RF Signal-Based UAV Detection and Mode Classification: A Joint Feature Engineering Generator and Multi-Channel Deep Neural Network Approach

With the proliferation of Unmanned Aerial Vehicles (UAVs) to provide diverse critical services, such as surveillance, disaster management, and medicine delivery, the accurate detection of these small devices and the efficient classification of their flight modes are of paramount importance to guarantee their safe operation in our sky. Among the existing approaches, Radio Frequency (RF) based methods are less affected by complex environmental factors. The similarities between UAV RF signals and the diversity of frequency components make accurate detection and classification a particularly difficult task. To bridge this gap, we propose a joint Feature Engineering Generator (FEG) and Multi-Channel Deep Neural Network (MC-DNN) approach. Specifically, in FEG, data truncation and normalization separate different frequency components, the moving average filter reduces the outliers in the RF signal, and the concatenation fully exploits the details of the dataset. In addition, the multi-channel input in MC-DNN separates multiple frequency components and reduces the interference between them. A novel dataset that contains ten categories of RF signals from three types of UAVs is used to verify the effectiveness. Experiments show that the proposed method outperforms the state-of-the-art UAV detection and classification approaches in terms of 98.4% and F1 score of 98.3%.

Deep Cascade Networks for Single 2D US Slice to 3D CT/MRI Image Registration

Background and Objective: Ultrasound (US) devices are often used in percutanous interventions. Due to their low image quality, the US image slices are aligned with pre-operative Computed Tomography/Magnetic Resonance Imaging (CT/MRI) images to enable better visibilities of anatomies during the intervention. This work aims at improving the deep learning one shot registration by using less loops through deep learning networks.Methods: We propose two cascade networks which aim at improving registration accuracy by less loops. The InitNet-Regression-LoopNet (IRL) network applies the plane regression method to detect the orientation of the predicted plane derived from the previous loop, then corrects input CT/MRI volume orientation and improves the prediction iteratively. The InitNet-LoopNet-MultiChannel (ILM) comprises two cascade networks, where an InitNet is trained with low resolution images toperform coarse registration. Then, a LoopNet wraps the high resolution images and result of the previous loop into a three channel input and trained to improve prediction accuracy in every loop. Results: We benchmark the two cascade networks on 1035 clinical images from 52 patients , yielding an improved registration accuracy with LoopNet. The IRL achieved an average angle error of 13.3° and an average distance error of 4.5 millimieter. It out-performs the ILM network with angle error 17.4° and distance error 4.9 millimeter and the InitNet with angle error 18.6° and distance error 4.9 millimeter. Our results show the efficiency of the proposed registration networks, which have the potential to improve the robustness and accuracy of intraoperative patient registration.

Data-Driven Remaining Useful Life Prediction for Lithium-Ion Batteries Using Multi-Charging Profile Framework: A Recurrent Neural Network Approach

Remaining Useful Life (RUL) prediction for lithium-ion batteries has received increasing attention as it evaluates the reliability of batteries to determine the advent of failure and mitigate battery risks. The accurate prediction of RUL can ensure safe operation and prevent risk failure and unwanted catastrophic occurrence of the battery storage system. However, precise prediction for RUL is challenging due to the battery capacity degradation and performance variation under temperature and aging impacts. Therefore, this paper proposes the Multi-Channel Input (MCI) profile with the Recurrent Neural Network (RNN) algorithm to predict RUL for lithium-ion batteries under the various combinations of datasets. Two methodologies, namely the Single-Channel Input (SCI) profile and the MCI profile, are implemented, and their results are analyzed. The verification of the proposed model is carried out by combining various datasets provided by NASA. The experimental results suggest that the MCI profile-based method demonstrates better prediction results than the SCI profile-based method with a significant reduction in prediction error with regard to various evaluation metrics. Additionally, the comparative analysis has illustrated that the proposed RNN method significantly outperforms the Feed Forward Neural Network (FFNN), Back Propagation Neural Network (BPNN), Function Fitting Neural Network (FNN), and Cascade Forward Neural Network (CFNN) under different battery datasets.

Multi-Channel Profile Based Artificial Neural Network Approach for Remaining Useful Life Prediction of Electric Vehicle Lithium-Ion Batteries

Remaining useful life (RUL) is a crucial assessment indicator to evaluate battery efficiency, robustness, and accuracy by determining battery failure occurrence in electric vehicle (EV) applications. RUL prediction is necessary for timely maintenance and replacement of the battery in EVs. This paper proposes an artificial neural network (ANN) technique to predict the RUL of lithium-ion batteries under various training datasets. A multi-channel input (MCI) profile is implemented and compared with single-channel input (SCI) or single input (SI) with diverse datasets. A NASA battery dataset is utilized and systematic sampling is implemented to extract 10 sample values of voltage, current, and temperature at equal intervals from each charging cycle to reconstitute the input training profile. The experimental results demonstrate that MCI profile-based RUL prediction is highly accurate compared to SCI profile under diverse datasets. It is reported that RMSE for the proposed MCI profile-based ANN technique is 0.0819 compared to 0.5130 with SCI profile for the B0005 battery dataset. Moreover, RMSE is higher when the proposed model is trained with two datasets and one dataset, respectively. Additionally, the importance of capacity regeneration phenomena in batteries B0006 and B0018 to predict battery RUL is investigated. The results demonstrate that RMSE for the testing battery dataset B0005 is 3.7092, 3.9373 when trained with B0006, B0018, respectively, while it is 3.3678 when trained with B0007 due to the effect of capacity regeneration in B0006 and B0018 battery datasets.

