21,503 research outputs found

    Towards Advantages of Parameterized Quantum Pulses

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    The advantages of quantum pulses over quantum gates have attracted increasing attention from researchers. Quantum pulses offer benefits such as flexibility, high fidelity, scalability, and real-time tuning. However, while there are established workflows and processes to evaluate the performance of quantum gates, there has been limited research on profiling parameterized pulses and providing guidance for pulse circuit design. To address this gap, our study proposes a set of design spaces for parameterized pulses, evaluating these pulses based on metrics such as expressivity, entanglement capability, and effective parameter dimension. Using these design spaces, we demonstrate the advantages of parameterized pulses over gate circuits in the aspect of duration and performance at the same time thus enabling high-performance quantum computing. Our proposed design space for parameterized pulse circuits has shown promising results in quantum chemistry benchmarks.Comment: 11 Figures, 4 Table

    Millimeter-wave channel measurements and path loss characterization in a typical indoor office environment

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    In this paper, a path loss characterization at millimeter-wave (mmWave) frequencies is performed in a typical indoor office environment. Path loss results were derived from propagation channel measurements collected in the 25–40 GHz frequency band, in both line-of-sight (LOS) and obstructed-LOS (OLOS) propagation conditions. The channel measurements were performed using a frequency-domain channel sounder, which integrates an amplified radio over fiber (RoF) link to avoid the high losses at mmWave. The path loss was analyzed in the 26 GHz, 28 GHz, 33 GHz and 38 GHz frequency bands through the close-in free space reference distance (CI) and the floating-intercept (FI) models. These models take into account the distance dependence of the path loss for a single frequency. Nevertheless, to jointly study the distance and frequency dependence of the path loss, multi-frequency models were considered. The parameters of the ABG (A-alpha, B-beta and G-gamma) and the close-in free space reference distance with frequency path loss exponent (CIF) models were derived from the channel measurements in the whole 25–40 GHz band under the minimum mean square error (MMSE) approach. The results show that, in general, there is some relationship between the model parameters and the frequency. Path loss exponent (PLE) values smaller than the theoretical free space propagation were obtained, showing that there are a waveguide effect and a constructive interference of multipath components (MPCs). Since the measurements were obtained in the same environment and with the same configuration and measurement setup, it is possible to establish realistic comparisons between the model parameters and the propagation behavior at the different frequencies considered. The results provided here allow us to have a better knowledge of the propagation at mmWave frequencies and may be of interest to other researchers in the simulation and performance evaluation of future wireless communication systems in indoor hotspot environments.This work has been funded in part by the MCIN/AEI/10.13039/501100011033/ through the I+D+i Project under Grant PID2020-119173RB-C21 and Grant PID2020-119173RB-C22, and by COLCIENCIAS in Colombia

    RAPID: Enabling Fast Online Policy Learning in Dynamic Public Cloud Environments

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    Resource sharing between multiple workloads has become a prominent practice among cloud service providers, motivated by demand for improved resource utilization and reduced cost of ownership. Effective resource sharing, however, remains an open challenge due to the adverse effects that resource contention can have on high-priority, user-facing workloads with strict Quality of Service (QoS) requirements. Although recent approaches have demonstrated promising results, those works remain largely impractical in public cloud environments since workloads are not known in advance and may only run for a brief period, thus prohibiting offline learning and significantly hindering online learning. In this paper, we propose RAPID, a novel framework for fast, fully-online resource allocation policy learning in highly dynamic operating environments. RAPID leverages lightweight QoS predictions, enabled by domain-knowledge-inspired techniques for sample efficiency and bias reduction, to decouple control from conventional feedback sources and guide policy learning at a rate orders of magnitude faster than prior work. Evaluation on a real-world server platform with representative cloud workloads confirms that RAPID can learn stable resource allocation policies in minutes, as compared with hours in prior state-of-the-art, while improving QoS by 9.0x and increasing best-effort workload performance by 19-43%

    Event-based tracking of human hands

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    This paper proposes a novel method for human hands tracking using data from an event camera. The event camera detects changes in brightness, measuring motion, with low latency, no motion blur, low power consumption and high dynamic range. Captured frames are analysed using lightweight algorithms reporting 3D hand position data. The chosen pick-and-place scenario serves as an example input for collaborative human-robot interactions and in obstacle avoidance for human-robot safety applications. Events data are pre-processed into intensity frames. The regions of interest (ROI) are defined through object edge event activity, reducing noise. ROI features are extracted for use in-depth perception. Event-based tracking of human hand demonstrated feasible, in real time and at a low computational cost. The proposed ROI-finding method reduces noise from intensity images, achieving up to 89% of data reduction in relation to the original, while preserving the features. The depth estimation error in relation to ground truth (measured with wearables), measured using dynamic time warping and using a single event camera, is from 15 to 30 millimetres, depending on the plane it is measured. Tracking of human hands in 3D space using a single event camera data and lightweight algorithms to define ROI features (hands tracking in space)

