Miracle-dz/UniFAR-FTUs huggingface dataset:
Underwater Robot Localization Using Magnetic Induction: Noise Modeling and Hardware Validation

Localization is a fundamental task in many swarm robotic applications, such as foraging and exploration. Magnetic induction communications, which rely on the magnetic component of an antenna's near-field, have gathered interest as means to perform localization in underground and underwater environments. MI signals propagate through lossy environments better than traditional RF signals, and can offer advantages over acoustics. In prior work, we developed a localization method based on MI signals to calculate the range between two moving MI antennas. A core aspect of the method is a particle filter that relies on the received signal strength and speed of the antennas to produce location estimates. In this paper, we first empirically find the amount of noise present in our signal strength measurements in underwater environments. Then we propose a model to capture the impact of the noise on the range calculations and apply it to improve the particle filter's location estimations.
Photonic-Electronic Ultra-Broadband Signal Processing: Concepts, Devices, and Applications

Combining photonic integrated circuits (PIC) with millimeter-wave electronics opens novel perspectives in generation and detection of ultra-broadband signals with disruptive potential for a wide variety of applications. Here, we will give an overview on our recent progress in the field of ultra-broadband photonic-electronic signal processing, covering device concepts such as silicon plasmonic integration, signal processing concepts such as Kramers-Kronig-based phase reconstruction of THz signals, as well as application demonstrations in the field of high-speed wireless data transmission.
Imaging Sonar based AUV Localization and 3D Mapping using Image Sequences

This paper proposes a three-dimensional (3D) sonar mapping method and autonomous underwater vehicle (AUV) localization method using two-dimensional (2D) sonar image sequences of an imaging sonar. The 2D sonar image sequences are registered to generate a 2D mosaic sonar map. In this process, we can estimate the displacement and rotation relationship between the sonar image pairs, and use this to estimate the position of the AUV. A 3D point cloud is generated from 2D sonar image sequences. This method takes advantage of the mobility of the AUV to reconstruct the height information, and partially solves the ambiguity issues in the elevation angle of the imaging sonar. The height map is generated by accumulating the 3D point cloud. By fusing two maps, we can generate a 3D sonar map. We verified the proposed method by conducting field experiments. Three types of artificial reef on the seabed were scanned, and a 3D sonar map was created. In the process, the trajectory of the AUV was estimated and compared with the trajectory of the dead reckoning.
Configuring Interface for IoT: Natural User Interface Study on Senior Adults in Japan

While Internet of Things has high affinity with houses and ability of customization, the customization process is complicated and hard for people not familiar with programming. Previous research reports that senior adults can independently come up with ideas by using cards. Three types of user interface were developed for experimental purpose to explore suitable user interface for senior adults; Voice recognition, touch interface, and image recognition. As a result, this study revealed that voice recognition was the most suitable. Furthermore, the results indicated that iterative interaction with Internet of Things may suggest image recognition with higher acceptance.
Analytical Review of the Methods of Dynamic Load Balancing under Conditions of Uncertainty in the Execution Time of Tasks

сomputationally complex problems can be solved using distributed computing resources, but the problem arises in the optimal way of distributing tasks between computing nodes in order to reduce the total execution time. However, the computation time for a task on the same device is not constant and can change dynamically over time. In this article, we explore methods of dynamic load balancing in order to minimize the computation time of the problem. We study three groups of methods based on the use of probability theory and mathematical statistics methods, evolutionary algorithms and artificial neural networks. We have shown that methods based on artificial neural networks can reduce the computation time of the problem and minimize the complexity of the dynamic load balancing algorithm.
Screw defect detection system based on AI image recognition technology

