296 research outputs found
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Improving next-generation wireless network performance and reliability with deep learning
A rudimentary question whether machine learning in general, or deep learning in particular, could add to the well-established field of wireless communications, which has been evolving for close to a century, is often raised. While the use of deep learning based methods is likely to help build intelligent wireless solutions, this use becomes particularly challenging for the lower layers in the wireless communication stack. The introduction of the fifth generation of wireless communications (5G) has triggered the demand for ânetwork intelligenceâ to support its promises for very high data rates and extremely low latency. Consequently, 5G wireless operators are faced with the challenges of network complexity, diversification of services, and personalized user experience. Industry standards have created enablers (such as the network data analytics function), but these enablers focus on post-mortem analysis at higher stack layers and have a periodicity in the time scale of seconds (or larger). The goal of this dissertation is to show a solution for these challenges and how a data-driven approach using deep learning could add to the field of wireless communications. In particular, I propose intelligent predictive and prescriptive abilities to boost reliability and eliminate performance bottlenecks in 5G cellular networks and beyond, show contributions that justify the value of deep learning in wireless communications across several different layers, and offer in-depth analysis and comparisons with baselines and industry standards. First, to improve multi-antenna network reliability against wireless impairments with power control and interference coordination for both packetized voice and beamformed data bearers, I propose the use of a joint beamforming, power control, and interference coordination algorithm based on deep reinforcement learning. This algorithm uses a string of bits and logic operations to enable simultaneous actions to be performed by the reinforcement learning agent. Consequently, a joint reward function is also proposed. I compare the performance of my proposed algorithm with the brute force approach and show that similar performance is achievable but with faster run-time as the number of transmit antennas increases. Second, in enhancing the performance of coordinated multipoint, I propose the use of deep learning binary classification to learn a surrogate function to trigger a second transmission stream instead of depending on the popular signal to interference plus noise measurement quantity. This surrogate function improves the users' sum-rate through focusing on pre-logarithmic terms in the sum-rate formula, which have larger impact on this rate. Third, performance of band switching can be improved without the need for a full channel estimation. My proposal of using deep learning to classify the quality of two frequency bands prior to granting the band switching leads to a significant improvement in users' throughput. This is due to the elimination of the industry standard measurement gap requirementâa period of silence where no data is sent to the users so they could measure the frequency bands before switching. In this dissertation, a group of algorithms for wireless network performance and reliability for downlink are proposed. My results show that the introduction of user coordinates enhance the accuracy of the predictions made with deep learning. Also, the choice of signal to interference plus noise ratio as the optimization objective may not always be the best choice to improve user throughput rates. Further, exploiting the spatial correlation of channels in different frequency bands can improve certain network procedures without the need for perfect knowledge of the per-band channel state information. Hence, an understanding of these results help develop novel solutions to enhancing these wireless networks at a much smaller time scale compared to the industry standards todayElectrical and Computer Engineerin
Practical global illumination for interactive particle visualization
ManuscriptParticle-based simulation methods are used to model a wide range of complex phenomena and to solve time-dependent problems of various scales. Effective visualizations of the resulting state will communicate subtle changes in the three-dimensional structure, spatial organization, and qualitative trends within a simulation as it evolves. We present two algorithms targeting upcoming, highly parallel multicore desktop systems to enable interactive navigation and exploration of large particle datasets with global illumination effects. Monte Carlo path tracing and texture mapping are used to capture computationally expensive illumination effects such as soft shadows and diffuse interreflection. The first approach is based on precomputation of luminance textures and removes expensive illumination calculations from the interactive rendering pipeline. The second approach is based on dynamic luminance texture generation and decouples interactive rendering from the computation of global illumination effects. These algorithms provide visual cues that enhance the ability to perform analysis and feature detection tasks while interrogating the data at interactive rates. We explore the performance of these algorithms and demonstrate their effectiveness using several large datasets
New single-ended objective measure for non-intrusive speech quality evaluation
