Miracle-dz/UniFAR-FTUs huggingface dataset:
The functional characterisation of novel sucrose transporters

Sucrose is the predominant form in which photosynthetically-fixed carbon is transported over long distances in many plant species. Sucrose moves in the plant from regions of net photosynthetic fixation of carbon or storage (source organs) to sinks, where active growth and/or storage product accumulation occurs. The phloem serves as the long-distance transport conduit. One or more symplasmic discontinuities may occur along the pathway from source to sink, invoking plasma membrane transport steps. For instance, phloem unloading of nutrients includes an apoplasmic step in a number of physiologically important sinks such as developing seeds and fleshy fruits. The uptake of sucrose into plant cells has been well described, and is mediated by sucrose/proton symporter proteins (SUTs). In contrast, little is known about the molecular identity of membrane transporters contributing to sucrose efflux in apoplasmic phloem loading and unloading. The aim of this study was to identify and characterise novel sucrose transporters involved in sucrose efflux. Seed coats of pea and bean were selected as source material, as they are functionally committed to sucrose efflux. Cloning by homology to known SUT sequences, five genes were isolated from legume seed coats three from pea (including the previously described PsSUT1) and two from French bean. Complementation of the yeast strain, SUSY7, demonstrated that each gene encodes a functional sucrose transporter. When functionally expressed in yeast, three of the five transporters studied exhibited pHand energy-independent sucrose transport that was shown to be bi-directional. These transport properties, together with counter transport, are consistent with a sucrose facilitator (SUF) function. In addition, and unlike H-coupled SUTs, their transport function was insensitive to diethylpyrocarbonate and did not bind maltose. Kinetically the SUFs functioned as low-affinity, high-capacity sucrose transporters. The physiological significance of these novel SUFs in mediating release of sucrose from coats to the seed apoplasm in developing pea and bean seeds is discussed. Cellular and subcellular localisation studies were also carried out to determine whether SUFs are present at putative sites of sucrose efflux. The cellular and subcellular localisation of PsSUT1, PsSUF1 and PsSUF4, cloned from pea, was carried out to determine their potential contribution to phloem loading and unloading of sucrose in planta. Transient expression of GFP-tagged protein showed that all three transporters
Alzheimer's disease detection using explainable AI on PET images

Automated detection of Alzheimer’s disease features from imaging is a widely worked on machine learning problem, facilitated by the amount of data available through programs such as ADNI. Consequently, “black‐box” neural networks are starting to become viable and accurate approaches to patient classification. However, such methods are typically opaque, and their results are difficult to interpret – limiting their deployment in clinical trials.
Direct 3D model-based tracking in omnidirectional images robust to large inter-frame motion

This paper tackles direct 3D model-based pose tracking. It considers the Photometric Gaussian Mixtures (PGM) transform of omnidirectional images as direct features. The contributions include an adaptation of the pose optimization to omnidirectional cameras and a rethink of the initialization and optimization rules of the PGM extent. These enhancements produce a giant leap in the convergence domain width. Application to images acquired onboard a mobile robot within an urban environment described by a large 3D colored point cloud shows significant robustness to large interframe motion, compared to approaches that directly use pixel brightness as direct features.
YO-VIO: Robust Multi-Sensor Semantic Fusion Localization in Dynamic Indoor Environments

Visual Simultaneous Localization and Mapping (SLAM) is widely employed in modern mobile service robots, which help robots to capture images indoors for estimating their pose. However, as part of visual SLAM, the typical visual odometry (VO) and visual-inertial odometry (VIO) systems only work in static environments. In many scenarios, they have to work in high-dynamic environments, which brings challenges to previous visual SLAM. In this paper, we proposed a novel monocular VIO for the challenging dynamic environments. Our method can make the robot locate accurately and robustly. Based on VINS-Mono, our system constructs a dynamic objects and feature points detection module. This module combines semantic object detection, multi-sensor-aided, and the geometric 3D vision constraints to remove the dynamic feature points. According to our experiments, the results demonstrate our system outperforms SOTA monocular VIO systems in accuracy and robustness, especially in high-dynamic indoor environments.
Growth kinetics of intermetallic compound in solder joints during thermal cycling: a review

A critical review is made in this paper to summarize the recently published studies about the growth kinetics of intermetallic compound (IMC) in solderjoints during thermal cycling in the applications of electronic packaging structures. The kinetics model based on diffusion mechanism has been well investigated for description of IMC growth at the solder and substrate interface under a constant high temperature. However, in the service condition, packaging structures are usually under varying temperature and the induced force due to the CTE mismatch between different material layers. In light of the fact that the mechanical properties of solderjoints are strongly affected by the IMC morphology, the coupled effects of stress and temperature on the growth of both the interfacial and isolated IMCs should be taken into account. In the literature, the published works regarding interfacial IMC growth kinetics can be classified into three categories: constant temperature, varying temperature with equivalent time and the couple effect of constant stress and temperature. The merits and demerits of these three categories are discussed from the solder joint mechanical reliability point of view. Meanwhile, the diffusion study regarding the isolated IMC morphology in the solid state is rare and some brief discussions are made to expect intensive investigations in the future.
A Power Conversion System For Large-Scale Reversible SOFC Energy Storage System

As the penetration rate of renewable energy in the utility grid increases, large-scale energy storage system (ESS) will become an essential infrastructure in the grid. Reversible solid oxide fuel cell (RSOFC) technology is a promising solution for large-scale ESS. However, the wide voltage range and low power of single RSOFC stack electrical characteristics have complicated the interface converter design. It is necessary to connect multiple stacks through a power conversion system to integrate the power because a single stack of RSOFC is small. This paper proposes a power conversion system for large-scale RSOFC ESS. The system consists of a cascaded H-bridge (CHB) converter and multiple multiport converters, and they are connected to a common dc bus. Because of the electrical characteristics of RSOFC, a two-stage CLLC based multiport converter topology is employed to achieve high voltage gain and operate at a wide voltage range. The control strategy of the proposed power conversion system is presented. Simulation tests verify the feasibility and effectiveness of the proposed conversion system
Performance Measurements of different Classification techniques for the Alzheimer’s Disease Neuroimaging Initiative

One of the neurogenerative disorders affected by many adults is Alzheimer’s Disease (AD). Disease prediction is also a difficult task. Except in the healthcare domain, the principles of artificial learning and data processing are commonly used. In this research paper, using the Machine Learning method, we applied various classification algorithms on the data sets and concluded which algorithms provide the best results. Also, we have used different possible methods to evaluate the model.
Motion Planning Framework for Autonomous Vehicle with LSTM-based Predictive Model

Accurate motion prediction of surrounding vehicles is of great importance to enable lower risk motion planning for autonomous vehicles. In this paper, a long short-term memory-based motion prediction approach is first adopted to predict the trajectories of surrounding vehicles, and the designed Long-Short Term network is trained and tested based on the Next Generation Simulation (NGSIM) datasets. More importantly, a motion planning technique is developed based the trajectory prediction model. Specifically, to avoid collisions with surrounding vehicles on highway, an artificial potential field-based risk assessment method is presented, and the road boundaries are also considered. Subsequently, the model predictive control algorithm is utilized to consider the risk of collision, road boundaries as well as the vehicle dynamics. Finally, the simulation results show the effectiveness of the proposed method.
HUMAN-CENTERED DESIGN OF A STOP-AND-GO VEHICLE CRUISE CONTROL

This paper presents design of a vehicle stop-and-go cruise control strategy based on analyzed results of the manual driving data. Human drivers driving characteristics have been investigated using vehicle driving data obtained from 100 participants on low speed urban traffic ways. The control algorithm has been designed to incorporate the driving characteristics of the human drivers and to achieve natural vehicle behavior of the controlled vehicle that would feel comfortable to the human driver under low speed stop-and-go driving conditions. Vehicle following characteristics of the cruise controlled vehicle have been investigated using a validated vehicle simulator and real driving radar sensor data.
Design and Development of an Instrumentation System to Detect the Bioelectric Signals of Plants

Bioelectric potential is generated in a plant by its physiological activities. Therefore, evaluation and monitoring of the plant activities can be done by measuring the changes in its bioelectric potential. The objective of this project was to develop a suitable instrumentation system that magnifies the plant bioelectric signals which are read at the surface of a leaf using a copper needle electrode. Further, it will also help in understanding the physiological behavior of plants which in turn may enable farmers to cultivate the plant crops in a more effective way. Copper needle electrodes were used as they are less susceptible to movement and also have less impendences compared to surface electrodes. Bioelectric potentials of the Bryophyllum plant were measured using copper needle electrodes. Bioelectric signal was amplified by an instrumentation amplifier (AD 620) and then converted to digital signal using a Data Acquisition device (DAQ) and monitored on a Cathode Ray Oscilloscope (CRO).
SYLOIN: Measuring Angle of Arrival of LoRa signals using Software Defined Radio

Several radio frequency direction finding algorithms exist for Internet of Things (IoT) applications that use angle-of-arrival (AoA) measurements to estimate target position. However, providing reliable and accurate direction system capability still remains a challenge. In this paper, we are interested in measuring the angle-of-arrival (AoA) of LoRa RF signal by using a software-defined radio (SDR) platform implemented with antenna-array interferometry method. The proposed system can determine the node’s direction using only dual interferometry antenna. The two antennas are operated by an individual universal software radio peripheral (USRP) SDR receiver. Furthermore, a new paradigm of LoRa digital carrier signal detection is investigated using GNU Radio and USRP B210 operating at a center frequency of 868 MHz. We present a solution that builds on the LoRa modulation properties to determine the presence of the user’s signal in the channel. The phase difference of the detected signal received by the two-channel USRP B210 is used to estimate the node’s AoA. The entire experimental setup was implemented in the GNU Radio Companion (GRC) and measured in a 8m 8m office environment. The measurement results show an AoA ×with a mean error of less than 5 degrees approximately. We therefore believe that our AoA system can be easily calculated in real time without the need for complicated post-processing algorithms.
Humans Prefer Collaborating with a Robot Who Leads in a Physical Human-Robot Collaboration Scenario

A question that many researchers in the area of human-robot interaction are addressing is how to create more human-like behaviour in robots to make the collaboration between a human and a robot more intuitive to the human partner. In this paper, we propose a novel method for robot control that aims to more accurately replicate human behaviour in human-robot collaborative tasks by considering how leader-follower relationships are established during physical human collaboration. How the leader/follower roles are allocated in a human dyad was learnt during a study on human-human collaboration. The performance of the proposed control method was evaluated using a reaching scenario where we compared task performance between individual tasks, tasks where two people were collaborating and tasks where a human was collaborating with a robot using the proposed control model. In addition, we were interested in observing how the subjects perceived the difficulty of each task type. This was evaluated using the Nasa Task Load Index and a short questionnaire. The results of the evaluation showed, that the robot control method is capable of replicating human behaviour to benefit the overall task performance of the subject in collaboration. Furthermore, the results in this work showed that the majority of subjects preferred to perform the task in collaboration with a robot that leads them, compared to performing the task on their own or in collaboration either with another human or a follower robot.
Dynamic Hierarchical Neural Network Offloading in IoT Edge Networks

In recent developments in machine learning, a trend has emerged where larger models achieve better performance. At the same time, deploying these models in real-life scenarios is difficult due to the parallel trend of pushing them on end-users or IoT devices with strong resource limitations. In this work, we develop a novel technique for executing parts of a single model successively through multiple devices (IoT, edge, cloud) while respecting each device’s resource limitations. For that, we introduce a new offloading mechanism where, during computation, a decision can be made to offload work, together with the ability to exit early in the computation with intermediate results. The decision itself is tuned through Deep Q-Learning.
Attitude and Heading Adaptive Estimation Using a Data Driven Approach

Accurate attitude and heading is required for many applications. For pedestrian navigation, the major challenge is to cope with linear accelerations and magnetic disturbances. To that end, a data driven adaptive complementary filter for attitude and heading estimation is proposed. The accelerometers and magnetometers weights in each axis are adjusted according to optimized gain maps. The proposed approach is compared to commonly used algorithms using experimental data representing commonly used smartphone dynamics. Results show the contribution of the proposed approach to the attitude accuracy.
A Semi-Autonomous Tele-Impedance Method based on Vision and Voice Interfaces

In tele-impedance the human can control the impedance of the remote robot through various interfaces, in addition to controlling the motion. While this can improve the performance of the remote robot in unpredictable and unstructured environments, it can add more workload to the human operator compared to the classic teleoperation. This paper presents a novel method for a semi-autonomous tele-impedance, where the controller exploits the robot vision to detect the environment and selects the appropriate impedance. For example, if vision detects a fragile object like glass, the controller autonomously lowers the impedance to increase the safety, while the human is commanding the motion to initiate and perform the interaction. If the vision algorithm is not confident in its detection, we developed an additional verbal communication interface that enables the human to confirm or correct the autonomous decision. Therefore, the method has four modalities: (i) perturbation rejection mode, (ii) object property detection mode, (iii) verbal confirmation mode, (iv) voice control mode. We conducted proof-of-concept experiments on a teleoperation setup, where the human operator performed position tracking and contact establishing tasks.
Parameter Estimation Method of Underwater Acoustic Chirp Spread Spectrum Signal

In non-cooperative underwater acoustic communication, there is a problem of insufficient accuracy in parameter estimation of Chirp spread spectrum signal. In this paper, a method combining short-time fractional Fourier transform and high-order cumulant is proposed to obtain the time-frequency distribution image of the signal, and then the Hough transform is used to process the image to get the parameter information of the signal. Simulation and measurement results show that the proposed method is more accurate and reliable than other methods.
REDO: A Reinforcement Learning-based Dynamic Routing Algorithm Selection Method for SDN

The current increase in the Internet traffic along with the global crisis have accelerated the roll-out of the next generation 5G network and key enabling technologies. In this context, addressing the end-to-end Quality of Service (QoS) provisioning in order to guarantee a sustainable service delivery to the end-users became of paramount importance. Some of the enabling technologies that could play a key role in this regard are Software Defined Network (SDN) and Machine Learning (ML). This paper proposes REDO, a Reinforcement lEarning-based Dynamic rOuting algorithm selection method that decides on the conventional routing algorithm to be applied on the traffic flows within a SDN environment. REDO will dynamically select the most appropriate routing algorithm from a set of centralized routing algorithms (MHA, WSP, SWP, MIRA) that maximizes the reward function from the network. The proposed REDO solution is implemented and evaluated using an experimental setup based on Mininet, Floodlight controller and Open vSwitch switches. The results show that REDO outperforms other state-of-the-art solutions.
A Workflow for Designing Video Processing Pipelines with PYNQ

Video processing acceleration is a vital task in a number of appications, including vision-based robotics. A workflow for the design of video pipelines based on the heterogeneous chip Zynq-7020 and the PYNQ-Z1 board is proposed. A video pipeline featuring a custom IP block based on AXI4-Stream interface is presented. The structure of a processing pipeline partitioned into programmable logic and the ARM processing system is outlined. The integration of the video pipeline with ROS nodes for hardware acceleration is proposed. The presented workflow is beneficial for education and can expedite research projects.
Coexistence Analysis Between 5G NR and LTE Network at 1.8 GHz

With the rapid growth of 5G new radio (NR) network, efficient spectrum resource utilization is one of the current biggest challenges. To solve this problem, the operator is beginning the construction of spectrum refarming which may cause the interference issue among different services. Thus, the study on interference coexistence between 5G NR and other networks is becoming more and more significant. Based on existing communication networks, this paper presents the sharing studies with 5G NR frequency division duplexing (FDD) and LTE time division duplexing (TDD) in 1.8 GHz band. Firstly, we provide an academic method, then we use the Monte Carlo method and apply system-level simulation. Finally, we conclude that whether in co-location and non-co-location topology, based on necessary adjacent channel interference ratio (ACIR), the coexistence of 5G NR FDD and LTE TDD can be achieved in the 1.8 GHz band.
Straight Line Detection Using Euclidean Distance Between Consecutive Pixels

Lines play a very important role in image processing and computer vision. They are a clue or a feature that enables further processing of an image, both in the image processing and computer vision fields. It is therefore imperative that they are accurately extracted or detected. The clues obtained from an image are used to calculate the line parameters, which tends to be an efficient way to detect lines. The strength of the proposed algorithm is its ability to limit the search area simply. The context of detecting straight lines in this research is mainly for the implementation of Autonomous Driving Systems (ADS) and Driver Assisted Systems (DAS) for trains. The proposed algorithm can be implemented in many image processing and computer vision procedures that require straight line detection.
A review of epileptic seizure detection using EEG signals analysis in the time and frequency domain

Epilepsy is a serious chronic neurological disorder and it can be detected by analyzing an Electroencephalogram acquired from non-invasive methods. Different strategies have been used for the identification of frequencies in EEG signals and the detection of an epileptic seizure, and also for the analysis in the time domain. This paper presents a comparative review of three of the most widely used methods for the analysis of EEG signals in the time and frequency domain: Fourier Transform, Wavelet Transform, and Hilbert Huang Transform. The results presented are obtained from real databases of patients diagnosed with epilepsy.
The Multi-Dimensional Scaling Algorithm with Centroid as Reference Point for Location in Wireless Senso Network

The multi-dimensional scaling(MDS) method is a kind of method used to analyze distance matrix or dissimilarity matrix to form data points in low-dimensional space. MDS algorithm uses the distance information between nodes in wireless sensor networks to estimate the relative location of nodes. Classical multi-dimensional scaling algorithm chooses any node in the network as reference point to estimate node location. With the increase of distance measurement error, the node position error estimated by this method also increases significantly. In order to improve the location performance, this paper proposes an MDS algorithm based on the centroid of all nodes in the network as a reference point, and derives the calculation formula of MDS algorithm based on centroid as reference point. Compared with the classical MDS algorithm, the simulation results show that the MDS algorithm base on centroid as the reference point has better positioning results.
A low-cost 3D printed mini underwater vehicle: Design and Fabrication

This paper introduces the conception, design, fabrication, and control of a modular remotely operated underwater vehicle. It is an affordable submarine robot intended for inspection operations in shallow depths. It has a 3D-Printed hull, and it can be adjusted according to the respective needs. It is equipped with six thrusters which allow it to have five Degrees of Freedom enabling increased maneuverability while it operates. The robot has marginally positive buoyancy which allows it to emerge in case of a malfunction, and the fins attached to the motors enhance its stability. The design, development, and construction as well as the components and electronics of the vehicle are presented. Moreover, the forces acting on the ROV and the buoyancy are introduced, along with the motion generation of the motors. The IMU sensor calibration is explaineed and a dataset of the rotational movement was analyzed into 3 axes. The proposed design features low cost, customizability and sturdiness allowing the user to configure and operate by himself.