Miracle-dz/UniFAR-FTUs huggingface dataset:
Smartphone Positioning Enhancement Using Several GNSS Satellite Selection Techniques

Smartphone positioning using Global Navigation Satellite Systems (GNSS) only is considered a challenge, especially in urban environments. Utilizing Raw GNSS Measurements (RGNSSM) on the android platform made smartphone navigation and tracking easier. Several researches have been conducted to improve the positioning information by removing the noise of pseudorange measurements, generating an optimized weighting covariance matrix, or integrating GNSS data with other sensors. In this paper, a new algorithm based on generating a modified weighting covariance matrix is proposed moreover, it is applied to several satellite selection techniques to improve android smartphone positioning information. This weighting covariance matrix depends on elevation angle, carrier-to-noise density, and also the measurement residual for each satellite. A real road trajectory was conducted to test the performance of the proposed algorithm. The results show that the 3D position was enhanced by 43.41% compared to the method suggested by the Google algorithm.
On the strain Assumption in a finite element model for plates and shells

Two different methods of assuming independent strain fields are examined for the nine node degenerate solid shell element. In the first case, the assumed strain field is chosen for the local orthogonal co-ordinate systems defined at the Gaussian integration points. In the second case, the independent strain is assumed for a local orthogonal co-ordinate system defined at the origin of the parent co-ordinates. The results of numerical tests involving simple example problems demonstrate that the second method is capable of exactly representing constant stress or moment states even when element geometries are distorted. In addition, both methods lead to a finite element model which is free of locking.
On the Limitations of Logic Locking the Approximate Circuits

Approximate Computing (AxC) is an emerging architectural paradigm, which promises improvements in area, performance or power consumption at the expense of limited and tolerable inaccuracies of computations. Circuits designed in typical AxC-friendly applications, such as neural network accelerators, visual computing and high-bandwidth communication, are vulnerable to supply-chain attacks. In this paper, we investigate logic locking-a protective technique against counterfeiting and overbuilding-in the context of AxC arithmetic primitives. We discuss a threat model that can hinder the adoption of logic locking on AxC based ICs. We show that modern SAT-resilient locking techniques are unsuitable for approximate circuits because of the low output corruption problem. Moreover, we explore the resistance of logic locking against an attacker who managed to obtain a subset of the correct locking key's bits. We show that an incorrectly unlocked circuit can still produce acceptable results, experimentally verifying this both at the level of primitives (adders and multipliers) and in a full application (neural network inference with approximate multiply-accumulate units).
Toward Congestion Control in Lossy Networks via Local Queue-Management Policies

Congestion control in intermittently connected lossy networks (ICLNs) is critical to enable efficient use of network resources, and mitigate congestion-induced packet losses and delays. Unfortunately, well-known protocols for end-to-end congestion control, such as the celebrated Transmission Control Protocol (TCP), are not well-suited for use in ICLNs. This work builds on the idea of hop-by-hop flow control and dynamic buffer-space allocation to develop a congestion-control approach able to react quickly to the onset of congestion with local congestion-control actions, and propagate direct and indirect congestion-control indicators toward the traffic sources. The local flow-control policy is developed using a reinforcement learning framework that captures queue-occupancy and sojourn-time indicators, and is coupled with a dynamic buffer-space allocation policy developed based on the Markowitz portfolio selection framework. The buffer-space allocation policy is responsible for allocating unused buffer space across all active flows in the node based on the relative traffic loads. The performance of the proposed framework when handling congestion locally is explored via numerical tests on a simulated environment.
Grid-Forming Type Converter Decoupling and Resynchronization for an AC Microgrid

The Microgrid (MG) operating modes are Grid-Tied Mode (GTM) and Islanded Mode (ISM). However, the transitions from GTM to ISM (GTM-ISM) and from ISM to GTM (ISM-GTM) are currently studied as operation modes. To correctly perform GTM-ISM and ISM-GTM, in this paper an Islanding Detection Algorithm (IDA) and Grid-Synchronization Algorithm (GSA) are developed. The development of IDA and GSA is based on the DQ transformation and the IEEE 1547 Standard. Both IDA and GSA operate together as a secondary control level to provide synchronizing signals to the primary control level. The results show a correct IDA and GSA performance, which allowed the Grid-Forming Type Converter (GFTC) to be capable of carrying out both GTM-ISM and ISM-GTM, correctly. This is possible because of the cascade control schemes outstandingly perform tasks such as: to extract mains power, to charge the battery, to regulate the DC bus voltage, and to synchronize and to regulate the GFTC output voltage.
Mathematical Modeling of Cyber Resilience

