Miracle-dz/UniFAR-FTUs huggingface dataset:
Output-Feedback Robust MPC for a class of Nonlinear system

This paper compares the performance of a robust model predictive control (MPC) with a nominal MPC. The robust scheme is applied to a nonlinear system with input constraints, bounded unknown additive disturbances, and uncertainties on the system states. The design of the robust controller relies on the linearized version of the nonlinear model along its trajectory. It comprises two components, one of which is obtained by a continuous-time MPC approach with tightened constraints for stabilizing the nominal system. The other one is chosen as standard LQR gain, ensuring that the error system trajectories lie in a particular invariant set. The controller is implemented in a digital environment and utilizes a continuous-time model and equations, vastly reducing the computational burden. Furthermore, with some plausible assumptions, the closed-loop system is demonstrated to have regional exponential stability around the origin. Simulation results verify the improvement in disturbance rejection property of the employed controller over nominal MPC.
Vetted Encryption ★

We introduce Vetted Encryption (VE), a novel cryptographic primitive, which addresses the following scenario: a receiver controls, or vets, who can send them encrypted messages. We model this as a lter publicly checking ciphertext validity, where the overhead does not grow with the number of senders. The lter receives one public key for veri cation, and every user receives one personal encryption key. We present three versions: Anonymous, Identi able, and Opaque VE (AVE, IVE and OVE), and concentrate on formal de nitions, security notions and examples of instantiations based on preexisting primitives of the latter two. For IVE, the sender is identi able both to the lter and the receiver, and we make the comparison with identity-based signcryption. For OVE, a sender is anonymous to the lter, but is identi ed to the receiver. OVE is comparable to group signatures with message recovery, with the important additional property of con dentiality of messages.
Key Mismatch Attack on NewHope Revisited

One of the NIST Post-Quantum Cryptography Standardization Process Round 2 candidates is the NewHope cryptosystem, which is a suite of two RLWE based key encapsulation mechanisms. Recently, four key reuse attacks were proposed against NewHope by Bauer et al., Qin et al., Bhasin et al. and Okada et al. In these attacks, the adversary has access to the key mismatch oracle which tells her if a given ciphertext decrypts to a given message under the targeted secret key. Previous attacks either require more than 26 000 queries to the oracle or they never recover the whole secret key. In this paper, we present a new attack against the NewHope cryptosystem in these key reuse situations. Our attack recovers the whole secret key with the probability of 100% and requires less than 3 200 queries on average. Our work improves state-of-the-art results for NewHope and makes the comparison with other candidates more relevant.
Modeling and trajectory tracking control for flapping-wing micro aerial vehicles

This paper studies the trajectory tracking problem of flapping-wing micro aerial vehicles ( FWMAVs ) in the longitudinal plane. First of all, the kinematics and dynamics of the FWMAV are established, wherein the aerodynamic force and torque generated by flapping wings and the tail wing are explicitly formulated with respect to the flapping frequency of the wings and the degree of tail wing inclination. To achieve autonomous tracking, an adaptive control scheme is proposed under the hierarchical framework. Specifically, a bounded position controller with hyperbolic tangent functions is designed to produce the desired aerodynamic force, and a pitch command is extracted from the designed position controller. Next, an adaptive attitude controller is designed to track the extracted pitch command, where a radial basis function neural network is introduced to approximate the unknown aerodynamic perturbation torque. Finally, the flapping frequency of the wings and the degree of tail wing inclination are calculated from the designed position and attitude controllers, respectively. In terms of Lyapunovʼ s direct method, it is shown that the tracking errors are bounded and ultimately converge to a small neighborhood around the origin. Simulations are carried out to verify the effectiveness of the proposed control scheme.
Design and Implementation of Intelligent Heart Rate Detection System Based on STM32

At present, people’s lifestyle is more and more diverse, and people’s eating habits are constantly changing. In this case, people’s health is facing great challenges. The sudden rate of various heart rate chronic diseases is also rising. Under the heart rate detection, it can be prevented in advance so that the disease can be effectively treated. This paper designs an intelligent heart rate detection system, which can real-time detect its own heart rate. When the heart rate beats too fast, the voice prompt will be given, and every time the heart rate value detected will be sent to the family through wireless transmission. The heart rate system satisfies the requirements of heart rate LCD display, voice prompt and Bluetooth transmission. It has certain application value for medical treatment.
An Optimal Robust Frequency Controller Design for a Two-Microgrid Cluster