Reconstruction of the Radar Reflectivity of Convective Storms Based on Deep Learning and Himawari-8 Observations

Radar reflectivity (RR) greater than 35 dBZ usually indicates the presence of severe convective weather, which affects a variety of human activities, including aviation. However, RR data are scarce, especially in regions with poor radar coverage or substantial terrain obstructions. Fortunately, the radiance data of space-based satellites with universal coverage can be converted into a proxy field of RR. In this study, a convolutional neural network-based data-driven model is developed to convert the radiance data (infrared bands 07, 09, 13, 16, and 16–13) of Himawari-8 into the radar combined reflectivity factor (CREF). A weighted loss function is designed to solve the data imbalance problem due to the sparse convective pixels in the sample. The developed model demonstrates an overall reconstruction capability and performs well in terms of classification scores with 35 dBZ as the threshold. A five-channel input is more efficient in reconstructing the CREF than the commonly used one-channel input. In a case study of a convective event over North China in the summer using the test dataset, U-Net reproduces the location, shape and strength of the convective storm well. The present RR reconstruction technology based on deep learning and Himawari-8 radiance data is shown to be an efficient tool for producing high-resolution RR products, which are especially needed for regions without or with poor radar coverage.

A Novel Disease Diagnosis Method Using Combining Knowledge Graph and Deep Learning

Disease diagnosis methods based on deep learning have some shortcomings in the auxiliary diagnosis process, such as relying heavily on labeled data and lack of doctor or expert experience knowledge. Based on the above background, this study proposes a disease diagnosis method combining medical knowledge atlas and deep learning (CKGDL). The core of the method is a knowledge-driven convolutional neural network (CNN) model. The structured disease knowledge in the medical knowledge map is obtained through entity link disambiguation and knowledge map embedding and extraction. The disease feature word vector and the corresponding knowledge entity vector in the disease description text are used as the multi-channel input of CNN, and different types of diseases are expressed from the semantic and knowledge levels in the convolution process. Through training and testing on multiple types of disease description text data sets, the experimental results show that the diagnostic performance of this method is better than that of a single CNN model and other disease diagnosis methods. And further verified that this method of joint training of knowledge and data is more suitable for the initial diagnosis of the disease.

Programmability of covariant quantum channels

A programmable quantum processor uses the states of a program register to specify one element of a set of quantum channels which is applied to an input register. It is well-known that such a device is impossible with a finite-dimensional program register for any set that contains infinitely many unitary quantum channels (Nielsen and Chuang's No-Programming Theorem), meaning that a universal programmable quantum processor does not exist. The situation changes if the system has symmetries. Indeed, here we consider group-covariant channels. If the group acts irreducibly on the channel input, these channels can be implemented exactly by a programmable quantum processor with finite program dimension (via teleportation simulation, which uses the Choi-Jamiolkowski state of the channel as a program). Moreover, by leveraging the representation theory of the symmetry group action, we show how to remove redundancy in the program and prove that the resulting program register has minimum Hilbert space dimension. Furthermore, we provide upper and lower bounds on the program register dimension of a processor implementing all group-covariant channels approximately.

Impact of The Correlation Between Channel Input And Side Information On Physical Layer Security Performances of Wireless Wiretap Channel

In this paper, a discrete memoryless wiretap channel with non-causal side information known at the encoder is considered. We (i) characterize capacity region for the Gaussian version of this channel by considering correlation between channel input and side information available at the transmitter; (ii) analyze the impact of correlation on the performance of physical layer security in a Rayleigh fading wiretap channel by deriving closed-form expressions on the average secrecy capacity (ASC) and secrecy outage probability (SOP). Further, to more show the impact of side information, asymptotic behavior of SOP is studied. Numerical evaluation of theoretical results is done finally.

Complex incremental ΣΔ ADC.

This thesis examines the theory and design of incremental Sigma-Delta (ΣΔ) modulators when applied to complex oversampling analog-to-digital converters (ADCs). Two different types of approaches for the complex ADC are analysed and compared. The first system is a traditional complex bandpass over-sampling ADC with incremental (time limited) ΣΔ architecture. This system uses cross-coupling switch capacitor (SC) integrators and quadrature two channel inputs. The second system uses a low-pass architecture with time interleaved integrators. This system does not have a mismatch between the in-phase and quadrature phase (I/Q) output channels. The input is frequency shifted down to DC during the conversion. A graphical user interface (GUI) design toolbox was created to design and simulate the two types of systems. The bandpass second-order system was fabricated in an IBM 130nm CMOS process with a 83kHz two channel input and 10kHz bandwidth at an OSR of 24.

Complex incremental ΣΔ ADC.

This thesis examines the theory and design of incremental Sigma-Delta (ΣΔ) modulators when applied to complex oversampling analog-to-digital converters (ADCs). Two different types of approaches for the complex ADC are analysed and compared. The first system is a traditional complex bandpass over-sampling ADC with incremental (time limited) ΣΔ architecture. This system uses cross-coupling switch capacitor (SC) integrators and quadrature two channel inputs. The second system uses a low-pass architecture with time interleaved integrators. This system does not have a mismatch between the in-phase and quadrature phase (I/Q) output channels. The input is frequency shifted down to DC during the conversion. A graphical user interface (GUI) design toolbox was created to design and simulate the two types of systems. The bandpass second-order system was fabricated in an IBM 130nm CMOS process with a 83kHz two channel input and 10kHz bandwidth at an OSR of 24.