Miracle-dz/UniFAR-FTUs huggingface dataset:
Updating Point Cloud Layer of High Definition (HD) Map Based on Crowd-Sourcing of Multiple Vehicles Installed LiDAR

A high-definition (HD) map is becoming an integral component of future mobility systems such as autonomous and connected vehicles. Advances in computing systems, LiDAR technologies, and vehicle communication technologies have enabled the HD map to directly treat a point cloud map (PCM), modeling road environments as LiDAR signal-level data. However, if actual road environments are changed, the PCM, modeling the environments before changes, can not be used for vehicle applications. Accordingly, the PCM has to stay up-to-date states by reflecting the environment changes continuously. This paper presents a crowd-sourcing framework to update the PCM from environment changes continuously using LiDAR and vehicle communication. Multiple intelligent vehicles installed with the LiDAR sensors download the PCM from a map server via wireless vehicle communication. To minimize the effects of environment changes, a robust localization based on a hierarchical Simultaneous Localization and Mapping (SLAM) estimates the pose (position and direction). The estimated pose is used to detect the differences between the PCM and environments, which are defined as map changes. The map changes are detected by the probabilistic and evidential theory considering the LiDAR characteristics, such as beam divergence and multi-echo. The detected map changes are uploaded to the map cloud server and merged into the PCM. The proposed crowd-sourcing framework to keep the PCM up-to-date is verified and evaluated via simulations and experiments in sites with road environment changes.
Production and Pricing Decisions in a Product-Service System Supply Chain

This research investigates a supply chain where a manufacturer sells products bundled with services to customers while satisfying the service level commitment. The demand is random and is sensitive to the selling price and the manufacturer’s service level commitment. The manufacturer simultaneously decides the production quantity, the amount of service capacity and the retail price before the selling season. We develop a stylized model to derive the manufacturer’s optimal decisions. We get the conditions in which the service capacity and the production quantity increase or decrease with the selling price. Numerical analysis shows that as the responsiveness of demand to the sales price increases, the service capacity and production quantity decrease.
Implementation Challenges and Opportunities in Beyond-5G and 6G Communication

As 5G New Radio (NR) is being rolled out, research effort is being focused on the evolution of what is to come in the post-5G era. In order to meet the diverse requirements of future wireless communication in terms of increased capacity and reduced latency, technologies such as distributed massive Multiple-Input Multiple-Output (MIMO), sub-millimeter wave and Tera-hertz spectrum become technology components of interest. Furthermore, to meet the demands on connectivity anywhere at anytime, non-terrestrial satellite networks will be needed, which brings about challenges both in terms of implementation as well as deployment. Finally, scaling up massive Internet-of-Things (IoT), energy harvesting and Simultaneous Wireless Information and Power Transfer (SWIPT) is foreseen to become important enablers when deploying a large amount of small, low-power radios. In this paper, we will discuss some of the important opportunities these technologies bring, and the challenges faced by the microwave and wireless communication communities.
Customer Satisfaction with Order Fulfillment in E-Retail Supply Chains in China: An Empirical Study

The Internet has changed the way how customers make their purchases and how retailers do business. With the development of B2C e-commerce, the retailers compete intensively to improve customer satisfaction and further secure their loyal customer bases. In this study, we investigate the relationship between customer satisfaction and customer loyalty in retail supply chains in B2C e-commerce in China. A conceptual model is developed to reveal the relationships and the factors influencing B2C e-customer satisfaction. Partial Least Squares is employed to assess the relationships between observable and unobservable variables and between unobservable variables. More specifically, SmartPLS is adopted to test and visualize the relationships. Based on the results, we suggest that e-retailers in China should improve their after-sales service activities and strengthen flexible and responsive order fulfillment activities in the hope of satisfying customers and further increasing loyal customer base.
Attention vs. precision: latency scheduling for uncertainty resilient control systems

In robotic systems, perception latency is a term that refers to the computing time, measured from the data acquisition to the moment in which perception output is ready to be used to compute control commands. There is a clear compromise between perception latency and the stability of the overall robotic system, referred here as the latency-precision trade-off. Opposed to periodic sampling, which forces a particular perception latency, we study for the first time the computation of the best sequence of latencies, obtaining different precision at different moments in time. Using this concept we give a formal problem formulation of the attention-precision trade-off. Moreover, we give a method to obtain sequences of latencies that improve the system performance which is verified by simulation experiments.
Deep Learning Method to Estimate Glucose Level from Heart Rate Variability

