1,720,977 research outputs found
Intelligent Chargers Will Make Mobile Devices Live Longer
No full textEditor's notes: Editor's note: Battery aging is becoming a major concern in mobile devices such as laptops or smartphones and often results in premature device replacement. This perspective article gives an overview of recent advances made in battery-health-aware charging and highlights the benefits of making chargers more intelligent to improve the cycle life of different battery-powered devices.
Scheduling Tasks on Intermittently-Powered Real-Time Systems
Full text linkBatteryless systems go through sporadic power on and off phases due to intermittently available energy; thus, they are called intermittent systems. Unfortunately, this intermittence in power supply hinders the timely execution of tasks and limits such devices’ potential in certain application domains, e.g., healthcare, live-stock tracking. Unlike prior work on time-aware intermittent systems that focuses on timekeeping [1, 2, 3] and discarding expired data [4], this dissertation concentrates on finishing task execution on time. I leverage the data processing and control layer of batteryless systems by developing frameworks that (1) integrate energy harvesting and real-time systems, (2) rethink machine learning algorithms for an energy-aware imprecise task scheduling framework, (3) develop scheduling algorithms that, along with deciding what to compute, answers when to compute and when to harvest, and (4) utilize distributed systems that collaboratively emulate a persistently powered system. Scheduling Framework for Intermittently Powered Computing Systems. Batteryless systems rely on sporadically available harvestable energy. For example, kinetic-powered motion detector sensors on the impalas can only harvest energy when the impalas are moving, which cannot be ascertained in advance. This uncertainty poses a unique real-time scheduling problem where existing real-time algorithms fail due to the interruption in execution time. This dissertation proposes a unified scheduling framework that includes both harvesting and computing. Imprecise Deep Neural Network Inference in Deadline-Aware Intermittent Systems. This dissertation proposes Zygarde- an energy-aware and outcome-aware soft-real-time imprecise deep neural network (DNN) task scheduling framework for intermittent systems. Zygarde leverages the semantic diversity of input data and layer-dependent expressiveness of deep features and infers only the necessary DNN layers based on available time and energy. Zygarde proposes a novel technique to determine the imprecise boundary at the runtime by exploiting the clustering classifiers and specialized offline training of the DNNs to minimize the loss of accuracy due to partial execution. It also proposes a single metric, η to represent a system’s predictability that measures how close a harvesterâs harvesting pattern is to a constant energy source. Besides, Zygarde consists of a scheduling algorithm that takes available time, available energy, impreciseness, and the classifier's performance into account. Scheduling Mutually Exclusive Computing and Harvesting Tasks in Deadline-Aware Intermittent Systems. The lack of sufficient ambient energy to directly power the intermittent systems introduces mutually exclusive computing and charging cycles of intermittently powered systems. This introduces a challenging real-time scheduling problem where the existing real-time algorithms fail due to the lack of interruption in execution time. To address this, this dissertation proposes Celebi, which considers the dynamics of the available energy and schedules when to harvest and when to compute in batteryless systems. Using data-driven simulation and real-world experiments, this dissertation shows that Celebi significantly increases the number of tasks that complete execution before their deadline when power was only available intermittently. Persistent System Emulation with Distributed Intermittent System. Intermittently-powered sensing and computing systems go through sporadic power-on and off periods due to the uncertain availability of energy sources. Despite the recent efforts to advance time-sensitive intermittent systems, such systems fail to capture important target events when the energy is absent for a prolonged time. This event miss limits the potential usage of intermittent systems in fault- intolerant and safety-critical applications. To address this problem, this dissertation proposes Falinks, a framework that allows a swarm of distributed intermittently powered nodes to collaboratively imitate the sensing and computing capabilities of a persistently powered system. This framework provides power-on and off schedules for the swamp of intermittent nodes which has no communication capability with each other.Doctor of Philosoph
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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FATE: A Fourier Accelerated Tensor Engine
