Range-resolved interferometric signal processing using sinusoidal optical frequency modulation

A novel signal processing technique using sinusoidal optical frequency modulation of an inexpensive continuous-wave laser diode source is proposed that allows highly linear interferometric phase measurements in a simple, self-referencing setup. Here, the use of a smooth window function is key to suppress unwanted signal components in the demodulation process. Signals from several interferometers with unequal optical path differences can be multiplexed, and, in contrast to prior work, the optical path differences are continuously variable, greatly increasing the practicality of the scheme. In this paper, the theory of the technique is presented, an experimental implementation using three multiplexed interferometers is demonstrated, and detailed investigations quantifying issues such as linearity and robustness against instrument drift are performed

Fibre segment interferometry using code-division multiplexed optical signal processing for strain sensing applications

A novel optical signal processing scheme for multiplexing fibre segment interferometers is proposed. The continuous-wave, homodyne technique combines code-division multiplexing with single-sideband modulation. It uses only one electro-optic phase modulator to achieve both range separation and quadrature interferometric phase measurement. This scheme is applied to fibre segment interferometry, where a number of long-gauge length interferometric fibre sensors are formed by subtracting pairs of signals from equidistantly placed, weak back reflectors. In this work we give a detailed account of the signal processing involved and, in particular, explore aspects such as electronic bandwidth requirements, noise, crosstalk and linearity, which are important design considerations. A signal bandwidth of ±20 kHz permits the resolution of phase change rates of 2.5 × 104 rad s-1 for each of the four 16.5 m long segments in our setup. We show that dynamic strain resolutions below 0.2 nanostrain Hz-0.5 at 2 m sensor gauge length are achievable, even with an inexpensive diode laser. When used in applications that require only relative strain change measurements, this scheme compares well to more established techniques and can provide high-fidelity yet cost-effective measurements

Energy-Aware Data Management on NUMA Architectures

The ever-increasing need for more computing and data processing power demands for a continuous and rapid growth of power-hungry data center capacities all over the world. As a first study in 2008 revealed, energy consumption of such data centers is becoming a critical problem, since their power consumption is about to double every 5 years. However, a recently (2016) released follow-up study points out that this threatening trend was dramatically throttled within the past years, due to the increased energy efficiency actions taken by data center operators. Furthermore, the authors of the study emphasize that making and keeping data centers energy-efficient is a continuous task, because more and more computing power is demanded from the same or an even lower energy budget, and that this threatening energy consumption trend will resume as soon as energy efficiency research efforts and its market adoption are reduced. An important class of applications running in data centers are data management systems, which are a fundamental component of nearly every application stack. While those systems were traditionally designed as disk-based databases that are optimized for keeping disk accesses as low a possible, modern state-of-the-art database systems are main memory-centric and store the entire data pool in the main memory, which replaces the disk as main bottleneck. To scale up such in-memory database systems, non-uniform memory access (NUMA) hardware architectures are employed that face a decreased bandwidth and an increased latency when accessing remote memory compared to the local memory. In this thesis, we investigate energy awareness aspects of large scale-up NUMA systems in the context of in-memory data management systems. To do so, we pick up the idea of a fine-grained data-oriented architecture and improve the concept in a way that it keeps pace with increased absolute performance numbers of a pure in-memory DBMS and scales up on NUMA systems in the large scale. To achieve this goal, we design and build ERIS, the first scale-up in-memory data management system that is designed from scratch to implement a data-oriented architecture. With the help of the ERIS platform, we explore our novel core concept for energy awareness, which is Energy Awareness by Adaptivity. The concept describes that software and especially database systems have to quickly respond to environmental changes (i.e., workload changes) by adapting themselves to enter a state of low energy consumption. We present the hierarchically organized Energy-Control Loop (ECL), which is a reactive control loop and provides two concrete implementations of our Energy Awareness by Adaptivity concept, namely the hardware-centric Resource Adaptivity and the software-centric Storage Adaptivity. Finally, we will give an exhaustive evaluation regarding the scalability of ERIS as well as our adaptivity facilities

Range-resolved optical interferometric signal processing

The ability to identify the range of an interferometric signal is very useful in interferometry, allowing the suppression of parasitic signal components or permitting several signal sources to be multiplexed. Two novel range-resolved optical interferometric signal processing techniques, employing very different working principles, are theoretically described and experimentally demonstrated in this thesis. The first technique is based on code-division multiplexing (CDM), which is combined with single-sideband signal processing, resulting in a technique that, unlike prior work, only uses a single, regular electro-optic phase modulator to perform both range-based signal identification and interferometric phase evaluation. The second approach uses sinusoidal optical frequency modulation (SFM), induced by injection current modulation of a diode laser, to introduce range-dependent carriers to determine phase signals in interferometers of non-zero optical path difference. Here, a key innovation is the application of a smooth window function, which, when used together with a time-variant demodulation approach, allows optical path lengths of constituent interferometers to be continuously and independently variable, subject to a minimum separation, greatly increasing the practicality of the approach. Both techniques are applied to fibre segment interferometry, where fibre segments that act as long-gauge length interferometric sensors are formed between pairs of partial in-fibre reflectors. Using a regular single-mode laser diode, six fibre segments of length 12.5 cm are multiplexed with a quadrature bandwidth of 43 kHz and a phase noise floor of 0.19 mrad · Hz -0.5 using the SFM technique. In contrast, the 16.5 m spatial resolution achieved with the CDM technique points towards its applicability in medium-to-long range sensing. The SFM technique also allows high linearity, with cyclic errors as low as 1 mrad demonstrated, and with modelling indicating further room for improvement. Additionally, in an industrial measurement, the SFM technique is applied to single-beam, multi-surface vibrometry, allowing simultaneous differential measurements between two vibrating surfaces

