Project: Sensor delay effects in High-Resolution FluoRespirometry
Oroboros Instruments GmbH is a leading laboratory equipment manufacturer, continuously working to improve its products. The company distributes the gold standard O2k-technology for High-Resolution FluoRespirometry (HRFR) world-wide. The devices now achieve high time resolution, which has been unreachable before. At the same time, sensor delay effects become relevant for many applications. Advanced mathematical models and methods are therefore essential to account for these delay effects in the measurement signals.
The aim of this project is to develop models and improved algorithms accounting for delay effects in the analyzers of Oroboros Instruments GmbH. In particular, the aim is to establish accurate mathematical models representing delay effects in the HRFR for mitochondria and cell research. Tools and from signal processing algorithms are established and implemented for model-based deburring of measurements signals. The developed tools should be accurate and stable, and example implementation will be established and implemented.
MitoEAGLE: The objective of the MitoEAGLE network is to improve our knowledge on mitochondrial function in health and disease related to Evolution, Age, Gender, Lifestyle and Environment.
Project: Classification of Network Traffic
Barracuda Networks offers network security and storage solutions. One of our internal, medium sized firewall handles approximately 40 million connections from about 30 clients during a typical working week. Our largest firewall models are designed to serve up to 8000 clients, with a theoretical maximum of about 400 billion connections per week. With this vast amount of network traffic it is difficult to identify suspicious or dangerous network traffic, like systematic network intrusion attempts or immanent hacker attacks, which intentionally try to go unnoticed.
Most of the network traffic follows certain patterns. For example: Many periodic tasks – like network backups or server synchronizations – always occur at approximately the same time of day, or only on certain weekdays, usually have similar data volume every time, and have internal network addresses as source and destination. As another example typical traffic from internet surfing occurs mostly between Monday and Friday during office hours, and usually has internal source and external destination address.
The goal of this project is twofold:
1. In a first stage an offline classification of collected network traffic should be performed. For each handled connection the firewall logs certain properties, like start and end time, source and destination network address, protocol, data volume and some more. From these collected data a set of descriptors should be derived, that can be used as feature vectors for classification by either a clustering method, a support vector machine, or similar algorithms. The result of the classification should reflect the most prominent classes of traffic. Further it should identify connections which do not fit well into any of the prominent classes, since these connections might be the suspicious or dangerous ones.
2. Using the results from the first stage, an online classification system (for example an artificial neuronal network) shall be trained such that it can then perform real-time (or almost real-time) classification of newly created network connections.
Project: Optimizing a parallel picking strategy on a component wafer to maximize the machine efficiency.
Company: BE Semiconductor Industries N.V.
Besi develops leading edge assembly processes and equipment for lead frame, substrate and wafer level packaging applications in a wide range of end-user markets including electronics, mobile internet, cloud server, computing, automotive, industrial, LED and solar energy.
Besi offers a wide range of die attach systems based on leading edge technology. The offering includes multi-chip bonders for advanced packaging, epoxy and soft solder bonders, high precision flip chip bonders for mass production, stacked die bonders and die sorting equipment.
Target: Optimize a parallel picking strategy on a component wafer to maximize the machine efficiency.
Details: On the component wafer are rectangular dies positioned on a regular grid. The dies Di,j for 0 ≤ i < M, 0 ≤ j < N can have the state ok (shall be picked) or bad (must not be picked). The number of good dies varies between 10% and 90% with a random structure. Typically, a component wafer consists of hundreds or thousands of components M > 20, N > 20. A picking device has the capability to pick more than one component in parallel but only in a fixed rectangular grid matching the grid of the component wafer.
This device can pick k x l neighbouring components, where k ≥ 1 and l ≥ 1, but k M and l ≪ N, typically 1 x 3 or 2 x 2. All Di,j marked good must be picked. The algorithm has to run on a standard PC (roughly i5 using a single CPU core and less than 4GB RAM) and needs to provide the first pick position(s) within 2-3 seconds. It is allowed to have a "stream" of pick positions, where every <0.5 s a new optimized position is provided. The target is to minimize the number of picking steps.
- minimize the distance between the pick positions
- interleaved picking device: only every n-th component is picked (checker board type)
Project: Speech intelligibility rating model
In order to complement the speech rehabilitation of our patients, this project aims to create a model that rates the intelligibility of speech. The model has to be generic to accept anything from single sounds to full sentences.
The inputs to the model are as follows: Text representation, Phonetic representation (IPA),Audio recording.
The desired output is: Annotation of text and phonetic representation with quality of the individual sounds, Segmentation and alignment of the textual representation with the audio (implicitly)
As such the model is conceptually similar to ASR (automatic speech recognition), but rather than generating a text transcription the model shall detect pronunciation anomalies. We will provide pointers to existing data corpora and suggestions for the model, however, the project is open for any model definition. Preference for models of English language, but German could also be interesting (language agnosticism may be too complex for this workshop).
- Contact person university: tba
Project: Product Portofolio Optimization
The Fraunhofer ITWM is a research institute whose focus is on the transfer of mathematical methods and technology into applications. The customers in industry come from a broad variety of sectors: from the automotive, mechanical engineering and textile industries to the energy and financial branches.
Let us assume a producer of fluid pumps is thinking about his product folio consisting of several pumps in different sizes ready to serve different customer needs. Traditionally, customer specifications cover in particular delivery height and delivery volume, which be seen as the nominal process point of the pump. Of course, each pump will be able to cope with a variety of process points in the neighborhood of the nominal point. But, deviations from nominal points will come with a loss of energy efficiency or with a loss of lifetime due to mechanical stress. On the other hand, a manufacturer will not offer individually produced pumps for any nominal process point nor will it be practically possible to stay close to nominal process points.
Given a cloud of orders from the past with specified process points how should a manufacturer set up a pump portfolio balancing pump species, customer needs and technical excellence ?
- Contact person Frauenhofer Institute: Karl-Heinz Küfer