No full textThe growing demand for matrix multiplication in artificial intelligence must be met with increased tensor computing efficiency and bandwidth improvements. While digital hardware accelerators gained popularity, improving AI compute throughput, the potential of analog circuits remains largely untapped. In this Thesis, we introduce a novel Fourier-Accelerated CMOS-based Tensor Engine (FATE) that aims to optimize the high complexity of matrix multiplication with computation and bandwidth efficiencies leveraging the Fourier Dot Product. We produce and simulate a transistor level version of FATE including op amps, filters, and Digital to Analog converters. Our design reduces the computational complexity of matrix multiplication from the traditional O(N^3) to O(N^3/f), where f is a number of frequency carriers up to N. Our circuit dramatically reduces the bandwidth required for moving vectors by encoding a vector as a summed sine series routed via a physical wire. The test circuit, designed with a 180 nm standard CMOS process, achieves a strong dot product linearity with an R^2 value of 0.940. By efficiently encoding vectors and computing dot products, the circuit demonstrates competitive energy use on the 180 nm process node. Despite the nonlinearities imparted by the analog components, extensive testing on standard machine learning models showed minimal (less than 1%) performance degradation, with some models even demonstrating improved accuracy up to 10% over control models. This work highlights the untapped potential of analog circuits in modern AI, offering a highly efficient solution to a critical bottleneck in AI computation
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Implementing Emulated Communication Models for Hybrid and Dynamic Network Topologies
No full textIn this thesis, network emulation is presented as a solution to testing, developing, and extending communication systems in a time- and cost-effective manner. Complex hybrid and dynamic wireless networks require extensive testing that is not easily conducted in hardware testbeds and may not be modeled accurately enough in network simulation tools. Network emulation provides the benefits of both hardware testbeds and simulation tools while also minimizing the shortcomings of each. This thesis evaluates the Extendable Mobile Ad-hoc Network Emulator (EMANE) as a network emulation tool by assessing its ability to emulate several complex network models. These models include hybrid wireless rural broadband deployments, an intelligent routing software development environment, and dynamic robot swarm networks. The emulated models were determined to be accurate enough to their hardware counterparts such that EMANE can be used as an effective tool for prototyping and testing communication systems
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Suggestive Audio Balance Tool
No full textAudio engineering is both a highly technical and uniquely artistic practice. Within this field, perhaps the most intense, heat-of-the-moment job is front of house engineering for live concerts. One of the duties of a front of house engineer is to balance the levels of the on-stage performers: each audio signal must be loud enough, but not too loud compared to other signals. Engineers, artists, and listeners each have individual preferences for this balance, so there is no single ground-truth for a mixture of music signals. Yet, each genre and style of music has some unquantifiable bounds for how present each voice or instrument should be in a mix, and artists want confidence that their live performance will be mixed within certain bounds. Thus, there is a need for an interdisciplinary Suggestive Audio Balance Tool (SABT) that can detect egregious level imbalances in a real-time audio mix based on provided examples of acceptable mixes. This thesis proposes an architecture for the SABT that incorporates Music Information Retrieval (MIR) datasets and audio features, classical machine learning and deep learning techniques, and statistical approaches. The result is a tool that can detect whether a stem is imbalanced with respect to other stems in a mix, based on a provided dataset of acceptably mixed songs
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Data Driven Mel Filter Bank Design for Environmental Sound Analysis
No full textAudio classification is a vital technique in environmental monitoring, facilitating the automatic categorization of audio data into predefined classes based on acoustic features. From identifying wildlife vocalizations to assessing urban noise pollution levels, its applications are diverse and pivotal in understanding and managing ecosystems and urban environments. The conventional audio classification method often utilizes Mel Frequency Cepstral Coefficients (MFCC) extracted from audio files as input to a Deep Neural Network (DNN) classifier. However, its effectiveness is limited by a fixed filterbank structure, designed for the human audio range but lacking optimization and adaptability to diverse datasets. To address this, we propose a customized MFCC approach (Pertinant Spectral Characteristic MFCC), aligning the filterbank with dataset-specific frequency power distribution peaks, thus enhancing classification accuracy and adaptability. Through a comparative analysis across various environmental datasets, including ESC50, UrbanSound8K, and Gunshot our study demonstrates the superiority of the Pertinant Spectral Characteristic MFCC (PSC-MFCC) approach. Specifically, we observed a notable 4.5% increase in classification accuracy and a 1.47% decrease in standard deviation compared to the traditional MFCC method, showcasing its potential to significantly enhance audio classification accuracy and precision. These findings underscore the practical utility and efficacy of the proposed methodology in environmental audio classification tasks. By accurately capturing and distinguishing features within diverse frequency ranges across classes, the PSC-MFCC approach offers a promising avenue for advancing audio classification techniques in environmental monitoring and conservation efforts
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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