Miracle-dz/UniFAR-FTUs huggingface dataset:
Broadband High-Efficiency Power Amplifier With Quasi-Elliptic Low-Pass Response

This paper presents a broadband high-efficiency harmonic-tuned power amplifier (PA) with quasi-elliptic low-pass responses. A combination of continuous Class-F−1 and extended continuous Class-F PA modes is employed to significantly expand the design space. A quasi-elliptic low-pass matching network is proposed, which can realize a broadband impedance matching in the predefined optimal impedance region desired by the combination of PA modes. Furthermore, two transmission zeros are generated near the passband, exhibiting high skirt selectivity and providing rapid impedance transition from the passband to the stopband. A wide stopband covering up to the third harmonic is achieved which shows good harmonic suppression. Design procedures of the proposed broadband PA are presented. To verify the proposed methodology, the broadband PA is fabricated and measured. The implemented PA achieves a bandwidth of 145.2% from 0.5 to 3.15 GHz. Over this frequency range, the drain efficiency is measured as 58–74.9% with the output power of greater than 39.03 dBm and a large signal gain ranging from 8.43 to 15.67 dB. A wide stopband is realized from 3.4 to 10 GHz, showing excellent quasi-elliptic low-pass responses. The measured adjacent leakage ratios (ACLRs) using a 20-MHz LTE signal with digital pre-distortion are below −45.06 dBc.
Hybrid Deep Learning Approach for Multi-Step-Ahead Daily Rainfall Prediction Using GCM Simulations

Deep Learning (DL) is an effective technique for dealing with complex systems. This study proposes a hybrid DL approach, a combination of one-dimensional Convolutional Neural Network (Conv1D) and Multi-Layer Perceptron (MLP) (hereinafter referred to as hybrid Conv1D-MLP model), for multi-step-ahead (1-day to 5-day in advance) daily rainfall prediction. Nine meteorological variables, closely associated with daily rainfall variation, are used as inputs to the hybrid model. The causal variables are obtained from a General Circulation Model (GCM). In general, simulation of meteorological variables from GCM is much better than rainfall estimate and observed records of meteorological variables is sparsely available, if not completely unavailable at many locations. Thus, proposed scheme helps to establish the effectiveness of the DL approach in augmenting the quality of rainfall prediction, exploiting the potential of GCM in simulating meteorological variables. The developed hybrid model is applied to twelve different locations in different climatic regimes in terms of daily precipitation characteristics. The proposed hybrid approach is compared with a DL approach namely, Multi-Layered Perceptron (deep MLP) and another machine learning approach namely, Support Vector Regression (SVR). It is also found that the performance of the model gradually decreases as the prediction lead time (in days) increases. Overall, this study establishes the fact that the hybrid Conv1D-MLP model is more effective in capturing the complex relationship between the causal variables and daily variation of rainfall. The benefit is due to the unification of potentials of individual approaches for extracting the hidden features of hydrometeorological association.
An Automated Breast Micro-Calcification Detection and Classification Technique Using Temporal Subtraction of Mammograms

Radiologists worldwide use mammography as a reliable tool for breast cancer screening. However, mammography assessment is challenging even for well-trained radiologists, leading to a pressing need for Computer Aided Diagnosis (CAD) systems. In this work, a novel technique for the detection and classification of breast Micro-Calcifications (MCs), which are diagnostically significant but difficult to detect findings, is presented. The proposed method is based on the subtraction of temporally sequential mammogram pairs, after pre-processing and image registration, followed by machine-learning. The classification was performed using several features extracted from the subtracted mammograms and selected during training to optimize the accuracy of the results. Six classifiers were tested in a leave-one-patient-out, 4, 5 and 10 fold cross-validation process. This technique was evaluated on a unique dataset, consisting of temporal sequences of mammograms from 80 patients taken between 1 to 6 years apart. The resulting 320 mammograms were reviewed by 2 radiologists who precisely marked each MC location. The accuracy of classifying MCs as benign or suspicious improved from 91.42% without temporal subtraction and an Ensemble of Decision Trees (EDT), to 99.55% with the use of sequential mammograms and Support Vector Machines (SVMs) with leave-one-patient-out validation. The improvement was statistically significant (p-value < 0.005). These results verify the accuracy and the effectiveness of the proposed technique should to be further evaluated on a larger dataset.
An Improved Method Based on Deep Reinforcement Learning for Target Searching