peer-reviewedThis article proposes a new output-based method for non-intrusive assessment of speech quality of voice communication systems and evaluates its performance. The method requires access to the processed (degraded) speech only, and is based on measuring perception-motivated objective auditory distances between the voiced parts of the output speech to appropriately matching references extracted from a pre-formulated codebook. The codebook is formed by optimally clustering a large number of parametric speech vectors extracted from a database of clean speech records. The auditory distances are then mapped into objective Mean Opinion listening quality scores. An efficient data-mining tool known as the self-organizing map (SOM) achieves the required clustering and mapping/reference matching processes. In order to obtain a perception-based, speaker-independent parametric representation of the speech, three domain transformation techniques have been investigated. The first technique is based on a perceptual linear prediction (PLP) model, the second utilises a bark spectrum (BS) analysis and the third utilises mel-frequency cepstrum coefficients (MFCC). Reported evaluation results show that the proposed method provides high correlation with subjective listening quality scores, yielding accuracy similar to that of the ITU-T P.563 while maintaining a relatively low computational complexity. Results also demonstrate that the method outperforms the PESQ in a number of distortion conditions, such as those of speech degraded by channel impairments.acceptedpeer-reviewe
The Impact Of The Development Of ICT In Several Hungarian Economic Sectors
As the author could not find a reassuring mathematical and
statistical method in the literature for studying the effect of
information communication technology on enterprises, the author
suggested a new research and analysis method that he also used to study the Hungarian economic sectors. The question of what
factors have an effect on their net income is vital for enterprises. At first, the author studied some potential indicators related to economic sectors, then those indicators were compared to the net income of the surveyed enterprises. The resulting data showed that the growing penetration of electronic marketplaces contributed to the change of the net income of enterprises to the greatest extent.
Furthermore, among all the potential indicators, it was the only indicator directly influencing the net income of enterprises.
With the help of the compound indicator and the financial data
of the studied economic sectors, the author made an attempt to find a connection between the development level of ICT and
profitability. Profitability and productivity are influenced by a lot of other factors as well. As the effect of the other factors could not be measured, the results â shown in a coordinate system - are not full but informative.
The highest increment of specific Gross Value Added was
produced by the fields of âManufacturingâ, âElectricity, gas and water supplyâ, âTransport, storage and communicationâ and
âFinancial intermediationâ. With the exception of âElectricity, gas and water supplyâ, the other economic sectors belong to the group of underdeveloped branches (below 50 percent).
On the other hand, âConstructionâ, âHealth and social workâ and
âHotels and restaurantsâ can be seen as laggards, so they got into the lower left part of the coordinate system.
âAgriculture, hunting and forestryâ can also be classified as a
laggard economic sector, but as the effect of the compound
indicator on the increment of Gross Value Added was less
significant, it can be found in the upper left part of the coordinate system. Drawing a trend line on the points, it can be made clear that it shows a positive gradient, that is, the higher the usage of ICT devices, the higher improvement can be detected in the specific Gross Value Added
Software Defined Based Pure VPN Protocol for Preventing IP Spoofing Attacks in IOT
The Internet of things (IoT) is the network of devices, vehicles, and home appliances that contain electronics, software, actuators, and connectivity which allows these things to connect, interact and exchange data. IoT involves extending Internet connectivity beyond standard devices, such as desktops, laptops, smart phones and tablets, to any range of traditionally dumb or non-internet-enabled physical devices and everyday objects. Embedded with technology, these devices can communicate and interact over the Internet, and they can be remotely monitored and controlled. Traditionally, current internet packet delivery only depends on packet destination IP address and forward devices neglect the validation of packetâs IP source address. It makes attacks can leverage this flow to launch attacks with forge IP source address so as to meet their violent purpose and avoid to be tracked. In order to reduce this threat and enhance internet accountability, many solution proposed in the inter domain and intra domain aspects. Furthermore, most of them faced with some issues hard to cope, i.e., data security, data privacy. And most importantly code cover PureVPN protocol for both inter and intra domain areas. The novel network architecture of SDN possess whole network PureVPN protocol rule instead of traditional SDN switches, which brings good opportunity to solve IP spoofing problems. However, use authentication based on key exchange between the machines on your network; something like IP Security protocol will significantly cut down on the risk of spoofing. This paper proposes a SDN based PureVPN protocol architecture, which can cover both inter and intra domain areas with encrypted format effectively than SDN devices. The PureVPN protocol scheme is significant in improving the security and privacy in SDN for IoT
