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Kalman-Filter Einführung in die Zustandsschätzung und ihre Anwendung für eingebettete Systeme
Dieses Lehrbuch befasst sich leicht verständlich mit der Theorie der Kalman-Filterung. Die Autoren geben damit eine Einführung in Kalman-Filter und...
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Adaptiver Kalman-Filter (ROSE-Filter)
Um das Kalman-Filter optimal zu nutzen, ist es von großer Wichtigkeit, die Kovarianz des Messrauschens...
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MEAs-Filter: a novel filter framework utilizing evolutionary algorithms for cardiovascular diseases diagnosis
Cardiovascular disease management often involves adjusting medication dosage based on changes in electrocardiogram (ECG) signals' waveform and...
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Nichtlineare Kalman-Filter
Nicht alle Differenzialgleichungssysteme lassen sich mit der linearen Zustandsraumdarstellung darstellen. Für solche Problemstellungen muss eine...
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Klassisches Kalman-Filter
Damit Kalman-Filter korrekt eingesetzt werden können, ist es wichtig, die Randbedingen zu kennen, unter der die Kalman-Gleichungen verwendet werden...
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Filter Effects
In addition to all the vector functionality offered by SVG (as you'd expect from a format called Scalable Vector Graphics), SVG also provides some...
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Harmonic reduction of second order sallen and key lowpass filter and second order MFB lowpass filter through closed loop PID controlled method
Filters play a crucial role in various sectors, and active filters, in particular, find widespread applications. This research paper focuses on...
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Automatic filter pruning algorithm for image classification
Network pruning is an essential technique for compressing and accelerating convolutional neural networks (CNNs). Existing pruning algorithms...
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Cascade-form odd-order stability-guaranteed tunable-bandwidth digital filter
This paper reveals a stability-constrained approach for designing an odd-order recursive digital filter whose frequency bandwidths are freely...
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Weighted least square filter via deep unsupervised learning
The weighted least square (WLS) filter is a popular edge-preserving image smoother that is particularly useful for detail enhancing and HDR tone...
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High-performance fractional anisotropic diffusion filter for portable applications
Anisotropic diffusion is one of the most effective methods used in image processing. It can be used to eliminate the small textures of an image while...
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FASS-pruner: customizing a fine-grained CNN accelerator-aware pruning framework via intra-filter splitting and inter-filter shuffling
Nowadays, with the increasing depth of CNNs, the number of computation and storage requirements with weights expands significantly, preventing their...
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Implementation of extended kalman filter for localization of ambulance robot
This paper focuses on the implementation of the Extended Kalman Filter for indoor localization of a semi-autonomous Ambulance Robot system named...
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Beispiel: Umfeldsensor mit ROSE-Filter
In dem folgenden Kapitel sollen die Vorteile des ROSE-Filters (Rapid Ongoing Stochastic covariance Estimation-Filter) an einem weiteren Beispiel...
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The Filter() Function
In Chapter 9 we have looked at filtering data using (fairly) simple comparisons applied as parameters to the...
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A new Legendre wavelet filter-based image super-resolution technique
The present article proposes a new Legendre wavelet (LW) filter-based image super-resolution technique. The LW basis vector is used in compact form...
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Filter feature selection methods for text classification: a review
Filter feature selection methods are utilized to select discriminative terms from high-dimensional text data to improve text classification...
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eBF: an enhanced Bloom Filter for intrusion detection in IoT
Intrusion Detection is essential to identify malicious incidents and continuously alert many users of the Internet of Things (IoT). The constant...
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Filter pruning based on evolutionary algorithms for person re-identification
Filter pruning has drawn much attention for releasing the limitations of person re-identification (ReID) in resource-constrained platforms due to the...
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Empirical evaluation of filter pruning methods for acceleration of convolutional neural network
Training and inference of deep convolutional neural networks are usually slow due to the depth of the network and the number of parameters in the...