Recently, there have been efforts by different researchers in implementing some Machine Learning (ML) and Deep Learning (DL) techniques in designing a model that will predict Glucose based solely on Heart Rate Variability parameters. However, each study uses an in-house dataset and thus the results differ from the rest. The aim of this paper is to explore the predictive capabilities of DL techniques in designing a model to predict glucose regulation from Heart Rate Variability. The clinical study was conducted on a dataset of 155 patients with long-term Electrocardiogram measurements. The best results are achieved with an architecture of three hidden layers (32, 256, and 64 neurons, respectively) with an Adam optimizer alongside a learning rate of 0.001 coupled by a Binary Cross entropy loss function. Furthermore, the Z score outlier removal method proved to lead to a higher accuracy value, whilst the IQR outlier removal method proved to lead to a higher Fl score value.
Antenna-integrated photodiode array with single optical input

In this paper we present the development of a photonic integrated circuit on InP with an array of antenna-integrated uni-travelling carrier photodiodes to increase the output power levels. Fabricated device consists of a single optical input and monolithically integrated optical power splitter. Due to fabrication issues the contact resistance was considerably above the expected values of 50 ohm and consequently recorded output power is much lower than expected. Nevertheless, the free-space emission of a 50 GHz signal has been successfully demonstrated as a proof of concept.
A Wireless Sensor Network Based Precision Agriculture

Agriculture is the main livelihood source for 58% of the population of India. The Indian food industry is on the verge of massive growth, which each year increases its contribution to world trade in food because of its immense potential for added value. Particularly, in the food-processing industry which contribute to 16% of total GDP and 10% of exports. In this paper monitor agricultural land and displaying the parameters that has been sensed through Wireless Sensor Network (WSN). If anyone wants to include technology in agriculture, he should be aware of agriculture process. In this proposed work, we monitor the agriculture field at a distance by using XBee technology. We have connected various sensors to monitor field and depending on sensor values necessary actions will be taken. Main advantage of proposed work is consumption of power will be very less to communicate over other wireless communication technologies.
Numerical design of Luenberger observers for nonlinear systems

In this paper, we propose a method to numerically design observers for nonlinear systems. The method relies on the theory of nonlinear Luenberger observers, which consist in mapping the nonlinear dynamics to a linear system, for which observer design is easy. Relying on results guaranteeing the existence of such mappings, we propose to approximate them by performing nonlinear regression on data simply generated by solving the system and observer dynamics. We detail different approaches for autonomous and excited systems and the choices made for data generation, pre-processing, and regression.
A Reinforcement Learning based Path Guidance Scheme for Long-range Autonomous Valet Parking in Smart Cities

Finding a parking slot in the city centre has always been a great challenge. In many cases, drivers spend a lot of time roaming around looking for an empty and suitable parking slot. The emerging machine learning technologies in intelligent transport system has made it more flexible for Electric Autonomous Vehicle (EAV) to find a parking slot and get parked. The Long-range Autonomous Valet Parking (LAVP) allows an EAV to drop user at a suitable drop-off spot and select an economical parking slot. With the evolution of battery operated vehicles, the primary concern is efficient use of battery resources. This can be done either by maximizing battery capacity or by smartly using battery with existing capacity. During the parking process, most of the energy is consumed by finding an optimal path to parking slot. The work proposed in this paper guides EAV from a random starting point to nearest drop-off spot and CP. A Reinforcement Learning based Autonomous Valet Parking technique (RL-LAVP) has been designed to guide EAV to drop-off spot, CP and minimize the total distance covered during this process. The RL-LAVP results show a significant improvement towards minimizing covered distance and consumed energy when compared with RaNdom (RN) parking and LAVP parking techniques.
Machine Learning and Feature Selection for Authorship Attribution: The Case of Mill, Taylor Mill and Taylor, in the Nineteenth Century

In this article we revisit a dividing issue as regards the corpus of one of the most famous nineteenth-century philosophers: John Stuart Mill. He was the author of two iconic texts in the history of political philosophy: On Liberty and The Subjection of Women. However, Mill attributed the first to collaboration with Harriet Taylor Mill, his wife, and characterized the second as a work of three minds: his own, his wife’s and her daughter, Helen Taylor. Experts disagree on this issue. Most think Mill was too generous sharing authorship credit. We use a training set consisted in manuscripts of the three above mentioned authors, to train a four-class problem (three authors and joint productions). For every manuscript in the training set we extract a set of features that are widely used in text analytics and classification. Then, we apply some pre-processing techniques to normalize the data and to reduce the number of features. Finally, we train three classifiers, namely k-nearest neighbours (k-NNs) with k = 1 and k = 2, support vector machines (SVMs), and decision trees (DTs) to attribute the texts of “disputed” authorship to one of the four potential authors. We routinely run the experiments using different feature sets every time, in order to identify the optimal combination of features that yield the best results on the test set. The best results are achieved with the SVMs, having as input the bigrams features and their principal components. The mean detection rate for all four classes is 100%. Similar results are achieved with the models built with the k-NNs (k = 1) and the DTs. The only classifier that consistently is returning significantly lower results is the k-NN with k = 2. All of the instances in the test set are attributed to John Stuart Mill.
An O(T2) Discrete-Time Adaptive Regulator for Uncertain MIMO Systems with Bounded Input Delays