Unmanned Aerial Vehicle (UAV), due to their high mobility and the ability to cover areas of different heights and locations at relatively low cost, are increasingly used for disaster monitoring and detecting. However, developing and testing UAVs in real world is an expensive task, especially in the domain of search and rescue, most of the previous systems are developed on the basis of greedy or potential-based heuristics without neural network. On the basis of the recent development of deep neural network architecture and deep reinforcement learning (DRL), in this research we improved the probability of success rate of searching target in an unstructured environment by combining image processing algorithms and reinforcement learning methods (RL). This paper aims at the deficiency of target tracking in unstructured environment, trying to propose an algorithm of stationary target positioning of UAV based on computer vision system. Firstly, a new input source is formed by acquiring depth information image of current environment and combining segmentation image. Secondly, the DQN algorithm is used to regulate the reinforcement learning model, and the specific flight response can be independently selected by the UAV through training. This paper utilizes open-source Microsoft UAV simulator AirSim as training and test environment based with Keras a machine learning framework. The main approach investigated in this research is modifying the network of Deep Q-Network, which designs the moving target tracking experiment of UAV in simulation scene. The experimental results demonstrate that this method has better tracking effect.
The B5-Modalized Propositional Logic

A B 5 -modalized propositional logic will be proposed in this paper which there are five modalities [t],[⊤],[⊥],[a],[f] to represent the five values t,⊤,⊥,a, f in B_5 , respectively, and a Gentzen-typed deduction system will be given so that the system is sound and complete with the B_5 -valued semantics of the logic.
CD70 as a critical mediator of tumor progression and immunosuppression in gliomas.

18Background: For malignant gliomas, current treatment modalities are rarely curative, necessitating development of novel therapies. Cancer immunotherapy has represented one of the most promising new treatment strategies for patients with gliomas based on the evidence that patients with brain tumors are able to mount immune responses against the autologous tumors. However, very few tumor specific targets have been discovered in this malignancy. Thus, identifying clinical useful targets for immunotherapeutic approaches is desperately needed. Methods: CD70 expression was tested from primary GBM and low grade gliomas patient tissues. CD70 gene expression and clinical outcomes were culled from TCGA datasets. CD70 inducing CD8 T cells death was performed by flowcytometry. Results: We demonstrate that CD70, a member of the TNF ligand family, was constitutively overexpressed by primary IDH-wild-type LGG and GBMs with mesenchymal gene signatures. Elevated CD70 expression was also found in recurrent tumors and cor.
Heterogeneity of Preictal Dynamics in Human Epileptic Seizures

It is generally understood that there is a preictal phase in the development of a seizure and this precictal period is the basis for seizure prediction attempts. The focus of this study is the preictal global spatiotemporal dynamics and its intra-patient variability. We analyzed preictal broadband brain connectivity from human electrocorticography (ECoG) recordings of 185 seizures (which included 116 clinical seizures) collected from 12 patients. ECoG electrodes record from only a part of the cortex, leaving large regions of the brain unobserved. Brain connectivity was therefore estimated using the sparse-plus-latent-regularized precision matrix (SLRPM) method, which calculates connectivity from partial correlations of the conditional statistics of the observed regions given the unobserved latent regions. Brain connectivity was quantified using eigenvector centrality (EC), from which a degree of heterogeneity was calculated for the preictal periods of all seizures in each patient. Results from the SLRPM method are compared to those from the sparse-regularized precision matrix (SRPM) and correlation methods, which do not account for the unobserved inputs when estimating brain connectivity. The degree of heterogeneity estimated by the SLRPM method is higher than those estimated by the SRPM and correlation methods for the preictal periods in most patients. These results reveal substantial heterogeneity or desynchronization among brain areas in the preictal period of human epileptic seizures. Furthermore, the SLRPM method identifies more onset channels from the preictal active electrodes compared to the SRPM and correlation methods. Finally, the correlation between the degree of heterogeneity and seizure severity of patients for SLRPM and SRPM methods were lower than that obtained from the correlation method. These results support recent findings suggesting that inhibitory neurons can have anti-seizure effects by inducing variability or heterogeneity across seizures. Understanding how this variability is linked to seizure initiation may lead to better predictions and controlling therapies.
Principles on Longitudinal Perching Dynamics of Fixed-Wing Flying Robots

Flying robot has been widely used for both civilian and military application due to its portability and high maneuverability. However, mission endurance decreases with shrinking size due to reduction in aerodynamic efficiency and poor energy density of battery. Inspired by birds perching after long-distance migration, extended mission duration would be desired if flying robot obtains the ability of perching. In present paper, longitudinal dynamics of fixed wing flying robot is explored in the process of post-stall perching. First, the longitudinal kinematical equations and dynamic equations are derived for the simplified model of a flying robot and solved using the methods of value iteration and time advancement. Then, typical perching trajectory is generated and features of perching behaviors are analyzed. Finally, an impact study is carried out to acquire the influence of approaching velocity and control sequence.
Personal, partner and public health benefits of testing for Chlamydia trachomatis infections in women

