We propose a low-cost, easy-to-fabricate, contactless microwave sensor for dielectric characterization of liquids. The design of the proposed sensor is based on a multiple complementary split-ring resonator (MCSRR) fabricated on a low-cost FR-4 substrate. A glass capillary tube having an inner diameter of 0.008λ0 is inserted in the high electric field region of the MCSRR to carry the liquid under test. The sensor is designed to operate at a resonant frequency of 2.45GHz for an empty tube and shifted resonant peaks are utilized for the dielectric characterization of different liquids. The maximum observed shifts in resonant frequency and Q factor are up to 400MHz and 31, respectively. The numerically established relations are experimentally verified through fabricated sensor for various binary mixtures of water and ethanol. The percentage errors between the calculated and reference permittivity of different samples are noticed to be less than 5%. The proposed device promises to be a cost-effective and convenient solution for accurate dielectric characterization of liquids and their binary aqueous solutions.

This paper proposes three-filters-to-normal (3F2N), an accurate and ultrafast surface normal estimator (SNE), which is designed for structured range sensor data, e.g., depth/disparity images. 3F2N SNE computes surface normals by simply performing three filtering operations (two image gradient filters in horizontal and vertical directions, respectively, and a mean/median filter) on an inverse depth image or a disparity image. Despite the simplicity of 3F2N SNE, no similar method already exists in the literature. To evaluate the performance of our proposed SNE, we created three large-scale synthetic datasets (easy, medium and hard) using 24 3D mesh models, each of which is used to generate 1800–2500 pairs of depth images (resolution: 480X640 pixels) and the corresponding ground-truth surface normal maps from different views. 3F2N SNE demonstrates the state-of-the-art performance, outperforming all other existing geometry-based SNEs, where the average angular errors with respect to the easy, medium and hard datasets are 1.66 degrees, 5.69 degrees and 15.31 degrees, respectively. Furthermore, our C++ and CUDA implementations achieve a processing speed of over 260 Hz and 21 kHz, respectively. Our datasets and source code are publicly available at sites.google.com/view/3f2n.

In order to meet their stringent dependability requirements, most modern communication networks support fast-recovery mechanisms in the data plane. While reactions to failures in the data plane can be significantly faster compared to control plane mechanisms, implementing fast recovery in the data plane is challenging, and has recently received much attention in the literature. This survey presents a systematic, tutorial-like overview of packet-based fast-recovery mechanisms in the data plane, focusing on concepts but structured around different networking technologies, from traditional link-layer and IP-based mechanisms, over BGP and MPLS to emerging software-defined networks and programmable data planes. We examine the evolution of fast-recovery standards and mechanisms over time, and identify and discuss the fundamental principles and algorithms underlying different mechanisms. We then present a taxonomy of the state of the art and compile open research questions.

This paper describes a deep latent variable model of speech power spectrograms and its application to semi-supervised speech enhancement with a deep speech prior. By integrating two major deep generative models, a variational autoencoder (VAE) and a normalizing flow (NF), in a mutually-beneficial manner, we formulate a flexible latent variable model called the NF-VAE that can extract low-dimensional latent representations from high-dimensional observations, akin to the VAE, and does not need to explicitly represent the distribution of the observations, akin to the NF. In this paper, we consider a variant of NF called the generative flow (GF a.k.a. Glow) and formulate a latent variable model called the GF-VAE. We experimentally show that the proposed GF-VAE is better than the standard VAE at capturing fine-structured harmonics of speech spectrograms, especially in the high-frequency range. A similar finding is also obtained when the GF-VAE and the VAE are used to generate speech spectrograms from latent variables randomly sampled from the standard Gaussian distribution. Lastly, when these models are used as speech priors for statistical multichannel speech enhancement, the GF-VAE outperforms the VAE and the GF.

This study introduces a receiver architecture for dual-functional communication and radar (RadCom) base-stations (BS), which exploits the spatial diversity between the received radar and communication signals, and performs interference cancellation (IC) to successfully separate these signals. In the RadCom system under consideration, both communication and radar systems employ orthogonal frequency-division multiplexing (OFDM) waveforms with overlapping subcarriers. Employing OFDM waveform allows the BS to simultaneously perform uplink channel estimation on the narrow-band subcarriers to efficiently obtain full channel state information (CSI) between the users (UEs) and the BS antenna elements. The estimated CSI matrix is then utilized to acquire uplink data streams from the UEs by suppressing the inter-user interference and radar signals which arrive at the BS through unknown channels. After acquiring the UEs’ data, radar signals are extracted from the received complex baseband signals by performing interference cancellation. The proposed method has been analyzed mathematically and verified by simulations under various conditions including CSI mismatch and high radar interference. The results show that 16QAM modulated uplink is outstandingly robust against radar interference and that having a large number of antennas significantly improves the performance of both communication and radar subsystems, cooperatively. This study shows that it is possible to distinguish radar and communication signals by employing large-scale antenna arrays to successfully realize a RadCom receiver for future communication networks