This paper proposes a discrete-time adaptive regulator for a MIMO linear time-invariant system with unknown, constant input time delays that may differ across the input channels. It is assumed the delay has a known upper-bound. In addition, the plant is subject to an unmeasurable exogeneous disturbance. To mitigate the effect of the disturbance, a secondorder delay disturbance observer is used. A stability analysis shows that the proposed regulator drives the plant state to zero asymptotically with an O(T2) bound on the regulation error and simulation results are shown to verify the approach.
Multi-Class Metabolic Pathway Prediction by Graph Attention-Based Deep Learning Method

Exploring the relationship between molecular structure and metabolic pathways plays a significant role in researching the metabolism and pharmacokinetic effects of drugs. Therefore, one usually want to know what kinds of metabolic pathways that a new synthetic drug compound may involve in. In this paper, we propose a novel deep learning framework based on GAT (Graph Attention neTwork) for such purpose. The framework mainly consists of a two-branch feature extractor and a FC (Fully Connected) layer. In the two-branch feature extractor, one is used to generate three kinds global features of the compounds, and the other one is used to learn the local structural features via two GAT layers. All features are embedded into the FC layer to output multiple probabilities, each of which corresponds to the probability of one kind of pathway that the compound belongs to. The comparing results to other five state-of-the-art representative methods on the KEGG (Kyoto Encyclopedia of Genes and Genomes) data set have shown that our method can achieve the highest prediction accuracy, illustrating that it is a promising tool that can be helpful to analyze the metabolic pathways of the drug compounds.
Millimeter-Wave Power Amplifier Integrated Circuits for High Dynamic Range Signals

The next-generation 5G and beyond-5G wireless systems have stimulated a substantial growth in research, development, and deployment of mm-Wave electronic systems and antenna arrays at various scales. It is also envisioned that large dynamic range modulation signals with high spectral efficiency will be ubiquitously employed in future communication and sensing systems. As the interface between the antennas and transceiver electronics, power amplifiers (PAs) typically govern the output power, energy efficiency, and reliability of the entire wireless systems. However, the wide use of high dynamic range signals at mm-Wave carrier frequencies substantially complicates the design of PAs and demands an ultimate balance of energy efficiency and linearity as well as other PA performances. In this review paper, we will first introduce the system-level requirements and design challenges on mm-Waves PAs due to high dynamic range signals. We will review advanced active load modulation architectures for mm-Wave PAs and power devices. We will then introduce recent advances in mm-Wave PA technologies and innovations with several design examples. Special design considerations on mm-Wave PAs for phased array MIMOs and high mm-Wave frequencies will be outlined. We will also share our vision on future technology trends and innovation opportunities.
Modernized Compartment With Safety Measures in Railways

The presented methodology is to monitor endlessly cracks in tracks, obstacles on rail tracks and any other train running on the same track oppositely by using sensors. When identified, the device will send an alert to the driver to prevent the accidents. The flame sensor detects it and sends a signal to the microcontroller and driver when the train compartment catches fire, then triggers the servo motor to disconnect the compartments. With the help of this paper we try to overcome few of the issues in railways as well as modernize the compartment.
IoT based animal classification system using convolutional neural network

The kingdom “Animalia” is used to represent all living creatures on the planet earth, which is fallen into six categories. The language is the most common factor to divide humans and animals. Numerous classification techniques can be used for classification purposes, and the classification commonly can be done acoustically and visually. The classification systems are playing a considerable role, and bioacoustics monitoring was a significant field of study. Visual classification of animals is done by using either satellite images or established camera images. Nevertheless, due to some circumstances, image processing techniques cannot be applied. Then the acoustical classification techniques are taken place to encounter those problems. Even with acoustical methods, a remote observing method is required due to a few issues. Applying an IoT based acoustic classification system was designed using Convolutional Neural Networks (CNN), which is beneficial for those who are interested in monitoring ecosystems such as animal scientists, zoologists, and environmentalists. The hardware implementation was designed to collect the data from the place it was placed. The audio clips were preprocessed using the Mel-frequency Cepstral Coefficient (MFCC). A CNN architecture based on TensorFlow was used for the training process. To train and test the network, 400 sound clips of two seconds, such that 40 per each ten animal species, which were gathered from online libraries and formatted using Audacity, were used. The network was trained by changing the different gradient descent optimizers and eventually obtained the confusion matrices for each. The best result was gained by the AdaDelta, Gradient Descent, and RMSProp optimizers with 91.3% accuracy for each. Among them, AdaDelta had the most stable and increasing learning approach. As a future extension, to improve accuracy, a large number of data will be used.
A Schema-Driven Synthetic Knowledge Graph Generation Approach With Extended Graph Differential Dependencies (GDDxs)

