (BO) failure rate estimation technique for high-sigma yield extraction. The BO-based IS method is combined with a kernel density estimator for finding the most probable failure events, which have significant contribution to the chip yield. By integration into a global optimization framework, we show how the proposed yield optimization method can be applied to high-sigma yield optimization problems, such as memory cells. The experimental results indicate that the proposed method consumes only 5% circuit simulations to achieve the same optimization effect as the state-of-the-art yield optimizer techniques.

We explore how discreet input can be provided using the tensor tympani - a small muscle in the middle ear that some people can voluntarily contract to induce a dull rumbling sound. We investigate the prevalence and ability to control the muscle through an online questionnaire (N=192) in which 43.2% of respondents reported the ability to “ear rumble”. Data collected from participants (N=16) shows how in-ear barometry can be used to detect voluntary tensor tympani contraction in the sealed ear canal. This data was used to train a classifier based on three simple ear rumble “gestures” which achieved 95% accuracy. Finally, we evaluate the use of ear rumbling for interaction, grounded in three manual, dual-task application scenarios (N=8). This highlights the applicability of EarRumble as a low-effort and discreet eyes- and hands-free interaction technique that users found “magical” and “almost telepathic”.

Printed electronics (PE) offers flexible, extremely low-cost, and on-demand hardware due to its additive manufacturing process, enabling emerging ultra-low-cost applications, including machine learning applications. However, large feature sizes in PE limit the complexity of a machine learning classifier (e.g., a neural network (NN)) in PE. Stochastic computing Neural Networks (SC-NNs) can reduce area in silicon technologies, but still require complex designs due to unique implementation tradeoffs in PE. In this paper, we propose a printed mixed-signal system, which substitutes complex and power-hungry conventional stochastic computing (SC) components by printed analog designs. The printed mixed-signal SC consumes only 35% of power consumption and requires only 25% of area compared to a conventional 4-bit NN implementation. We also show that the proposed mixed-signal SC-NN provides good accuracy for popular neural network classification problems. We consider this work as an important step towards the realization of printed SC-NN hardware for near-sensor-processing.

Modern mobile phones are equipped with many sensors, which can increasingly be used to sense various environmental phenomena. In particular, mobile sensing has enabled crowdsourced data collection at an unprecedented scale. However, as laypersons are involved in this, concerns regarding the data quality arise. This work explores the gamification of smartphone-based measurement processes in practice by embedding a sensing task into a mobile minigame. The underlying idea is – rather than to educate the user on how to correctly perform a measurement task – to opportunistically execute the measurement in the background once the smartphone is in a suitable context. To this end, this paper presents the design and evaluation of SpaceMaze, a smartphone game with the goal of minimising user error by introducing appropriate game mechanics to influence the phone context, using the example of mobile noise level monitoring. A large user study that compares SpaceMaze to two non-gamified apps for noise level monitoring (N = 360 in total) shows that SpaceMaze can successfully reduce user errors when compared to simple non-gamified ambient noise level monitoring applications and that the minigame is generally perceived as being enjoyable. Solutions for remaining problems, such as noise generated by the players, are discussed.

Neuromorphic computing systems have demonstrated many advantages for popular classification problems with significantly less computational resources. We present in this paper the design, fabrication and training of a programmable neuromorphic circuit, which is based on printed electrolytegated field-effect transistor (EGFET). Based on printable neuron architecture involving several resistors and one transistor, the proposed circuit can realize multiply-add and activation functions. The functionality of the circuit, i.e. the weights of the neural network, can be set during a post-fabrication step in form of printing resistors to the crossbar. Besides the fabrication of a programmable neuron, we also provide a learning algorithm, tailored to the requirements of the technology and the proposed programmable neuron design, which is verified through simulations. The proposed neuromorphic circuit operates at 5V and occupies 385mm2of area.

Due to the aggressive technology downscaling, process variations are becoming pre-dominent, causing performance fluctuations and impacting the chip yield. Therefore, individual circuit components have to be designed with very small failure rates to guarantee functional correctness and robust operation. The assessment of high-sigma failure rates however cannot be achieved with conventional Monte Carlo (MC) methods due to the huge amount of required time-consuming circuit simulations. To this end, Importance Sampling (IS) methods were proposed to solve the otherwise intractable failure rate estimation problem by focusing on high-probable failure regions. However, the failure rate could largely be underestimated while the computational effort for deriving them is high. In this paper, we propose an eXtended Bayesian Optimized IS (XBOIS) method, which addresses the aforementioned shortcomings by deployment of an accurate surrogate model (e.g. delay) of the circuit around the failure region. The number of costly circuit simulations is therefore minimized and estimation accuracy is substantially improved by efficient exploration of the variation space. As especially memory elements occupy a large amount of on-chip resources, we evaluate our approach on SRAM cell failure rate estimation. Results show a speedup of about 16x as well as a two orders of magnitude higher failure rate estimation accuracy compared to the best state-of-the-art techniques.