Solar-powered photovoltaic (PV) system encounters a significant amount of losses due to different derating factors of PV modules throughout its lifespan. Thus, proper investigation is much needed to grasp the technical and economic impact of derating factors on the solar PV system, specially the one which is connected to the utility grid. This study performs a comprehensive discussion on various PV loss parameters followed by a techno-economic-environmental assessment of combined derating factor on an grid-connected and optimally tilted PV system located at Hatiya, Bangladesh using HOMER (Hybrid Optimization Model for Multiple Energy Resources) software. Some criteria linked with derating factor such as PV degradation and ambient temperature are further explored to analyze their impact on the aforementioned power system. Simulation results show that the system provides the best technical performance concerning annual PV production, power trade with grid, and renewable fraction with less emissions at a higher value of derating factor since it represents the lower impact of loss parameters. Similarly, financial performance in terms of net present cost (NPC), levelized cost of energy (LCOE), and grid power exchange cost is found lower when derating factor value is higher.

This paper presents a novel algorithm for measuring the linewidth enhancement factor of semiconductor lasers and the optical feedback level factor in a semiconductor laser with an external cavity. The proposed approach is based on analysis of the self-mixing phase equation to deduce equations for finding parameters given only knowledge of the perturbed phase. The effectiveness of the method has been validated with accuracy of 8.6%and 1.7% for ‘C’ and alpha respectively while covering all feedback regimes.

In dynamic environment, the suddenly appeared human or other moving obstacles can affect the safety of the bridge crane. For such dangerous situation, the bridge crane must predict potential collisions between the payload and the obstacle, keep safe distance while the swing of the payload must be considered in the mean time. Therefore, the safe distance is not a constant value, which must be adaptive to the relative speed of the bridge crane. However, as far as we know, the mathematical model between the safe distance and the relative speed of the bridge crane has never been fully discussed. In this paper, we propose a safe distance prediction method using model prediction control (MPC), which can make sure that the crane can stop before the obstacle, and avoid possible collisions, while the relative speed and anti-swing are both considered. The experimental results prove the effectiveness of our idea.

The Poisson multi-Bernoulli mixture (PMBM) filter is extended for distributed implementation using a wireless sensor network. At the core of the proposed networking approach, the PMBM posterior is decomposed into two parts corresponding to the undetected and detected targets, respectively. Fusion is motivated to be performed with regard to the latter only which is represented by MBM based on a distributed flooding algorithm for internode communication, which iteratively shares the MBMs between neighbor sensors. Then, a suboptimal “best-fit-ofmixture” principle is followed at each local sensor to find a MBM that best fits the mixture of MBMs aggregated from distinct sensors, leading to an arithmetic average (AA) of these MBMs. We prove the exact closure of the MBM-AA fusion and discuss its sub-optimality and limitations. Simulation demonstrates the effectiveness and limitations of our approach.

This study presents an investigation on sixteen pretrained CNNs for classification of COVID-19 using a large public database of CT scans collected from COVID-19 patients and non-COVID-19 subjects. The results update CNNs that achieve very high performance on the classification task and discover that implementation of transfer learning with direct input of whole image slices and without the use of data augmentation provide better classification results than the use of data augmentation. {\it Conclusions:} The findings alleviate the task of data augmentation and manual extraction of regions of interest on CT images, which are adopted by current implementation of deep-learning models, and can facilitate the rapid deployment of AI tools to contain the spread of the coronavirus disease.

In the proposed research work; the COVID-19 is detected using transfer learning from CT scan images decomposed to three-level using stationary wavelet. A three-phase detection model is proposed to improve the detection accuracy and the procedures are as follows; Phase1- data augmentation using stationary wavelets, Phase2- COVID-19 detection using pre-trained CNN model and Phase3- abnormality localization in CT scan images. This work has considered the well known pre-trained architectures, such as ResNet18, ResNet50, ResNet101, and SqueezeNet for the experimental evaluation. In this work, 70% of images are considered to train the network and 30% images are considered to validate the network. The performance of the considered architectures is evaluated by computing the common performance measures.

In the cross-silo federated learning setting, one kind of data partition according to features, which is so-called vertical federated learning (i.e. feature-wise federated learning) (Yang et al. 2019), is to apply to multiple datasets that share the same sample ID space but different feature spaces. Simultaneously, the image dataset can also be partitioned according to labels. To improve the model performance of the isolated parties based on feature-wise (i.e. label-wise) results, the most effective method is to federate the model results of the isolated parties together. However, it is a non-trivial task to allow the participating parties to share the model results without violating the data privacy of the parties. In this paper, within the framework of principal component analysis (PCA), we propose a Federated-PCA machine learning approach, in which the PCA method is used to reduce the dimensionality of sample data for all parties and extract the principal component feature information to improve the efficiency of subsequent training work. This process will not reveal the original data information of each party. The federal system can help each side build a common profit strategy. Under this federal mechanism, the identity and status of each party are the same. By comparing the federated results of the isolated parties and the result of the unseparated party through multiple sets of comparative experiments, we find that the experimental results of these two settings are close, and the proposed method can effectively improve the training model performance of most participating parties.

