Miracle-dz/UniFAR-FTUs huggingface dataset:
ITO-Free Organic Light-Emitting Transistors with Graphene Gate Electrode

In this work, we report on the fabrication and characterization of organic light-emitting transistors (OLETs) within an indium–tin-oxide (ITO)-free platform, using graphene-based transparent conductive electrodes in place of ITO as gate electrode. A direct comparison between twin bottom-gate/top-contacts OLETs, where a standard ITO layer is replaced with a film made of a few graphene layers, shows that comparable electrical characteristics can be obtained along with a clear improvement in the electroluminescence generation characteristics. Our experimental findings pave the way to the exploitation of graphene-based transparent conductive electrodes within this class of emerging devices on flexible substrates, further promoting the novel era of flexible organic electronics.
Hybrid Machine Learning Model Using Particle Swarm Optimization for Effectual Diagnosis of Alzheimer's Disease from Handwriting

Alzheimer's disease (AD) is a condition that weakens nerves, impairs cognitive and memory functions, and eventually results in amnesia. An improved approach for early diagnosis of AD is crucial to identify and control the decline as they advance. Machine Learning (ML) approaches have proved successful in intelligently identifying patterns in the data to categorize it into different classes. Hybrid ML models with Swarm Intelligence (SI) based feature selection will result in robust and efficient model for diagnosis and prediction. In this work, Particle Swarm Optimization has been used for feature selection to identify the best performing feature dimension resulting in a hybrid ML model with good performance. Six classifier models have been explored in the hybrid ML.
Single-Sample Direction-of-Arrival Estimation by Hankel-matrix Decompositions

Modern networked robotic platforms operating autonomously on the ground, in the air, or in space over highfrequency bands (e.g., mm-wave or future THz) require rapid and effective estimation of the direction of arrival (DoA) of signals of interest to maintain high data rate connectivity with each other and avoid interference from external in-band sources. High robotic platform mobility limits -or completely negates- our ability to wait and collect the necessary statistically stationary sequence of antenna-array-front measurements. As a result, conventional statistical DoA estimation optimization methods may not be applicable. In this paper, we present for the first time in the literature a single-sample DoA estimation algorithm based on Hankel-matrix-representation and singular-value decomposition (SVD) of the individual antenna-array snapshot. We compare the newly proposed estimator against the Maximum Likelihood (ML) single-sample estimator of the DoA of a signal observed in white Gaussian noise and -arguably surprisingly- demonstrate significant superiority in each metric of interest, such as meansquare estimation error, bias, and variance.
Realization of Fractional order Multivibrator using different approximation techniques

The working of free running multivibrators using fractional order capacitor is studied in this paper. Initially rational approximation methods for the fractional order operator are deeply investigated. Then an equivalent RC circuit is synthesized. The synthesized circuit is used in place of fractional order capacitor. The performance of the circuit is studied by making use of different approximation techniques. It has been observed that the curve fitting based approximation has produced higher frequencies compared to others when fourth order approximation is used. All simulations are carried out using TINA software.
Bearing Health Condition Monitoring- A Brief Exposition

Industrial machines run continuously to meet the set target. These machines comprise different components, and the rolling element bearing is one such component. Bearings provide smooth relative motion between two moving parts. In an industry where the machines are working continuously in hostile operating conditions, the bearings experience heavy loads. Hence, it leads to faster deterioration of the bearing's health and starts surfacing in the form of various defects in the bearing components. If these defects are not identified in the developing stage, it will cause the disastrous failure of the complete system. Hence, bearing condition health monitoring is a crucial aspect of machinery health monitoring, and a substantial amount of study has been done in this area. Bearing fault signatures can be identified by using vibration data, acoustic signals, bearing temperature, wear debris analysis, etc. or in a combination of these parameters. The vibration data measurement is a quick, accurate and mostly followed technique in the bearing condition monitoring field. Various time, frequency & time-frequency domain vibration analysis methods are used to extract the bearing fault signatures from the vibration signal accurately. This review paper consolidates the methods used in bearing condition monitoring using vibration data and evaluates them based on their merits, demerits and limitations.
A Deep Learning Approach for Doppler Unfolding in Automotive TDM MIMO Radar