Printed Electronics technology is a key-enabler for smart sensors, soft robotics, and wearables. The inkjet printed electrolyte-gated field effect transistor (EGFET) technology is a promising candidate for such applications due to its low-power operation, high field-effect mobility, and on-demand fabrication. Unlike conventional silicon-based technologies, inkjet printed electronics technology is an additive manufacturing process where multiple layers are printed on top of each other to realize functional devices such as transistors and their interconnections. Due to the additive manufacturing process, the technology has limited routing layers. For routing of complex circuits, insulating crossovers are printed at the intersection of routing paths to isolate them. The crossover can alter the electrical properties of a circuit based on specific location on a routing path. In this work, we propose a crossover-aware placement and routing (COPnR) methodology for inkjet-printed circuits by integrating the crossover constraints in our design framework. Our proposed placement methodology is based on a state-of-the-art evolutionary algorithm while the routing optimization is done using a genetic algorithm. The proposed methodology is compared with the industrial standard placement and routing (PnR) tools. On average, the proposed methodology has 38% fewer crossovers and 94% fewer failing paths compared to the industrial PnR tools applied to printed circuit designs.

In many application domains, in particular automotives, guaranteeing a very low failure rate is crucial to meet functional and safety standards. Especially, reliable operation of memory components such as SRAM cells is of essential importance. Due to aggressive technology downscaling, process and runtime variations significantly impact manufacturing yield as well as functionality. For this reason, a thorough memory failure rate assessment is imperative for correct circuit operation and yield improvement. In this regard, Monte Carlo (MC) simulations have been used as the conventional method to estimate the variability induced failure rate of memory components. However, MC methods become infeasible when estimating rare events such as high-sigma failure rates. To this end, importance sampling (IS) methods have been proposed which reduce the number of required simulations substantially. However, existing methods still suffer from inaccuracies and high computational efforts, in particular for high-sigma problems. In this article, we fill this gap by presenting an efficient mixture IS approach based on Bayesian optimization, which deploys a surface model of the objective function to find the most probable failure points. Its advantages include constant complexity independent of the dimensions of design space, the potential to find the global extrema, and the higher trustworthiness of the estimated failure rate by accurately exploring the design space. The approach is evaluated on a 6T-SRAM cell as well as a master-slave latch based on a 28-nm FDSOI process. The results show an improvement in accuracy, resulting in up to 63× better accuracy in estimating failure rates compared to the best state-of-the-art solutions on a 28-nm technology node.

Printed electronics (PE) circuits have several advantages over silicon counterparts for the applications where mechanical flexibility, extremely low-cost, large area, and custom fabrication are required. The custom (personalized) fabrication is a key feature of this technology, enabling customization per application, even in small quantities due to low-cost printing compared with lithography. However, the personalized and on-demand fabrication, the non-standard circuit design, and the limited number of printing layers with larger geometries compared with traditional silicon chip manufacturing open doors for new and unique reverse engineering (RE) schemes for this technology. In this paper, we present a robust RE methodology based on supervised machine learning, starting from image acquisition all the way to netlist extraction. The results show that the proposed RE methodology can reverse engineer the PE circuits with very limited manual effort and is robust against non-standard circuit design, customized layouts, and high variations resulting from the inherent properties of PE manufacturing processes.

Printed Electronics is perceived to have a major impact in the fields of smart sensors, Internet of Things and wearables. Especially low power printed technologies such as electrolyte gated field effect transistors (EGFETs) using solution-processed inorganic materials and inkjet printing are very promising in such application domains. In this paper, we discuss a modeling approach to describe the variations of printed devices. Incorporating these models and design flows into our previously developed printed design system allows for robust circuit design. Additionally, we propose a reliability-aware routing solution for printed electronics technology based on the technology constraints in printing crossovers. The proposed methodology was validated on multiple benchmark circuits and can be easily integrated with the design automation tools-set.

