Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatial Optimization of Piezoelectric Energy Scavenger from Current-Carrying Wire

Harvesting energy by coupling a magnetic proof mass with current flowing through a wire has recently been investigated as a method to power wireless sensor networks. However, the location of the cantilever and magnet in relation to the wire is critical to optimize performance. The configuration of the wire and the stiffness of the cantilever are also critical for device performance. This paper investigates optimizing the spatial location of the energy harvester and magnetic proof mass in relation to the wire for smart grid applications. Two different types of wires (solid and braided) copper wires were used with varying current up to 20A. This is conducted using a macro-scale piezoelectric cantilever with the goal to gain insight to apply to micro-electromechanical devices. Two different piezoelectric cantilevers with varying stiffness were tuned to operate at 60 Hz resonant frequency, using NdFeB magnet. The magnets act as a proof mass to lower the frequency while also coupling to the magnetic field from the current carrying wire, generating a sinusoidal force. Experimental and finite element modelling determined that the optimal location of the magnet for a solid wire was between 33° and 40° depending on the cantilever stiffness.
Intermediate layer to improve the performances and the frequency control of acoustic power transfer systems

Powering remote sensor nodes placed behind walls without drilling holes is of great interest for many industrial processes and applications. Acoustic power transfer using two piezoelectric transducers placed on both sides of the wall is a proven technology to answer this need. In this paper, we demonstrate that the use of an intermediary layer placed between the emitter and the wall results in a strong increase of the transmitted power: experimentally, the transmitted power is multiplied by two for the same driving voltage. We also show that intermediary layers strongly simplify the identification and the control of the optimal excitation frequency.
Conceptual framework for the formation of the integrated intelligent transport system in Ukraine

Introduction. The research is dedicated to the conceptual framework of the integrated intelligent transport system (ITS) formation in Ukraine. The author presents different approaches and technologies in the field of the functional structure of the integrated intelligent transport system optimisation and describes the influence of the integrated ITS creation on political, economic and social situation in Ukraine. Purpose . To develop a conceptual framework of the integrated intelligent transport system as a balanced synergetic factor of the transport sector development in Ukraine. Methods . In order to achieve the goal set in the article, the appropriate methods have been used based on the systemic approach, artificial intelligence, management theory and decision-making theory. The system analysis was used to define the scientific task. Results . The study related to the development of the conceptual framework for the formation of the integrated intelligent transport system has made it possible to introduce approaches and technologies in the field of ITS functional structure optimisation and determine its principal components. The functional structure of the proposed integrated intelligent transport system includes implementation of a modern methodology of mathematical transport modelling, which provides an objective, comprehensive and advanced approach to planning and solving problems of the transport industry of Ukraine. Conclusions . The global experience and the results of the implementation of ITS are invaluable. The creation of the integrated ITS in Ukraine should provide not only solutions to specific problems, but also improve the efficiency of the transport system in general. The proposed conceptual framework for the formation of the integrated intelligent transport system in Ukraine may be useful for government authorities, transport enterprises and consumers of transport services.
Integrated pedestrian navigation using GNSS, MEMS IMU, Magnetometer and Baro-altimeter

Man motion is one of the most challenging applications for a navigation system. A common approach to integrated pedestrian navigation (IPN) is to integrate GNSS user equipment and inertial sensors with magnetometers and a barometric altimeter. However, there are a number of different approaches to the use of the inertial sensors, which may be characterised by • The number and quality of inertial sensors to be used; • Whether to mount them on the shoes or the body; • Whether to use conventional inertial navigation algorithms, supported by zero velocity updates (ZVUs), pedestrian dead reckoning (PDR) or both. The trade-offs are discussed. The Micro Electro-Mechanical System (MEMS) Inertial Measurement Unit (IMU) GPS Receiver Module (MIGRM), developed for the UK Ministry of Defence (MOD) is described and the benefits of adding PDR, ZVU, magnetometer and baro measurements to an INS/GPS system with body-mounted sensors are demonstrated. The paper then compares the performance of two new internally-funded systems, developed from MIGRM. IPN system A uses a body-mounted IMU with inertial navigation, ZVUs and PDR. IPN system B uses a shoemounted IMU with inertial navigation and ZVUs during the stance phase of every stride. Both systems successfully bridged a 60 s GPS outage with a position error of less than 10 m. The system B solution exhibited less drift during the outage, but was also noisier. System A was also used to successfully demonstrate PDR during running and jogging motion, though performance was not as good as during walking. Some preliminary results on adopting different step length estimation models for the different motion regimes are then presented.
A 2.4-5.25GHz Balun-LNA in 22nm CMOS Technology

This paper presents a $2.4\sim 5.25{\mathrm {GHz}}$ single to differential low-noise amplifier (balun-LNA) using 22nm CMOS technology. Current-reuse technique is introduced to make a compromise between gain and linearity. A balanced buffer is used to reduce the gain difference and phase difference of the differential outputs. The contradiction between linearity and NF can also be resolved through variable gain control. The post-simulation results show that it achieves a voltage gain of 30. 0dB, an NF of 1. 49dB, the phase mismatch of 0.3°, and the gain mismatch of 0.1 dB in the high-gain mode. The IIP3 is 9. 0dBm, and IP1dB is 3.0 dBm in the low-gain mode. At 1V supply voltage, the power consumption is 1S.43mW, and the layout is 0.63mm2.
Root Canal Therapy Evaluation Based on Rule Embedded Neural Networks

Root canal therapy is a procedure to treat a tooth so that a person can get a bad tooth re-paired. However, due to different levels of doctors, different patients often get different repair results. This technique leads to the result of tooth repair, which is not what we want. We always hope that patients can get better treatment results. In this paper, a score scheme for root canal treatment is proposed to enable doctors to have a better understanding of their own technical level, so as to improve the dental restoration results of users. We worked out a rule scoring system, according to which the skill level of the doctor was determined. This model first convolved the image of the doctor's root canal treatment teeth with ResNet to obtain the tooth characteristics, then combined with the rule system, and finally output the classification scoring of the teeth. Our model finally got the correct rate of 91.6%.
A Semi-Supervised Learning Approach for Ranging Error Mitigation Based on UWB Waveform

Localization systems based on ultra-wide band (UWB) measurements can have unsatisfactory performance in harsh environments due to the presence of non-line-of-sight (NLOS) errors. Learning-based methods for error mitigation have shown great performance improvement via directly exploiting the wideband waveform instead of handcrafted features. However, these methods require data samples fully labeled with actual measurement errors for training, which leads to time-consuming data collection. In this paper, we propose a semi-supervised learning method based on variational Bayes for UWB ranging error mitigation. Combining deep learning techniques and statistic tools, our method can efficiently accumulate knowledge from both labeled and unlabeled data samples. Extensive experiments illustrate the effectiveness of the proposed method under different supervision rates, and the superiority compared to other fully supervised methods even at a low supervision rate.
Strapdown Inertial Navigation Algorithm Based on MIMU and Its Application in Missile Ejection Test

A strapdown inertial navigation algorithm is proposed, which is suitable for the measurement of the parameters of missile in the ejection test with short movement time, small changes of velocity and position. The transformation relationship between the missile-boy coordinate frame and the navigation coordinate frame is described by the attitude matrix. The initial attitude angles of the missile are determined by the initial alignment processing static data. The navigation solution of missile motion parameters is realized by using the attitude update model, the velocity update model and the position update model. Numerical simulation results show that the proposed algorithm can calculate the position, velocity and attitude angles of the missile.
Using Transfer Learning in Building Federated Learning Models on Edge Devices

Today’s state-of-the-art machine learning techniques, such as deep networks, are typically trained using cloud platforms, leveraging the elastic scalability of the cloud. For such processing, data from various sources need to be transferred to a cloud server. While this works well for some application domains, it is not suitable for all due to privacy and network overhead concerns. Federated learning is a machine learning setting designed for learning from distributed data over edge devices. In federated learning, data does not leave users’ devices. Instead, each user trains a local model with their data and shares the model updates with a federated server to aggregate and build a global model. Such training respects users’ privacy while reducing the network overhead for transferring data to a central server. Despite all advantages, each user usually has limited data that might not be enough to train a well-performing local model. In this work, to solve this challenge while keeping the client data private, we combine transfer learning with federated learning, where we train a base model with a public dataset. The base model is passed to the federated users to be fine-tuned for the target task. We have built a federated image classification model for the CIFAR-100 dataset using transfer learning and have studied how different parameters affect the model accuracy. Using this process, we achieve a model accuracy similar to a model trained in a central fashion while keeping the data private.
Research on Defence Method of Blackmail Virus Based on Trusted Computing

Relying on traditional defence methods, the information system is easily to be attacked by blackmail viruses. A blackmail virus defence method and system based on trusted computing is proposed in the paper. The customized trusted architecture of important network assets is formed by facing specific application scenarios and security requirements. Access control policy is established by trusted defence system, and the white list is based on according to trusted access list. The common area, key areas and other logical networks are isolated to avoid the spread of unsafe factors. The security strategy can be implemented more effectively by reasonably dividing the network area. The trusted defence system will immediately intercept the untrusted IP captured and prevent it from accessing the real host. The trusted defence system will not intercept the untrusted IP to continue to visit the honeypot, and continue to let the untrusted IP to visit the honeypot. It can quickly detect the behaviour of blackmail virus and other malicious programs, and effectively and timely curb them, or control the risk of blackmail virus and other malicious programs in a micro isolation area.
SiloSolver: Developing an Algorithm for Automatic Aggregation and Testing of Isolated Customer Segments in Facebook Ads Campaigns

Data silo problem refers to related datasets located in different databases, systems, or files. In the case of online advertising, performance metrics are fragmented in different campaigns and ad sets, making it difficult to compare customer segments. In this research, we present SiloSolver, an algorithm that (a) retrieves performance metrics for different customer segments across all campaigns, (b) aggregates the values for each customer segment in mean values, and (c) runs a statistical comparison (Student’s t-test) on the performance differences between the segments. The algorithm is evaluated using a real Facebook Ads dataset from an e-commerce company consisting of hundreds of campaigns from over five years. Using SiloSolver, advertisers using Facebook Ads are better able to understand their market segments across multiple seemingly disparate campaigns.
Internal Knowledge Sharing by Infrastructure Maintenance Engineers in Small and Medium Size Construction Companies in Japan

It is necessary to increase the capability of engineers to carry out maintenance management for keeping infrastructure safe and serviceable. Many regional training programs have been established to develop engineers into “maintenance experts. ” While small and medium size construction companies and engineering consultants play an active role in infrastructure maintenance, they have limited human and economic resources for joining the training programs, so the sharing of knowledge acquired from the programs becomes important for improving the capability of the company as a whole. The objective of this research is to investigate how engineers who participated in regional training programs shared their knowledge inside their organization using an online questionnaire survey. It was found that leadership is an important factor for driving knowledge sharing activities, particularly for adapting the knowledge acquired from the training program to the company’s environment. However, the lack of experience serves as a barrier to the deeper transfer of knowledge, such as on-the-job training or seminars, as these methods of knowledge sharing require expert knowledge. For organizations to improve their knowledge sharing practices, it is necessary to consider the type of knowledge to be shared and the appropriate method for sharing, along with the improvement of the leadership for knowledge sharing in the organization.
Beyond $L_{p}$ Norms: Delving Deeper into Robustness to Physical Image Transformations

With the increasing adoption of deep learning in computer vision-based applications, it becomes critical to achieve robustness to real-world image transformations, such as geometric, photometric, and weather changes, even in the presence of an adversary. However, earlier work has focused on only a few transformations, such as image translation, rotation, or coloring. We close this gap by analyzing and improving robustness against twenty-four different physical transformations. First, we demonstrate that adversarial attacks based on each physical transformation significantly reduce the accuracy of deep neural networks. Next, we achieve robustness against these attacks based on adversarial training, where we show that single-step data augmentation significantly improves robustness against these attacks. We also demonstrate the generalization of robustness to these types of attacks, where robustness achieved against one attack also generalizes to some other attack vectors. Finally, we show that using an ensemble-based robust training approach, robustness against multiple attacks can be achieved simultaneously by a single network. In particular, our proposed method improves the aggregate robustness, against twenty-four different attacks, from 21.4% to 50.0% on the ImageNet dataset.
Smart Region System Architecture and Technologies

Based upon the synopsis of information technology development and smart region system, this paper puts forward the architecture, the constitution and the related functions of smart region system, and sets forth a series of related technologies, such as lifecycle data safe control technology, intelligent and joint operation technology, and system architecture, network and software expansibility technology, and moreover, the effectiveness analysis is carried out with a performance comparison between the existed smart region system and the before region system. This will help to advance the further evolution of smart region system and the continual improvement of IOT+ engineering.
Low Complexity Neural Network-Aided NMS LDPC Decoder

For recent years, deep learning has been studied and utilized among decoders in wireless and optical communication. For low density parity check (LDPC) codes, most works use neural network (NN) to implement the belief propagation (BP) decoder and unfold its iterations into layers activated by different functions. However, the whole decoder composed by neural network performs lots of non-linear operations, which are time consuming and unfriendly to hardware implementation. In this work, a neural network-aided normalized min-sum (NN-aided NMS) decoder for LDPC code is proposed, in which the normalization factors are inferred from input log likelihood ratio (LLR) by apre-trained NN, while maintaining check and variable nodes processing the same as in min-sum (MS) decoder. In order to test the proposed decoder with irregular LDPC code, a rate-7/8 one was constructed using progressive edge growth (PEG) algorithm, showing better error correction capability than the standard of consultative committee for space data systems (CCSDS) LDPC (8176,7154) code by 0.08 dB with the same rate. Simulation results show that, NN-aided NMS decoder outperforms MS decoder by 0.3 dB, and its performance approaches BP decoder with a gap of 0.02 dB, which is 50% closer than the best NMS decoder using 0.7 as the normalization factor. The proposed decoder has good performance for both regular and irregular LDPC codes, and needs much less computation complexity than BP decoder. It introduces more linear computation than NMS decoder but achieves better bit error rate (BER) performance, thus becoming a good complement between BP and NMS decoder when trading off among complexity, error correction performance and hardware implementation for wireless and satellite communication.
Research on Construction Method of Protein-Protein Interaction Network Cluster Based on Hadoop

These massive protein interaction data provide rich data sources for building protein interaction network and systematically exploring the life body activity rules. At the same time, it also puts forward a new challenge to deal with, mine, analyse and effectively use these data. In view of the above problems, we build a processing platform of protein-protein interaction network based on Hadoop to collect, store, analyse and apply massive protein-protein interaction data, realize aggregation and integration, improve work efficiency, and provide important basis for the construction and analysis of protein-protein interaction network.
Chinese Named Entity Recognition for a Power Customer Service Intelligent Q&A System

Power customer service intelligent Q&A system can greatly improve the efficiency of power customer service and reduce labor costs. In order to deal with the questions that need to be solved by reasoning, it is necessary to build the power customer service knowledge graph and accurately understand the questions. One of the key tasks is to implement a named entity recognizer using the historical log data of power customer service Q&A. Recently, lattice based neural networks have gained great advantages in Chinese named entity recognition. However, lattice based models rely heavily on an external predetermined dictionary, and the quality of the dictionary may interfere with entity boundary learning. As the power customer service Q&A is a form of oral conversation. , it is difficult to build the specialized dictionary, which seriously restricts the application of the original menthod of lattice structure based neural network for Chinese named entity recognition in the field of power customer service. Therefore, this paper proposes a method of using entity boundary locally in lattice based neural networks for Chinese named entity recognition. Through joint learning of entity boundary and entity recognition, without any external dictionary, experiments on data sets in the field of power customer service show that this method has very good potential.
Wireless Power Transfer by Self-biased Magnetoelectric Laminate for Biomedical Implants

Magnetoelectric (ME) wireless power transfer (WPT) is becoming an important topic in the field of biomedical implants. Implantable ME WPT receivers have potential safety, size, and convenience advantages over alternative methods (i.e. inductive, far-field RF, and acoustic). However, for optimal performance, ME devices need some method to apply a DC bias magnetic field. To overcome the DC bias problem, this paper investigates self-biased ME laminates using the magnetization grading approach. We experimentally characterize the voltage and power performance of multi-layer self-biased ME laminates as a function of pre-magnetizing field. We demonstrate devices made of Metglas, Ni, and PZT of 0.05 cm3 in size that can generate ~250 μW from an applied 130 μT AC field with no DC field bias. This size, power, and AC magnetic field combination makes these laminates attractive for powering biomedical implants.
A Digitigrade Wheeled Bipedal Robot and Its Obstacle Avoidance Actions Library

The current wheel-legged robots are mainly deformed wheeled or multi-legged robots, and there is little research on humanoid wheel-legged robots. To integrate wheel-legged robots into human society further, we propose a wheeled bipedal robot with bionic joints and establish an obstacle avoidance actions library to enhance its active ability to overcome obstacles. We use closed vector equations and the Modified Denavit-Hartenberg (MDH) method to build the single-leg model. The bionic joints introduce two closed kinematics loops. We handle this by converting these closed loops into spanning trees. We prove that the robot can actively overcome different types of obstacles by calling the actions in the library. This research provides an idea for enhancing the motion capability of wheel-legged robots, which further enhances the possibility of wheel-legged robots serving human society in reality.
Research on the Secure Routing Mechanism for Software Defined Internet of Things Based on IPv6

The Internet of things (IOT) has gained great popularity in the past decade. With the increase of connected devices, IPv4 protocol has exposed the problems of insufficient address and insecurity. IPv6 is an improved solution to the problem of IPv4. IETF proposed RPL (Routing Protocol for Low-Power and Lossy Networks) routing mechanism based on IPv6. The RPL routing protocol initially did not consider the security of the network, making the RPL protocol vulnerable to many malicious attacks. Software defined network (SDN) has become a significant solution to the problem of heterogeneous device interconnection and network security. SDN-WISE is a software defined IoT solution. Under the SDN-WISE architecture, we will develop an RPL secured routing mechanism based on a trust mechanism to provide secure routing theory technology and a method for deploying the Internet of Things based on IPv6.