Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-objective Filtering for Discrete-time Systems in the Presence of Data Packet Drops

This paper studies the multi-objective filter design for linear time-invariant discrete-time systems over communication networks, where data packet dropout occurs from the observation side to the filter. The desired filter is able to achieve the optimal estimation error covariance under white Gaussian noises, together with a guaranteed worst case performance against disturbance/model uncertainty. By game theoretical approach, a bounded Nash equilibrium strategy is analytically conducted in terms of two cross-coupled modified algebraic Riccati equations. Moreover, a numerical example is included to show the validity of the current results.
Slotted Folded Patch Antenna With Double-T-slots for Platform-Insensitive UHF Tag Design

A compact folded patch antenna, which has a dimension of 42 mm <inline-formula> <tex-math notation="LaTeX">$\times50$ </tex-math></inline-formula> mm <inline-formula> <tex-math notation="LaTeX">$\times1.6$ </tex-math></inline-formula> mm (<inline-formula> <tex-math notation="LaTeX">$0.128\lambda \times 0.152\lambda \times 0.0049\lambda $ </tex-math></inline-formula>) is proposed. Multiple C-shaped slotlines are incorporated for tuning down the resonant frequency of the folded patch. A pair of double-T-slots, which acts as the impedance matching network, is etched at the center of the patch for achieving conjugate match between the antenna and the chip. By combining the analytical method and the particle swarm optimization algorithm, an equivalent circuit model is constructed for analyzing the impedance characteristics of the proposed tag antenna. With the use of 4 W EIRP, a maximum read distance of 11.5 m is achievable by placing the proposed tag antenna on a dielectric object. Also, the tag antenna is found to be able to reach ~5.2 m on metal. The operating frequency of the proposed tag antenna is very stable for both the dielectric and metallic objects, which is very desirable for practical applications.
Control-theoretic methods for biological networks

Feedback is both a pillar of control theory and a pervasive principle of nature. For this reason, control-theoretic methods are powerful to analyse the dynamic behaviour of biological systems and mathematically explain their properties, as well as to engineer biological systems so that they perform a specific task by design. This paper illustrates the relevance of control-theoretic methods for biological systems. The first part gives an overview of biological control and of the versatile ways in which cells use feedback. By employing control-theoretic methods, the complexity of interlaced feedback loops in the cell can be revealed and explained, and layered feedback loops can be designed in the cell to induce the desired behaviours, such as oscillations, multi-stability and activity regulation. The second part is mainly devoted to modelling uncertainty in biology and understanding the robustness of biological phenomena due to their inherent structure. Important control-theoretic tools used in systems biology are surveyed. The third part is focused on tools for model discrimination in systems biology. A deeper understanding of molecular pathways and feedback loops, as well as qualitative information on biological networks, can be achieved by studying the “dynamic response phenotypes” that appear in temporal responses. Several applications to the analysis of biological systems are showcased.
Understanding the Network Traffic Constraints for Deep Packet Inspection by Passive Measurement

The Deep Packet Inspection (DPI) system examines each captured network packet to find malicious events. The quality of network traffic set an upper limit of DPI system's functionality, such as traffic integrity and asymmetric routing. To better understand these constraints on a DPI system, we setup a series of indicators to quantify these factors, these indicators can be classified as basic information (e.g. average packet sizes), link stability (e.g. out of order packets number), connection integrity (e.g. missing packets number) and asymmetric routing (e.g. one-way flow). There are two challenges in measuring these indicators on real network traffic. The first is how to measure these indicators on high-speed traffic with limited resources. The second is how to track TCP flows across multiple inspection points. We tackle this problem with a scalable passive measurement system, which adopts fast packet I/O technique to capture network traffic, and Spark to process the collected data. To prove its practicability, we deploy the system in a carrier grade network that has six data centers. We have found that 1) over 90% of TCP SYN packets have no subsequent data packet, 2) over 90% of TCP flows are asymmetric, unordered or retransmitted, and 3) over 80% of TCP flow's round trip time are less than 400ms.
Evaluating Human Behavior in Manual and Shared Control via Inverse Optimization

