Middle ear effusion is a common symptom of otitis media, the reactive physical manifestation of otitis media (OM) in children's middle ear. However, diagnosing MEE for little children at home is troublesome due to...
Middle ear effusion is a common symptom of otitis media, the reactive physical manifestation of otitis media (OM) in children's middle ear. However, diagnosing MEE for little children at home is troublesome due to their difficulty cooperating and the caregiver's lack of medical knowledge. To this end, we propose EarSonar, a novel acoustic-based MEE diagnostic system. The principle behind EarSonar is that the acoustic absorption effect exists in ear scenarios, and the volume of middle ear fluid can markedly affect the absorbed spectrum energy. By automatically eliminating the impact of potential interference factors and identifying the representative frequency range with the typical reaction of acoustic absorption, EarSonar captures fine-grained signal features on absorbed spectrum energy and models the intrinsic relationship between acoustic absorption and the volume of the filler fluid in the eardrum. On that basis, EarSonar extracts the features of the MEE signal segment and uses k-means clustering to classify middle ear effusion status. We conducted a test on 112 adolescents aged 4–6. We divided the degree of middle ear effusion into three grades. The final average detection accuracy rate exceeds 92%, which is 8 % higher than the previous method. We have implemented a proof-of-concept prototype of EarSonar by building upon earphones embedded with a microphone and speaker. Experimental results demonstrate a feasible and effective way to turn earphones into potential home-use MEE screening tools.
This paper proposes a hybrid Quantum Federated Learning (QFL) method, called QQFL, a revolutionary approach for Dynamic Security Assessment (DSA) optimized for modern smart grids. Built on the synergy of measurement-d...
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ISBN:
(数字)9781728190549
ISBN:
(纸本)9781728190556
This paper proposes a hybrid Quantum Federated Learning (QFL) method, called QQFL, a revolutionary approach for Dynamic Security Assessment (DSA) optimized for modern smart grids. Built on the synergy of measurement-device-independent QKD (MDI-QKD) and Variational Quantum Circuit (VQC), QQFL uniquely addresses the challenges of centralized structures and vulnerabilities in existing ML-based DSA techniques. It enables accurate label predictions for quantum states encoded from classical DSA data while ensuring data security via QKD networks. A novel mechanism, the DNN-based MDI-QKD optimizer, ensures optimal secret key exchange. Unlike traditional methods reliant solely on classical CPUs, QQFL integrates QPUs for executing computational tasks. Given the imperative of frequent data transmission in modern rapidly changing smart grid environment, QQFL emphasizes swift online learning and dynamic deployment. Testing on the synthetic Illinois 49-machine 200-bus system affirms QQFL's superior the DSA accuracy while upholding the data privacy of smart grids. Ultimately, QQFL enhances the security, reliability, confidentiality, and robustness of sophisticated smart grids.
Promoting sustainable water usage is a critical imperative across all sectors of society. Households are no exception since a significant portion of water is wasted daily due to inefficient appliances or improper habi...
Promoting sustainable water usage is a critical imperative across all sectors of society. Households are no exception since a significant portion of water is wasted daily due to inefficient appliances or improper habits. Thus, there is a need for innovative solutions that not only improve water utilization but also raise residents' awareness about this issue. This paper presents a promising solution leveraging the Internet of Things (IoT) and Machine Learning (ML) techniques to detect water wastage stemming from sink usage automatically. We have designed and developed a low-cost prototype equipped with an array of sensors, including a microphone, an ultrasonic sensor, and a PIR, to monitor sink usage. A deep learning model based on Gated Recurrent Units (GRU) has been trained to classify the wastage events. To validate our concept, we have gathered a small dataset relative to nine common daily water usage activities through the IoT prototype. Our preliminary findings demonstrate the feasibility of our solution, with an average accuracy exceeding 90% in detecting wastage events.
In massive multiple-input-multiple-output (mMIMO) systems, the importance of power control (PC) cannot be overstated. The PC is estimated by various algorithms, including the weighted mean square error (WMMSE) algorit...