Due to aggressive technology downscaling, process and runtime variations have a strong impact on the correct functionality in the field as well as manufacturing yield. The assessment of the yield and failure rate is extremely crucial for design optimization. The common practice is to use Monte Carlo simulations in order to account for device variations and estimate failure rate. However, Monte Carlo methods are infeasible for estimating rare events such as high sigma failure rates, and hence, various importance sampling methods have been proposed. In this paper, we present an efficient importance sampling approach based on Bayesian optimization. Its advantages include constant complexity independent of the dimensions of design space, the potential to find the global extrema, and higher trustworthiness of the estimated failure rate. We evaluated the approach on a 6T SRAM cell based on a 28nm FDSOI process. The results show significant speedup and more than two orders of magnitude better accuracy in failure rate estimation, compared to the best state-of-the-art technique.

Recent machine learning models find a widespread use whether in respect of data mining and forecasting or in the classification domain. However, real-world situations comprise complex estimation tasks that carry a certain semantic load and bring a certain degree of fuzziness with them. This is a fuzziness which humans, due to their common sense knowledge and their personal experience, can easily understand by linking the underlying concepts together, while machines may from scratch not. A vast amount of both training data and time are necessary in order for a computational model to be capable of learning such kind of relations and adapting to new situations. In this work, we show that letting explicit semantic knowledge flow into a predictive model leads to an improved performance with regard to training time, accuracy and robustness. In particular, we propose adding an auxiliary semantic layer to the model, whose role is to provide it with information about the semantic interrelation of the treated classes creating in this way shortcuts and saving valuable training time while improving its quality at the same time. We explore several versions of our approach and we illustrate their functionality in a semantic location prediction scenario using 2 different real-world datasets.

Passive Haptic Learning (PHL) describes the learning of a motion, sequence or pattern without voluntary involvement of attention, focus or motivation through a haptic interface. In previous PHL studies about teaching Morse code, we suspect that active learning processes were at least partially involved in causing extraordinarily high learning rates. Therefore we conduct a similar, 50-participant user study to investigate whether PHL is able to teach Morse code without active attention. Our study design differs from previous studies by preventing active learning explicitly (no feedback given) and implicitly (non-Morse patterns, tests prevent learning by rule of elimination) for three groups. Two other groups get the same feedback as was present in other studies. Our results show much lower learning rates when there is no active learning possible. Including active learning, we replicate the same learning results as have been reported. This suggests that our suspicion was correct and that PHL of Morse Code is possible, but greatly limited. Further studies about PHL must pay attention to whether their study design allows participants to learn actively - even small amounts of information that is actively learned may have a great impact on learning performance.

Proactive mobile applications and services have the advantage of providing their users with timely and customized solutions improving in this way the human-machine interaction. For this reason, Location Based Services (LBS) rely increasingly on predictive models that estimate how likely it is for a user to visit a certain location. Recently, Artificial Neural Networks, and especially recurrent architectures such as the LSTMs, have shown a particularly good performance in this field. In this work, we extend a LSTM network by applying Sequence to Sequence (Seq2Seq) learning on human semantic trajectories. In particular, we explore whether and to what extent Attention-based Seq2Seq learning in combination with neural networks can contribute to improving the accuracy in a location prediction scenario. We compare the performance of our framework with the performance of a standard LSTM, a semantic trajectory tree-based approach and a probabilistic graph of first and higher order on two different real-world datasets. It can be shown that Sequence to Sequence learning may well be used to model semantic trajectories and predict future human movement patterns.

Sleep breathing disorder is a serious threat to a large share of the population. This paper presents a low-cost, tiny sensor system based on Volatile Organic Component (VOC) sensing for the detection of sleep apnea/hypopnea. We present two designs, discuss wearability aspects and show that the sensor works similar to gold standard Polysomnography (PSG).

Human Activity Recognition (HAR) with body-worn sensors has been studied intensively in the past decade. Existing approaches typically rely on data from inertial sensors. This paper explores the potential of using point cloud data gathered from wearable depth cameras for on-body activity recognition. We discuss effects of different granularity in the depth information and compare their performance to inertial sensor based HAR. We evaluated our approach with a total of sixteen participants performing nine distinct activity classes in three home environments. 10-fold cross-validation results of KNN and Random Forests classification exhibit a significant increase in F-score from inertial data to depth information (by > 12 percentage points) and show a further improvement when combining low-resolution depth matrices and sensor data. We discuss the performance of the different sensor types for different contexts and show that overall, depth sensors prove to be suitable for HAR.

