Miracle-dz/UniFAR-FTUs huggingface dataset:
Multipass Circular SAR 3D Imaging via $L_{q}$-based Complex Approximate Message Passing algorithm

Multipass circular synthetic aperture radar (MCSAR) three-dimensional (3D) imaging is a challenging problem. For anisotropic scattering observation targets, the strategy of subaperture incoherent 3D imaging is generally adopted. When the target is reconstructed along the elevation direction in the multipass circular SAR subaperture image stack, the reconstruction method via $L_{q}$-norm $(0 < \mathrm{q} < 1)$ regularization is proposed in this paper. In subaperture 3D target reconstruction, the $L_{1/2}$-based complex approximate message passing (CAMP) reconstruction algorithm combined with the Bayesian information criterion (BIC) is proposed in this paper. Combined with BIC, the estimation of the sparsity in elevation can be obtained, and the $L_{1/2}$-based CAMP algorithm can be used to reconstruct the target in elevation rapidly and accurately. Compared with the target reconstruction algorithm in elevation with multipass circular SAR data, such as the $L_{1}$ norm-based method, the clear observed target 3D image with fewer desultory points can be obtained by the proposed method. Detailed experiments on real MCSAR data demonstrate the advantages of the proposed method.
Detection and Staging of Lung Cancer from CT scan Images by Deep Learning

Lung cancer is jeopardized disease which easily demolish human body with great impact. Lung Cancer is most prominent disease nowadays. To diagnosis this minacious disease CT scan is one of the methods which is more often used. Manual detection of anomalies from medical images It takes a lot of time and is prone to aberration. Several computer-aided diagnosis methods have been developed for the accurate diagnosis of cancers from images. With the advent of deep learning approaches in image processing, researchers have moved to developing new and efficient models for cancer diagnosis and classification. In this work, a convolution neural network-based approach has been presented for the prediction and categorization of cancers from lung CT scans. After image preprocessing and resizing, the class imbalance is taken care of by augmentation. Batch normalization, max pooling, and flattening are some of the steps used in the model. Finally, fully connected layer functioned to classify the images into four classes, namely benign, primary lung cancer, malignant, and suspicious malignant. The results obtained are comparable to the ones achieved by other researchers so far.
Effects of Carasau Dough Composition on the Microwave Dielectric Spectra up to 20 GHz

Carasau bread is a traditional product from Sardinia (IT). This flat bread is experiencing industrial advancement, through automation, and has great market potential. However, there is lack of understanding of how the composition (water content, salt, and yeast concentration) affects the product quality. In this work, a microwave dielectric spectroscopy study is performed to investigate how the composition of Carasau bread doughs influences the spectra of this food product up to 20 GHz. A third-order Cole–Cole model was used for the physical and quantitative understanding of the electromagnetic properties of this food product. Then, it has been studied how salt, yeast, and water variations affected the model parameters. This work could pave the route to the development of non-destructive, contactless microwave sensors for Carasau bread quality assessment.
Analysis of Different Feature Selection Techniques on Different Machine Learning Approaches to Classify Leukemia Subclasses

The correct identification of specific cancer subclasses is one of the significant challenges in the treatment of leukemia. Utilizing genomic profiles from microarrays is a useful tool for classifying different cancer subtypes. It is difficult since using vast amounts of gene expression data will demand a lot of processing power. The findings in our study focus on comparing several feature selection techniques in order to identify the genetic expressions that are extremely lethal. The feature selection procedure improves the algorithms’ capacity for prediction. The computational cost and data volume can be significantly reduced by choosing the appropriate feature. The correct identification of specific cancer subtypes is one of the major difficulties in treating leukemia. Utilizing genetic profiles from microarrays is a useful tool for classification. To classify Acute Myeloid Leukemia (AML) and Acute Lymphoblastic Leukemia (ALL) on features chosen in three different ways, namely the Filter method, Wrapper method, and Embedded method, six machine learning algorithms, and an Artificial Neural Network was constructed. One process achieved a success rate of 100% and the rest of the procedures procured about 95% accuracy on average.
Diospyros kaki’s phytochemical mediated inhibition of GBM by targeting PDK-1: An in-silico docking and machine learning model.