We provide a conceptual and theoretical analysis of nonsinusoidal antennas with emphasis on how electromagnetics and communication theories can be integrated to propose ideas for near-field (NF) communications systems utilizing future antennas. It is shown through rigorous analysis that in nonsinusoidal antennas it is possible to derive and solve ordinary differential equations giving specialized time-domain excitation signals that lead to exact cancellation of the near field at specific radiation spheres. This opens the door to building NF communications systems with far-field-like communication receiver infrastructures utilized if the receive antenna is placed at the special sphere where the NF component is made to vanish.

As the local energy sources are mostly uncertain and fluctuating in nature, the ‘energy risk’ due to discrepancies between committed energy transactions and metered measurements is prominent in peer to peer (P2P) markets. We propose a P2P market settlement mechanism which lowers this risk and maximizes the welfare of buyers and sellers. The risk in energy production is modeled using Markowitz portfolio theory and the best point where energy return per unit risk is maximum is obtained from the efficient frontier by using the modified Sharpe ratio. The energy portfolio thus obtained is used as a constraint while optimizing the conflicting prosumer benefits using multi-objective stochastic weight trade-off chaotic non-dominated sorting particle swarm optimization (SWTC-NSPSO). In effect, only a reliable proportion of total energy demand submitted in the bid is cleared in the market, foreseeing the real-time fluctuations. The proposed market settlement mechanism also gives room to the existing distribution system operators by assigning them the duty of 1) optimally allocating energy among buyers and sellers in accordance with their competitive bids 2) providing the infrastructure, managing the market and charging for the service and 3) checking the technical feasibility by performing load flow and monitoring power transfer sensitivities to encourage short distance transactions. The energy allocation is done in CIGRE LV benchmark microgrid with ten peers having solar and wind generation. The allocated energy is found to be closer to the metered measurements and hence the reserve cost is observed to be low.

Complete face recovering (CFR) is to recover the complete face image of a given partial face image of a target person whose photo may not be included in the gallery set. The CFR has several attractive potential applications but is challenging. As far as we know, the CFR problem has yet to be explored in the literature. This paper therefore proposes an identity-preserved CFR approach (IP-CFR) to addressing the CFR. First, a denoising auto-encoder based network is applied to acquire the discriminative feature. Then, we propose an identity-preserved loss function to keep the personal identity information. Furthermore, the acquired features are fed into a new variant of the generative adversarial network (GAN) to restore the complete face image. In addition, a two-pathway discriminator is leveraged to enhance the quality of the recovered image. Experimental results on the benchmark datasets show the promising result of the proposed approach.

A document by Ming YiDong . Click on the document to view its contents.

Pretrained language models such as BERT, GPT have shown great effectiveness in language understanding. The auxiliary predictive tasks in existing pretraining approaches are mostly defined on tokens, thus may not be able to capture sentence-level semantics very well. To address this issue, we propose CERT: Contrastive self-supervised Encoder Representations from Transformers, which pretrains language representation models using contrastive self-supervised learning at the sentence level. CERT creates augmentations of original sentences using back-translation. Then it finetunes a pretrained language encoder (e.g., BERT) by predicting whether two augmented sentences originate from the same sentence. CERT is simple to use and can be flexibly plugged into any pretraining-finetuning NLP pipeline. We evaluate CERT on three language understanding tasks: CoLA, RTE, and QNLI. CERT outperforms BERT significantly.

The Coronavirus disease (COVID-19) is an infectious disease that primarily affects lungs. This virus has spread in almost every continent. Countries are racing to slow down the spread by testing and treating patients. To diagnose the infected people, reverse transcription-polymerase chain reaction (RT-PCR) test is used. Because of colossal demand; PCR kits are under shortage, and to overcome this; radiographic techniques such as X-rays and CT-scan can be used for diagnostic purpose. In this paper, deep learning technology is used to diagnose COVID-19 in subjects through chest CT-scan. EfficientNet deep learning architecture is used for timely and accurate detection of coronavirus with an accuracy 0.897, F1 score 0.896, and AUC 0.895. Three different learning rate strategies are used, such as reducing the learning rate when model performance stops increasing (reduce on plateau), cyclic learning rate, and constant learning rate. Reduce on plateau strategy achieved F1-score of 0.9, cyclic learning rate and constant learning rate resulted in F1-score of 0.86 and 0.82, respectively. Implementation is available at github.com/talhaanwarch/Corona\_Virus/tree/master/CT\_scan