Reading and understanding source code is a major task in software development. Code comprehension depends on the quality of code, which is impacted by code structure and identifier naming. In this paper we empirically investigated whether longer but more descriptive identifier names improve code comprehension compared to short names, as they represent useful information in more detail. In a web-based study 88 Java developers were asked to locate a semantic defect in source code snippets. With descriptive identifier names, developers spent more time in the lines of code before the actual defect occurred and changed their reading direction less often, finding the semantic defect about 14% faster than with shorter but less descriptive identifier names. These effects disappeared when developers searched for a syntax error, i.e., when no in-depth understanding of the code was required. Interestingly, the style of identifier names had a clear impact on program comprehension for more experienced developers but not for less experienced developers.

Acoustic Doppler shift estimation is a cost-effective way to implement Human-Computer Interaction applications across existing smart devices such as smart phones and smart spekaers. However, due to the inherent uncertainty principle in the traditional time-frequency analysis, it remains challenging to profile motions accurately and timely. In this paper, phase offset in acoustic OFDM signal is leveraged for developing Phascope, a fine-grained, fast and flexible motion profiling scheme. We evaluate Phascope using simulation and experiment on COTS devices. Sub-millisecond response time is achieved for Phascope in our experiment. Besides, with optimal subcarrier selection and SNR of 30 dB over all subcarriers, Phascope can estimate motion speed of 0.1 m/s with 6.75% root-mean-square error (RMSE) compared to optimized FFT method.

Trajectory modelling, trajectory analysis and trajectory prediction have become very important tools in the hands of mobile service providers, whether in respect to resource management (e.g., mobile network management), or to building intelligent, context-aware mobile applications. Most of the existing modelling approaches are highly data-driven. For this reason, the need of large, high-quality datasets has become enormous in the recent years. It is very costly and time-consuming to collect real-world data such as human trajectory data. Moreover, new privacy laws and restrictions make it even more harder. Thus, data turned into a bottleneck for algorithm developers of all kinds. Synthetic data generators provide a solution for this problem. There exists a variety of approaches for producing synthetic trajectories and many extra features have been investigated such as the transportation mode, the proximity to friends and the activity to name but a few. However, none of them has explored the use of psychological features, such as the personality and the emotional state of the users. In this work, we try to give insight into the impact of the aforementioned features on the generation process of (semantic) location trajectories. For this purpose, we designed a novel multi-agent synthetic trajectory generator that takes, among others, these features explicitly into account. We refer to it as Affective Semantic Trajectory Generator (ASTG). In order to evaluate our approach and the use of personality and emotions, we compared the produced trajectories with the outcome of two large-scale studies (> 25.000 participants each) conducted in Germany and Chicago, USA in 2008. It can be shown that dynamic data, such as emotions, can lead to a better performance, a fact that makes ASTG particularly interesting for further investigation.

Wearables like smartwatches or fitness trackers are increasingly entering everyday life. This offers new and unexplored opportunities to use their unique features - like the ability to act as vibrotactile actuators - as interfaces in computer games to increase game immersion. Current tactile cues in games are mostly restricted to event based-feedback and practically exclusive to console gaming. This paper presents LifeTact, a system which informs players about remaining hit points through a tactile heartbeat. The Tactile heartbeat is emitted via existing wearables like smartwatches or fitness trackers. The system was implemented and evaluated in a between-subject study with gamers, who rated the system as innovative, beneficial to the user experience and immersive.

With over 1 billion vehicles in operation over the world1and steadily growing cities, intelligent traffic management has become inevitable in order to preserve quality of life as we know it. Analyzing and predicting the movement behavior of traffic participants helps providing forward-looking solutions and plays a major role in intelligent traffic systems (ITS), in the field of location and handoff management, and in location aware systems in general. In this paper, we introduce a novel semantic location prediction approach that provides user-specific predictions based on their past semantic trajectories. For this purpose, we adopt and adapt an item recommendation method called FPMC. FPMC relies on a combination of Matrix Factorization and Markov Chains. We evaluate our algorithm against the user-independent standard Matrix Factorization (MF) and the Factorized Markov Chains (FMC) and show that our approach clearly surpasses the performance of the former mentioned methods.