In view of personalised laboratory testing for the presence of infections with Chlamydia trachomatis the paper by Wiesenfeld fits perfectly (1). The paper starts with a case of a 19-year-old woman visiting her physician for preventive reasons. She is sexually active and has no complaints. The question is raised whether she should be screened for Chlamydia trachomatis. The answer is yes and Wiesenfeld gives a number of good arguments why women at the reproductive age or others at increased risk for infection should be (regularly) tested.
Induction-Machine-Based Starter/Generator Systems: Techniques, Developments, and Advances

The starter/generator (SG) system is widely used in various applications, including more-electric/hybridelectric aircraft (MEA/HEA) and hybrid electric vehicles (HEVs). The electric machine operates as a motor to provide the required torque to start the engine. Once the engine starts, the same electric machine transforms to a generator to produce electrical power. With the advantages of a simple and robust structure, low cost and maintenance, and a good overload capability, the cage-type induction machine (IM) is a competitive candidate for SG systems.
A Novel Telerehabilitation System Based on Bilateral Upper Limb Exoskeleton Robot

In this paper, a novel bilateral upper limb telerehabilitation training system is presented. By wearing two MYO armbands, one worn in the middle of the upper arm and the other in the middle of the forearm, the therapist could remotely control a 12 degree-of-freedom (DOF) bilateral upper limb exoskeleton robot to perform post-stroke rehabilitation training for stroke survivors. Through IMU sensors and EMG sensors embedded in MYO armbands, the telerehabilitation system acquires the joint angles and EMG signals of the therapist during upper limb movements and reconstructs these movements on the rehabilitation exoskeleton robot to help the patient perform rehabilitation training. In order to confirm the effectiveness and availability of this system, we analyzed the angles change obtained by the IMU sensors during the upper limb movements and simulated these actions in MATLAB; we also compared the classification accuracy by KNN, LDA, logistic regression and SVMs. At last, giving priority to efficiency, combination of five principal components and Gaussian kernel SVM is meeting for the real-time classification requirements. The results of experiments prove the feasibility of the novel telerehabilitation system.
The Promising Future of Process Mining with the Internet of Events in Context-Aware Environments

Process Mining comprises a consolidated set of techniques that aim at discovering, checking and improving processes. Originally designed to cope with business processes, process mining techniques fed on log files automatically generated by enterprise resource planning (ERP) systems and software alike. However, nowadays, with the unstoppable deployment of the Internet of Things, multiple events are continuously generated and stored by distributed, highly uncoupled and heterogeneous devices able to sense their surroundings and report precise and timely updates on the environment. By embracing the Internet of Events, recently available contextual information opens the door to the application of process mining techniques in new domains, hence, generating opportunities for the analysis of contextual data with multiple implications in hot research areas such as, smart environments (e.g., smart cities, smart buildings, smart homes), cognitive systems, and industry 4.0. In this article, we discuss the opportunities and challenges of process mining in these new, events-rich, context-aware environments.
Implementation and Analysis of Fusion in Multibiometrics

Biometrics are being implemented to overcome the major security concerns faced by the traditional security systems such as passwords and pincode. However, over the past few years, many researchers have deployed unimodal biometrics, which makes use of only one biometric trait. Despite the fact that unimodal biometrics have solved many security issues, they are experiencing several challenges such high error rates, noisy data and intra-class variations. To address these issues, researchers are exploring multimodal biometrics, which makes use of more than one biometric trait. Despite ongoing researches, the most appropriate level at which fusion has to be done in multibiometric has still not been established. In this work, the four levels of fusions, that is, sensor level, feature level fusion, score level and decision level are being explored and experimented on various datasets using face biometrics. The performance at each level is being evaluated and the results reveal that score level fusion beats the other levels of fusion by providing a recognition rate of nearly 98.9.
Why We Need Automation Models: Handling Complexity in Industry 4.0 and the Internet of Things

With the introduction of computers occurring more than 50 years ago, automation systems have become more powerful, comprehensive, and complex. The key to handling this complexity was the introduction of many aspects, including architecture models that structure and standardize functions, information, and its exchange; procedures; and the interaction of subsystems. With the recent Industry 4.0 and industrial Internet of Things (IIoT) movements, such models have received fresh attention and have grown in complexity. This article reviews the evolution of automation models. It compares several recent approaches, their standardization, and recent computing paradigms, and discusses their practical use and importance for modern automation systems.
Targeting MUC16 in Cancer Therapy

Over the recent decades, there has been a number of studies investigating the role of mucins in the pathogenesis of various cancers such as breast, lung, ovarian, gastrointestinal and pancreatic malignancies. Since then, it has been discovered that mucins play a critical role in tumorigenesis as they can mediate cell proliferation, metastasis and resistance to chemotherapy. Thus, mucins have been explored as a potential therapeutic target as well as a biomarker, as cancer cells often have an aberrant expression of mucins. MUC16 is a glycoprotein coded by one of the 21 mucin genes. CA125, the extracellular domain of MUC16, is a well-established biomarker for ovarian cancer, however there is no in depth literature review on MUC16 as a target for anti-cancer therapy. Thus, this review summarises the existing literature on MUC16, the current therapies targeting on MUC16 and highlights future avenues for targeting mucin-producing cancers.
Fully Homomorphic Encryption: An Antidote to Cloud Data Security and Privacy Concems.