Shared control systems have a great potential to contribute to a safer human-machine interaction. A great body of literature has been concerned with the design of the automation the human is sharing control with. At the same time, an adequate design is connected to the availability of reliable models of human behavior. A promising modeling approach is given by optimal control theory, where human behavior arises from the minimization of a cost function. However, most of the work found in literature focus on determining the cost function of the human in a situation without any haptic interaction with a partner, i.e. in manual control tasks. Motivated by several studies which indicate that human behavior changes when completing a task cooperatively, this paper proposes an optimal control approach for human behavior modeling in a shared control scenario. We further hypothesize that the human cost functions in a shared control scenario change significantly when compared to the ones which arise from a human performing a control task alone. We apply an inverse optimization approach in order to identify the cost function in both scenarios. In order to evaluate our hypothesis, a study was conducted where 42 participants performed a tracking task in a manual mode and then sharing control with an assistance system. The findings show that the model is able to describe human behavior in both shared and manual control. Furthermore, the results confirm that the human cost function changes considerably between both scenarios.
Development of High Isolation Ping-Pong DDS-PLL Based Frequency Synthesizer for Fast Frequency Hopping Applications

High isolation ping pong based fast frequency hopping source is presented in this paper. The Direct Digital Synthesis (DDS) driving Phase Locked Loop (PLL) is adopted to design the frequency synthesizer. The proposed ping pong configuration not only minimizes the frequency switching time and tuning step but also reduces the self-interference between synthesizers. The measurement results shows that the single path of the proposed ping pong structure, a DDS-PLL frequency synthesizer with 80 MHz DDS output as PLL reference input, has the frequency switching time of less than $20 \vert 1\text{S}$ with 3 MHz hopping distance, the output frequency resolution of about 3 Hz, the spurious of better than −70 dBc, and the phase noise of −95 dBc/Hz at an offset of 10kHz for the centre frequency of 2.4 GHz. The switching time of 2 ping-pong synthesizers is no more than 1 μs and the isolation of that is up to 73 dB.
A Novel Concept for 2D Butler Matrix with Multi-Layers Technology

A novel 2D beamformer with 2D sixteen beam switching capability was proposed in this paper. Different from just reducing 1D Butler matrix size to achieve compact 2D beamforming network (BFN), the proposed architecture also reduces the complexity of interface connection between the BFN and antenna array. Thanks to the new design concept proposed in this paper, the circuit size is only $\mathbf{2.3\lambda_{0}\times 2.3\lambda_{0}\times 0.2\lambda_{0}}$. This 2D beamformer is very suitable for fabricating by multi-layer technology. It is very convenient to integrate with antenna array due to the similar plane size. Furthermore, the less loss would be achieved due to the elimination of long coaxial cables. To the best of the author's knowledge, this is the first demonstration of such sixteen beams of 2D beamformer by multi-layer PCB technology.
A Deep Learning Approach for Intrusion Detection in Internet of Things using Bi-Directional Long Short-Term Memory Recurrent Neural Network

Internet of Things (IoT) is one of the most rapidly evolving technologies nowadays. It has its impact in various industrial sectors including logistics tracking, medical fields, automobiles and smart cities. With its immense potentiality, IoT comes with crucial security concerns that need to be addressed. In this paper, we present a novel deep learning technique for detecting attacks within the IoT network using Bi-directional Long Short-Term Memory Recurrent Neural Network (BLSTM RNN). A multi-layer Deep Learning Neural Network is trained using a novel benchmark data set: UNSWNB15. This paper focuses on the binary classification of normal and attack patterns on the IoT network. The experimental outcomes show the efficiency of our proposed model with regard to precision, recall, f-1 score and FAR. Our proposed BLSTM model achieves over 95% accuracy in attack detection. The experimental outcome shows that BLSTM RNN is highly efficient for building high accuracy intrusion detection model and offers a novel research methodology.
Gaussian Process Regression for Nonlinear Time-Varying System Identification

