Miracle-dz/UniFAR-FTUs huggingface dataset:
LightVO: Lightweight Inertial-Assisted Monocular Visual Odometry with Dense Neural Networks

Monocular visual odometry (VO) is one of the most practical ways in vehicle autonomous positioning, through which a vehicle can automatically locate itself in a completely unknown environment. Although some existing VO algorithms have proved the superiority, they usually need another precise adjustment to operate well when using a different camera or in different environments. The existing VO methods based on deep learning require few manual calibration, but most of them occupy a tremendous amount of computing resources and cannot realize real-time VO. We propose a highly real-time VO system based on the optical flow and DenseNet structure accompanied with the inertial measurement unit (IMU). It cascade the optical flow network and DenseNet structure to calculate the translation and rotation, using the calculated information and IMU for construction and self- correction of the map. We have verified its computational complexity and performance on the KITTI dataset. The experiments have shown that the proposed system only requires less than 50% computation power than the main stream deep learning VO. It can also achieve 30% higher translation accuracy as well.
Automatic and Adaptive Signal- and Background-ROIs With Analytic-Representation-Based Processing for Robust Webcam-Based Heart-Rate Estimation

Determining a suitable, adaptive region of interest (ROI) automatically for extracting information related to cardiac activity (signal-ROI/S-ROI), and another containing information on ambient light-fluctuation (background-ROI/B-ROI, as close as possible to the signal-ROI), and robust signal processing are important in webcam based heart-rate (HR) estimation – in real life situations. We describe a novel method of automatically determining both the ROIs. The forehead is the candidate for the S-ROI, due to its uniformity and minimum vulnerability for deformation. We first identify the skin-pixels within the face-region detected by the Viola-Jones (VJ) algorithm. The forehead-region, and a uniform sub-rectangle within it not containing hair – determined by using variance as a measure – yields the S-ROI. The B-ROI – consisting of 3 rectangles – each of the same size as that of S-ROI, at the two sides and the top of the VJ-rectangle – is used to generate a reference signal for an adaptive noise-cancellation scheme. The situation arising from (possibly simultaneous) facial expressions deforming the S-ROI, is addressed – by extracting the phase sequence associated with the analytic representation of the signal. Experiments conducted with 21 healthy subjects, using this novel array of techniques, have produced good correspondence with the ground truth obtained from a standard finger-pulse transducer – as reflected by the Bland-Altman plot.
Direct Sensorless Model Predictive Control of Induction Motor for Three-Level Inverter based on Extended Kalman Filter

In this paper, an enhanced sensorless Model Predictive Control (MPC) based on Extended Kalman Filter (EKF) supplied from three-level Neutral Point Clamped (NPC) voltage source inverter for three-phase induction motor (IM) is presented, simulated and discussed. The main idea for the estimation of the non-measurable internal states of the machine, those are the rotor speed, flux, and load torque is the utilization of the EKF for the nonlinear machine equations and the selection of the covariance matrix coefficients in order to fast track the internal states of the machine. The backbone of the power unit in the whole scheme is the 3L inverter that offers 27 different voltage space vectors; which one will be selected, then applied to the machine this is done using the MPC controller, which predicts firstly all future values of the states of the machine and then establishes a convergence mechanism to follow the reference values of the desired Speed, torque and flux. Sectors and areas of the applied space vectors and the corresponding machine states will be focused and will be shown graphically. The suggested technique is verified by simulation results to demonstrate the feasibility of the drive during different speeds.
Mindfulness Based Cognitive Therapy (MBCT) as An Addition toPsychiatric Care as Usual Useful for HIV-infected Patients with MentalHealth Symptoms

