Miracle-dz/UniFAR-FTUs huggingface dataset:
A mink-fur farm livestock management platform The case of WelMink Project

Livestock Management is a complex and demanding process that requires specialized hardware and software. Advancements in software technologies during the last decade have made the development, deployment and usage of animal-farm management platforms more accessible even for smaller, specialized production lines, such as mink-fur farms. Additionally, the utilization of cloud platforms offered as services in various software solutions, can increase development speed, system availability and reduce cost. In this paper we present a low-cost mink-fur farm livestock management platform based on cloud technologies developed for small enterprises in the context of the WelMink project.
The Design of a Pedestrian Aware Contextual Speed Controller for Autonomous Driving

Social acceptance is a major hurdle for autonomous vehicle technology, central to which is ensuring both passengers and nearby pedestrians feel safe. This idea of ‘feeling safe’ and perceived safety is highly subjective and rooted in human intuition. As such, traditional analytical approaches to autonomous navigation often fail to cater for the social expectations of individuals. Therefore, this paper proposes an approach to capture the complexity of social expectations and integrate this complexity into a 3-layered Contextual Speed Controller. The layers are; the legal road speed limit, the socially acceptable speed given the number of nearby pedestrians, and the socially acceptable speed based on proximity to nearby pedestrians. An implementation of this layered approach was tested in areas of both low and high density of vehicle-pedestrian interactions. As social acceptability is a subjective measure, the system is assessed through comparative metrics against a human driver operating within a similar urban context. From the experiments conducted, the lower two layers were seen working in tandem to modulate the vehicle speed to appropriate levels that mimicked conservative human driver behaviour. In this paper, the need for different driving schemes to navigate different road environments was identified, building the foundation for future human user studies to validate this approach for perceivably safe autonomous driving.
RGBDTCalibNet: End-to-end Online Extrinsic Calibration between a 3D LiDAR, an RGB Camera and a Thermal Camera

For applications in robotics and autonomous vehicles, fusing 3D LiDAR, RGB camera, and thermal camera can improve the perception in day and night environments. To fuse the sensors, accurate extrinsic calibration is important. However, most existing methods use offline calibration, which is tedious, as it requires the use of special targets and human intervention. Meanwhile, among the online calibration methods, little attention has been paid to LiDAR and thermal camera online calibration. Thus, in this paper, an online extrinsic calibration method is proposed to solve the problem, called RGBDTCalibNet. It can achieve the extrinsic calibration between a 3D LiDAR and an RGB camera, as well as a 3D LiDAR and a thermal camera. It leverages CNN-based deep learning methods for feature extraction and feature matching. The proposed network only takes a pair of images and point cloud as the input and outputs the extrinsic parameters in a single shot. It is trained and tested on two datasets: KITTI360 and our own dataset collected in the NTU campus, using two types of LiDAR: Velodyne HDL-64 and Livox Horizon. Both demonstrate the effectiveness, robustness and accuracy of RGBDTCalibNet. The code will be open-sourced at https://github.com/sanatmharolkar/RGBDTCalibNet.git
Nonlinear Temperature Mapping for Infrared-Based Landing Site Detection

This paper proposes a novel nonlinear temperature mapping algorithm for infrared sensors for landing field detection and relative positioning for integrity monitoring of an INS/SBAS navigation solution during landing approach. Traditional linear temperature mapping algorithms, which clip the temperature range to increase contrast, come at the cost of losing details at the remote ends of the temperature scale. The proposed algorithm does not clip the temperature range but uses parametrized nonlinear S-shaped mapping curves to increase contrast while also retaining details at the remote ends of the temperature range. The influence of the maximum curve gradient and inflection point temperature are investigated. The performance of the proposed algorithm is discussed using recorded flight-test data of landing approaches by fixed-wing as well as VTOL aircraft.
Pyroelectric properties of (1−x)Pb(Mg1∕3Nb2∕3)O3-xPbTiO3 and (1−x)Pb(Zn1∕3Nb2∕3)O3-xPbTiO3 single crystals measured using a dynamic method

The pyroelectric coefficients of 0.72Pb(Mg1∕3Nb2∕3)O3-0.28PbTiO3 (PMN-28PT), 0.67Pb(Mg1∕3Nb2∕3)O3-0.33PbTiO3 (PMN-33PT), and 0.92Pb(Zn1∕3Nb2∕3)O3-0.08PbTiO3 (PZN-8PT) single crystals, oriented and poled along three different crystallographic directions, [001]C, [011]C, and [111]C, have been measured using a dynamic method. The suffix C indicates reference to the prototypic cubic coordinate system. Large coefficients of up to 1070μCm−2K−1 have been observed for the [111]C oriented, rhombohedral PMN-28PT and PMN-33PT samples, where the measurement direction corresponds to the polar axis of the crystal. For each composition and orientation two different poling procedures are compared, a commonly used field cooling method and a low temperature poling protocol. It was found that in otherwise pseudoorthorhombic samples of PZN-8PT, a room temperature rhombohedral phase could be stabilized by application of a field along the [111]C direction while heating from −70°C.
Variable-Friction-Based In-Hand Manipulation of Fabrics Applied to Unfolding Operations

The automatic state recognition and handling of fabrics, which are typical deformable objects, is difficult to realize. The development of related technologies for fabric manipulation remains impractical. This research was inspired by the process by which humans handle fabrics using their fingers. We proposed an in-hand manipulation method for fabrics based on variable friction and verified its effectiveness by sliding a single sheet of fabric and unfolding using in-hand manipulation functions. Experimental results demonstrate that the in-hand manipulation functions proposed in this study have the potential to solve complex problems in fabric handling.
Evaluation of UWB Radar Module for Snow Water Equivalent Monitoring

Remote longterm monitoring of Snow Water Equivalent (SWE) is essential for prediction of hydro electric power production, climate models and more. In this paper we present a low-power, low-cost upward looking radar system compliant with the FCC/ETSI UWB regulations for unlicensed longterm remote SWE monitoring. The SWE is estimated using the two-way time of arrival distance measurement of a fixed reflector. To filter radar clutter and seasonal external environmental interference, the reference reflector is oscillating at 0.6 Hz normal to the radar to enable pulse doppler signal domain processing and robust operation. A SNR of 8 dB is measured trough $3.15m$ dry old snow considered sufficient for most dry snow applications.
Dialogue State Tracking with Zero-Shot and Few-Shot Learning for Generalization: A Review

Research on Dialogue State Tracking (DST) has achieved meaningful advancements in benchmark datasets. However, the generalization ability of DST models to handle unseen data robustly remains an issue. Hence, recent studies on DST with zero-shot and few-shot learning are reviewed in this paper. For a task-oriented dialogue system, DST is explained by introducing datasets and evaluation metrics. DST models could be categorized into four groups: DST based on a pre-trained model, DST using a description, DST using a prompt, and DST with cross-task. Characteristics of each model are described and the performance of the model experimented under the same conditions is summarized.
Sensor Scheduling Strategies for 1-to-N Multi-Object Tracking

We present and compare myopic with non-myopic algorithms for the sensor scheduling problem in 1-to-N multi-target tracking applications. A UAV, equipped with limited sensor resources is tasked to search and track multiple dispersed ground objects. In general, not all observations can be carried out simultaneously. Therefore, a scheduling algorithm that produces an observation sequence is required. We propose an object detection prediction model, which supports the scheduling algorithm in choosing observations with high expected utility.
Where to go: Agent Guidance with Deep Reinforcement Learning in A City-Scale Online Ride-Hailing Service

Online ride-hailing services have become a prevalent transportation system across the world. In this paper, we study a challenging problem of how to direct vacant taxis around a city such that supplies and demands can be balanced in online ride-hailing services. We design a new reward scheme that considers multiple performance metrics of online ride-hailing services. We also propose a novel deep reinforcement learning method named Deep-Q-Network with Action Mask (AM-DQN) masking off unnecessary actions in various locations such that agents can learn much faster and more efficiently. We conduct extensive experiments using a city-scale dataset from Chicago. Several popular heuristic and learning methods are also implemented as baselines for comparison. The results of the experiments show that the AM-DQN attains the best performances of all methods with respect to average failure rate, average waiting time for customers, and average idle search time for vacant taxis.
Real-Time Object Detection in Microscopic Image of Indian Herbal Plants using YOLOv5 on Jetson Nano

Object detection has been evolving greatly in recent years and the advancements in hardware and software technologies have made it possible to perform object detection with ease. Due to the enhanced capabilities of the modern processors and Graphics Processing Unit (GPU) of doing an exponentially complex and extensive number of iterations in very less time. Real-time object detection has become highly popular and the center of attention in recent years because most of the hardware owned by common users is powerful enough to compute that which unlocks whole new possibilities for implementing real-time object detection in numerous applications in various domains. Real-time herbal plant detection is one such topic that has many applications in the field of ayurvedic medicines and many other pharmaceutical applications that can be used to spike the efficiency in identifying these herbal plants that can be used as a precaution and even as a cure for numerous health problems. There are many existing algorithms for real-time detection, but the evolution of new Artificial Neural Network (ANN) and Machine Learning (ML) techniques unlocks new ways to implement recent and advanced algorithms to apply for real-time detection of such powdered microscopic images to achieve better performance in various aspects compared to already existing methods. Our model is trained for detecting three types of microscopic herbal plants.
Improving Power Density of a Three-Level ANPC Structure Using the Electro-Thermal Model of Inverter and a Modified SPWM Technique

Multilevel inverter structures have become an interesting substitute for the well-known two-level inverters in a variety of applications due to their exceptional characteristics when compared to conventional inverters. One major issue regarding multilevel structures is the unequal junction temperature of the switches which deteriorates the power density and increase the cost of the inverter by reducing the maximum achievable output power with a given thermal model and cooling system. Hence, on account of the aggressive goals of power density and cost in a majority of power electronic applications, this article proposes a new technique for reducing the maximum junction temperature of switches in a three-level active neutral-point clamped (ANPC) inverter based on a junction temperature estimation method and a modified SPWM control scheme. This technique can ensure up to a 12% rise in the power density value when compared to basic SPWM techniques with no loss distribution algorithm. Moreover, the suggested approach can be used as a protection stage in the inverter which protects the inverter in transient loads, while allowing for reaching the maximum power capability of the inverter. Finally, the simulations of the proposed technique are validated with experimental results of a 400 V, 20 kW three-level ANPC inverter, controlled by the conventional and proposed techniques.
AD PerDevKit: An Autonomous Driving Perception Development Kit using CARLA simulator and ROS

Every now and then a new dataset appears that aims to help training computer vision models for autonomous vehicles (AVs), being KITTI [1] the most famous, but others like Waymo [2] or nuScenes [3] can be found that try to innovate by adding more sensors, giving more data about the ego-vehicle surroundings or offering new scenarios. These approaches tend to produce a high quality dataset, but at the cost of time and money. The key difficulty is to create datasets with enough data to generalize multiple road scenarios and hazard use-cases in the training process of perception models for AVs. To address this concern, we propose AD PerDevKit, a perception development kit for autonomous driving based on CARLA simulator [4] able to generate an infinite set of annotated data referred to the ego-vehicle by recording virtual car sensors (camera, LiDAR, Radar) in virtual environments. Our ground-truth generator provides real-time information of the objects surrounding the vehicle that are visible for each sensor by publishing them on a ROS [5] topic. In addition, a dataset generated with this tool is also presented for some specific challenging scenarios and an evaluation of its contribution in the improvement of 2D object detection methods on real data is included. The code of our tool and its database is available in the following git repository: https://github.com/Javier-DlaP/ad_perdevkit.
Investigation on influences of phase delay on performance of resonator gyroscopes

In this paper, the influence of phase shift induced by the front-end amplifier and the digital control circuits on the quadrature suppression and the force-to-rebalance control loops is theoretically analyzed. Due to the existence of a phase shift, the output of the resonator gyroscope is affected not only by the damping mismatch, but also by the frequency splitting of the gyroscope which is the main contribution to the nonlinearity of scale factor of a RG. Based on the analytical results and established mathematical model of scale factor, the phase delay was characterized and compensated in the room temperature. Detailed experiments of phase compensation were conducted and results were analyzed. Experimental results show that with phase compensation, the quadrature suppression signal changes far less with the variation of external angular rate than that before phase compensation. This means that the coupling between the quadrature suppression signal and the force-to-rebalance control signal is greatly decreased. In addition, residual errors of the scale factor of the resonator gyroscope were also greatly decreased. It is shown that after compensation of the phase delay the residual error is reduced by a factor of 45 and the linearity of the scale factor of the resonator gyroscope was extremely increased by more than 50 times compared with results before compensation.
Hard-Rock TBM Thrust Prediction Using an Improved Two-Hidden-Layer Extreme Learning Machine

It is difficult for tunnel boring machine (TBM) operators to respond for safe and high-efficient construction without accurate reference parameters such as the TBM thrust. A new hybrid model (MRFO-AT-TELM) combining an improved two-hidden-layer extreme learning machine (AT-TELM) and manta ray foraging optimization (MRFO) algorithm is proposed to predict TBM thrust with 12 selected input featuring parameters. The affine transformation (AT) activation function is used to improve the performance of TELM. Input weights and bias of AT-TELM are optimized using the MRFO algorithm. The performance of the proposed model is validated with TBM construction data collected from the Yin-Song Project in China and compared with other models. Input data of the first 30, 60, and 90 seconds of the rising period are analyzed. Results show that the proposed model is superior to the other models and with 90-second data as input outperforms that with 30 and 60-seconds data. The proposed model and the selected input features are validated in a new project. The thrust prediction model can be embedded into the TBM construction intelligence system and thus help improve construction efficiency.
Optimizing Performance and Storage of Memory-Mapped Persistent Data Structures

Persistent data structures represent a core component of high-performance data analytics. Multiple data processing systems persist data structures using memory-mapped files. Memory-mapped file I/O provides a productive and unified programming interface to different types of storage systems. However, it suffers from multiple limitations, including performance bottlenecks caused by system-wide configurations and a lack of support for efficient incremental versioning. There-fore, many such systems only support versioning via full-copy snapshots, resulting in poor performance and storage capacity bottlenecks. To address these limitations, we present Privateer 2.0, a virtual memory and storage interface that optimizes performance and storage capacity for versioned persistent data structures. Privateer 2.0 improves over the previous version by supporting userspace virtual memory management and block compression. We integrated Privateer 2.0 into Metall, a C++ persistent data structure allocator, and LMDB, a widely-used key-value store database. Privateer 2.0 yielded up to 7.5× speedup and up to 300× storage space reduction for Metall incremental snapshots and 1.25× speedup with 11.7× storage space reduction for LMDB incremental snapshots.
On the Characterization of the Performance-Productivity Gap for FPGA

Today, FPGA vendors provide a C++/C-based programming environment to enhance programmer productivity over using a hardware-description language at the register-transfer level. The common perception is that this enhanced pro-ductivity comes at the expense of significantly less performance, e.g., as much an order of magnitude worse. To characterize this performance-productivity tradeoff, we propose a new composite metric, II, that quantitatively captures the perceived discrepancy between the performance and productivity of any two given FPGA programming languages, e.g., Verilog vs. OpenCL. We then present the implications of our metric via a case study on the design of a Sobel filter (i.e., edge detector) using three different programming models - Verilog, OpenCL, oneAPI - on an Intel Arria 10 GX FPGA accelerator. Relative to performance, our results show that an optimized OpenCL kernel achieves 84% of the performance of an optimized Verilog version of the code on a 7680×4320 (8K) image. Conversely, relative to productivity, OpenCL offers a 6.1 x improvement in productivity over Verilog, while oneAPI improves the productivity by an additional factor of 1.25 x over OpenCL.
Premerger localization of gravitational-wave standard sirens with LISA: Harmonic mode decomposition

The continuous improvement in localization errors (sky position and distance) in real time as LISA observes the gradual inspiral of a supermassive black hole binary can be of great help in identifying any prompt electromagnetic counterpart associated with the merger. We develop a new method, based on a Fourier decomposition of the time-dependent, LISA-modulated gravitational-wave signal, to study this intricate problem. The method is faster than standard Monte Carlo simulations by orders of magnitude. By surveying the parameter space of potential LISA sources, we find that counterparts to supermassive black hole binary mergers with total mass $M\ensuremath{\sim}{10}^{5}\char21{}{10}^{7}{\mathrm{M}}_{\ensuremath{\bigodot}}$ and redshifts $z\ensuremath{\lesssim}3$ can be localized to within the field of view of astronomical instruments ($\ensuremath{\sim}{\mathrm{deg}}^{2}$) typically hours to weeks prior to coalescence. This will allow a triggered search for variable electromagnetic counterparts as the merger proceeds, as well as monitoring of the most energetic coalescence phase. A rich set of astrophysical and cosmological applications would emerge from the identification of electromagnetic counterparts to these gravitational-wave standard sirens.
Decision-making and Planning Framework with Prediction-Guided Strategy Tree Search Algorithm for Uncontrolled Intersections

Uncontrolled intersections are important and challenging traffic scenarios for autonomous vehicles. Vehicles not only need to avoid collisions with dynamic vehicles instantaneously but also predict their behavior then make long-term decisions in reaction. To solve this problem, we propose a cooperative framework composed of a Primary Driver (PD) for motion planning and a Subordinate Driver (SD) for decision-making. SD is essentially the combination of a prediction module and a high-level behavior planner, which develops a prediction-guided strategy tree to determine the optimal action sequence. Especially, under the guidance of the prediction results, the tree branches are evaluated in security metrics, then get trimmed in action and observation space to reduce the dimensional complexity. With the assistance of SD, PD works as a collision checker and a low-level motion planner to generate a safe and smooth trajectory. We use the INTERACTION dataset to validate our method and achieve more than 90% success rate with efficiency improvement in various situations.
Segmentation of Skin Lesions using U-Net with EfficientNetB7 Backbone

Skin cancer is one of the most common types of cancer worldwide. Early diagnosis of malignant skin cancer is important in terms of removing the tumor completely from the body and increasing the chance of survival of the patient. Segmentation is an important process used in medical image analysis, such as detecting diseases, determining the disease region and monitoring morphological changes in the disease region. In this study, skin lesions on dermoscopic images are segmented using deep learning. For this purpose, U-net model with an encoder and decoder structure is used as the segmentation method. In the encoder part of the model, EfficientNetB7 model is proposed as the backbone structure in order to extract the features from the images. The proposed U-Net model with EfficientNetB7 backbone was trained and tested on ISIC 2017 dataset containing 2750 dermoscopic images. According to the experimental results, 0.889 sensitivity, 0.994 specificity, 0.945 accuracy, 0.884 dice constant and 0.809 Jaccard index value were reached. According to the Jaccard index value, which is frequently used in the evaluation of segmentation performance, a better result was obtained than the existing methods in the literature.
Classification and correction of the radar bright band with polarimetric radar

The quantitative estimation of precipitation with weather radars is error prone due to many different physical factors that affect the relation of radar measurements to surface observations (Austin 1986). The inhomogeneity of precipitation with height is one such problem due to the occurrence of bright bands (BB). The BB is an annular feature that occurs due to an enhancement of radar reflectivity returns from regions of melting snow (Austin and Bemis 1950). The horizontal reflectivity (Zh) returns received by the radar are related to precipitation estimates through a power law equation. This can cause overestimations of rainfall intensity and accumulation at the surface by up to a factor of 5 (Joss and Waldvogel 1990) In order to reduce this overestimation the areal extent of the BB in radar scans must be classified and then corrected.