Time-division multiplexing (TDM) is an easy way to achieve waveform orthogonality in automotive multiple-input multiple-output (MIMO) radar to synthesize a large virtual array to achieve high-resolution radar imaging for autonomous driving. Under TDM, only one transmit antenna is scheduled to transmit at each time slot. As a result, the maximum unambiguous detectable Doppler or radial speed is reduced by a factor of the number of transmit antennas. Additionally, there is phase migration in the array response for moving targets that needs to be compensated before angle estimation. The staggered pulse repetition intervals (PRIs) in consecutive frames can be used for Doppler unfolding. However, it has high computational complexity in pairing multiple targets. In this paper, we propose a fast and robust solution using a one-dimensional convolutional neural network to unfold the Doppler estimation in automotive TDM MIMO radar. The input of the neural network is the beam vector of the virtual array and the output is the unfolded velocity estimate for phase compensation. Extensive numerical experiments show that accurate Doppler estimation is achieved at a reasonable signal-to-noise ratio (SNR). Radar field experiments using Texas Instruments imaging radar support our theoretical investigations.
LiDAR Odometer Based on Visual Enhancement

A single LiDAR odometer is prone to location failure in similar scenarios using only geometric information, where its location accuracy is relatively low. A novel LiDAR odometer based on visual enhancement is proposed in this paper to solve those problems. Firstly, the point cloud from LiDAR is attached with visual information through the fusion of the image and the point cloud. Then the colour continuum surface is constructed to transform the discrete colour information of the point cloud into the continuous colour information, from which photometric constraint is built. Finally, the odometer is established by photometric constraint and geometric constraint. Experiments on the KITTI data set show that the proposed odometer is more accurate and robust than other single LiDAR odometers such as ICP-SVD, ICP-GN, NDT and LOAM under the same conditions, which verifies the progressive nature of the proposed method.
The Non Dominated Set in Bayesian Decision Problems with Convex Loss Functions

Bayesian decision problems require subjective elicitation of the inputs: beliefs and preferences. Sometimes, elicitation methods may not represent perfectly the judgements of the decision maker. Several foundations propose to overlay this problem using robust approaches. In these models, beliefs are modelled by a class of probability distributions and preferences by a class of loss functions. Then, we are in the conditions of a Pareto order. Hence the solution concept is the set of non dominated alternatives. In this article we focus on the computation of the efficient set when the preferences are modeled by a class of convex loss functions.
Efficient Multi-task Adaption for Crossbar-based In-Memory Computing

ReRAM crossbar Non-volatile memory (NVM) based In-Memory Computing (IMC) has been widely investigated as a highly parallel, fast, and energy-efficient computing platform for Deep Neural Networks (DNNs), especially for one specific task inference. However, due to the intrinsic high energy consumption of weight re-programming and the relatively low endurance issue, adapting the ReRAM crossbar-based IMC hardware for continual learning or multi-task learning has not been well explored. In this paper, we discuss a crossbar-aware learning method with a 2-tier masking technique that could enable efficient and fast new task adaption for a deployed DNN model with minor hardware overhead.
Deep Reinforcement Learning for Interference Management in Millimeter-Wave Networks

Interference is a long-lasting problem in cellular networks. As cell deployment becomes denser, interference becomes a major obstacle to improving network throughput. In this paper, we propose using deep reinforcement learning for interference management via joint beamforming and power control in multi-cell networks. The goal is to maximize the signal-to-interference-plus-noise ratio (SINR) in the dynamic environment of millimeter-wave multi-cell networks. The proposed algorithm works for an arbitrary number of cells to improve the network performance, measured by achievable SINR and sum-rate. Simulation results show that our algorithm performs significantly better than state-of-the-art and is close to the optimal scenario in terms of coverage and sum-rate performance.
Resource Allocation with Vickrey-Dutch Auctioning Game for C-RAN Fronthaul

The network slicing concept divides physical networks into logical networks and abstracts the network resources. With the help of virtualization technologies, these abstracted network resources can be allocated to service providers and resources can dynamically be added to these slices based on users' demands. The infrastructure sharing model with slicing makes it possible for services to lease the resources of the infrastructure provider. This study considers optical network resource allocation from a profit generation perspective with a game, in which service providers bid to lease C-RAN fronthaul paths via auctioning with Vickrey-Clarke-Groves outcomes. The game aims to distribute fronthaul resources with a social-welfare maximizing outcome. Service providers maximize their revenue by predicting user demand and requesting bandwidth resources from the infrastructure provider by bidding in the auction. Users have the option to change their association and switch between the service providers to maximize their utility. The results display that a balanced profit and social welfare trade-off can be achieved in converged optical and mmWave radio networks infrastructure sharing scenario with Vickrey-Dutch auctioning and distributed decision-making.
Maximum Likelihood Structured Covariance Matrix Estimation and connections to SBL: A Path to Gridless DoA Estimation