Glioblastoma Multiforme (GBM), a grade 4 brain tumor, is resistant to standard treatments and exhibits a 100 percent recurrence rate. By obstructing the primary pathways involved in tumour sustenance and progression, GBM can be completely eliminated. Since high levels of p-AKT (Phosphorylated Protein Kinase B) have been detected in recurring GBM, the AKTmTOR pathway has received the most attention in GBM therapies. AKT can be activated by PDK-I (3-phosphoionsitide-dependent kinase1) phosphorylation. Targeting and hampering PDK-I can prevent such activation. Through in-silico analysis, this study seeks to investigate several phytochemicals that have the potential to target PDK-I. The phytochemical profile of Diospyros kaki holds immense significance. Carotenoids, tannins, and phenolic compounds impart Diospyros kaki excellent anti-oxidant potential making it a promising candidate for anticancer drug compound extraction. After docking 40 selected phytochemicals against PDK-I protein, the results revealed that Diospyrin and Neodiospyrin show maximum binding energies (-10.6 and -10.4kcal/mol respectively) comparable to that of the standard inhibitor 2-(5-{[2R)-2-amino-3- phenylpropyloxy}pyridine-3-yl)8,9dimethoxybenzo[c] [2], [7]naphthy ridine-4-amine (ID-SII) compound (-10.1kcal/mol). Validating the docking results by machine learning, multiple analyses were carried out to find the drug likeness, bioactivity and bioavailability of the selected phytochemicals so as to imply them as a therapeutic agent against GBM.
Multi-label Classification of Hosts Observed through a Darknet

To observe compromised hosts at Internet-scale, a darknet or network telescope collects Internet background radiation that includes large-scale phenomena like DDoS (Distributed Denial-of-Service) or scanning. Gathered data is however very partial and labeling such traffic to precise activities thanks to external databases is far from being satisfactory (8.4% of IP addresses in our case). In addition, as compromised hosts are used for multiple malicious activities, they cannot be classified in a unique category. We propose in this paper a new multi-label classification method by representing traffic generated by a host as a graph and leveraging machine learning algorithms (Node embedding and Graph Convolutional Networks). From partial information about IP addresses, our method can label addresses with a precision of 0.80 and recall of 0.81.
Thelxinoë for Emotion Recognition and Touch Processing

This paper presents the framework for the creation of touch in virtual reality (VR) with the processing of emotions from multiple sensors, and the processing of touch and sensation generation. We use Gradient Boosting (GB) and Long Short-Term Memory (LSTM) models to process the aggregated emotion data from brain waves, physiological changes, pupillometry, facial recognition, speech, and background effects to recreate the emotional state of a person in VR. In our real life day-to-day existence we connect with our environment, and be it real life or VR, we connect. Emotion creates that connection that enhances in person communication and by adding these two to the virtual world, we are pulling more of the real world into the experience which can only enhance it. Touch is the first sense we experience at birth. The contribution of this paper is the introduction of a multi-sensory collection framework called Thelxinoë that collects emotion data from multiple sensors including brain waves, facial expression, pupillometry, speech, and body movement. This is then aggregated within a black box that generates an output matching the emotions at the time of collection. Those emotions are used to facilitate the action of touch generated by the parties by creating sensations based on the initiated touch.
A Survey on Handover Optimization in Beyond 5G Mobile Networks: Challenges and Solutions

