We present two constant-time dynamic data-structures that support insertions, deletions, and queries with one-sided errors: a space-efficient dynamic (key-only) filter and a compact dynamic data-structure that combine...
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ISBN:
(纸本)9783031929311
We present two constant-time dynamic data-structures that support insertions, deletions, and queries with one-sided errors: a space-efficient dynamic (key-only) filter and a compact dynamic data-structure that combines retrieval and filtering (called a key-value filter). A one-sided error occurs when a query for a key not in the dataset is issued and the outcome is wrong, i.e., a "yes" in a filter or a non-null in the key-value filter. The response to a query with a key in the dataset always returns the correct answer, i.e., a "yes" in a filter and the correct value in a key-value filter. The probability of the one-sided error in our data-structures is Ω(1/poly(logn)), where n is the maximum cardinality of the dataset, and the probability space is over the random bits of the data-structure (i.e., random choice of hash function). The computational framework is the Word RAM model. We differentiate between accesses to non-modifiable memory (i.e., read-only memory that stores the program instructions, hash function seed or tables, etc.) and accesses to modifiable memory (i.e., read-write memory that stores the representation of the dataset). We are not aware of previous works that make this distinction in the context of data-structures. Our dynamic filter design requires only a single access to the modifiable memory per operation in the worst-case. We also present a dynamic key-value filter for values of O(loglogn) bits that requires 1+o(1) accesses to the modifiable memory per operation in expectation. Previous dynamic filter designs require, in the worst case, at least two accesses to modifiable memory for queries with keys not in the dataset. Previous dynamic retrieval data-structure designs always require two dictionary accesses for queries with keys not in the dataset even for single bit values. We prove bounds on the number of balls that overflow in a dynamic balls-into-bins random process for a range of bin capacities that extends the Iceberg Lemma of [Bender et
Template matching is a well-known computer vision algorithm that involves scanning a template across various parts of an image. The template is correlated within this algorithm using a similarity or matching score, su...
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ISBN:
(数字)9798331509422
ISBN:
(纸本)9798331509439
Template matching is a well-known computer vision algorithm that involves scanning a template across various parts of an image. The template is correlated within this algorithm using a similarity or matching score, such as the Pearson correlation coefficient (PCC). A chieving a m ore a ccurate match necessitates searching many regions using the PCC metric, which is hindered by the Von Neumann Bottleneck, resulting in increased energy consumption and delays. Therefore, this paper proposes an energy-efficient, c omprehensive memristive in-memory computing architecture for template matching with its physical design, where the PCC computation unit sensor readout unit, DAC, demultiplexers, in-memory memristive computing array, ADC, running sum module, fixed point operation unit and comparator. The PCC equation is approximated, considering the limitations of the hardware characteristics and application requirements. The proposed approximated memristive in-memory based template-matching scheme demonstrates competitive performance compared to the Von Neumann system and achieves around 678× improvement in the power-delay product. Lastly, a threshold-based optimization strategy is suggested to reduce energy consumption in the application.
We consider a source node deployed in a real-time monitoring application that needs to sample a stochastic process and convey its state timely and accurately to a destination over a wireless ON/OFF channel. The source...
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In recent years, classical knowledge-driven approaches for inverse problems have been complemented by data-driven methods exploiting the power of machine and especially deep learning. Purely data-driven methods, howev...
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Electric vehicles(EVs)are gradually being deployed in the transportation *** they have a high impact on reducing greenhouse gas emissions,their penetration is challenged by their random energy demand and difficult sch...
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Electric vehicles(EVs)are gradually being deployed in the transportation *** they have a high impact on reducing greenhouse gas emissions,their penetration is challenged by their random energy demand and difficult scheduling of their optimal *** cope with these problems,this paper presents a novel approach for photovoltaic grid-connected microgrid EV charging station energy demand *** present study is part of a comprehensive framework involving emerging technologies such as drones and artificial intelligence designed to support the EVs’charging scheduling *** using predictive algorithms for solar generation and load demand estimation,this approach aimed at ensuring dynamic and efficient energy flow between the solar energy source,the grid and the electric *** main contribution of this paper lies in developing an intelligent approach based on deep recurrent neural networks to forecast the energy demand using only its previous ***,various forecasters based on Long Short-term Memory,Gated Recurrent Unit,and their bi-directional and stacked variants were investigated using a real dataset collected from an EV charging station located at Trieste University(Italy).The developed forecasters have been evaluated and compared according to different metrics,including R,RMSE,MAE,and *** found that the obtained R values for both PV power generation and energy demand ranged between 97%and 98%.These study findings can be used for reliable and efficient decision-making on the management side of the optimal scheduling of the charging operations.
