Cost-effective upgrade of WDM all-optical networks using overlay fibres and hop reduction links

Fibre overlay is a cost-effective technique to alleviate wavelength blocking in some links of a wavelength-routed optical network by increasing the number of wavelengths in those links. In this letter, we investigate the effects of overlaying fibre in an all-optical network (AON) based on GÉANT2 topology. The constraint-based routing and wavelength assignment (CB-RWA) algorithm locates where cost-efficient upgrades should be implemented. Through numerical examples, we demonstrate that the network capacity improves by 25 per cent by overlaying fibre on 10 per cent of the links, and by 12 per cent by providing hop reduction links comprising 2 per cent of the links. For the upgraded network, we also show the impact of dynamic traffic allocation on the blocking probability. Copyright © 2010 John Wiley & Sons, Ltd.

Experimental demonstration of cost-Effective intensity-modulation and direct-detection optical fast-OFDM over 40km SMF transmission

We demonstrate the first experimental implementation of intensity-modulation and direct-detection 7.6Gb/s DBPSK-based DSB optical Fast-OFDM with a reduced subcarrier spacing equal to half of the symbol rate per subcarrier over 40km SMF. © 2012 OSA.

Cost-effective and eco-friendly synthesis of novel and stable N-doped ZnO/g-C3N4 core-shell nanoplates with excellent visible-light responsive photocatalysis

N-doped ZnO/g-C3N4 hybrid core–shell nanoplates have been successfully prepared via a facile, cost-effective and eco-friendly ultrasonic dispersion method for the first time. HRTEM studies confirm the formation of the N-doped ZnO/g-C3N4 hybrid core–shell nanoplates with an average diameter of 50 nm and the g-C3N4 shell thickness can be tuned by varying the content of loaded g-C3N4. The direct contact of the N-doped ZnO surface and g-C3N4 shell without any adhesive interlayer introduced a new carbon energy level in the N-doped ZnO band gap and thereby effectively lowered the band gap energy. Consequently, the as-prepared hybrid core–shell nanoplates showed a greatly enhanced visible-light photocatalysis for the degradation of Rhodamine B compare to that of pure N-doped ZnO surface and g-C3N4. Based on the experimental results, a proposed mechanism for the N-doped ZnO/g-C3N4 photocatalyst was discussed. Interestingly, the hybrid core–shell nanoplates possess high photostability. The improved photocatalytic performance is due to a synergistic effect at the interface of the N-doped ZnO and g-C3N4 including large surface-exposure area, energy band structure and enhanced charge-separation properties. Significantly, the enhanced performance also demonstrates the importance of evaluating new core–shell composite photocatalysts with g-C3N4 as shell material.

Fiber optically integrated cost-effective spectrometer for optical coherence tomography

A tilted fiber Bragg grating (TFBG) was integrated as the dispersive element in a high performance biomedical imaging system. The spectrum emitted by the 23 mm long active region of the fiber is projected through custom designed optics consisting of a cylindrical lens for vertical beam collimation and successively by an achromatic doublet onto a linear detector array. High resolution tomograms of biomedical samples were successfully acquired by the frequency domain OCT-system. Tomograms of ophthalmic and dermal samples obtained by the frequency domain OCT-system were obtained achieving 2.84 μm axial and 10.2 μm lateral resolution. The miniaturization reduces costs and has the potential to further extend the field of application for OCT-systems in biology, medicine and technology. © 2014 SPIE.

Cost-effective optical coherence tomography spectrometer based on a tilted fiber Bragg grating

A compact, fiber-based spectrometer for biomedical application utilizing a tilted fiber Bragg grating (TFBG) as integrated dispersive element is demonstrated. Based on a 45° UV-written PS750 TFBG a refractive spectrometer with 2.06 radiant/μm dispersion and a numerical aperture of 0.1 was set up and tested as integrated detector for an optical coherence tomography (OCT) system. Featuring a 23 mm long active region at the fiber the spectrum is projected via a cylindrical lens for vertical beam collimation and focused by an achromatic doublet onto the detector array. Covering 740 nm to 860 nm the spectrometer was optically connected to a broadband white light interferometer and a wide field scan head and electronically to an acquisition and control computer. Tomograms of ophthalmic and dermal samples obtained by the frequency domain OCT-system were obtained achieving 2.84 μm axial and 7.6 μm lateral resolution. © 2014 SPIE.

Cost-effective broadband GaAs IQ modulator array for long-reach OFDM-PONs

A novel modulator array integrating eight GaAs electro-optic IQ modulators is characterized and tested over long-reach direct-detected multi-band OFDM-PONs. The GaAs IQ modulators present > 22 GHz bandwidth with 3V Vpi, being suitable for a 100-km 40-Gb/s OOFDM-PON supporting up to 1024 users.

Novel cost-effective PON-to-mm-wave RoF photonic bridge for multigigabit access networks

A novel low-cost photonic PON-to-RoF bridge is presented, allowing simultaneous wireline and wireless multi-Gbps data transmission with minor impact on deployed PON networks. Simulation results show that the proposed scheme does not require narrow-linewidth lasers when envelope detector mobile terminals are used, since the transmission performance is not limited by the phase noise but by the RIN and the frequency difference between the two beating lasers. © 2013 IEEE.

Cost-effective node monitoring for online hot event detection in sina weibo microblogging

We propose a cost-effective hot event detection system over Sina Weibo platform, currently the dominant microblogging service provider in China. The problem of finding a proper subset of microbloggers under resource constraints is formulated as a mixed-integer problem for which heuristic algorithms are developed to compute approximate solution. Preliminary results show that by tracking about 500 out of 1.6 million candidate microbloggers and processing 15,000 microposts daily, 62% of the hot events can be detected five hours on average earlier than they are published by Weibo.

Reductions of peak-to-average power ratio and optical beat interference in cost-effective OFDMA-PONs

The peak-to-average power ratio (PAPR) and optical beat interference (OBI) effects are examined thoroughly in orthogonal frequency-division multiplexing access (OFDMA)-passive optical networks (PONs) at a signal bit rate up to ∼ 20 Gb/s per channel using cost-effective intensity-modulation and direct-detection (IM/DD). Single-channel OOFDM and upstream multichannel OFDM-PONs are investigated for up to six users. A number of techniques for mitigating the PAPR and OBI effects are presented and evaluated including adaptive-loading algorithms such as bit/power-loading, clipping for PAPR reduction, and thermal detuning (TD) for the OBI suppression. It is shown that the bit-loading algorithm is a very efficient PAPR reduction technique by reducing it at about 1.2 dB over 100 Km of transmission. It is also revealed that the optimum method for suppressing the OBI is the TD + bit-loading. For a targeted BER of 1 × 10-3, the minimum allowed channel spacing is 11 GHz when employing six users. © 2013 Springer Science+Business Media New York.

Practical and cost-effective high-fidelity optical carrier dissemination using coherent communication techniques

We report a unidirectional frequency dissemination scheme for high-fidelity optical carriers deployable over telecommunication networks. For the first time, a 10 Gb/s Binary Phase Shift Keying (BPSK) signal from an ultra-narrow linewidth laser was transmitted through a field-installed optical fibre with round-trip length of 124 km between Cork City and town of Clonakilty, without inline optical amplification. At the receiver, using coherent communication techniques and optical injection-locking the carrier was recovered with noise suppression. The beat signal between the original carrier at the transmitter and recovered carrier at the receiver shows a linewidth of 2.8 kHz. Long term stability measurements revealed fractional instabilities (True Allan deviation) of 3.3 × 10-14 for 1 s averaging time, prior to phase noise cancellation.