This paper presents a new robust design approach to control the frequency of a two-microgrid cluster together with the power of the coupling line. To this end, a two-microgrid cluster including both dispatchable and non-dispatchable source is considered as a study case. The non-dispatchable sources cause power imbalance due to stochastic behavior. The stochastic behavior of these sources leads to the frequency deviation. Furthermore, it affects the power of the coupling line connecting two microgrids. Thereby, a two-level design process is addressed. It includes inner and outer optimization. The outer level determines the weighting functions for the inner level. The inner level designs the robust regulator by solving an optimization problem constrained by a linear matrix inequality in three stages. The first stage, the state feedback is determined in order to provide robust behavior. The second stage, an observer is obtained to estimate the states used for the state feedback. The last stage, the controller is formed using state feedback as well as the state observer. Then, a series of simulations are carried out using the designed controller. The results show that the robust controller can improve the operation of microgrids in comparison with the conventional proportional-integral controller.
FinSNet: End-to-End Separation of Overlapped Fingerprints Using Deep Learning

Latent fingerprints found at a crime scene are an important evidence in a criminal investigation. However, latent fingerprints are often damaged by the surface of an object or obscured by other latent fingerprints. Herein, we propose an end-to-end overlapped fingerprint separation method using a deep learning algorithm. The neural network, FinSNet, takes an overlapped fingerprint image as an input and is trained to restore the component fingerprint from the input directly. Our network can remove redundant fingerprints and background images with only a single inference, resulting in improved computational efficiency compared to conventional methods that require background removal to be preceded separately. First, we introduce strategies to design the network and build a training dataset. Next, we demonstrate results of separating real-world fingerprints, using Tsinghua overlapped fingerprint datasets. We then evaluate our proposed method using commercial fingerprint identification software to perform fingerprint matching. Finally, we thoroughly analyze our method in terms of the training dataset, background removal, and angular deviation of overlapped fingerprints.
On the use of human-assisted optimisation for the optimal camera placement problem and the surveillance of urban events

The optimal camera placement problem is that of determining the best possible set of camera positions and orientations in order to meet application-specific constraints and objectives. This paper focuses on one application of the problem: global area surveillance. Given an area to be covered, the question is to design a camera network which fully covers critical subareas and proceeds in a best-effort manner in the rest of the environment, given a limited budget. This is achieved through the integration of user-provided input into a mixed combinatorial model which brings together two variants of a popular optimisation problem. Time-efficient algorithms then allow for regular user interaction in between solving iterations. This human-assisted design is based off requirements set by experts and decision makers and yields components of a decision support system to support law enforcement officers and officials when designing video surveillance infrastructure.
Modelling the system of agents in the presence of random moments of transactions*

This paper presents the approach to modelling the system of agents making transactions at random time. The two main ideas are, to obtain the agents’ optimal control in the form of synthesis (feedback) and, secondly, to make the aggregate dynamics stock-flow consistent on the average, not strictly at any moment of time. We present a model of a large number of consumers and producers that take loans from the bank to buy consumption goods or investment. The moments of deals form described the Poisson flow. Consumers and producers optimally solve their stochastic optimal control problems. The solution to the OC problems are in the closed-loop form, obtained using asymptotic methods for large frequency of transactions. The optimal policy functions appear to be linear in the state variables, if time is far from the planning horizon. This enables aggregation across a large population of consumers or producers. As a result, the description of the dynamics of their aggregate state might be substituted by deterministic dynamics. The system of equations for the aggregate dynamics is reduced to one differential equation. The equation is studied numerically and the results are presented.
Takagi-Sugeno (TS) Fuzzy model-basedobserver design for glucose-insulin system in diabetes type 1: An LMI approach

Paper This proposes a Takagi-Sugeno fuzzy model-based observer for glucose-insulin system for estimating the states of glucose-insulin system in diabetes type 1. In this approach, the Takagi-Sugeno (TS) fuzzy modeling is employed and linear matrix inequalities (LMIs) are utilized to derive the estimation conditions. Nonlinear minimal Bergman model as a description of glucose-insulin process in type 1 diabetes is considered.
Stock Market Prediction-by-Prediction Based on Autoencoder Long Short-Term Memory Networks

This paper proposes a strategy for the stock market closing price prediction-by-prediction using the autoencoder long short-term memory (AE-LSTM) networks. To integrate technical analysis with deep learning methods, technical indicators and oscillators are added to the raw dataset as features. The wavelet transformation is used as a noise-removal technique in the stock index. Anomaly detection in dataset is also performed through the z-score method. First, the autoencoder is trained to represent the data. Then, the encoder extracts feature and puts them into the LSTM network for predicting the closing price of the stock index. Afterwards, the system predicts subsequently based on the previous predictions. To evaluate the theoretical results, the proposed method is experimented on the standard and poor's 500 (S&P 500) stock market index through several simulation studies. To analyze the results, several performance criteria are used to compare the results with the generative adversarial network (GAN). The simulation studies are conducted to show the effectiveness of the proposed method in the Python environment, and the results show that the proposed prediction-by-prediction method outperforms GAN in terms of daily adjusted closing price prediction.
Improved P&O Algorithm for Maximum Power Point Tracking at the Photovoltaic Array Using an Interleaved Boost Converter