Cloud Computing has witnessed tremendous growth in the last decades. Security and privacy of cloud data are two major concerns that have limited the growth and researches witnessed in the area of cloud computing. With Artificial Intelligence and Deep Learning taking prominent role in the decade, individuals and organizations are becoming more uneasy to the issues of security and privacy of cloud data. This article aims to provide an introduction to Fully Homomorphic Encryption as an antidote to the challenges of security and privacy of cloud data computation. The milestones and review of works in the area of Homomorphic Encryption was discussed. An insight into future research direction in the field of Fully Homomorphic Encryption was specified.
An Elbow Joint Angle Estimation Method Based on sEMG Feature and the Extra IMF Feature

With the tendency of aging society, the assistant robot for the aged has become a research hotspot, such as exoskeleton robot. Surface electromyography (sEMG) is a kind of biological signals generated by muscle, and the joint angle estimated by sEMG can be used to construct efficient control strategy for the assistant robot. Estimating joint angle requires large amount of sEMG training data; however, due to the muscle fatigue, it is inapplicable to collect sEMG signals from each subject for a long time. To improve the angle estimation accuracy based on less data, this paper proposes a novel joint angle estimation method with empirical mode decomposition (EMD), and selects the elbow joint as the research joint. This estimation method utilizes extra intrinsic mode functions (IMF) feature extracted from IMF and conventional sEMG features including auto-regressive model (AR model) and root mean square (RMS). This research adopts support vector regression (SVR) to estimate elbow joint angle by IMF features and conventional sEMG features. The experimental results show that with the help of extra IMF features the accuracy of estimation result is substantially improved. To be specific, the average regression mean square error (MSE) is reduced from 0.2459 to 0.1712 and the average R2 is improved from 0.7546 to 0.8242. According to the experimental results, it can be concluded that the estimation method with extra IMF feature can estimate the elbow joint angle efficiently, which paves a solid foundation for the future control strategy of the assistant robots.
Blueprinting the Workflow of Medical Diagnosis through the Lens of Machine Learning Perspective

The association of machine learning into medical data and healthcare communities embraces substantial improvement in both health care and machine learning itself. Many companies are racing to integrate machine learning into medical diagnosis process that boosts the automatic medical decision, reducing the inferior effects of data overload and increasing the accurate prediction and time effectiveness. It is one of today's most rapidly growing technical fields, lying at the intersection between health care and computer science in general. Thus, there is an urgent need to optimize medical processes, guidelines and workflows to increase the workload capacity while reducing costs and improving efficiencies. Moreover, no medical doctor or experts can manually keep pace today due to increasingly large and complex datasets. In this paper, the authors aim at addressing the mentioned issue by proposing a workflow of medical diagnosis through the lens of the machine learning perspective. An intensive comparison has been conducted applying 5 well-known machine learning algorithms on 8 real-world categorized datasets. A mobile application has been also deployed to enhance the incorporation from hospital experts.
Privacy Leakage through Exploitation of Vulnerable Inter-App Communication on Android

Android applications can access only the resources for which they have explicitly been granted permission by the user. These permissions may be granted at install-time or runtime and ensure that privacy of users’ data is always preserved. Inter-app communication on Android; however, is vulnerable to privacy leakage because there is no mechanism which would preclude an app from receiving data from another app, for which it does not have the requisite permissions. This vulnerability may be un-intentional due to developer negligence or it may intentionally be placed in apps to launch a collusion attack. In this paper, we present Inter Application Data Flow (IADF) analyzer: an android app analyzer which can be used to detect the existence of this vulnerability. IADF works by reverse engineering, extracting and correlating apps’ intents, activities and manifest features. To prove its effectiveness, we analyze 28 android apps through IADF. The first app we used for analysis is called Sieve which has been published for testing purposes and is known to have many vulnerabilities. The remaining 27 apps used in our analysis are legitimate, in-the-wild apps, published on the Google Play store. Among these apps, we discovered the existence of privacy leakage through Inter-app communication vulnerability in a social media app. Discovery of this vulnerability in a very small set of legitimate apps points to the possibility of its existence in other apps on the Google Play store.