    Offline and Online Models for Learning Pairwise Relations in Data

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    Pairwise relations between data points are essential for numerous machine learning algorithms. Many representation learning methods consider pairwise relations to identify the latent features and patterns in the data. This thesis, investigates learning of pairwise relations from two different perspectives: offline learning and online learning.The first part of the thesis focuses on offline learning by starting with an investigation of the performance modeling of a synchronization method in concurrent programming using a Markov chain whose state transition matrix models pairwise relations between involved cores in a computer process.Then the thesis focuses on a particular pairwise distance measure, the minimax distance, and explores memory-efficient approaches to computing this distance by proposing a hierarchical representation of the data with a linear memory requirement with respect to the number of data points, from which the exact pairwise minimax distances can be derived in a memory-efficient manner. Then, a memory-efficient sampling method is proposed that follows the aforementioned hierarchical representation of the data and samples the data points in a way that the minimax distances between all data points are maximally preserved. Finally, the thesis proposes a practical non-parametric clustering of vehicle motion trajectories to annotate traffic scenarios based on transitive relations between trajectories in an embedded space.The second part of the thesis takes an online learning perspective, and starts by presenting an online learning method for identifying bottlenecks in a road network by extracting the minimax path, where bottlenecks are considered as road segments with the highest cost, e.g., in the sense of travel time. Inspired by real-world road networks, the thesis assumes a stochastic traffic environment in which the road-specific probability distribution of travel time is unknown. Therefore, it needs to learn the parameters of the probability distribution through observations by modeling the bottleneck identification task as a combinatorial semi-bandit problem. The proposed approach takes into account the prior knowledge and follows a Bayesian approach to update the parameters. Moreover, it develops a combinatorial variant of Thompson Sampling and derives an upper bound for the corresponding Bayesian regret. Furthermore, the thesis proposes an approximate algorithm to address the respective computational intractability issue.Finally, the thesis considers contextual information of road network segments by extending the proposed model to a contextual combinatorial semi-bandit framework and investigates and develops various algorithms for this contextual combinatorial setting

    Теорія систем мобільних інфокомунікацій. Системна архітектура

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    Навчальний посібник містить опис логічних та фізичних структур, процедур, алгоритмів, протоколів, принципів побудови і функціонування мереж стільникового мобільного зв’язку (до 3G) і мобільних інфокомунікацій (4G і вище), приділяючи увагу розгляду загальних архітектур мереж операторів мобільного зв’язку, їх управління і координування, неперервності еволюції розвитку засобів функціонування і способів надання послуг таких мереж. Посібник структурно має сім розділів і побудований так, що складність матеріалу зростає з кожним наступним розділом. Навчальний посібник призначено для здобувачів ступеня бакалавра за спеціальністю 172 «Телекомунікації та радіотехніка», буде також корисним для аспірантів, наукових та інженерно-технічних працівників за напрямом інформаційно-телекомунікаційних систем та технологій.The manual contains a description of the logical and physical structures, procedures, algorithms, protocols, principles of construction and operation of cellular networks for mobile communications (up to 3G) and mobile infocommunications (4G and higher), paying attention to the consideration of general architectures of mobile operators' networks, their management, and coordination, the continuous evolution of the development of the means of operation and methods of providing services of such networks. The manual has seven structural sections and is structured in such a way that the complexity of the material increases with each subsequent chapter. The textbook is intended for applicants for a bachelor's degree in specialty 172 "Telecommunications and Radio Engineering", and will also be useful to graduate students, and scientific and engineering workers in the direction of information and telecommunication systems and technologies

    Reinforcement Learning-based User-centric Handover Decision-making in 5G Vehicular Networks