Handover (HO) management is essential in mobile cellular networks. It ensures seamless connectivity to the User Equipment (UE) while moving from one Base Station (BS) to another within the coverage area. HO optimization refers to the adoption of intelligent and automatic HO techniques in mobile networks. HO optimization is gaining more importance in Fifth-Generation (5G) and Beyond (B5G) systems because of the requirements and specifications that B5G targets. The requirements of B5G include global connectivity, ultra-low latency, big data analytics, extreme data rate transmissions, a massive number of devices in a small area, and new technologies that will support the B5G network, such as Millimeter Wave (mmWave), Terahertz (THz) communication, and Ultra-Dense Networks (UDNs). All these factors cause new HO optimization challenges and require new solutions for HO optimization techniques. This study comprehensively provides HO optimization challenges and solutions in B5G. First, it provides a research background and explanation of HO in legacy. It then investigates the HO optimization challenges in B5G, including future research directions. The paper then discusses the most prominent and recent techniques and technological solutions for HO optimization management in B5G. Finally, we highlight potential techniques for HO optimization in B5G.
Multifractal Properties of Solar Magnetograms

We explore the multiscale properties of magnetic fleld distributions using magnetograms obtained from the Michelson Doppler Imager (MDI) on board the Solar and Heliospheric Observatory (SOHO). Multifractal spectra were estimated with the help of microcanonical formalism applying Choquet capacities. We have extracted spectra both for active regions (AR) and for regions outside of AR. It has been found that background fleld multifractal spectra are stable and do not change their forms during approximately 24 hours. The spectra of active regions are more changeable, but there is no evidence that their variability is associated with the onset of the ∞ares. To verify the magnetic fleld multifractal scaling existence, we construct a toy model with three symbols, a version of the Chaos Game. The empirical measures and those of the toy model (obtained from the solution of inverse problem, by means of Iterated Functions System (IFS) with Markov random choice of the functions) were compared. As the constructed models provide a satisfactory flt to theoretical and empirical measures of toy models, we conclude that obtained scaling estimations of the magnetograms can really result from the multifractal properties of the solar magnetic fleld dynamics.
Predicting Mobility Management Demands of Cellular Networks based on User Behavior

Mobile network operators continuously ! aim to optimize network resources to satisfy user demands and be energy efficient. In the era of network function virtualization, predicting the demands for certain functional resources becomes a necessity. Resource optimization is usually based on modeling the user behavior and demands – which gets more challenging if the behavior and demand change dynamically. This applies to the mobile network control plane, where session and mobility management load could have certain seasonality, although the ever-changing user behavior modulates the models. As user behavior keeps varying, traditional mathematical time series analysis cannot provide sub-optimal results. This paper proposes using machine learning methods to predict certain mobility management efforts, such as handovers and tracking area updates. In the cellular network, HO (Handover) and TAU (Tracking Area Update) messages are used for user mobility management, among others. These appear in the S1AP protocol for LTE and 5G NSA (Non-Standalone Architecture). In this paper, we use real-life HO and TAU message records and aim to predict their volumetric appearance for the upcoming time window. We compare the prediction results for a baseline, a linear, a CNN (Convolutional Neural Network), and an LSTM (Long Short-Term Memory NN) model. The ultimate aim of these predictions is to estimate the expected network demands for certain network functions.
Integration of Machine Learning and Kalman Filter Approach for Fingerprint Indoor Positioning

Fingerprint-based indoor positioning systems are simple and widely used to determine the location of a device inside a building or other enclosed area. However, the accuracy and reliability are still a major concern due to the turbulence in the environment and the presence of noise in the data. This paper presents a machine learning integrated with Kalman filter approach for improving the accuracy of fingerprint-based indoor positioning systems. The proposed approach combines the power of machine learning techniques for feature extraction and classification with the noise-filtering capabilities of the Kalman filter. Implementation is achieved by a real-world dataset collected from multiple Bluetooth low energy access points. The experiment results indicate that the proposed approach significantly improves the accuracy of fingerprint-based indoor positioning compared to traditional machine learning approaches. This study also offers a potential of cost-effective and high accuracy algorithm for the indoor positioning applications.
Robust Mean Estimation in High Dimensions: An Outlier-Fraction Agnostic and Efficient Algorithm