A cost-effective steam-driven RO plant for brackish groundwater

Desalination is a costly means of providing freshwater. Most desalination plants use either reverse osmosis (RO) or thermal distillation. Both processes have drawbacks: RO is efficient but uses expensive electrical energy; thermal distillation is inefficient but uses less expensive thermal energy. This work aims to provide an efficient RO plant that uses thermal energy. A steam-Rankine cycle has been designed to drive mechanically a batch-RO system that achieves high recovery, without the high energy penalty typically incurred in a continuous-RO system. The steam may be generated by solar panels, biomass boilers, or as an industrial by-product. A novel mechanical arrangement has been designed for low cost, and a steam-jacketed arrangement has been designed for isothermal expansion and improved thermodynamic efficiency. Based on detailed heat transfer and cost calculations, a gain output ratio of 69-162 is predicted, enabling water to be treated at a cost of 71 Indian Rupees/m3 at small scale. Costs will reduce with scale-up. Plants may be designed for a wide range of outputs, from 5 m3/day, up to commercial versions producing 300 m3/day of clean water from brackish groundwater.

Efficient asynchronous scheduling algorithms for cost-effective buffered crossbar switches

Buffered crossbar switches have recently attracted considerable attention as the next generation of high speed interconnects. They are a special type of crossbar switches with an exclusive buffer at each crosspoint of the crossbar. They demonstrate unique advantages over traditional unbuffered crossbar switches, such as high throughput, low latency, and asynchronous packet scheduling. However, since crosspoint buffers are expensive on-chip memories, it is desired that each crosspoint has only a small buffer. This dissertation proposes a series of practical algorithms and techniques for efficient packet scheduling for buffered crossbar switches. To reduce the hardware cost of such switches and make them scalable, we considered partially buffered crossbars, whose crosspoint buffers can be of an arbitrarily small size. Firstly, we introduced a hybrid scheme called Packet-mode Asynchronous Scheduling Algorithm (PASA) to schedule best effort traffic. PASA combines the features of both distributed and centralized scheduling algorithms and can directly handle variable length packets without Segmentation And Reassembly (SAR). We showed by theoretical analysis that it achieves 100% throughput for any admissible traffic in a crossbar with a speedup of two. Moreover, outputs in PASA have a large probability to avoid the more time-consuming centralized scheduling process, and thus make fast scheduling decisions. Secondly, we proposed the Fair Asynchronous Segment Scheduling (FASS) algorithm to handle guaranteed performance traffic with explicit flow rates. FASS reduces the crosspoint buffer size by dividing packets into shorter segments before transmission. It also provides tight constant performance guarantees by emulating the ideal Generalized Processor Sharing (GPS) model. Furthermore, FASS requires no speedup for the crossbar, lowering the hardware cost and improving the switch capacity. Thirdly, we presented a bandwidth allocation scheme called Queue Length Proportional (QLP) to apply FASS to best effort traffic. QLP dynamically obtains a feasible bandwidth allocation matrix based on the queue length information, and thus assists the crossbar switch to be more work-conserving. The feasibility and stability of QLP were proved, no matter whether the traffic distribution is uniform or non-uniform. Hence, based on bandwidth allocation of QLP, FASS can also achieve 100% throughput for best effort traffic in a crossbar without speedup.

Utilisation of Glass Reinforced Plastics as Cost Effective Alternative to Traditional Materials in Access Panel Design for Waste Water Treatment Plants

Composites are fast becoming a cost effective option when considering the design of engineering structures in a broad range of applications. If the strength to weight benefits of these material systems can be exploited and challenges in developing lower cost manufacturing methods overcome, then the advanced composite systems will play a bigger role in the diverse range of sectors outside the aerospace industry where they have been used for decades.
This paper presents physical testing results that showcase the advantages of GRP (Glass Reinforced Plastics), such as the ability to endure loading with minimal deformation. The testing involved is a cross comparison of GRP grating vs. GRP encapsulated foam core. Resulting data gained within this paper will then be coupled with design optimization (utilising model simulation) to bring forward layup alterations to meet the specified load classifications involved.