Recent experiments have demonstrated that synthesized complex-frequency waves can impart a virtual gain to molecule sensing systems,which can effectively restore information lost due to intrinsic molecular *** enhance...
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Recent experiments have demonstrated that synthesized complex-frequency waves can impart a virtual gain to molecule sensing systems,which can effectively restore information lost due to intrinsic molecular *** enhancement notably amplifies the signal of trace molecular vibrational fingerprints,thereby substantially improving the upper limit of sensitivity.
Subwavelength manipulation of light waves with high precision can enable new and exciting applications in spectroscopy,sensing,and medical *** these applications,miniaturized spectrometers are desirable to enable the ...
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Subwavelength manipulation of light waves with high precision can enable new and exciting applications in spectroscopy,sensing,and medical *** these applications,miniaturized spectrometers are desirable to enable the on-chip analysis of spectral *** particular,for imaging-based spectroscopic sensing mechanisms,the key challenge is to determine the spatial-shift information accurately(i.e.,the spatial displacement introduced by wavelength shift or biological or chemical surface binding),which is similar to the challenge presented by super-resolution ***,we report a unique"rainbow"trapping metasurface for on-chip spectrometers and *** with super-resolution image processing,the low-setting 4×optical microscope system resolves a displacement of the resonant position within 35 nm on the plasmonic rainbow trapping metasurface with a tiny area as small as0.002 *** unique feature of the spatial manipulation of efficiently coupled rainbow plasmonic resonances reveals a new platform for miniaturized on-chip spectroscopic analysis with a spectral resolution of 0.032 nm in wavelength *** this low-setting 4×microscope imaging system,we demonstrate a biosensing resolution of 1.92×109exosomes per milliliter for A549-derived exosomes and distinguish between patient samples and healthy controls using exosomal epidermal growth factor receptor(EGFR)expression values,thereby demonstrating a new on-chip sensing system for personalized accurate bio/chemical sensing applications.
Pupil dilation is a fundamental marker of emotional response, and indicates emotional arousal independently of stimulus valence. As such, pupil dilation provides invaluable insight into emotional engagement and arousa...
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Contributing significantly to the accomplishment of renewable energy goals and the overall decrease in carbon emissions, this is crucial in verifying and recording the production of renewable energies. However, a few ...
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This paper explores the effectiveness of molecular verbal exchange in network Coding (NC) for enhancing community overall performance in Wi-Fi applications. Molecular communique (MC) is an emerging nanoscale verbal ex...
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ISBN:
(纸本)9798350383348
This paper explores the effectiveness of molecular verbal exchange in network Coding (NC) for enhancing community overall performance in Wi-Fi applications. Molecular communique (MC) is an emerging nanoscale verbal exchange approach that allows efficient and dependable transmission of information and indicators over Wi-Fi. By utilizing the variety of molecules, the performance of molecular communique can be appreciably multiplied. This paper examines how network Coding with molecular communication may be applied to improve network performance. Using a case evaluates the efficacy of this approach by looking at a wireless sensor community deployed in a temperate indoor environment. Consequences display growth in facts fee of as much as 30%, indicating a potential development in network performance via using network Coding with molecular conversation. The paper additionally discusses how network Coding can improve community safety, records accuracy, and general latency. Those potential upgrades are promising and future studies are needed to prove the effectiveness of community Coding with molecular verbal exchange in wireless applications. Molecular communique in community coding has emerged as a critical studies place due to its potential to enhance the bodily-layer overall performance of wireless networks. This paper evaluates the community and gadget's overall performance with molecular conversation in community coding for Wi-Fi packages. A theoretical evaluation, pc simulations, and experiments examine the gadget's performance. Moreover, the consequences of diverse parameters such as channel length, range of relay nodes, number of symbols transmitted, and coding fee are evaluated. The results imply that the overall performance of molecular conversation in network coding is superior to regular bodily-layer coding strategies, providing improved community throughput and conversation reliability. Additionally, the power efficiency of the proposed gadget is likewise tes
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