The problem of robust mean estimation in high dimensions is studied, in which a certain fraction (less than half) of the datapoints can be arbitrarily corrupted. Motivated by compressive sensing, the robust mean estimation problem is formulated as the minimization of the <inline-formula> <tex-math notation="LaTeX">$\ell _{0}$ </tex-math></inline-formula>-‘norm’ of an <italic>outlier indicator vector</italic>, under a second moment constraint on the datapoints. The <inline-formula> <tex-math notation="LaTeX">$\ell _{0}$ </tex-math></inline-formula>-‘norm’ is then relaxed to the <inline-formula> <tex-math notation="LaTeX">$\ell _{p}$ </tex-math></inline-formula>-norm (<inline-formula> <tex-math notation="LaTeX">$0< p\leq 1$ </tex-math></inline-formula>) in the objective, and it is shown that the global minima for each of these objectives are order-optimal and have optimal breakdown point for the robust mean estimation problem. Furthermore, a computationally tractable iterative <inline-formula> <tex-math notation="LaTeX">$\ell _{p}$ </tex-math></inline-formula>-minimization and hard thresholding algorithm is proposed that outputs an order-optimal robust estimate of the population mean. The proposed algorithm (with breakdown point <inline-formula> <tex-math notation="LaTeX">$\approx 0.3$ </tex-math></inline-formula>) does not require prior knowledge of the fraction of outliers, in contrast with most existing algorithms, and for <inline-formula> <tex-math notation="LaTeX">$p=1$ </tex-math></inline-formula> it has near-linear time complexity. Both synthetic and real data experiments demonstrate that the proposed algorithm outperforms state-of-the-art robust mean estimation methods.
FPGA-Based Modified Ring Oscillator Physical Unclonable Function for Internet of Vehicles

The Internet of Things (IoT) has opened up a world of opportunities for improving our daily lives. One offshoot of the IoT is the Internet of Vehicles (IoV), which aims to enhance traffic safety and provide low route information. However, the security of information exchanged between moving vehicles and wireless mediums is crucial. Physical Unclonable Function (PUF) is an effective security technology that creates a hardware fingerprint based on the unique characteristics of a device. The Ring Oscillator (RO) PUF is the most commonly used PUF. This study proposes an enhanced RO PUF design that generates a 64-bit secret key well-suited for IoV applications. The proposed design demonstrates significant improvements in PUF performance, with a uniqueness, reliability, and uniformity of 49.83%, 99.93%, and 49.75%, respectively. These findings suggest that the enhanced RO PUF design provides effective and reliable solutions for securing IoT devices through PUF-based authentication.
Innovative Fuzzy Models for Mapping Acacia catechu Using Semi-Hypertemporal Satellite Images

The use of semi-hypertemporal (SH) satellite data can play an important role in species differentiation based on the physiological and phenological characteristics of different plant species in a forest. Mapping of plant species is essential for addressing problems related to their management and conservation. This study has been conducted to map Acacia catechu from a heterogeneous forest area using SH images in fuzzy-based classifiers. This study has claimed that an individual sample as mean (ISM) training approach outperforms the conventional mean-based approach for selecting the training samples for fuzzy-based possibilistic $c$ means (PCM), noise clustering (NC), and modified PCM (MPCM) algorithms. Furthermore, the SH dataset is found to be better for a particular species extraction in contrast to the single date image, using both the conventional and ISM approaches. Moreover, an increasing number of images in the SH dataset produces significantly better classification outputs. This study can be used to map the endangered species with improved classification accuracy and for better management practices.
Performance Investigation of Polarity Controllable-Ferroelectric-FET for Hydrogen Gas Sensing Applications

In the present study, the sensing performance of Polarity Controllable-Ferroelectric-FET (PC-FE-FET) is extensively investigated for hydrogen gas sensing applications. The study employs the baseline approach wherein simulated data is solved self-consistently with Landau Khalatnikov equation. It will be demonstrated that ferroelectric insulator present in gate stack of PC-FE-FET aids to improve sensing performance in terms of enhanced ON-state current and reduced subthreshold swing values. Further, the proposed PC-FE-FET based hydrogen gas sensor exhibits average sensitivity value of transconductance generation factor (δgm/IDS) as high as 96.53 (3.14) for n-(p-) mode operation in comparison to conventional polarity controllable gas sensor that exhibits δgm/IDS ≈ 49.54 (1.19).
Mobile Spaghetti Analysis in Grocery Store with Smartphone Sensing

