557 research outputs found
Power packet transferability via symbol propagation matrix
Power packet is a unit of electric power transferred by a power pulse with an
information tag. In Shannon's information theory, messages are represented by
symbol sequences in a digitized manner. Referring to this formulation, we
define symbols in power packetization as a minimum unit of power transferred by
a tagged pulse. Here, power is digitized and quantized. In this paper, we
consider packetized power in networks for a finite duration, giving symbols and
their energies to the networks. A network structure is defined using a graph
whose nodes represent routers, sources, and destinations. First, we introduce
symbol propagation matrix (SPM) in which symbols are transferred at links
during unit times. Packetized power is described as a network flow in a
spatio-temporal structure. Then, we study the problem of selecting an SPM in
terms of transferability, that is, the possibility to represent given energies
at sources and destinations during the finite duration. To select an SPM, we
consider a network flow problem of packetized power. The problem is formulated
as an M-convex submodular flow problem which is known as generalization of the
minimum cost flow problem and solvable. Finally, through examples, we verify
that this formulation provides reasonable packetized power.Comment: Submitted to Proceedings of the Royal Society A: Mathematical,
Physical and Engineering Science
Channel Phase Processing in Wireless Networks for Human Activity Recognition
The phase of the channel state information (CSI) is underutilized as a source
of information in wireless sensing due to its sensitivity to synchronization
errors of the signal reception. A linear transformation of the phase is
commonly applied to correct linear offsets and, in a few cases, some filtering
in time or frequency is carried out to smooth the data. This paper presents a
novel processing method of the CSI phase to improve the accuracy of human
activity recognition (HAR) in indoor environments. This new method, coined Time
Smoothing and Frequency Rebuild (TSFR), consists of performing a CSI phase
sanitization method to remove phase impairments based on a linear regression
and rotation method, then a time domain filtering stage with a Savitzy-Golay
(SG) filter for denoising purposes and, finally, the phase is rebuilt,
eliminating distortions in frequency caused by SG filtering. The TSFR method
has been tested on five datasets obtained from experimental measurements, using
three different deep learning algorithms, and compared against five other types
of CSI phase processing. The results show an accuracy improvement using TSFR in
all the cases. Concretely, accuracy performance higher than 90\% in most of the
studied scenarios has been achieved with the proposed solution. In few-shot
learning strategies, TSFR outperforms the state-of-the-art performance from
35\% to 85\%.Comment: submitted to IEEE Transactions on Mobile Computing (under review
Decentralized Algorithms for Consensus-Based Power Packet Distribution
Power packets are proposed as a transmission unit that can deliver power and
information simultaneously. They are transferred using the store-and-forward
method of power routers. A system that achieves power supply/demand in this
manner is called a power packet network (PPN). A PPN is expected to enhance
structural robustness and operational reliability in an energy storage system
(ESS) with recent diverse distributed sources. However, this technology is
still in its early stage, and faces numerous challenges, such as high cost of
implementation and complicated energy management. In this paper, we propose a
novel power control based on decentralized algorithms for a PPN. Specifically,
the power supply is triggered and managed by communications between power
routers. We also discuss the mechanism of the decentralized algorithm for the
operation of power packets and reveal the feasibility of the given control
method and application by forming biased power flows on the consensus-based
distribution.Comment: This paper was submitted to Nonlinear Theory and Its Applications,
IEICE on October 29, 202
Stochastic Power Processing through Logic Operation of Power Packets
This article presents an application of the recently proposed logic operation
of power based on power packetization. In a power packet dispatching system,
the power supply can be considered as a sequence of power pulses, where the
occurrence of pulses follows a probability that corresponds to the capacity of
the power sources or power lines. In this study, we propose a processing scheme
to reshape a stream of power packets from such stochastic sequences to satisfy
the load demand. The proposed scheme is realized by extending the concept of
stochastic computing to the power domain. We demonstrate the operation of the
proposed scheme through experiments and numerical simulations by implementing
it as a function of a power packet router, which forms a power packet
dispatching network. The stochastic framework proposed in this study provides a
new design foundation for low-power distribution networks as an embodiment of
the close connection between the cyber and physical components
A General Framework for Analyzing, Characterizing, and Implementing Spectrally Modulated, Spectrally Encoded Signals
Fourth generation (4G) communications will support many capabilities while providing universal, high speed access. One potential enabler for these capabilities is software defined radio (SDR). When controlled by cognitive radio (CR) principles, the required waveform diversity is achieved via a synergistic union called CR-based SDR. Research is rapidly progressing in SDR hardware and software venues, but current CR-based SDR research lacks the theoretical foundation and analytic framework to permit efficient implementation. This limitation is addressed here by introducing a general framework for analyzing, characterizing, and implementing spectrally modulated, spectrally encoded (SMSE) signals within CR-based SDR architectures. Given orthogonal frequency division multiplexing (OFDM) is a 4G candidate signal, OFDM-based signals are collectively classified as SMSE since modulation and encoding are spectrally applied. The proposed framework provides analytic commonality and unification of SMSE signals. Applicability is first shown for candidate 4G signals, and resultant analytic expressions agree with published results. Implementability is then demonstrated in multiple coexistence scenarios via modeling and simulation to reinforce practical utility
Demonstrating Advantages of Neuromorphic Computation: A Pilot Study
Neuromorphic devices represent an attempt to mimic aspects of the brain's
architecture and dynamics with the aim of replicating its hallmark functional
capabilities in terms of computational power, robust learning and energy
efficiency. We employ a single-chip prototype of the BrainScaleS 2 neuromorphic
system to implement a proof-of-concept demonstration of reward-modulated
spike-timing-dependent plasticity in a spiking network that learns to play the
Pong video game by smooth pursuit. This system combines an electronic
mixed-signal substrate for emulating neuron and synapse dynamics with an
embedded digital processor for on-chip learning, which in this work also serves
to simulate the virtual environment and learning agent. The analog emulation of
neuronal membrane dynamics enables a 1000-fold acceleration with respect to
biological real-time, with the entire chip operating on a power budget of 57mW.
Compared to an equivalent simulation using state-of-the-art software, the
on-chip emulation is at least one order of magnitude faster and three orders of
magnitude more energy-efficient. We demonstrate how on-chip learning can
mitigate the effects of fixed-pattern noise, which is unavoidable in analog
substrates, while making use of temporal variability for action exploration.
Learning compensates imperfections of the physical substrate, as manifested in
neuronal parameter variability, by adapting synaptic weights to match
respective excitability of individual neurons.Comment: Added measurements with noise in NEST simulation, add notice about
journal publication. Frontiers in Neuromorphic Engineering (2019
Molecular Dynamics Simulation
Condensed matter systems, ranging from simple fluids and solids to complex multicomponent materials and even biological matter, are governed by well understood laws of physics, within the formal theoretical framework of quantum theory and statistical mechanics. On the relevant scales of length and time, the appropriate âfirst-principlesâ description needs only the Schroedinger equation together with Gibbs averaging over the relevant statistical ensemble. However, this program cannot be carried out straightforwardlyâdealing with electron correlations is still a challenge for the methods of quantum chemistry. Similarly, standard statistical mechanics makes precise explicit statements only on the properties of systems for which the many-body problem can be effectively reduced to one of independent particles or quasi-particles. [...
Model-based Filtering of Interfering Signals in Ultrasonic Time Delay Estimations
In dieser Arbeit werden modellbasierte algorithmische AnsĂ€tze zur Interferenz-invarianten ZeitverschiebungsschĂ€tzung vorgestellt, die speziell fĂŒr die SchĂ€tzung kleiner Zeitverschiebungsdifferenzen mit einer notwendigen Auflösung, die deutlich unterhalb der Abtastzeit liegt, geeignet sind. Daher lassen sich die Verfahren besonders gut auf die Laufzeit-basierte Ultraschalldurchflussmessung anwenden, da hier das Problem der Interferenzsignale besonders ausgeprĂ€gt ist. Das Hauptaugenmerk liegt auf der Frage, wie mehrere Messungen mit unterschiedlichen Zeitverschiebungen oder Prozessparametern zur UnterdrĂŒckung der Interferenzsignale in Ultraschalldurchflussmessungen verwendet werden können, wobei eine gute Robustheit gegenĂŒber additivem weiĂen GauĂ\u27schen Rauschen und eine hohe Auflösung erhalten bleiben sollen. Zu diesem Zweck wird ein Signalmodell angenommen, welches aus stationĂ€ren Interferenzsignalen, die nicht von wechselnden Zeitverschiebungen abhĂ€ngig sind, und aus Zielsignalen, die den Messeffekt enthalten, besteht.
ZunĂ€chst wird das Signalmodell einer Ultraschalldurchflussmessung und sein dynamisches Verhalten bei Temperatur- oder Zeitverschiebungsschwankungen untersucht. Ziel ist es, valide SimulationsdatensĂ€tze zu erzeugen, mit denen die entwickelten Methoden sowohl unter der PrĂ€misse, dass die Daten perfekt zum Signalmodell passen, als auch unter der PrĂ€misse, dass Modellfehler vorliegen, getestet werden können. Dabei werden die Eigenschaften der Signalmodellkomponenten, wie Bandbreite, StationaritĂ€t und TemperaturabhĂ€ngigkeit, identifiziert. Zu diesem Zweck wird eine neue Methode zur Modellierung der TemperaturabhĂ€ngigkeit der Interferenzsignale vorgestellt. Nach der Charakterisierung des gesamten Messsystems wird das Signalmodell -- angepasst an die Ultraschalldurchflussmessung -- als Grundlage fĂŒr zwei neue Methoden verwendet, deren Ziel es ist, die Auswirkungen der Interferenzsignale zu reduzieren.
Die erste vorgeschlagene Technik erweitert die auf der Signaldynamik basierenden AnsĂ€tze in der Literatur, indem sie die Voraussetzungen fĂŒr die erforderliche Varianz der Zeitverschiebungen abschwĂ€cht. Zu diesem Zweck wird eine neue Darstellung von mehreren Messsignalen als Punktwolken eingefĂŒhrt. Die Punktwolken werden dann mithilfe der Hauptkomponentenanalyse und B-Splines verarbeitet, was entweder zu Interferenz-invarianten ZeitverschiebungsschĂ€tzungen oder geschĂ€tzten Interferenzsignalen fĂŒhrt. In diesem Zusammenhang wird eine neuartige gemeinsame B-Spline- und RegistrierungsschĂ€tzung entwickelt, um die Robustheit zu erhöhen.
Der zweite Ansatz besteht in einer regressionsbasierten SchĂ€tzung der Zeitverschiebungsdifferenzen durch das Erlernen angepasster SignalunterrĂ€ume. Diese UnterrĂ€ume werden effizient durch die Analytische Wavelet Packet Transformation berechnet, bevor die resultierenden Koeffizienten in Merkmale transformiert werden, die gut mit den Zeitverschiebungssdifferenzen korrelieren. DarĂŒber hinaus wird ein neuartiger, unbeaufsichtigter Unterraum-Trainingsansatz vorgeschlagen und mit den konventionellen Filter- und Wrapper-basierten Merkmalsauswahlmethoden verglichen.
SchlieĂlich werden beide Methoden in einem experimentellen Ultraschalldurchflussmesssystem mit einem hohen MaĂ an vorhandenen Interferenzsignalen getestet, wobei sich zeigt, dass sie in den meisten FĂ€llen den Methoden aus der Literatur ĂŒberlegen sind. Die QualitĂ€t der Methoden wird anhand der Genauigkeit der ZeitverschiebungsschĂ€tzung bewertet, da die Grundwahrheit fĂŒr die Interferenzsignale nicht zuverlĂ€ssig bestimmt werden kann. Anhand verschiedener DatensĂ€tze werden die AbhĂ€ngigkeiten von den Hyperparametern, den Prozessbedingungen und, im Falle der regressionsbasierten Methode, dem Trainingsdatensatz analysiert
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