Participatory Sensing, i.e. collaboratively taking sensor measurements with mobile devices in a Citizen Science fashion, has become increasingly popular. Because such scenarios often require a critical mass of users, applying gamification to different areas in order to increase user engagement has been proposed. However, existing attempts often default to the standard points, badges, and leaderboards and fail to recognize the potential of exploiting game design elements beyond creating user engagement. We propose not to think of Gamified Participatory Sensing when designing such systems, but rather of Sensified Gaming. To this end, this work presents a collection of design patterns and game mechanics that can be used to identify or design suitable games, into which participatory sensing tasks can be embedded. We identified four core tasks from participatory environmental sensing and sensor networks research, reviewed hundreds of design patterns and map each of the 63 selected patterns to the core tasks.

This work presents the bPart, a highly integrated autonomous sensor platform for use with mobile phones and devices. It consists of a Bluetooth Low Energy (BLE) radio and several MEMS sensors, all integrated in a volume of less than 1cm³, including the battery. Aside from the wireless transceiver, the bPart features sensors for ambient illumination, 3D-acceleration, temperature and relative humidity. In addition, there is a button and a magnetic switch for binary input and a RGB-LED for user feedback. A secondary LED in the infrared spectrum enables camera-assisted identification and tracking of the node. Runtimes of several years are possible on the included CR2023 lithium coin cell, through the low energy radio, onboard power-conversion and low-power sleep modes. The latter is rated below 2µW and a single data packet consumes about 75µWs. Its low energy consumption makes the bPart suitable for operation with energy harvesting, which we have validated with a 33cm² solar cell in indoor lighting conditions.

Displaying three-dimensional content on a flat display is bound to reduce the impression of depth, particularly for mobile video see-trough augmented reality. Several applications in this domain can benefit from accurate depth perception, especially if there are contradictory depth cues, like occlusion in a x-ray visualization. The use of stereoscopy for this effect is already prevalent in head-mounted displays, but there is little research on the applicability for hand-held augmented reality. We have implemented such a prototype using an off-the-shelf smartphone equipped with a stereo camera and an autostereoscopic display. We designed and conducted an extensive user study to explore the effects of stereoscopic hand-held augmented reality on depth perception. The results show that in this scenario depth judgment is mostly influenced by monoscopic depth cues, but our system can improve positioning accuracy in challenging scenes.

We consider the detection of activities from non-cooperating individuals with features obtained on the radio frequency channel. Since environmental changes impact the transmission channel between devices, the detection of this alteration can be used to classify environmental situations. We identify relevant features to detect activities of non-actively transmitting subjects. In particular, we distinguish with high accuracy an empty environment or a walking, lying, crawling or standing person, in case-studies of an active, device-free activity recognition system with software defined radios. We distinguish between two cases in which the transmitter is either under the control of the system or ambient. For activity detection the application of one-stage and two-stage classifiers is considered. Apart from the discrimination of the above activities, we can show that a detected activity can also be localized simultaneously within an area of less than 1 meter radius.

This paper proposes using a 2D waveguide for a novel means of authentication in public Wi-Fi infrastructures. The design of the system is presented, and its practicability and usability is comparatively discussed with that of five other tag and context based authentication schemes, two of which have not been previously realized. In accordance with the presented application scenarios, all of the schemes were implemented in a platform-independent fashion built on web technology.

This paper presents a mobile, low-cost particulate matter sensing approach for the use in Participatory Sensing scenarios. It shows that cheap commercial off-the-shelf (COTS) dust sensors can be used in distributed or mobile personal measurement devices at a cost one to two orders of magnitude lower than that of current hand-held solutions, while reaching meaningful accuracy. We conducted a series of experiments to juxtapose the performance of a gauged high-accuracy measurement device and a cheap COTS sensor that we fitted on a Bluetooth-enabled sensor module that can be interconnected with a mobile phone. Calibration and processing procedures using multi-sensor data fusion are presented, that perform very well in lab situations and show practically relevant results in a realistic setting. An on-the-fly calibration correction step is proposed to address remaining issues by taking advantage of co-located measurements in Participatory Sensing scenarios. By sharing few measurement across devices, a high measurement accuracy can be achieved in mobile urban sensing applications, where devices join in an ad-hoc fashion. A performance evaluation was conducted by co-locating measurement devices with a municipal measurement station that monitors particulate matter in a European city, and simulations to evaluate the on-the-fly cross-device data processing have been done.