In this paper, an improved Perturb & Observe algorithm proposed to maximum power point tracking in a photovoltaic array. In this work, the interleaved boost converter used, which can extract more power than a single converter structure. In the suggested approach, to improve performance of Perturb & Observe algorithm versus tracking accuracy, the reference voltage of this algorithm has been a function of irradiation and temperature of the photovoltaic module surface, which at any moment, enables the algorithm to create a voltage for the controller of the converter input voltage that this voltage, proportionate to maximum power point of a photovoltaic module. Simulation outcomes and comparative investigations with classical Perturb & Observe algorithm used in single converter structure, verify the adequate performance of the recommended method versus extract more power from the photovoltaic array.
Rethinking Divide and Conquer—Towards Holistic Interfaces of the Computing Stack

We argue that the abstractions between the layers of the computing stack and the components of computing systems, especially the HW/SW interface, have to be rethought to cope with the ever-growing complexity of problem domains and their manifestations in the underlying computing systems. The divide and conquer approach to hardware/software with a minimal interface is unable to cope with complexity. Rethinking the abstractions and interfaces between the application, system, and architecture can lead to significant benefits in improving performance, efficiency, resilience, security, and programmability, at the same time.
Fabrication of Surface Acoustic Device Based on 2D Ruddlesden Popper Perovskite (RPP)

In this paper a Surface Acoustic Wave (SAW) device with the wavelength of $80\ \mu\mathrm{m}$ is proposed. Inter Digital Transducers (IDTs) of the device are fabricated through the photolithography method. (BA)2PbI4, a quasi 2D Ruddlesden Popper Perovskite (RPP), is used as piezoelectric material on the top of IDTs. The RF measurements of the SAW device are investigated and the resonance frequencies at 71.43 MHz and 289.83 MHz show the accurate matching of transmission (S21) and reflection (S11) spectrums.
Dynamic hand gesture recognition based on short-term sampling neural networks

Hand gestures are a natural way for human-robot interaction. Vision based dynamic hand gesture recognition has become a hot research topic due to its various applications. This paper presents a novel deep learning network for hand gesture recognition. The network integrates several well-proved modules together to learn both short-term and long-term features from video inputs and meanwhile avoid intensive computation. To learn short-term features, each video input is segmented into a fixed number of frame groups. A frame is randomly selected from each group and represented as an RGB image as well as an optical flow snapshot. These two entities are fused and fed into a convolutional neural network ( ConvNet ) for feature extraction. The ConvNets for all groups share parameters. To learn long-term features, outputs from all ConvNets are fed into a long short-term memory ( LSTM ) network, by which a final classification result is predicted. The new model has been tested with two popular hand gesture datasets, namely the Jester dataset and Nvidia dataset. Comparing with other models, our model produced very competitive results. The robustness of the new model has also been proved with an augmented dataset with enhanced diversity of hand gestures.
A Distributed Power Amplifier Design with a High Power Gain

In this paper we present a distributed power amplifier that has a good gain-bandwidth product in comparison with other recent discrete distributed power amplifiers. A TGF2023-01 bare die transistor with $. 25\mu\mathrm{m}$ GaN HEMT technology is utilized in our work. Our distributed power amplifier provides a better matching that leads to a more flat frequency response and uses tapering technique for both gate and drain transmission lines to boost the output power and efficiency. This design demonstrates a 41.6 dBm saturated power and a 22.2 dB small signal gain from DC to 3.4 GHz with a power added efficiency (PAE) of 27%.
PRESENT Hardware Implementation for Concurrent Fault Detection Architecture

Efficient fault tolerant implementation of lightweight cryptography hardware is a main challenge of secure hardware in IoT, RFID and sensor network. In this paper, we propose low cost PRESENT architecture using 80-bit key and 64-bit input data with fault detection capability. Our proposed architecture utilizes different S-Boxes implementations in which both encryption and decryption processes are integrated in one data path. The simulation results of our fault detection structure are compared with previous related works. The results are compared in terms of area, throughput and efficiency. This comparison illustrate that our fault detection architecture outperforms related works in the comparison metrics.
An AoA-aided Low Complexity Pilot Decontamination Scheme for Multi-Cell Massive MIMO Systems