This paper presents a method for nonlinear system identification with Gaussian process regression. The unsupervised method is able to generate an approximation of the system with correct extrapolation behaviour, that is refined with input/output-data in the typical working area and sampled online data. Therefore, an offline model is generated, which consists of a nominal model set up by the extrapolation behaviour and a detailed model for the refinement. The method is able to keep track of time-varying systems by using the confidence information to incorporate new measurements into the online model. The performance of the proposed method is tested on different numerical examples.
Analysis of the Influence of Color Normalization in the Classification of Non-Hodgkin Lymphoma Images

In this work, a method is proposed to analyze the influence of color normalization in the classification lymphoma images. The approach combines multidimensional fractal techniques, curvelet transforms and Haralick features. The method considered a feature selection technique and different classification approaches to evaluate the combinations, such as decision tree, random forest, support vector machine, naive bayes and k-star. The classifications were analyzed considering three common lymphoma classes: mantle cell lymphoma, follicular lymphoma and chronic lymphocytic leukemia. The best result was achieved for the extraction from input images, features obtained mostly from lacunarity and percolation from curvelet sub-images, using random forest classifier. The tests were considered with 10-fold cross-validation and the result was a rate of AUC=0.963. The color normalization was not able to provide relevant classification rates. The obtained performance with the analysis over different types of features, classifiers and color normalization influence are important contributions to the identification of the lymphoma cancer.
A New Two-Phase Classifier for Face Recognition

Sparse representation plays an important role in many applications, such as face recognition and image denoising. The sparse representation based on regularization is proposed by many researchers. And it has not only very high computational efficiency but also has a simple solution. In this paper, we propose the sparse representation method that bases on the regularization and tikhonov regularization. Face recognition is divided into two steps: First, we use the Sparse representation based representation to Select the top M of Suspected classes for each test samples. Then we use the sparse representation method that bases on the regularization and tikhonov regularization to train the M Suspected classes. At last, testing our method with test samples. The experiment's results demonstrate that the improved algorithm has a better performance in image classification.
Evaluating Engagement Level and Analytical Support of Interactive Visualizations in Virtual Reality Environments

Interactive visualizations are external cognitive artifacts aimed at supporting users' exploratory and sense-making activities. In recent years, there has been an explosion of commercial virtual reality (VR) head-mounted displays (HMD). These VR devices are meant to offer high levels of engagement and improve users' analytical exploration of the displayed content. However, given their rapid market introduction, the possible influences and usefulness that VR could bring in terms of supporting users' exploration with interactive visualizations remain largely underexplored. We attempt to fill this gap and provide results of an empirical study of an interactive visualization tool that we have developed for a VR HMD system. This tool is aimed at facilitating exploratory and analytical reasoning activities with 3D shapes and their transformational processes. Overall, the results show that the tool is supportive of users' exploratory and analytical activities based on the significant improvement in their post-experiment test scores (when compared to their pre-experiment ones) and their engagement level measured via a user engagement questionnaire and participants' comments. The results shed a positive light on the use of visualizations in VR environments and can inform the design of these tools of domains beyond 3D transformational geometry.
Stereo with Zooming

This paper investigates stereo matching with zooming information. In the proposed technique, two zoom lens cameras are used to capture multiple stereo image pairs with different focal length. With the assistance of zoom image pairs captured from the same camera, it is possible to increase the correctness of stereo matching results. In addition, a zoom rectification method is proposed to simplify the zoom vector computation. In the experiments, we use the bad pixel rate to compare the results obtained from the real scene images using conventional stereo matching techniques and the proposed stereo with zooming approach. Moreover, we test the proposed algorithm on the real image with optical zoom to demonstrate the effectiveness of our work. It is shown that our technique is able to reduce the bad pixel rate using both the local and global stereo matching algorithms.
Backside-Illuminated CMOS Photodiodes With Embedded Polysilicon Grating Reflectors

In this study, we investigated the feasibility of replacing metal mirrors with polysilicon gratings to serve as compact optical back reflectors in thin backside-illuminated CMOS photodiodes (BSI CPDs). The unique reflective properties of polysilicon grating reflectors can be implemented within a very small area (<inline-formula> <tex-math notation="LaTeX">$12~\mu \text{m}^{2}$ </tex-math></inline-formula>); i.e., between the contact vias of BSI CPDs. The proposed scheme achieves high optical reflectivity for TE-polarized light at wavelengths exceeding 100 nm, while improving responsivity at near-infrared wavelengths. The resulting BSI CPDs were shown to enhance photocurrent by <inline-formula> <tex-math notation="LaTeX">$1.45\times $ </tex-math></inline-formula> in a polarization-dependent manner (<inline-formula> <tex-math notation="LaTeX">$\text{I}_{\mathrm {TE}}/\text{I}_{\mathrm {TM}} =1.148$ </tex-math></inline-formula>) at a wavelength of 980 nm.
Evomorph: Morphological Modularization in A.I. for Machine Vision Inspired by Embryology

Nature likely implements modularization in multicellular developmental biology using the chemical context of the cell, cell division generational distance, and genetic triggers. Inspired in this, Evomorph is a proposed heuristic method of Artificial Intelligence that pairs these concepts with Evolutionary Computation. It is described here as a flexible template matching for object detection in Machine Vision.
On consensus protocols allowing joint-agent interactions

In this note we consider consensus protocols where an agent would not be influenced by any of his neighbours singularly taken, but might be sensitive to the simultaneous and coherent influence of two or more of them. This may resemble several common behaviours in social, economic and opinion networks (i.e. conformity, risk aversion, social inertia, herding). We derive novel graph-theoretical concepts to describe and analyze the ability of general networks with joint-agent interactions to converge towards consensus. Interestingly, and for the first time, we borrow to this end the language of Petri Nets as a convenient way to describe bipartite directed graphs, showing how the notion of siphon is helpful in characterizing the flow of information across the network and its ability to induce attainment of consensus among agents in the considered set-up.
On the structure of kinematic systems with complete symmetry

This paper provides a new perspective on the structure of kinematic systems with complete symmetry. These systems naturally occur as models for mechanical systems with symmetry, for example flying or submersible robots. The configuration space of such systems is a homogeneous space of the symmetry Lie group, and it is well known that their kinematics can be lifted to equivariant kinematics on the symmetry group thus allowing global state observer constructions. We provide explicitly checkable sufficient differential-algebraic conditions on the symmetry that will lead to a lifted system in the form of standard left or right invariant kinematics on the symmetry group. Previously known conditions for one of these two cases required finding a velocity lift map with particular properties for which there was no general construction known.
Position Control of Dielectric Elastomer Actuators Based on Port-Hamiltonian Framework

This paper deals with position control of an actuator system consisting of a dielectric elastomer membrane biased with a combination of a linear and a bi-stable spring. The highly nonlinear response of dielectric elastomer material, in conjunction with the bi-stable biasing spring, makes the control of the overall system challenging. To systematically address the design of the control system, a novel approach is proposed based on port-Hamiltonian (PH) theory. First, based on a recently developed PH model of the dielectric elastomer material, a PH model of the overall actuator system is obtained. Subsequently, several control laws are designed by means of PH theory, i.e., interconnection and damping assignment passivity-based control (IDA-PBC), and IDA-PBC with integral of non-passive output for robust regulation. The developed control laws are finally compared with a conventional PID, showing improved dynamic performance in the overall actuation range.
Covert Visuospatial Attention (VSA) for EEG-Based Asynchronous Control of Robot