We identify quantitative characteristics of responses to cyber compromises that can be learned from repeatable, systematic experiments. We model a vehicle equipped with an autonomous cyber-defense system and which also has some inherent physical resilience features. When attacked by malware, this ensemble of cyber-physical features (i.e., “bonware”) strives to resist and recover from the performance degradation caused by the malware's attack. We propose parsimonious continuous models, and develop stochastic models to aid in quantifying systems' resilience to cyber attacks.
Finite-time Second order sliding mode control for master-slave manipulator with time delays

In this paper a finite-time second order sliding mode control for the teleoperation system in presence of uncertainties, disturbances and time delay is exposed. First, the second order fast nonsingular terminal sliding mode surface is designed to guarantee a finite time convergence and to eliminate the singularity problem. Then, a robust control law is developed to achieve high precision tracking and strong robustness against parametric uncertainties, disturbances, and time delay. The proposed idee consist to use the discontinuous function in the time derivative of the control input. Then, the proposed control, obtained by integration, is applied to the system in order to eliminate the chattering phenomenon and reduces the amplitude of the control effort. The stability of the system is demonstrated using the Lyapunov theory.
An Analysis on Different Distance Measures in KNN with PCA for Android Malware Detection

As Majority of the market is presently occupied by Android consumers, Android operating system is a prominent target for intruders. This research shows a dynamic Android malware detection approach that classifies dangerous and trustworthy applications using system call monitoring. While the applications were in the execution phase, dynamic system call analysis was conducted on legitimate and malicious applications. Majority of relevant machine learning-based studies on detecting android malware frequently employ baseline classifier settings and concentrate on selecting either the best attributes or classifier. This study examines the performance of K Nearest Neighbor (KNN), factoring its many hyper-parameters with a focus on various distance metrics and this paper shows performance of KNN before and after performing Principal Component Analysis (PCA). The findings demonstrate that the classification performance may be significantly improved by using the adequate distance metric. KNN algorithm shows decent accuracy and improvement of efficiency such as decreasing the training time After PCA.
External Torque Estimation for Mobile Manipulators: A Comparison of Model-based and LSTM Methods

Online monitoring of external forces and torques is highly important for safety and robustness in certain manipulation tasks and close interaction with humans. For fixed-base manipulators, methods using explicit dynamic models as well as neural networks are popular. In this paper, we address the problem of estimating external torques on a mobile manipulator, where the mobile base introduces additional dynamic effects on the manipulator joints. We adapt a model-based method that is established for fixed-base manipulators to the mobile manipulator case. We identify the relevant dynamic parameters and use a momentum observer for online torque estimation. A learning-based method using long short-term memory (LSTM) neural networks is presented afterwards. The accuracy of the two methods is compared in an evaluation with a real mobile manipulator with attached weights.
Multi-tenant Cloud SaaS Application for a meeting to task transition via deep learning models

Thought digitalisation and the post pandemic period online meetings increase by 12.9% and number of attendees by 13.5%. According to Zoom, Google Meets, and MS Teams they collectively increased there amount of active users from March 2020 until june 2021 by 253%. Additionally virtual meetings are becoming inefficient, unproductive and unstructured, where results are not documented correctly. The intention is to shift those statistics by revolutionising the way online meeting are held. The paper outlines the technical requirements for a user-friendly web application that allows to reproduce a virtual meeting including agreements, its participants interactions and highlighting relevant sequences, is outlined. The developed AI meeting wizard autonomously documents the discussed content in the form of notes, to-dos tasks and integrates them directly into task management, mail or calendar tools. Reinforcing the focus on the discussion by increased interaction and productivity instead of task management and note taking.
PrimitivePose: 3D Bounding Box Prediction of Unseen Objects via Synthetic Geometric Primitives

This paper studies the challenging problem of 3D pose and size estimation for multi-object scene configurations from stereo views. Most existing methods rely on CAD models and are therefore limited to a predefined set of known object categories. This closed-set constraint limits the range of applications for robots interacting in dynamic environments where previously unseen objects may appear. To address this problem we propose an oriented 3D bounding box detection method that does not require 3D models or semantic information of the objects and is learned entirely from the category-specific domain, relying on purely geometric cues. These geometric cues are objectness and compactness, as represented in the synthetic domain by generating a diverse set of stereo image pairs featuring pose annotated geometric primitives. We then use stereo matching and derive three representations for 3D image content: disparity maps, surface normal images and a novel representation of disparity-scaled surface normal images. The proposed model, PrimitivePose, is trained as a single-stage multi-task neural network using any one of those representations as input and 3D oriented bounding boxes, object centroids and object sizes as output. We evaluate PrimitivePose for 3D bounding box prediction on difficult unseen objects in a tabletop environment and compare it to the popular PoseCNN model-a video showcasing our results can be found at: https://preview.tinyurl. com/2pccumvt.
Intelligent Reflecting Surface-based Secrecy Rate Enhancement in Multicast Multigroup Tactical Communication Systems

Improving wireless transmission capacity and security is substantial in today's tactical networks due to the explosion of the data-intensive applications. In this paper, we investigate a framework that can successfully convey the messages from the transmitter to the receivers with the supports of intelligent reflecting surfaces (IRSs) to achieve high data rate. In particular, we manage the actual transmitted data instead of sending all source data in IRSs-aided multicast multigroup systems. A key challenging issue of this problem consists in obtaining a closed-form expression for the complicated distribution of the signal-to-noise-plus-interference (SINR) in IRSs-aided wireless systems. Therefore, we propose a deep neural network (DNN)-based framework to obtain a high-accuracy prediction of the long-term network capacity. Based on predicted results, we solve the joint power control and data reduction ratio selection problem to maximize the total received quality of service (QoS). Furthermore, we also consider the physical layer security design where data leakage among users' groups is prevented. We adapt well-known zero-forcing (BD) and block diagonalization (BD) techniques in achieving high-efficient secure solutions in IRSs-aided multicast multi-group systems. Numerical results confirm the efficiency of the proposed design. The information leakage can decrease down to 0 thanks to our proposed framework.
RTC: Route to Controller Algorithm Providing SDN Capabilities in MANETs

Decentralized network architectures like MANETs make it difficult to leverage SDN capabilities like centralized path construction since routing is managed by participants' local topology knowledge. Such a distributed routing behavior characterizes MANETs as robust as a certain number of node failures can be covered by participants. However, distributed routing based on inconsistent and incomplete topology knowledge of participants leads to inefficient path constructions. This decreases the residual transmission capacity of the MANETs which could be improved with complete and up-to-date topology knowledge. Previous research proposed RFTKR, an algorithm providing up-to-date topology knowledge stored on a controller in the MANET. Thus, routing can leverage SDN capabilities, where path construction is outsourced to the controller. Participants forward flows based on these paths. However, an open question is still how to maintain a route to this controller for each participant to request new routes. This requirement challenges even more since nodes are moving continuously. Also, control and routing messages must be kept minimal since transmission capacities in MANETs are restricted. This research tackles this challenge and presents RTC, an algorithm that rearranges participants of the MANET to a tree. Participants of this tree, maintain their route to their controller while keeping the message overhead as minimal as possible. Results prove the correct functionality of RTC. In addition, further simulations verify that tree rearrangements minimize the message overhead at runtime.
A Computer-Aided Brain Tumor Detection Integrating Ensemble Classifiers with Data Augmentation and VGG16 Feature Extraction

Early detection of brain tumors is important to increase the rate of complete recovery from it without risking the lives of patients. Nowadays, the medical domain aims to use magnetic resonance to achieve early detection of Brain Tumors (BT), as 40 out of 100 people survive their cancer for 1 year or more[6], therefore the early detection of the tumors helps in the recovery. Magnetic resonance imaging (MRI) and X-Ray images are used in the early diagnosis of BT to eliminate its spreading. In this paper, we build an ensemble classifier model that integrates data augmentation with the VGG16 deep-learning feature extraction model for early detection of multi-class brain tumor types of patient infection. We perform the BT classification using the proposed model on a dataset that has a multiclass classification (Glioma tumor, Meningioma tumor, No tumor, and Pituitary tumor), it will classify the type of the tumor if it exists in the MRI. Our model results in an accuracy of 96.8% using the proposed model.
DSFL: Dynamic Sparsification for Federated Learning

Federated Learning (FL) is considered the key, enabling approach for privacy-preserving, distributed machine learning (ML) systems. FL requires the periodic transmission of ML models from users to the server. Therefore, communication via resource-constrained networks is currently a fundamental bottleneck in FL, which is restricting the ML model complexity and user participation. One of the notable trends to reduce the communication cost of FL systems is gradient compression, in which techniques in the form of sparsification are utilized. However, these methods utilize a single compression rate for all users and do not consider communication heterogeneity in a real-world FL system. Therefore, these methods are bottlenecked by the worst communication capacity across users. Further, sparsification methods are non-adaptive and do not utilize the redundant, similar information across users' ML models for compression. In this paper, we introduce a novel Dynamic Sparsification for Federated Learning (DSFL) approach that enables users to compress their local models based on their communication capacity at each iteration by using two novel sparsification methods: layer-wise similarity sparsification (LSS) and extended top- $K$ sparsification. LSS enables DSFL to utilize the global redundant information in users' models by using the Centralized Kernel Alignment (CKA) similarity for sparsification. The extended top-$K$ model sparsification method empowers DSFL to accommodate the heterogeneous communication capacity of user devices by allowing different values of sparsification rate $K$ for each user at each iteration. Our extensive experimental results11All code and experiments are publicly available at: https://github.com/mahdibeit/DSFL. on three datasets show that DSFL has a faster convergence rate than fixed sparsification, and as the communication heterogeneity increases, this gap increases. Further, our thorough experimental investigations uncover the similarities of user models across the FL system.
A Digital Twin Study: Particle Swarm Optimization of ACC Controller for Follow Acceleration Maneuver

A digital twin (DT) is a technology that allows to create a virtual copy of a real-world system or product. This allows real-world activities to be synchronized, validated and production costs to be reduced. In this study, a DT model is developed to simulate the maneuvers of the vehicle in the virtual environment and optimize the vehicles' control parameters within the scope of Advanced Driver Assistance Systems (ADAS) and Automated Driving (AD) applications. In order to create and validate the DT, we focused on the follow acceleration maneuver (FAM). Firstly, road tests were carried out based on the cases, and vehicle dynamics relevant data were collected with the sensors. The collected data were used to generate scenario files containing static and dynamic environmental information, and movements of road users using ASAM OpenDRIVE® (ODR) and ASAM OpenSCENARIO® (OSC) scenario modelling standards, respectively. The Model. CONNECT™ co-simulation platform (CSP), which enables subsystems containing vehicle model, controller, and scenario information to work together, was used to simulate maneuvers regarding the generated scenarios. While creating the DT, the parameters in the vehicle model were tuned by performing the coastdown maneuver as well as FAM. After all, subsystems were integrated into the AVL Model. CONNECT™ (MC), FAM is performed and controller parameters were optimized using the particle swarm optimization (PSO) algorithm. The main goal of this study is to analyze and validate the DT model, then optimize controller parameters of adaptive cruise control (ACC) with the DT.
Grid-Feeding Power Converter in an AC Microgrid with a Photovoltaic Array and a Battery Pack

In this paper, a grid-feeding power converter topology is proposed so that a photovoltaic array and the mains, if necessary, can charge a battery pack. The proposal presented also considers extracting the maximum power of the photovoltaic array. The grid-feeding power converter topology is composed by a boost DC-DC converter and a bidirectional DC-AC converter connected to the mains, the control strategies for each converter are also presented. The control scheme for the boost DC-DC converter includes a maximum power point tracking algorithm. The control scheme for the bidirectional DC-AC converter has two operating modes, current-control mode and voltage-control mode. To validate the performance of the grid-feeding converter simulation results are presented. The grid-feeding converter control strategy successfully carries out the power management between the mains, the PV array and the battery.
Integrating Human Intelligence to Bypass Information Asymmetry in Procurement Decision-Making

President Biden's Executive Order on Improving the Nation's Cybersecurity included two core components to enhance the security and integrity of the software supply chain: Labels and Software Bills of Materials. The National Institute of Standards and Technology (NIST) was tasked with establishing a security labeling program. Its initial design was based on Energy Star, a voluntary labeling program established by the Environmental Protection Agency (EPA) to allow businesses to communicate energy consumption information to consumers. Similarly, the National Telecommunications and Information Administration (NTIA) defined the minimum elements for the Software Bill of Materials (SBOM). These SBOMs are analogous to nutrition facts labels, as they detail all the software components used in a product. What combination of information, on labels or bills of materials, should be provided at each stage of the acquisition lifecycle? To answer this question we built on previous research on labels, procurement standards, best practices for IoT and software, and information proposed for labeling and SBOM programs. From that, we identified candidate features (and the purposes of those features) that were potentially salient during the acquisition process. We recruited participants from the Department of Defense community to sort those features according to their importance. We conclude that neither a single label nor a list of information can adequately support risk-informed decision-making across the acquisition process. We report how participants' information requirements correlated with their work roles. We offer recommendations for the next steps to design an effective label system to support cybersecurity-aware procurement.
Broadly tunable high repetition rate femtosecond optical parametric oscillator

A tunable, singley-resonant optical parametric oscillator provides femtosecond light pulses in the infrared wavelength band. The oscillator includes an internally mounted thin crystal of KTiOPO4 which is synchronously pumped by femtosecond pulses from a colliding-pulse passively mode-locked dye laser. Rotation of the crystal varies the wavelength of the oscillator. Prisms within the oscillator cavity control group velocity dispersion to limit pulse width, and the oscillator as stabilized by a feedback network which varies the length of the cavity in response to spectral changes.
The Dimensionality of the Aliasing Problem in Models with Rational Spectral Densities

This paper reconsiders the aliasing problem of identifying the parameters of a continuous time stochastic process from discrete time data. It analyzes the extent to which restricting attention to processes with rational spectral density matrices reduces the number of observationally equivalent models. It focuses on rational specifications of spectral density matrices since rational parameterizations are commonly employed in the analysis of the time series data. (This abstract was borrowed from another version of this item.)