In this paper, we revisit the framework for maximum likelihood estimation (MLE) as applied to parametric models with an aim to estimate the parameter of interest in a gridless manner. The approach has inherent connections to the sparse Bayesian learning (SBL) formulation, and naturally leads to the problem of structured matrix recovery (SMR). We therefore pose the parameter estimation problem as a SMR problem, and recover the parameter of interest by appealing to the Carathéodory-Fejér result on decomposition of positive semi-definite Toeplitz matrices. We propose an iterative algorithm to estimate the structured covariance matrix; each iteration solves a semi-definite program. We numerically compare the performance with other gridless schemes in literature and demonstrate the superior performance of the proposed technique.
Comparative Analysis of Bone Age Assessment Techniques Using Hand X-Ray Images and Gender Feature

Estimated bone age is an essential measure of the growth and development of a person. As an indicator, the chronological and bone ages of the person predict the height and maturity time of the body. Statistic shows that men have greater average height than women, indicating that gender plays a role in human growth. This research aimed to utilize machine learning methods to predict bone age using hand X-ray images and analyze the effect of the presence of gender features on bone age prediction ability. Aiming at this problem, this paper proposes the use of convolutional neural network (CNN) with transfer learning methods in predicting bone age. We used VGG19, ResNet50V2, and DenseNet201 architectures as the pretrained models for comparison. The models were trained using hand X-ray images with and without gender variables. In this paper, the models are trained on a dataset consisting of 5000 hand X-ray images from RSNA2017. The images came with the corresponding gender and actual bone age ranging from 0 to 228 months old. In the experimental design, our models are divided into two scenarios. In the first scenario, we used our proposed transfer learning methods with three architectures, but gender information was not included as an input. In the second scenario, we used the same proposed models but with gender provided as an additional feature input. In this research, the models were evaluated by Mean Absolute Error (MAE) in months with a smaller MAE indicating better performance. The experimental results indicate that models added with the gender feature have lower MAE than the same models without gender feature. Models with VGG-19 architecture also have lower MAE than other architecture models. The model with VGG-19 that takes only hand X-ray image achieves an MAE of 14.544 months, whereas the same model that takes both hand X-ray image and gender feature achieved an MAE of 11.507 months.
Quantum-size effects in n-type bismuth thin films

Oscillatory thickness dependences of the electrical conductivity, Hall coefficient, charge carrier mobility, and Seebeck coefficient were obtained at room temperature for n-type thin Bi films (d=3–300 nm) fabricated by the thermal evaporation of a bismuth crystal in a vacuum and deposition on mica substrates at 380 K. We attribute this oscillatory behavior to quantum-size effects, which are observable when the electron mean-free path and Fermi wave length exceed the film thickness d.
Anisotropic interfacial strain in InP/InGaAs/InP quantum wells studied using degree of polarization of photoluminescence

Room-temperature, polarization-resolved photoluminescence from a (001) surface has been used to investigate InP/InGaAs/InP quantum wells grown by gas source molecular beam epitaxy. The degree of polarization of photoluminescence from a (001) surface, DOP001, is a direct measure of the anisotropy of polarization of luminescence between [110] and [11 0] directions. DOP001 is observed to be strongly dependent on the quantum well thickness, composition (strain), and the gas switching time at the growth-interrupted interface. Results show that the anisotropy of polarization may be due to an effect of an anisotropic strain field that is associated with strained bonds at the interfaces of the quantum well.
Research on Traffic Situational Awareness System Based on Software-Defined Network

With the progress of science and technology, the scale of the network structures built by people is getting larger and larger, and the types of network structures are becoming more and more complex. The Software-Defined Network makes operating and deploying the network more manageable by splitting the data forwarding and control functions. However, the complexity of application scenarios has significantly increased the difficulty of controlling the entire network. However, Cyberspace Traffic Situational Awareness enables multi-source network data extraction, fusion, analysis, and prediction of future trends. At present, most of the network situations such as traffic and survival type are less studied. The traditional situation prediction model is suitable for historical event sequence data that is relatively stable, less sudden, and less dynamic, which does not apply to SDN networks with strong dynamics. In order to solve the above problems, this paper proposes a traffic situational awareness system based on the software-defined network and related evaluation index system. By implementing the traffic situational assessment function and prediction function on the SDN framework, the performance management strategy of the SDN network is further improved.
Emotion Recognition Based on EEG Brain Rhythm Sequencing Technique

This work proposes a technique that analyzes electroencephalography (EEG) using brain rhythms (<inline-formula> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\alpha $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula>) presented in a sequential format and applies it for emotion recognition. Although brain rhythms are regarded as reliable parameters in EEG-based emotion recognition, to achieve high accuracy by considering fewer optimal multichannel rhythmic features (MCRFs) has not been addressed in detail. Thus, the rhythm sequence for each channel is generated by choosing the strongest brain rhythm having the maximum instantaneous power for every 200-ms time bin. A <inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-nearest neighbor (<inline-formula> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula>-NN) classifier is employed for evaluating the rhythmic features extracted from different sequences, and the experimental validation was performed on three well-known emotional databases (DEAP, MAHNOB, and SEED). The results showed that approximately 30% of MCRFs for as high as 87%–92%, achieving high classification accuracies with a small number of data. Further investigation revealed that the frontal and parietal regions are active during the emotional process, as consistent as earlier studies. Therefore, the proposed technique demonstrates its availability and reliability for emotion recognition. It also provides a novel solution to find optimal channel-specific rhythmic features in EEG signal analysis.
Asymmetric Bilinear Inference for Joint Communications and Environment Sensing