DR9.3 Final report of the JRRM and ASM activities
Deliverable del projecte europeu NEWCOM++This deliverable provides the final report with the summary of the activities carried out in NEWCOM++ WPR9, with a particular focus on those obtained during the last year. They address on the one hand RRM and JRRM strategies in heterogeneous scenarios and, on the other hand, spectrum management and opportunistic spectrum access to achieve an efficient spectrum usage. Main outcomes of the workpackage as well as integration indicators are also summarised.Postprint (published version
Visual vocabularies for category-level object recognition
This thesis focuses on the study of visual vocabularies for category-level object recognition. Specifically, we state novel approaches for building visual codebooks. Our aim is not just to obtain more discriminative and more compact visual codebooks, but to bridge the gap between visual features and semantic concepts. A novel approach for obtaining class representative visual words is presented. It is based on a maximisation procedure, i. e. the Cluster Precision Maximisation (CPM), of a novel cluster precision criterion, and on an adaptive threshold refinement scheme for agglomerative clustering algorithms based on correlation clustering techniques. The objective is to increase the vocabulary compactness while at the same time improve the recognition rate and further increase the representativeness of the visual words. Moreover, we describe a novel clustering aggregation based approach for building efficient and semantic visual vocabularies. It consist of a novel framework for incorporating neighboring appearances of local descriptors into the vocabulary construction, and a rigorous approach for adding meaningful spatial coherency among the local features into the visual codebooks. We also propose an efficient high-dimensional data clustering algorithm, the Fast Reciprocal Nearest Neighbours (Fast-RNN). Our approach, which is a speeded up version of the standard RNN algorithm, is based on the projection search paradigm. Finally, we release a new database of images called Image Collection of Annotated Real-world Objects (ICARO), which is especially designed for evaluating category-level object recognition systems. An exhaustive comparison of ICARO with other well-known datasets used within the same context is carried out. We also propose a benchmark for both object classification and detection
Visual vocabularies for category-level object recognition
This thesis focuses on the study of visual vocabularies for category-level object recognition. Specifically, we state novel approaches for building visual codebooks. Our aim is not just to obtain more discriminative and more compact visual codebooks, but to bridge the gap between visual features and semantic concepts. A novel approach for obtaining class representative visual words is presented. It is based on a maximisation procedure, i. e. the Cluster Precision Maximisation (CPM), of a novel cluster precision criterion, and on an adaptive threshold refinement scheme for agglomerative clustering algorithms based on correlation clustering techniques. The objective is to increase the vocabulary compactness while at the same time improve the recognition rate and further increase the representativeness of the visual words. Moreover, we describe a novel clustering aggregation based approach for building efficient and semantic visual vocabularies. It consist of a novel framework for incorporating neighboring appearances of local descriptors into the vocabulary construction, and a rigorous approach for adding meaningful spatial coherency among the local features into the visual codebooks. We also propose an efficient high-dimensional data clustering algorithm, the Fast Reciprocal Nearest Neighbours (Fast-RNN). Our approach, which is a speeded up version of the standard RNN algorithm, is based on the projection search paradigm. Finally, we release a new database of images called Image Collection of Annotated Real-world Objects (ICARO), which is especially designed for evaluating category-level object recognition systems. An exhaustive comparison of ICARO with other well-known datasets used within the same context is carried out. We also propose a benchmark for both object classification and detection
Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions
Visual localization enables autonomous vehicles to navigate in their
surroundings and augmented reality applications to link virtual to real worlds.
Practical visual localization approaches need to be robust to a wide variety of
viewing condition, including day-night changes, as well as weather and seasonal
variations, while providing highly accurate 6 degree-of-freedom (6DOF) camera
pose estimates. In this paper, we introduce the first benchmark datasets
specifically designed for analyzing the impact of such factors on visual
localization. Using carefully created ground truth poses for query images taken
under a wide variety of conditions, we evaluate the impact of various factors
on 6DOF camera pose estimation accuracy through extensive experiments with
state-of-the-art localization approaches. Based on our results, we draw
conclusions about the difficulty of different conditions, showing that
long-term localization is far from solved, and propose promising avenues for
future work, including sequence-based localization approaches and the need for
better local features. Our benchmark is available at visuallocalization.net.Comment: Accepted to CVPR 2018 as a spotligh
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