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In autonomous driving, accurately interpreting the movements of other road users and leveraging this knowledge to forecast future trajectories is crucial. This is typically achieved through the integration of map data...
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This paper proposes a Complex-Valued Neural Network (CVNN) for glucose sensing in milli-meter wave (mmWave). Based on the propagation characteristics of millimeter wave in glucose medium, we obtain the S21 parameter o...
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This article is devoted to the actual topic of studying scenarios for adapting a network infrastructure to a network of remote patient monitoring systems (PCMS). The paper presents an overview and analysis of scenario...
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The popularity of cryptocurrencies is increasing, as they have become an economy of their own, due to the observed returns of investments in cryptocurrencies and digital assets. This led to an increasing interest in t...
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ISBN:
(纸本)9798350309850
The popularity of cryptocurrencies is increasing, as they have become an economy of their own, due to the observed returns of investments in cryptocurrencies and digital assets. This led to an increasing interest in the prediction of their prices over the past few years. Ethereum is one of the most popular cryptocurrencies that has witnessed an increase of prices since 2015 while having the second largest market cap. Ethereum is a decentralized platform that incorporates several interactions, not limited to asset trading only; it is also a platform of smart contracts execution and token trading. In this work, we aim at reflecting the interactions perceived in the Ethereum network on Ether prices using Topological Data Analysis (TDA). We introduce a method to extract the TDA features of the indicators of different interaction networks; traded volumes, smart contracts, and transactions between accounts. We conducted an analysis of the effect of using TDA features on Ether price prediction and extended our method to predict the prices of eight Ethereum tokens. Our method resulted in 0.75%, 4.9%, and 13.75% MAPE in hourly, daily, and weekly forecasts respectively, outperforming the previously reported results.
Grant-free non-orthogonal multiple access (GF-NOMA) technique is considered as a promising solution to address the bottleneck of ubiquitous connectivity in massive machine type communication (mMTC) scenarios. One of t...
Grant-free non-orthogonal multiple access (GF-NOMA) technique is considered as a promising solution to address the bottleneck of ubiquitous connectivity in massive machine type communication (mMTC) scenarios. One of the challenging problems in uplink GF-NOMA systems is how to efficiently perform user activity detection and data detection. In this paper, a novel complexity-reduction weighted block coordinate descend (CR-WBCD) algorithm is proposed to address this problem. To be specific, we formulate the multi-user detection (MUD) problem in uplink GF-NOMA systems as a weighted $l_{2}$ minimization problem. Based on the block coordinate descend (BCD) framework, a closed-form solution involving dynamic user-specific weights is derived to adaptively identify the active users with high accuracy. Furthermore, a complexity reduction mechanism is developed for substantial computational cost saving. Simulation results demonstrate that the proposed algorithm enjoys bound-approaching detection performance with more than three-order of magnitude computational complexity reduction.
The development of the transportation sector towards green infrastructures plays a critical role towards mitigating climate change. Specifically, the trend of transitioning from Gas to Electric Vehicles (EVs) requires...
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ISBN:
(数字)9798350356694
ISBN:
(纸本)9798350356700
The development of the transportation sector towards green infrastructures plays a critical role towards mitigating climate change. Specifically, the trend of transitioning from Gas to Electric Vehicles (EVs) requires city planning for the optimal placements of EV Charging Stations (CS). With a future vision of converting all vehicles to electric, this paper examines the impact of CS locations placement on normal taxi operations. Optimized placement of stations have a significant impact on charger utilization and minimize charging queues leading to lower user wait times. A simulator was developed for modeling the taxi service in the Manhattan Borough of New York City. Factors such as taxi regions, DC stations, as well as real user data were used to closely mimic the system. The Particle Swarm Optimizer (PSO) was developed to optimize the CS locations showing the consequences of optimized CS placement on the taxi infrastructure in Manhattan. The results showed that the city of NYC would need approximately 80 DC fast CS, or 480 level 2 chargers placed mostly within midtown Manhattan.
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