Human Activity Recognition (HAR) using accelerometers has been studied intensively in the past decade. Recent HTML5 methods allow sampling a mobile phone's sensors from within web pages. Our objective is to leverage this for the creation of individual activity recognition modules that can be included into web applications to allow them to gain context-awareness. In this work, jActivity, a first prototype of such a platform-independent HTML5/JavaScript framework is presented, along with experiments to determine the general feasibility and challenges for HAR in web applications. Our results indicate that the realization looks promising, albeit so far limited to certain devices/user agents.

Context prediction is the task of inferring information about the progression of an observed context time series based on its previous behaviour. Prediction methods can be applied at several abstraction levels in the context processing chain. In a theoretical analysis as well as by means of experiments we show that the nature of the input data, the quality of the output, and finally the flow of processing operations used to make a prediction, are correlated. A comprehensive discussion of basic concepts in context prediction domains and a study on the effects of the context abstraction level on the context prediction accuracy in context prediction scenarios is provided. We develop a set of formulae that link scenario-dependent parameters to a probability for the context prediction accuracy. It is demonstrated that the results achieved in our theoretical analysis can also be confirmed in simulations as well as in experimental studies.

Intelligent Environments are currently implemented with standard WSN technologies using conventional connection-based communications. However, connection-based communications may impede progress towards IE scenarios involving high mobility or massive amounts of sensor nodes. We present a novel approach based on collective transmission for item level tagging using printed organic electronics, which implements robust, collective, approximate read-out of large numbers of simple tags. Our approach uses mechanisms for calculation by simultaneous transmission. We detail the collective transmission approach, discuss its implementation in the organic printed label scenario, and show first results of experiments conducted with our smart label test bed. We conclude with an outlook on the potential of collective transmission, and argue that collective transmission is a fundamental building block for realizing distributed intelligence.

In wireless sensor networks (WSN), collaboration is a way to improve the quality of data communication between sensor nodes with restricted resources in terms of memory, processing and energy storage. For receive collaboration, various array processing schemes such as receive beamforming and collaborative channel equalization (CCE) can be used for aggregating data received by each node in the network. The key challenge is the limitation on the number of nodes which can collaborate because of the increased computational load and memory demand when the multiple signals are aggregated. This problem arises when sensor nodes in a CDMA based WSN collaborate, although the low power property of CDMA technique makes it suitable for WSN applications. Here receive collaboration is investigated in CDMA networks using CCE as the collaboration algorithm. We present two novel distributed signal aggregation algorithms: partial and hierarchical aggregation, which distribute computational load and memory demands on collaborative nodes. The positive impacts of receive collaboration on the signal quality and reliability are confirmed experimentally in a WSN scenario using software radios. Then the requirements of collaborative reception using CCE combined with the novel aggregation methods in terms of computational and memory load, as well as energy consumption are evaluated. The results indicate that the distributed signal aggregation algorithms, especially hierarchical aggregation, have computational and memory requirements less than that of centralized CCE, providing greater flexibility and scalability which enables collaboration in WSNs on a larger scale than previously possible.

We derive an asymptotically sharp bound on the synchronization speed of a randomized black box optimization technique for closed-loop feedback-based distributed adaptive beamforming in wireless sensor networks. We also show that the feedback function that guides this synchronization process is strong multimodal. Given this knowledge that no local optimum exists, we consider an approach to locally compute the phase offset of each individual carrier signal. With this design objective, an asymptotically optimal algorithm is derived. Additionally, we discuss the concept to reduce the optimization time and energy consumption by hierarchically clustering the network into subsets of nodes that achieve beamforming successively over all clusters. For the approaches discussed, we demonstrate their practical feasibility in simulations and experiments.

In this paper, a new type of collaboration in wireless sensor networks (WSN) is suggested that exploits array processing algorithms to improve the reception of a signal. For receive collaboration, the transmission power during intra-cluster transmissions decreases at the expense of increasing the inter-cluster communications. It is shown that, as a result of using receive collaboration, the destination node's power consumption and the network interference level decrease which considerably improve the data transmission performance and network life time. This method is applicable both for cluster based and non-cluster based WSNs. In order to show the feasibility of receive collaboration and also to evaluate its performance, an LS-CMA based channel equalization scheme is also simulated which is performed during cooperation between cluster nodes. The comparison of the output BER between random distributed and uniform linear distributed cases shows a good performance of receive collaboration.