BUILDING EFFICIENT AND COST-EFFECTIVE CLOUD-BASED BIG DATA MANAGEMENT SYSTEMS

In today’s big data world, data is being produced in massive volumes, at great velocity and from a variety of different sources such as mobile devices, sensors, a plethora of small devices hooked to the internet (Internet of Things), social networks, communication networks and many others. Interactive querying and large-scale analytics are being increasingly used to derive value out of this big data. A large portion of this data is being stored and processed in the Cloud due the several advantages provided by the Cloud such as scalability, elasticity, availability, low cost of ownership and the overall economies of scale. There is thus, a growing need for large-scale cloud-based data management systems that can support real-time ingest, storage and processing of large volumes of heterogeneous data. However, in the pay-as-you-go Cloud environment, the cost of analytics can grow linearly with the time and resources required. Reducing the cost of data analytics in the Cloud thus remains a primary challenge. In my dissertation research, I have focused on building efficient and cost-effective cloud-based data management systems for different application domains that are predominant in cloud computing environments. In the first part of my dissertation, I address the problem of reducing the cost of transactional workloads on relational databases to support database-as-a-service in the Cloud. The primary challenges in supporting such workloads include choosing how to partition the data across a large number of machines, minimizing the number of distributed transactions, providing high data availability, and tolerating failures gracefully. I have designed, built and evaluated SWORD, an end-to-end scalable online transaction processing system, that utilizes workload-aware data placement and replication to minimize the number of distributed transactions that incorporates a suite of novel techniques to significantly reduce the overheads incurred both during the initial placement of data, and during query execution at runtime. In the second part of my dissertation, I focus on sampling-based progressive analytics as a means to reduce the cost of data analytics in the relational domain. Sampling has been traditionally used by data scientists to get progressive answers to complex analytical tasks over large volumes of data. Typically, this involves manually extracting samples of increasing data size (progressive samples) for exploratory querying. This provides the data scientists with user control, repeatable semantics, and result provenance. However, such solutions result in tedious workflows that preclude the reuse of work across samples. On the other hand, existing approximate query processing systems report early results, but do not offer the above benefits for complex ad-hoc queries. I propose a new progressive data-parallel computation framework, NOW!, that provides support for progressive analytics over big data. In particular, NOW! enables progressive relational (SQL) query support in the Cloud using unique progress semantics that allow efficient and deterministic query processing over samples providing meaningful early results and provenance to data scientists. NOW! enables the provision of early results using significantly fewer resources thereby enabling a substantial reduction in the cost incurred during such analytics. Finally, I propose NSCALE, a system for efficient and cost-effective complex analytics on large-scale graph-structured data in the Cloud. The system is based on the key observation that a wide range of complex analysis tasks over graph data require processing and reasoning about a large number of multi-hop neighborhoods or subgraphs in the graph; examples include ego network analysis, motif counting in biological networks, finding social circles in social networks, personalized recommendations, link prediction, etc. These tasks are not well served by existing vertex-centric graph processing frameworks whose computation and execution models limit the user program to directly access the state of a single vertex, resulting in high execution overheads. Further, the lack of support for extracting the relevant portions of the graph that are of interest to an analysis task and loading it onto distributed memory leads to poor scalability. NSCALE allows users to write programs at the level of neighborhoods or subgraphs rather than at the level of vertices, and to declaratively specify the subgraphs of interest. It enables the efficient distributed execution of these neighborhood-centric complex analysis tasks over largescale graphs, while minimizing resource consumption and communication cost, thereby substantially reducing the overall cost of graph data analytics in the Cloud. The results of our extensive experimental evaluation of these prototypes with several real-world data sets and applications validate the effectiveness of our techniques which provide orders-of-magnitude reductions in the overheads of distributed data querying and analysis in the Cloud.