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    The advancement of 5G technologies and Vehicular Networks open a new paradigm for Intelligent Transportation Systems (ITS) in safety and infotainment services in urban and highway scenarios. Connected vehicles are vital for enabling massive data sharing and supporting such services. Consequently, a stable connection is compulsory to transmit data across the network successfully. The new 5G technology introduces more bandwidth, stability, and reliability, but it faces a low communication range, suffering from more frequent handovers and connection drops. The shift from the base station-centric view to the user-centric view helps to cope with the smaller communication range and ultra-density of 5G networks. In this thesis, we propose a series of strategies to improve connection stability through efficient handover decision-making. First, a modified probabilistic approach, M-FiVH, aimed at reducing 5G handovers and enhancing network stability. Later, an adaptive learning approach employed Connectivity-oriented SARSA Reinforcement Learning (CO-SRL) for user-centric Virtual Cell (VC) management to enable efficient handover (HO) decisions. Following that, a user-centric Factor-distinct SARSA Reinforcement Learning (FD-SRL) approach combines time series data-oriented LSTM and adaptive SRL for VC and HO management by considering both historical and real-time data. The random direction of vehicular movement, high mobility, network load, uncertain road traffic situation, and signal strength from cellular transmission towers vary from time to time and cannot always be predicted. Our proposed approaches maintain stable connections by reducing the number of HOs by selecting the appropriate size of VCs and HO management. A series of improvements demonstrated through realistic simulations showed that M-FiVH, CO-SRL, and FD-SRL were successful in reducing the number of HOs and the average cumulative HO time. We provide an analysis and comparison of several approaches and demonstrate our proposed approaches perform better in terms of network connectivity

    Improving Random Access with NOMA in mMTC XL-MIMO

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    The extra-large multiple-input multiple-output (XL-MIMO) architecture has been recognized as a technology for supporting the massive MTC (mMTC), providing very high-data rates in high-user density scenarios. However, the large dimension of the array increases the Rayleigh distance (dRayl), in addition to obstacles and scatters causing spatial non-stationarities and distinct visibility regions (VRs) across the XL array extension. We investigate the random access (RA) problem in crowded XL-MIMO scenarios; the proposed grant-based random access (GB-RA) protocol combining the advantage of non-orthogonal multiple access (NOMA) and strongest user collision resolutions in extra-large arrays (SUCRe-XL) named NOMA-XL can allow access of two or three colliding users in the same XL sub-array (SA) selecting the same pilot sequence. The received signal processing in a SA basis changes the dRayl, enabling the far-field planar wavefront propagation condition, while improving the system performance. The proposed NOMA-XL GB-RA protocol can reduce the number of attempts to access the mMTC network while improving the average sum rate, as the number of SA increases.Comment: 13 pages, 5 figures, 1 table, conference VTC 2023. arXiv admin note: substantial text overlap with arXiv:2303.0053

    Perceptual Requirements for World-Locked Rendering in AR and VR

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    Stereoscopic, head-tracked display systems can show users realistic, world-locked virtual objects and environments. However, discrepancies between the rendering pipeline and physical viewing conditions can lead to perceived instability in the rendered content resulting in reduced realism, immersion, and, potentially, visually-induced motion sickness. The requirements to achieve perceptually stable world-locked rendering are unknown due to the challenge of constructing a wide field of view, distortion-free display with highly accurate head- and eye-tracking. In this work we introduce new hardware and software built upon recently introduced hardware and present a system capable of rendering virtual objects over real-world references without perceivable drift under such constraints. The platform is used to study acceptable errors in render camera position for world-locked rendering in augmented and virtual reality scenarios, where we find an order of magnitude difference in perceptual sensitivity between them. We conclude by comparing study results with an analytic model which examines changes to apparent depth and visual heading in response to camera displacement errors. We identify visual heading as an important consideration for world-locked rendering alongside depth errors from incorrect disparity

    Quantifying the Roles of Visual, Linguistic, and Visual-Linguistic Complexity in Verb Acquisition

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    Children typically learn the meanings of nouns earlier than the meanings of verbs. However, it is unclear whether this asymmetry is a result of complexity in the visual structure of categories in the world to which language refers, the structure of language itself, or the interplay between the two sources of information. We quantitatively test these three hypotheses regarding early verb learning by employing visual and linguistic representations of words sourced from large-scale pre-trained artificial neural networks. Examining the structure of both visual and linguistic embedding spaces, we find, first, that the representation of verbs is generally more variable and less discriminable within domain than the representation of nouns. Second, we find that if only one learning instance per category is available, visual and linguistic representations are less well aligned in the verb system than in the noun system. However, in parallel with the course of human language development, if multiple learning instances per category are available, visual and linguistic representations become almost as well aligned in the verb system as in the noun system. Third, we compare the relative contributions of factors that may predict learning difficulty for individual words. A regression analysis reveals that visual variability is the strongest factor that internally drives verb learning, followed by visual-linguistic alignment and linguistic variability. Based on these results, we conclude that verb acquisition is influenced by all three sources of complexity, but that the variability of visual structure poses the most significant challenge for verb learning
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