Summary form only given. Embedded systems driven by future applications will be tightly coupled with the increasing complexity of the real world. Consisting of myriads of wireless networked devices, of heterogeneous architectures, distributed and interacting in a number of ways and serving a multitude of purposes systems have to adapt and take advantage of conditions unpredictable at design time. In their realisation software both on a system and on an application level is playing an increasingly important role that cannot be designed independently. Dominant design factors are the severe resource constraints, the unreliability of the wireless medium and the dynamics of both the applications and the environment. Selected challenges in the area of wireless sensor networks are addressed.

This paper presents an experience report illustrating the design of the uPart tiny low-power sensor network platform: from the analysis phase over the definition of the application, design and construction of hardware, the implementation of the software and network to the application set-up. uPart sensor nodes were given away in the conference badge to 500 voluntary attendees of the Ubicomp 2005. In our demo application, uParts were able to recognize activities of attendees of the Ubicomp 2005 conference. Design was carried out under serve time and budget restrictions. The paper focuses on reporting design decisions and presents tech-nical details of uPart hardeware, firmware and applications. It also shows first qualitative experiences with the run of the system at the conference. The outcome of the paper is a general meta-guideline for designing sensor network systems under similar conditions.

Current sensor network platforms perform multiple processes including sensor sampling, communication, and various computational tasks. When deployed in unpredictable environments, complex schedules of those processes may arise. Typical sensor network qualities like periodic sampling of sensors, avoidance of process starvation and automatic energy management are required to be maintained in such situations. We propose a scheduling framework for senor nodes consisting of a scheduler, a dispatcher and a controller for an on-line adaptation of the process execution. The key components are a controller and an enhanced dispatcher implementing strategies like jitter correction and starvation avoidance. Further, the framework is aware of the energy consumption of sensors. We show that our controlled scheduling framework performs significantly better than a non-controlled single scheduler in unpredictable environments. Our proposed measures are efficient to implement. Results are shown by extensive simulations and a first implementation on the Particle Computer platform.

This paper presents a sensor-based, networked embedded system, referred to as the particle computer system. It is comprised of tiny wireless sensor nodes, capable of communication with each other, as well as connectivity with backend, PC-based systems, thereby facilitating software development and data analysis in an integrated systems package. The core design principles of the sensor nodes enable operation in very mobile settings and truly ad-hoc, peer-to-peer interoperation without the intervention of a master or explicit middleware layer. The two main system properties highlighted in this paper are: 1) information distribution to all components within the system and 2) the usage of a common communication language in all system components. This language has been proprietarily developed for the particle system and is known as ConCom. As a result of these system properties, we have found the particle system to be very extensible and applicable in many everyday scenarios. The paper presents insights to the implementation of the particle computer system, including software development and data analysis capabilities, and the overall system integration.

Synchronous distributed jam signalling (SDJS) is a new transmission scheme targeted to highly mobile and ad hoc wireless systems. It is based on the synchronous, parallel and superimposing emission of jam signal on the physical layer. SDJS is intended to be implemented as a feature on existing standards. It enables those system with the ability to fast estimate statistical parameters. This paper presents SDJS in general and then focuses on the application to estimate the parameter number of devices in a mobile setting. We studied SDJS and its application through a mathematical model and simulations and proved the idea in a real-world implementation on a mobile network, where we estimated the number of devices in real time (within 5 ms), increasing the estimation speed compared to traditional approaches by factor 1000.

Article Free Access Share on Assistant for an information database Authors: Michael Früchtl SAP AG Walldorf, Human Resources Management Systems, P.O.Box 1461, 69185 Walldorf, Germany SAP AG Walldorf, Human Resources Management Systems, P.O.Box 1461, 69185 Walldorf, GermanyView Profile , Jürgen Kreuziger SAP AG Walldorf, R/3 Services SAP AG Walldorf, R/3 ServicesView Profile , Michael Beigl Telecooperation Office, Institute of Telematics, University of Karlsruhe, Vincenz-Priessnitz-Strasse 1, 76131 Karlsruhe, Germany Telecooperation Office, Institute of Telematics, University of Karlsruhe, Vincenz-Priessnitz-Strasse 1, 76131 Karlsruhe, GermanyView Profile Authors Info & Claims CIKM '97: Proceedings of the sixth international conference on Information and knowledge managementJanuary 1997 Pages 230–237https://doi.org/10.1145/266714.266903Online:01 January 1997Publication History 1citation237DownloadsMetricsTotal Citations1Total Downloads237Last 12 Months8Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF