3,022 research outputs found
Chronic bronchial infection in stable COPD: To treat or not to treat
Chronic bronchial infection; COPDInfección bronquial crónica; EPOCInfecció bronquial crònica; MPO
Modelling and Analysis of the Effect of Process and Geometry Parameters on Multiphase Flow Formation in ACLR Atomizers
The atomization of liquids into droplets is an essential process operation that is used in different industrial processes to promote heat and mass transfer. Usually for applications such as surface coating, combustion technology, or spray drying. The various models of atomizers differ mainly in: the atomization energy source, the form of energy transfer into the liquid, and the range of solid contents and viscosities in the liquid feed that can be handled.
The Air-Core-Liquid-Ring (ACLR) atomization is an innovative internal-mixing pneumatic atomization technique, suitable for energy-efficient spray drying because of its ability to handle highly viscous liquid feeds with high solid contents. The ACLR nozzle forms an annular flow with a pressurized gas core surrounded by a thin liquid falling film, which forms a cone and breaks up into droplets, when the flow leaves the nozzle and the gas expands
Frequency Modulation Aggregation for Federated Learning
Federated edge learning (FEEL) is a framework for training models in a
distributed fashion using edge devices and a server that coordinates the
learning process. In FEEL, edge devices periodically transmit model parameters
to the server, which aggregates them to generate a global model. To reduce the
burden of transmitting high-dimensional data by many edge devices, a broadband
analog transmission scheme has been proposed. The devices transmit the
parameters concurrently using a linear analog modulation, which are aggregated
by the superposition nature of the wireless medium. However, linear analog
modulations incur in an excessive power consumption for edge devices and are
not suitable for current digital wireless systems. To overcome this issue, in
this paper we propose a digital frequency broadband aggregation. The scheme
integrates a Multiple Frequency Shift Keying (MFSK) at the transmitters and a
type-based multiple access (TBMA) at the receiver. Using concurrent
transmission, the server can recover the type (i.e., a histogram) of the
transmitted parameters and compute any aggregation function to generate a
shared global model. We provide a extensive analysis of the communication
scheme in an AWGN channel and compare it with linear analog modulations. Our
experimental results show that the proposed scheme achieves the same
performance, although it requires 14 dB less in peak-to-average power ratio
(PAPR) than linear analog modulations.Comment: Paper submitted to 2023 IEEE Global Communications Conferenc
LoRa-based Over-the-Air Computing for Sat-IoT
Satellite Internet of Things (Sat-IoT) is a novel framework in which
satellites integrate sensing, communication and computing capabilities to carry
out task-oriented communications. In this paper we propose to use the Long
Range (LoRa) modulation for the purpose of estimation in a Sat-IoT scenario.
Then we realize that the collisions generated by LoRa can be harnessed in an
Over-the-Air Computing (AirComp) framework. Specifically, we propose to use
LoRa for Type-based Multiple Access (TBMA), a semantic-aware scheme in which
communication resources are assigned to different parameters, not users. Our
experimental results show that LoRa-TBMA is suitable as a massive access
scheme, provides large gains in terms of mean squared error (MSE) and saves
scarce satellite communication resources (i.e., power, latency and bandwidth)
with respect to orthogonal multiple access schemes. We also analyze the
satellite scenarios that could take advantage of the LoRa-TBMA scheme. In
summary, that angular modulations, which are very useful in satellite
communications, can also benefit from AirComp.Comment: Paper accepted in 2023 European Signal Processing Conference
(EUSIPCO
LoRa Modulation for Split Learning
In this paper we introduce a task-oriented communication design for split
learning (SL) over a communication channel. Our approach involves the
Expressive Neural Network (ENN), a novel neural network featuring adaptive
activation functions (AAF) based on the Discrete Cosine Transform (DCT). This
architecture does not only provide better learning capabilities, but also
facilitates data transmission using the Long Range (LoRa) modulation. The
frequency nature of LoRa is adequate for the communication side of the problem,
while allowing to construct the AAFs at the receiver. Additionally, we propose
orthogonal chirp division multiplexing (OCDM) for multiple access and a
modified modulation aimed at preserving communication bandwidth. Our
experimental results demonstrate the effectiveness of this scheme, achieving
high accuracy in challenging scenarios, including low signal to noise Ratio
(SNR) and absence of channel state information (CSI) for both additive white
Gaussian noise (AWGN) and Rayleigh fading channels.Comment: Accepted in 2023 IEEE International Workshop on Computational
Advances in Multi-Sensor Adaptive Processing (CAMSAP 2023
DCT-based Air Interface Design for Function Computation
With the integration of communication and computing, it is expected that part
of the computing is transferred to the transmitter side. In this paper we
address the general problem of Frequency Modulation (FM) for function
approximation through a communication channel. We exploit the benefits of the
Discrete Cosine Transform (DCT) to approximate the function and design the
waveform. In front of other approximation schemes, the DCT uses basis of
controlled dynamic, which is a desirable property for a practical
implementation. Furthermore, the proposed modulation allows to recover both the
measurement and the function in a single transmission. Our experiments show
that this scheme outperforms the double side-band (DSB) modulation in terms of
mean squared error (MSE). This can also be implemented with an agnostic
receiver, in which the function is unknown to the receiver. Finally, the
proposed modulation is compatible with some of the existing transmission
technologies for sensor networks.Comment: Paper accepted in IEEE Open Journal of Signal Processing (2023
Adaptive function approximation based on the Discrete Cosine Transform (DCT)
This paper studies the cosine as basis function for the approximation of
univariate and continuous functions without memory. This work studies a
supervised learning to obtain the approximation coefficients, instead of using
the Discrete Cosine Transform (DCT). Due to the finite dynamics and
orthogonality of the cosine basis functions, simple gradient algorithms, such
as the Normalized Least Mean Squares (NLMS), can benefit from it and present a
controlled and predictable convergence time and error misadjustment. Due to its
simplicity, the proposed technique ranks as the best in terms of learning
quality versus complexity, and it is presented as an attractive technique to be
used in more complex supervised learning systems. Simulations illustrate the
performance of the approach. This paper celebrates the 50th anniversary of the
publication of the DCT by Nasir Ahmed in 1973.Comment: Accepted paper in 26th International Conference on Circuits, Systems,
Communications and Computers (CSCC
From mobile phone data to the spatial structure of cities
Pervasive infrastructures, such as cell phone networks, enable to capture
large amounts of human behavioral data but also provide information about the
structure of cities and their dynamical properties. In this article, we focus
on these last aspects by studying phone data recorded during 55 days in 31
Spanish metropolitan areas. We first define an urban dilatation index which
measures how the average distance between individuals evolves during the day,
allowing us to highlight different types of city structure. We then focus on
hotspots, the most crowded places in the city. We propose a parameter free
method to detect them and to test the robustness of our results. The number of
these hotspots scales sublinearly with the population size, a result in
agreement with previous theoretical arguments and measures on employment
datasets. We study the lifetime of these hotspots and show in particular that
the hierarchy of permanent ones, which constitute the "heart" of the city, is
very stable whatever the size of the city. The spatial structure of these
hotspots is also of interest and allows us to distinguish different categories
of cities, from monocentric and "segregated" where the spatial distribution is
very dependent on land use, to polycentric where the spatial mixing between
land uses is much more important. These results point towards the possibility
of a new, quantitative classification of cities using high resolution
spatio-temporal data.Comment: 14 pages, 15 figure
Uncovering the spatial structure of mobility networks
The extraction of a clear and simple footprint of the structure of large,
weighted and directed networks is a general problem that has many applications.
An important example is given by origin-destination matrices which contain the
complete information on commuting flows, but are difficult to analyze and
compare. We propose here a versatile method which extracts a coarse-grained
signature of mobility networks, under the form of a matrix that
separates the flows into four categories. We apply this method to
origin-destination matrices extracted from mobile phone data recorded in
thirty-one Spanish cities. We show that these cities essentially differ by
their proportion of two types of flows: integrated (between residential and
employment hotspots) and random flows, whose importance increases with city
size. Finally the method allows to determine categories of networks, and in the
mobility case to classify cities according to their commuting structure.Comment: 10 pages, 5 figures +Supplementary informatio
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