Range-resolved signal processing for fibre segment interferometry applied to dynamic long-gauge length strain sensing

A range-resolved interferometric signal processing technique using sinusoidal optical frequency modulation is applied to fibre segment interferometry. Here, six optical fibre segments of gauge length 12.5 cm are used as interferometric strain sensors and are formed between seven weak, broadband fibre Bragg gratings, acting as in-fibre partial reflectors. In a very simple and cost-effective optical setup using injection current modulation of a laser diode source, interferometric measurement of acoustic wave propagation in a metal rod is used to demonstrate the capabilities of the technique

Simultaneous laser vibrometry on multiple surfaces with a single beam system using range-resolved interferometry

A novel range-resolved interferometric signal processing technique that uses sinusoidal optical frequency modulation is applied to multi-surface vibrometry, demonstrating simultaneous optical measurements of vibrations on two surfaces using a single, collimated laser beam, with a minimum permissible distance of 3.5 cm between surfaces. The current system, using a cost-effective laser diode and a fibre-coupled, downlead insensitive setup, allows an interferometric fringe rate of up to 180 kHz to be resolved with typical displacement noise levels of 8 pm Hz-0.5. In this paper, the system is applied to vibrometry measurements of a table-top cryostat, with concurrent measurements of the optical widow and the sample holder inside. This allows the separation of common-mode vibrations of the whole cryostat from differential vibrations between the window and the sample holder.EPSR

SMIX: Self-managing indexes for dynamic workloads

As databases accumulate growing amounts of data at an increasing rate, adaptive indexing becomes more and more important. At the same time, applications and their use get more agile and flexible, resulting in less steady and less predictable workload characteristics. Being inert and coarse-grained, state-of-the-art index tuning techniques become less useful in such environments. Especially the full-column indexing paradigm results in many indexed but never queried records and prohibitively high storage and maintenance costs. In this paper, we present Self-Managing Indexes, a novel, adaptive, fine-grained, autonomous indexing infrastructure. In its core, our approach builds on a novel access path that automatically collects useful index information, discards useless index information, and competes with its kind for resources to host its index information. Compared to existing technologies for adaptive indexing, we are able to dynamically grow and shrink our indexes, instead of incrementally enhancing the index granularity

SMIX Live - A Self-Managing Index Infrastructure for Dynamic Workloads

As databases accumulate growing amounts of data at an increasing rate, adaptive indexing becomes more and more important. At the same time, applications and their use get more agile and flexible, resulting in less steady and less predictable workload characteristics. Being inert and coarse-grained, state-of-the-art index tuning techniques become less useful in such environments. Especially the full-column indexing paradigm results in lot of indexed but never queried data and prohibitively high memory and maintenance costs. In our demonstration, we present Self-Managing Indexes, a novel, adaptive, fine-grained, autonomous indexing infrastructure. In its core, our approach builds on a novel access path that automatically collects useful index information, discards useless index information, and competes with its kind for resources to host its index information. Compared to existing technologies for adaptive indexing, we are able to dynamically grow and shrink our indexes, instead of incrementally enhancing the index granularity. In the demonstration, we visualize performance and system measures for different scenarios and allow the user to interactively change several system parameters

Resolution enhancement in Fabry-Perot interferometers through evaluation of multiple reflection using range-resolved interferometry

This work presents a novel approach for improving interferometer resolution with a relatively simple setup by combining the use of range-resolved interferometry and a high-finesse Fabry-Perot setup utilizing multiple reflections in the cavity to gradually increase the resolution. This approach could enable the measurement of small displacements with a potentially much higher resolution than current interferometry methods. A simple proof-of concept setup demonstrated the evaluation of up to four Fabry-Perot passes, while theoretically much higher sensitivity improvement factors should be possible

Query Processing on Prefix Trees Live

Modern database systems have to process huge amounts of data and should provide results with low latency at the same time. To achieve this, data is nowadays typically hold completely in main memory, to benefit of its high bandwidth and low access latency that could never be reached with disks. Current in-memory databases are usually column-stores that exchange columns or vectors between operators and suffer from a high tuple reconstruction overhead. In this demonstration proposal, we present DexterDB, which implements our novel prefix tree-based processing model that makes indexes the first-class citizen of the database system. The core idea is that each operator takes a set of indexes as input and builds a new index as output that is indexed on the attribute requested by the successive operator. With that, we are able to build composed operators, like the multi-way-select-join-group. Such operators speed up the processing of complex OLAP queries so that DexterDB outperforms state-of-the-art in-memory databases. Our demonstration focuses on the different optimization options for such query plans. Hence, we built an interactive GUI that connects to a DexterDB instance and allows the manipulation of query optimization parameters. The generated query plans and important execution statistics are visualized to help the visitor to understand our processing model