Brain-Computer Interface (BCI) is an effective modality for direct communication between brain and computer, bypassing brain's conventional communication pathway of the nerves and muscles. The objective of this work is to demonstrate EEG-based asynchronous control to a robotic device (Sphero) using covert visuospatial attention (VSA). Alpha and beta power band features in conjunction with a forward search technique for feature selection are used as input features for training a classifier model. The selected features are used to train Linear Discriminant Analysis (LDA) model. Average validation accuracy of 71.25% and average test accuracy of 66 % are obtained from eight subjects. We also investigated the effect of inter-session variations in feature patterns and accuracy of control using a multiple online VSA session. It is observed that there is a shift of discriminative features within subject across sessions. An asynchronous control session is also done and average test accuracy of 62.7% is reported which validates our objective for the work.
The Geometric Structure of Generalized Softmax Learning

In this paper, we formulate the generalized softmax learning (GSL) problem, as a symmetric extension of the softmax regression problem. We further study the geometric structure of GSL and demonstrate the equivalence of GSL and the original softmax regression problem. Besides, this geometric structure indicates the symmetry between a neural network and its reverse network, and the symmetric roles of the weights and feature in a neural network. Finally, we present a numerical simulation to verify these symmetry properties in neural networks.
Controlling Home Appliances through Thought Commands

Brain-Computer Interfaces (BCI) is a field which has shown rapid advancement over the past few decades. With the availability of low-cost electroencephalography (EEG) signal acquisition devices, it is becoming more feasible to develop hands-free environmental control systems which can be used for everyday use. These systems may be used as an assistive technology for disabled individuals or as an alternative form of control for healthy individuals. Hands-free environmental control systems often lack user-friendliness and intuitiveness due to the difficulty of mapping the users’ intentions, to control commands that can be used to control the appliances in a 3D environment. Therefore, this paper reports on an ongoing research to develop a hands-free environmental control system based on P300 responses of EEG signals of a user. The proposed solution does not use a screen for visual feedback, therefore, it improves the intuitiveness and user friendliness than existing solutions.
Stabilité et fonctionnalité des glycoprotéines de l’enveloppe du VIH-1 recombinant CRF01_AE : rôle de l’histidine en position 375

ii Table des matières. iii Liste des tableaux . vi Liste des figures . vii Liste des abréviations . viii Remerciements . x Introduction . 1 1. Problématique . 1 1.1 L’épidémie du VIH/SIDA. 1 1.2 Traitements actuels . 1 2 Les rétrovirus . 4 2.1 Classification. 4 2.2 Origine . 4 2.3 Génome . 5 2.4 Structure . 6 2.5 Cycle réplicatif . 7 2.6 Rôles de protéines virales . 9 2.6. 1 Rôle des protéines accessoires dans la réplication virale. 9 2.6. 1.1 Nef. 9 2.6. 1.2 Vpu . 10 2.6. 1.3 Vif . 10 2.6. 1.4 Vpr . 11 2.7 Diversité génétique . 11 2.7. 1 Répartition géographique des clades et sous-types du VIH-1 . 12 2.7. 2 Les formes recombinantes circulantes (CRF) . 13 2.7. 2.1 Définition et classification des CRF . 13
Design Theory of Dual-Band Wilkinson Power Divider with Different Frequency Ratio Ranges

In this paper, a design theory of dual-band Wilkinson power divider (WPD) with different frequency ratio ranges is presented. By selecting different physical lengths ratio of two transmission lines (TLs). , the frequency ratio range can be selected accordingly. , where physical lengths ratio is defined as $\pmb{l_{1}}/\pmb{l_{2}}$. A $\Pi$ -type transformer is used to replace the first TL in WPD application to enhance the spurious band suppression. The design theory with $\pmb{l_{1}} < \pmb{l_{2}}$ has been reported. , and the second TL was replaced by the $\Pi$ -type transformer in the former work. Compared with the previous work. , it is proved by this work that the case of $\pmb{l_{1}} > \pmb{l_{2}}$ also can be realized physically. The design charts and design examples are summarized. The comparison of S-parameters between two cases is also given in this paper. Finally. , one experiment with $\Pi$ -type transformer is fabricated. , the simulated and experimental results are matched very well.