Knowledge Graphs (KGs), as one of the key trends which are driving the next wave of technologies, have now become a new form of knowledge representation, and a cornerstone for several applications from generic to specific industrial use cases. However, in some specific domains such as law enforcement, a real and large domain-oriented KG is often unavailable due to data privacy concerns. In such domains it is necessary to generate a synthetic KG which mimics the properties of a real KG in the domain. Although during the last two decades, a variety of graph data generators has been proposed to achieve the generation of different kinds of networks, the state-of-the-art synthetic graph data generators are not feasible to generate a realistic and synthetic KGs because KGs always contain data characteristics with specified semantics. In this work, we propose a schema-driven synthetic KG generation approach with extended graph differential dependencies ( $GDD^{x}$ ), which is an extension of the recently developed graph entity/differential dependencies that represent formal constraints for graph data to enable the generation of desired graph patterns in synthetic KG. Next, we develop an effective KG generation algorithm that employs the schema and the pre-defined $GDD^{x}\text{s}$ . Finally, we evaluate our synthetic KG generator and compare with several state-of-the-art synthetic graph generators. The results from the experiments show that our KG generation method can generate KGs that exhibit the desired graph patterns, node attributes and degree distributions associated with each entity type in the graph’s schema.
Application of Lightweight Multi-pooling Convolutional Neural Network in Satellite Cloud Image Recognition

Thunderstorm weather and other small-scale meteorology will have a very serious impact on people's personal and property safety. At this stage, early warning of thunderstorm weather in satellite cloud images still requires very rich professional knowledge, and it is also difficult to meet the real-time requirements of thunderstorm warning. In view of the above problems, this paper proposes a network structure, which uses deep separable convolution and residual blocks to combine with each other, and uses a multi-pooling structure for global information extraction, while ensuring accuracy and reducing the number of network parameters. Meet the real-time requirements. Experiments show that the use of the proposed lightweight multi-pooling network structure has a certain improvement in the accuracy of segmentation of thunderstorm cloud clusters, and can have a shorter response time in a satellite cloud image, compared with commonly used image segmentation, the network has also improved.
Virus Named Entity Recognition based on Pre-training Model

Virus plays an important role in the earth’s ecosystem. It maintains the balance of the ecosystem. It infects host cells, causing damage or death to the host. Understanding the relationship between virus and host is the key to preventing viral diseases. There are a large number of proven relationships between viruses and hosts in the literature. Extracting the relationships between viruses and hosts in these literature and organizing them into a virus-host knowledge base is of great significance to medical and biological research. Virus named entity recognition (VNER) is the key prerequisite step of relationship extraction. The complexity of virus naming and classification makes the identification of virus named entities challenging. In this paper, we provide a labeled corpus for the task of VNER. Furthermore, we use different pre-training models to compare its performance on downstream virus entity recognition tasks. Finally, BioBERT_ BiLSTM_ CRF got best result on the task of VNER. The precision value, recall value and F1 value are 92.18%, 91.28% and 91.85%, respectively.
Real-Time Learning of Non-Gaussian Uncertainty Models for Autonomous Racing

Performance and robustness targets have been considered for controller design for decades. However, robust controllers usually suffer from performance limitations due to conservative uncertainty assumptions made a priori to system operation. The increased number of systems (e.g. autonomous vehicles) which require high-performance operation in safety- critical environments is motivating research in novel design methods. Recently, machine learning methods have emerged as a promising way to reduce conservatism, based on data gathered during system operation. We propose a combination of a recursive least squares estimator with a recursive quantile estimator to identify feature-dependent upper and lower uncertainty bounds. We give conditions under which the estimator converges to a robust invariant set, such that the resulting bounds cover a target proportion of the samples up to small error. In contrast to widely applied Gaussian process regression or Bayesian linear regression approaches, we do not imply any assumptions about the probability distribution of the samples. We demonstrate that the estimated bounds achieve the desired data coverage in contrast to state-of-the-art approaches on academic examples, as well as a motion control example for an autonomous race car. Furthermore, the approach exhibits very low computational requirements and is therefore suitable for application on embedded systems.