This paper investigates the pilot contamination issue in a multi-cell massive MIMO system. To this end, utilizing both the angular information and the distance information, a framework is presented to formulate a pilot contamination-aware pilot assignment. In the following, a pilot assignment strategy is proposed, where compared to the conventional algorithms, an alternative optimization problem with low complexity is extracted. To do so, the pilot assignment problem is reformulated by another problem whose variables are continuous. After that, the resultant optimization is treated via an iterative method, where the sequential convex programming (SCP) is employed. The simulation results show that the proposed algorithm can achieve a performance that is comparable to the conventional method with a complexity decrease, at the cost of a negligible reduction in the uplink sum rate per cell.
A Stacked Resonant High Voltage DC Modulator for Industrial Magnetron

This paper presents a design of a 8.8kV DC modulator for anode power supply of a 10kW magnetron. The DC modulator requires to follow a specific output voltage profile from 6.8kV to 8.8kV, while the magnetron switches from the preheating state to the steady operation of full power. The design is based on series resonant converter for its high power density, feature of soft switching and inherent safety. Initially, a single resonant converter based design is detailed, and then it is argued that stacking of multiple modules is advantageous for ease in testing, maintenance and components selection. So, a design based on three stacked converters is presented, and simulated for its working, and characterization of the transient response and to help select the components of suitable ratings and to help design a control system. Hence, this work has laid a strong foundation for fabrication of the required DC modulator.
Flow based Traffic Scheduling in Software Defined Network

The Software Defined Network (SDN) offers centralized traffic flow control from a global view of the underlying network, covering the route for SDN's various traffic engineering research. The continuous development of virtual machine migration, data backup, or MapReduce operations, the scale of the current network become more and more enormous. Equal-Cost Multi-Path (ECMP) is a method of multipath routing to distribute the flows using static hashing method. These flows do not reveal degrading due to static hashing for the resulting collisions. Traffic overloads due to long delay and low throughput will frequently cause connection congestion and result in less flow transmission performance. The paper focuses on the Flow based Traffic Scheduling FTS scheme that provides rerouting solution by scheduling the flow to the maximum available bandwidth path between the several paths. The experimental evaluations are used to show the efficiency of FTS in relation to ECMP and Hedera with reference to average throughput and flow completion time (FCT).
Teeth Detection and Dental Problem Classification in Panoramic X-Ray Images using Deep Learning and Image Processing Techniques

Deep convolutional neural networks, have gained a lot popularity in medical research due to their impressive results in detection, prediction and classification. Analysis of panoramic dental radiographies help specialists observe problems in poor visibility areas, inside the buccal cavity or in hard to reach areas. However, poor image quality or fatigue can cause the diagnosis to vary, which can ultimately hinder the treatment. In this paper we propose a novel approach of automatic teeth detection and dental problem classification using panoramic X-Ray images which can aid the medical staff in making decisions regarding the correct diagnosis. For this endeavor panoramic radiographies were collected from three dental clinics and annotated, highlighting 14 different dental issues that can appear. A CNN was trained using the annotated data for obtaining semantic segmentation information. Next, multiple image processing operations were performed for segmenting and refining the bounding boxes corresponding to the teeth detections. Finally, each tooth instance was labeled and the problem affecting it was identified using a histogram-based majority voting within the detected region of interest. The implemented solution was evaluated with respect to several metrics like intersection over union for the semantic segmentation and accuracy, precision, recall and F1-score for the generated bounding box detections. The results were compared qualitatively with the data obtained from other approaches illustrating the superiority of the proposed solution.
Electrical and Optoelectronic Properties Analysis in Two-dimensional Multilayer WSe2 Phototransistor for High Speed Device Applications

In this work, we fabricated back gated multilayer WSe2 phototransistor with Au/Ti as electrodes whose electrical properties were investigated over a wide range of temperature T from T = 5.4 to T = 350 K from which several electrical parameters including mobility μ and barrier height ϕms were extracted to analyze their T-dependencies. For example, the T-dependent mobility μ was characterized by a peak at ~ 250 K after which point μ decreased according to μ ~ T-0.71, attributed to phonon scattering. Additionally, ϕms was found to be 29 meV at VG = 0 V which decreased as VG increased in alignment with barrier modulation theory. Finally, optoelectronic properties of our Au/Ti/WSe2 field effect transistors (FETs) were investigated by exposing the device to a tunable laser, where the photoresponsivity R was calculated to be ~ 43 A/W at an optical power of 0.786 nW. The phototransistor also showed very short response time with τrise = 10 ms, and Tfall = 3 ms. Our work demonstrates excellent potential of WSe2 as a promising 2D semiconductor for high speed electronics and opto-electronics.