Beyond-fifth-generation (B5G), sixth-generation (6G) and Internet-of- Things (IoT) applications, such as autonomous driving and robot-assisted living anticipate scenarios where multiple user equipment (UE) not only communicate with multiple access points (APs), but also collaborate among themselves and utilize communication signals in the detection and identification of objects in the environment. Motivated by this emerging paradigm, joint communications and sensing (JCAS) systems have been heavily investigated in recent years. This paper contributes to this field with a novel JCAS algorithm to extract environmental information from the scattered paths of communication signals typical of the high frequency bands envisioned for B5G and 6G systems. In particular, we model the environment as a voxelated occupancy grid and employ a bilinear Gaussian belief propagation (BiGaBP) framework to jointly estimate the voxel occupancies (filled/empty space) and the data symbols. Simulation results verify the effectiveness of the proposed algorithm in both signal and environment estimation, where the three-dimensional (3D) environment objects are perfectly reconstructed with sufficient signal-to-noise ratio (SNR) while achieving a low communications error rate.
A Kernel-Based Real-Time Adaptive Dynamic Programming Method for Economic Household Energy Systems

Modern home energy management systems (HEMSs) have great flexibility of energy consumption for customers, but at the same time, bear a range of problems, such as the high system complexity, uncertainty and time-varying nature of load consumptions, and renewable sources generation. This has brought great challenges for the real-time control. To solve these problems, we propose an HEMS that integrates a kernel-based real-time adaptive dynamic programming (K-RT-ADP) with a new preprocessing short-term prediction technique. For the preprocessing short-term prediction, we propose a gated recurrent unit-bidirectional encoder representations from the transformer (GRU-BERT) model to improve the forecasting accuracy of electrical loads and renewable energy generation. In particular, we classify household appliances into the temperature-sensitive loads, human activity sensitive loads, and insensitive/constant loads. The GRU-BERT model can incorporate weather and human activity information to predict load consumption and solar generation. For real-time control, we propose and employ the K-RT-ADP HEMS based on the GRU-BERT prediction algorithm. The objective of the K-RT-ADP HEMS is to minimize the electricity cost and maximize the solar energy utilization. To enhance the nonlinear approximation ability and generalization ability of the adaptive dynamic programming (ADP) algorithm, the K-RT-ADP algorithm leverages kernel mapping instead of neural networks. Hardware-in-the-loop experiments demonstrate the superiority of the proposed K-RT-ADP HEMS over the traditional ADP control through comparison.
A Distinguished Method for Network Intrusion Detection using Random Initialized Viterbi Algorithm in Hidden Markov Model

Intrusion Detection System (IDS) is a system that surveils the dubious network activity. There are several approaches which deal with intrusion detection and cyber-attack detection, but the most optimal IDS would be the one which can predict the upcoming threats along with detecting the present attacks. The Machine Learning probabilistic models work remarkably in prediction of threats among these models, Hidden Markov Model (HMM) outperforms all other models. HMM is widely used in cryptanalysis, gene prediction, computational linguistic, speech analysis as well as its synthesis and network attacks detection and prediction. In this paper, we have proposed a distinct methodology using Viterbi algorithm of HMM which is initialized with the random parameter. Our methodology significantly upsurges the detection accuracy of the current state along with all states, it also enhances the prediction accuracy of the next feasible state when compared to existing approaches.
Hypergraph 1-Spectral Clustering with General Submodular Weights

We develop a spectral clustering algorithm for general submodular hypergraphs based on their 1-Laplacians. More precisely, we utilize the eigenvector associated with the second smallest eigenvalue of the hypergraph 1-Laplacian to cluster the vertices. The computation of the eigenvector is based on the inverse power method, the key of which is to solve its inner-loop optimization problem. Efficient solutions to this inner problem exist when submodular hypergraphs are equipped with hyperedge splitting functions with special structures such as when they are cardinality based or graph reducible. In this paper, we present a solution to the inner problem for general submodular splitting functions by adopting a random coordinate descent method together with the Fujishige-Wolfe algorithm. Numerical experiments using real-world data demonstrate the effectiveness of the proposed clustering algorithm.