Objectives: HIV-infected patients suffer from both physical and mental health symptoms and treatment of both symptoms is important. The 8-week Mindfulness Based Cognitive Therapy (henceforth MBCT) was originally developed for both mental and physical symptoms and is easy to implement. It is unknown, however, whether the addition of MBCT to psychiatric care as usual is effective in the long term and feasible for this group of patients. We measured depressive and anxiety symptoms, suicidal thoughts and fatigue in HIV-infected patients receiving psychiatric care as usual with or without MBCT, at intake and after one year. Methods: The study was conducted at the outpatient clinic for HIV and Mental Health at GGZ inGeest in Amsterdam, the Netherlands, between March 2006 and September 2009. MBCT was offered as a group therapy in addition to individual care as usual to all (N=208) patients in their first year of psychiatric treatment. Results: In the research period, 58 patients opted for the MBCT and 150 did not. Twenty patients did not complete the MBCT and questionnaires of both time points were available from 22 patients with and from 60 patients without MBCT. During their first year of psychiatric care, patients in both treatment groups showed significant improvement on depressive and anxiety symptoms, suicidal thoughts and fatigue. Conclusion: Measured after one year, the addition of MBCT to psychiatric care as usual for HIV-infected patients had no measurable added effect on the treatment of depressive and anxiety symptoms, suicidal thoughts and fatigue. The present study was an explorative study to investigate the role of additional MBCT to psychiatric care as usual. It seems that general addition of MBCT is not feasible in this group of patients. However, further research, for example in a randomized controlled trial (RCT) is necessary to confirm the resu
Computation Offloading Game for Multi-Task Multi-Helper Fog Networks

Fog computing has risen as an evolving architecture to support delay-sensitive applications in Internet of Things (IoT) and next generation mobile networks. For a typical heterogeneous fog network consisting of many fog nodes, some of them have different computation tasks while some have spare computation resources, which forms a multi-task multi-helper (MTMH) network. How to effectively map multiple tasks into multiple helper nodes to reduce the service delay is a key issue to be resolved. To tackle this issue, a computation offloading problem minimizing every taskâ€™s delay is considered, from the perspective of individuals. This problem is further formulated into a non-cooperative game, i.e., MTMH computation offloading (MTMHCO) game, to model the competition among tasks for helpers. The existence of Nash equilibrium (NE) is guaranteed and an efficient distributed algorithm is developed to achieve an NE for the MTMHCO game. Theoretical analysis and simulation results show that the proposed algorithm can offer the nearoptimal performance in system average delay and achieve more number of beneficial task nodes, at two orders of magnitude lower complexity than a centralized optimal algorithm.
Grant-Free Massive Machine-Type Communications with Backward Activity Detection

As one of the three major scenarios of fifth-generation (5G) communication system, massive machine type communications (mMTC) raises more challenges for development of new radio access technology. Unlike the human-to-human communications, the mMTC typically exhibit features such as massive number of devices, small-sized packets and sporadic transmission, which requires novel solutions to satisfy these features. In this paper, we propose a novel grant-free compressive sensing based solution with system level design to jointly conduct the active devices detection, channel estimation and data recovery without knowing the number of active devices. Specifically, in proposed solution, we integrate the sparsity information of channel coefficients and data symbols and exploit the constellation information of modulation and the data diversity of different devices to enhance the performance of receiver. Simulation results demonstrate that the proposed solutions have improve the performance of active device detection, channel estimation, data recovery and the throughput of the system.
Eco-Friendly Fully Water-Driven HfGdO ${_{x}}$ Gate Dielectrics and Its Application in Thin-Film Transistors and Logic Circuits

In this work, HfGdO<sub><italic>x</italic></sub> gate dielectric thin films have been prepared via a nontoxic and environmental-friendly fully water-driven (WD) method. An in-depth investigation has been conducted to reveal the evolution of physical properties in the as-prepared HfGdO<sub><italic>x</italic></sub> thin films as a function of annealing temperature. Based on the measurements of electrical parameters of the HfGdO<sub><italic>x</italic></sub> thin films, it is observed that the 500 °C-annealed HfGdO<sub><italic>x</italic></sub> thin films have shown optimized properties, including a smooth surface, lower leakage current density, and a larger areal capacitance. To validate the feasibility of the HfGdO<sub><italic>x</italic></sub> gate dielectrics in thin-film transistors (TFTs), In<sub>2</sub>O<sub>3</sub>/HfGdO<sub><italic>x</italic></sub> TFTs have been constructed. The optimized In<sub>2</sub>O<sub>3</sub>/HfGdO<sub><italic>x</italic></sub> TFT exhibited excellent electrical performances, including high field-effect mobility of 11.2 cm<sup>2</sup> V<sup>−1</sup> s<sup>−1</sup>, a high ON-/OFF-state current ratio of <inline-formula> <tex-math notation="LaTeX">${4.1} \times {10}^{{6}}$ </tex-math></inline-formula>, a small sub-threshold swing of 105.2 mV/decade, and a threshold voltage shift of 0.58 V after 7200-s bias stress, respectively. Finally, based on the In<sub>2</sub>O<sub>3</sub>/HfGdO<sub><italic>x</italic></sub> TFT, an inverter is assembled by connecting an external 3-<inline-formula> <tex-math notation="LaTeX">$\text{M}\Omega $ </tex-math></inline-formula> resistor. High gain of 8.73 and excellent swing characteristics have been obtained. As a result, it can be inferred that fully WD In<sub>2</sub>O<sub>3</sub>/HfGdO<sub><italic>x</italic></sub> TFTs with high performance will pave the way for achieving low-power-consumption, low-cost, and large-area electronics.
Towards Understanding of Bus Mobility for Intelligent Vehicular Networks Using Real-World Data

Understanding the mobility of vehicles plays a fundamental role in the design of solutions for intelligent vehicular networks. Considering that different types of vehicles have different characteristics of mobility, we are interested in investigating how bus mobility impacts the formation of these networks. In this sense, we present clear understanding the bus mobility for vehicular networks. Our analysis is based on a real mobility trace of buses from several days of Dublin, Ireland. Particularly, our study reveals key features of a vehicular network obtained from a real bus mobility trace such as the network structure over the days and how the components of the networks are arranged in the space and time. Additionally, we investigate the potential of bus mobility for urban sensing. In summary, due to network fragmentation identified in our analysis, data dissemination mechanisms that use store-carry-and-forward and street-centric routing are more indicated for intelligent vehicular networks based on bus mobility. Moreover, we show how the use of buses as sensors can compose a powerful urban sensing infrastructure.
Obsea: A Decadal Balance for a Cabled Observatory Deployment

The study of the effects of climate change on the marine environment requires the existence of sufficiently long time series of key parameters. The study of these series allows both to characterize the range of variability in each particular region and to detect trends or changes that could be attributed to anthropogenic causes. For this reason, networks of permanent cabled observation systems are being deployed in the ocean. This paper presents a balance of a decade of activity at the OBSEA cabled observatory, as an example of ocean monitoring success and drawbacks. It is not the objective of this article to analyze the scientific and technical aspects already presented by the authors in different publications (Table 4). We will evaluate the overall experience by retracing the different steps of infrastructure deployment and maintenance, focusing on routines for in situ control, damages experienced, breakdowns and administrative constraints by local administrations. We will conclude by providing a set of guidelines to improve cabled observatories scientific outreach, societal projection, and economic efficiency. As a result of this work, a 10-years dataset has been published in Pangaea that is available for the community.
Outage Performance of Full-Duplex Overlay CR-NOMA Networks with SWIPT

In this paper, we propose a novel non-orthogonal multiple access (NOMA)-assisted cooperative overlay spectrum-sharing network, in which a full- duplex (FD) secondary transmitter (ST) is selected for relaying the primary transmitter's information and transmitting its own to the primary receiver and secondary receivers, respectively, using NOMA approach. In order to encourage the ST to perform secondary cooperative transmission and maintain its operation, simultaneous wireless information and power transfer (SWIPT) technique is exploited to scavenge energy from the primary signal. To assess the performance of the proposed system, the outage probabilities for the primary and secondary networks are derived in tight closed-form approximations. Numerical results show that the combination of FD, SWIPT, and NOMA technologies greatly benefits the cooperative overlay spectrum- sharing network in terms of outage probability, when compared with that of half-duplex NOMA and conventional orthogonal multiple access networks.
A mm-Wave Low-Loss Transition from Microstrip Line to Air-Filled Substrate Integrated Wavguide on Printed Circuit Board Technology

This paper introduces the design of an air-filled substrate integrated waveguide (AFSIW), and a transition design from microstrip line (ML) to AFSIW at 60 GHz. The proposed structure is realized by printed circuit board (PCB) technology so that the fabrication cost is low and the whole structure is easy to be integrated with other planar components. An equivalent circuit model of the proposed ML-AFSIW transition is proposed and verified by full-wave simulation. To verify the proposed idea, a back-to-back transition is designed, fabricated and measured. The measured return loss is higher than 15 dB from 60.6 to 64.5 GHz. The minimum of measured insertion loss is 0.2 dB at 61 GHz. The measured results show that the proposed ML-AFSIW transition is suitable for millimeter-wave (mm-Wave) application.
A Review on Bidirectional Matrix-Based AC-DC Conversion for Modular Solid-State-Transformers

This paper has presented an extensive review of the bidirectional phase-modular and semi-modular Solid-State- Transformer (SST) topologies, specific to the matrix-type variants with single-stage AC-DC conversion. This includes a qualitative and mathematical explanation of each topology and the corresponding modulation(s). A comparative analysis incorporating the criteria of power density, failure rate, cost and component count is also done among all these topologies along with the conventional Cascaded-Modular-SST (CMSST) topology with two-stage AC-DC conversion. The matrix-based topologies clearly prove to be better than the CMSST topology in terms of power density and reliability, with the entirely phase-modular topologies proving to be superior overall. The AC-DC DAB type submodule has been estimated to have the best characteristics in terms of all the comparison indices and thus it can be considered to be the best among the grid-connected matrix-based SST topologies for the application-type under consideration.
Development of Prototype MGB2 Superconducting Solenoid Magnet for High-Efficiency Klystron Applications

A wind-and-react MgB2 solenoid magnet for klystrons has been developed. While the current normal-conducting (Cu) magnet consumes 20 kW per magnet, this MgB2 magnet consumes less than 3 kW in refrigerator power. The conduction-cooled half coil of the magnet is 337 mm in inner diameter; the winding pack, 19.4 mm wide × 136.6 mm high, uses 2.7 km of 10 filament circular conductor, which is insulated with glass 0.83 mm in diameter, and is reacted after being wound onto a stainless steel bobbin. The coil has Cu plates of 0.2 mm in thickness between each coil layer and on the inner and outer sides. The magnet has two coils and produces 0.8 T in the center and its stored energy is 11.8 kJ. Together with the above-mentioned coil structure, these coils can consume stored energy in itself at quench without a special quench protection system. A performance test of the magnet was successful.
Self-Polarization in Reference-Less Electrolyte-Gated Organic Field-Effect Transistors

Electrolyte-gated organic field-effect transistors (EGOFETs) were fabricated using 6,13-Bis(triisopropylsilylethynyl)pentacene (TIPS-Pentacene) as a p-type organic semiconductor. The devices were characterized as reference-less (RL) EGOFETs, and the self-polarization mechanism was studied. The fact that EGOFETs can exhibit field-effect behavior even without the gate electrode was demonstrated. This work provides useful guidelines for optimizing RL-EGOFET technology.
Network Energy Efficient Mobile Edge Computing with Reliability Guarantees

This paper proposes a novel algorithmic solution for dynamic computation offloading, aimed at reducing the energy consumption of a mobile network endowed with multi-access edge computing. The dynamic evolution of the system is modeled through three queues: a local queue at the user side, a computation queue at the edge server, and a queue of results at the network access point. The optimization problem is cast as the minimization of the long-term average energy consumption of the whole system, comprising user devices, servers, and access points. Quality of service constraints for end users are imposed in terms of probability that the \textit{sum of the queues} exceeds a given threshold. A suitable weighting parameter can be tuned to drive the system toward a user-centric, a network-centric, or a hybrid solution. Exploiting stochastic optimization tools, the problem is solved thanks to a dynamic optimization algorithm, based on the solution of deterministic convex problems in each time slot. The algorithm does not assume any knowledge on the task input and output random sizes and the radio channel statistics. Several numerical results illustrate the advantages of the proposed method.
Incentive Mechanism and Relay Selection for D2D Relaying in Cellular Networks