Spaghetti analysis is a survey conducted for the purpose of studying customer shopping behavior in a store. Each customer's behavior is observed visually by a hired researcher. The human cost of the survey is a significant challenge. In this study, we propose a framework for mobile spaghetti analysis that can be performed using sensor data obtained from customers' smartphones and a skeleton map, which is a map of the store interior. The performance of the proposed framework was evaluated in a rigorous laboratory environment and in an actual store to confirm its usefulness. In addition, a spaghetti analysis was conducted on more than 500 customers in an actual store using the proposed method, and from the results, it was possible to understand customer behavior patterns that were not known by the store staff without conducting a large-scale survey.
State-Space Average Modeling Based PID Control for Three Novel Topologies of DC-DC Boost Converter

The objectives of this paper are to obtain state-space average models of the three new topologies of the DC-DC boost converter and apply Proportional Integral Derivative (PID) controller on them to analyze their performance. One of the most important applications of DC-DC boost converter in modern world is in Photovoltaic (PV) systems. It boosts up the low DC voltage coming from the PV panels to a high DC bus voltage and it drives the DC-AC inverter. Thus, the efficiency and the control of it play very important role in this entire process. Recently, some new topologies have been proposed to improve the efficiency of this converter. In order to inspect these topologies, proper mathematical modeling is required, and the state-space average model is one of the efficient techniques to do that. Among various control techniques, PID controller is widely used to get the best-desired output voltage of this converter. To get the expected result, MATLAB Simulink models and MATLAB m-file code will be used in this paper.
Feature-Based Registration Framework for Pedicle Screw Trajectory Registration Between Multimodal Images

Pedicle screw placement for vertebral fixation is a complicated surgery for orthopaedic surgeons. The main challenge is to estimate the accurate trajectory’s position to minimize post-operative complications related to pedicle screw placement. Different types of 3D to 2D registration techniques have been employed to avoid the misplacement of the screw during the surgery. However, these techniques cannot be applied directly to MR to X-ray registration due to differences in image intensity and tissue non-correspondence. To overcome these limitations, feature-based 3D to 2D registration technique was developed to map a trajectory position in the intra-operative X-ray image on to the pre-operative MR image. The registration framework validated by generating projection images that perfectly matched simulated X-ray images, then back-projecting the trajectory position on the pre-operative MR image using the estimated transformation parameters. The accuracy of the registered trajectory evaluated by measuring the displacement and directional errors between the registered and planned trajectory. The proposed method successfully registered the trajectory position in the simulated X-ray to pre-operative MR to estimate the trajectory position. A number of experiments are performed on the simulated dataset to assess the effectiveness of the proposed method. The Euclidean distance between the entry and end points and the directional error of the registered trajectory from the planned trajectory were below 1mm in AP, Lateral, and a combination of both planes. The mean trajectory length difference between the planned and registered trajectory was less than 1mm.
Denoising ECG signals and their analysis using Hybrid Deep learning model

Proactive illness diagnosis with AI and related technologies has been an intriguing and productive field in the last ten years. Cardiovascular illnesses are among the medical conditions that need regular monitoring. Arrhythmia, a type of coronary heart disease, is frequently observed by clinicians using electrocardiography (ECG). In humans, an ECG records electrical activity and cardiac rhythm. In recent decades, there has been a substantial surge in the use of neural networks to detect cardiovascular abnormalities. It has been shown that using the denoised signal as compared to the raw input signal increases the probability of better identification of arrhythmias. In this paper, rigorous, three-step preprocessing is done to improve classification accuracy. Firstly, denoising is done using a wavelet transform, then, for baseline artifact filtering, five filters have been applied to ECG signals, and lastly, an R peak is detected. A hybrid (CNN+LSTM) model is implemented to automate arrhythmia categorization on a denoised ECG signal. Comparative analysis demonstrates that the suggested model outperforms contemporary models in terms of various performance factors.