The performance of the cell edge users (CUEs) can be improved if they transmit their data via suitable relay UEs (RUEs) exploiting device-to- device (D2D) communication. The critical aspect of the whole relaying concept is to offer convenient incentives for the RUEs to motivate them to act as relays. The contribution of this paper is twofold. First, we propose a new incentive mechanism for the RUEs that can exploit certain amount of resources allocated to the CUE. Depending on the preferences of users, the CUEs/RUEs can benefit from relaying in terms of capacity enhancement, reduction of energy consumption or both. In this respect, we provide a detailed analysis on how and when relaying is of benefit for both sides. Second, we propose a low-complexity greedy relay selection algorithm incorporating the incentive mechanism that increases capacity up to 32.1% and/or reduces energy consumption by up to 36.1% when compared to state-of-the-art schemes. Moreover, we show that the greedy approach gives close-to-optimal performance.
Maximum Power Point Tracking For a Stand Alone PV System Under Shading Conditions Using Flower Pollination Algorithm

in this paper, an optimal study is introduced to obtain the maximum out power for a standalone PV system under different shading conditions applying flower pollination optimization algorithm. The characteristics of photovoltaic systems may have several power peaks under partially shading conditions. This may cause that the traditional maximum power point tracking (MPPT) methods to fail in local peak while the global peak doesn’t be detected. The maximum power point can be achieved using artificial intelligence (AI) tool of search. This paper introduces a global MPPT method when partial shading condition is applied. The method is based on perturbation of random variables using flower pollination AI algorithm. The simulation is performed on a 100 kW photovoltaic station and the results proved the superiority of the method.
Auditory Assist Method to Indicate Steering Start Timing in Reverse Parking for Improvement of Driver Performance

The development of fully automating the operation of automobiles is rapidly progressing. However, challenges related to driving skill improvement of drivers exist because of the necessity of manual operation, and these are expected to continue in the future. Despite the fact that maintaining and improving manual driving performance will be indispensable in the near future, there are few studies on parking assistance systems aimed at improving the skills of the drivers. Therefore, we focused on steering timing as a candidate factor for skill improvement in reverse parking. In this article, we developed an assist method based on auditory presentation that indicates the timing for starting of steering in order to follow the target trajectory during reverse parking. From the results of verification tests of the assistance using the driving simulator, various effects such as improvement of driver's parking accuracy and reduction of workload were confirmed.
Detection System for Detecting Worms using Hybrid Algorithm of Naïve Bayesian classifier and K-Means

According to a huge rampage of malware attacks especially worms which the attackers use in their attacks to the network. Detection systems are needed to stop these worm attacks. In this paper, we proposed and designed a detection system that detects worms depending on a dataset was taken from tow web sites which are the Kasperski web site and the McAfee web site, these two companies' websites links are: (https://www.kaspersky.com/resource-center/threats/resource-for-virus-threats-definitions, https://home.mcafee.com/virusinfo). The information of the worms that had been used in this paper and had been detected by the detection system were all from this data set that had been gathered from these two websites the Kasperski and McAfee. The proposed system has been used in host-based environment and it consists of two hybrid algorithms, which are the Naïve Bayesian classification and K-Means clustering and called NBKM. The proposed NBKM is a hybrid between Naïve Bayesian classification and K-Means clustering, which they are both Data mining and Machine learning techniques. This hybrid model has the benefits of the two algorithms the Naïve Bayesian classification and the K-Means clustering, the data detection could be achieved by two phases, which are classification and clustering. The NB classifier was used to make a comparison among the results with the previous proposed model NBKM to check the NBKM model performance. In addition, as the previous model, it will classify the data of incoming packets as ones and zeros, the ones for the threat data and the zeros for the benign, and it would check the ports that would the worm could get access to the network with the local IP address (127.0.0.1). We applied this proposed algorithm on the dataset that involves the worms and as a result, we gained 88% accuracy, 81% of Detection Rate and 21% of False Alerts.