In the past ten years, smart manufacturing has been widely discussed and gradually introduced into various manufacturing fields. Since Germany proposed the concept of “Industry 4.0” in 2011, it has been spreading and feverish all over the world. For Industry 4.0, information digitization, intelligent defect detection and database platform management are their main core technologies. Aiming at a large screw industry manufacturing field in central and southern Taiwan, this paper proposes a screw defect detection system based on AI image recognition technology to detect damage to the nut during the “molding” process in the screw production process, and it is determined whether the inspected screw passes the inspection. The recognition result is given as shown in Figure 1. This paper uses 500 non-defective screw samples and 20 defective screw samples provided by the screw factory. The above samples collected real-time images through the sampling structure designed in this article, and we adopt Microsoft Corporation's ML.NET suite to model AI images, and uses the following four deep learning models: ResNetV2 50, ResNetV2 101, InceptionV3, MoblieNetV2 for learning; in the process of learning, this article divides the data set into three types of data sets (one is the unknown set that is not used for training but mixed with correct and defective samples, and the other is used for post-training verification of mixed samples with correct and defective samples. The third is a training set for training a mixture of correct and defective samples) This arrangement is used for subsequent verification models; after training, a PC-based screw defect detection system is implemented as shown in Figure 2; finally, with Detect screw defects in the form of instant photography. After the experiment, in 1,000 repeated tests, the success rate of defect detection reached 97%, while the false positive rate was only 2%.
Predictive Exoskeleton Control for Arm-Motion Augmentation Based on Probabilistic Movement Primitives Combined With a Flow Controller

There are many work-related repetitive tasks where the application of exoskeletons could significantly reduce the physical effort by assisting the user in moving the arms towards the desired location in space. To make such control more user acceptable, the controller should be able to predict the motion of the user and act accordingly. This letter presents an exoskeleton control method that utilizes probabilistic movement primitives to generate predictions of user movements in real-time. These predictions are used in a flow controller, which represents a novel velocity-field-based exoskeleton control approach to provide assistance to the user in a predictive way. We evaluated our approach with a haptic robot, where a group of twelve participants had to perform movements towards different target locations in the frontal plane. We tested whether we could generalize the predictions for new and unknown target locations whilst providing assistance to the user without changing their kinematic parameters. The evaluation showed that we could accurately predict user movement intentions while at the same time significantly decrease the overall physical effort exerted by the participants to achieve the task.
The d and q Axes Technique for Suppression Zero-Sequence Circulating Current in Directly Parallel Three-Phase PWM Converters

The directly parallel pulse width modulated converter (DPC) method is used to construct large-scale converter systems to increase system performance and reduce costs. However, circulating currents arise when the converters supply unbalanced currents or have different converter parameters. Circulating currents cause distortions in the current, increase the current coming from each converter, and degrade the system performance. This paper proposes a circulating current control (CCC) methodology to eliminate circulating currents through the analysis and approximation of the circulating current of the fundamental harmonic current at 150 Hz. By using the phase shift technique, this new model of the circulating current in the 0-axis was extended to the $0d$ - and $0q$ -axes. The proposed CCC was then designed according to the new circulating current model and added to the conventional controller. The performance of the proposed CCC converter was verified via simulation and experimentally with three directly parallel 5.5 kVA converters. The proposed CCC enhanced controller and conventional controller were tested with various current values, and all resulting circulating currents were recorded. The experimental results showed that the proposed CCC eliminated nearly all the circulating currents and that it had a fast, dynamic response to step-change currents. Using ramp current sharing, the experimental results were confirmed. Additionally, the efficiency of the proposed CCC in the DPC was significantly higher than the conventional controller, and the system’s efficiency was the same as that of a single converter connection. Thus, using the proposed CCC in DPC systems is economical, highly efficient, modular, and flexible.
Unbalanced-/Balanced-to-Unbalanced Diplexer Based on Dual-Mode Dielectric Resonator

The design of compact unbalanced-/balanced-to- unbalanced diplexer (U2U/B2U) based on full dual-mode dielectric resonator (DR) is investigated for the first time. The relationships of the external quality factors (<inline-formula> <tex-math notation="LaTeX">$Q_{e}$ </tex-math></inline-formula>) under the two modes are analyzed. It is found that the <inline-formula> <tex-math notation="LaTeX">$Q_{e}$ </tex-math></inline-formula> of two modes can be controlled independently, which makes the design procedure simple and efficient. Based on the analysis, both of the proposed U2U and B2U diplexer can be easily built by properly adding feeding probes for the DR without increasing the circuit size by altering the location of the feeding probes according to the distribution of the electromagnetic fields. To verify the proposed design concept, the diplexers mentioned above are simulated, implemented and measured. The isolation is achieved by the orthogonality between modes while the low insertion loss is obtained due to the high unloaded quality factor (<inline-formula> <tex-math notation="LaTeX">$Q_{u}$ </tex-math></inline-formula>) of the dual-mode DR. The measurements are in accordance with the simulated results, showing low loss and good selectivity.
Research of Neural Networks Efficiency in Video Generation Problem

This paper describes the research of video generation using neural networks. The main ideas of several existing approaches based on the generative adversarial networks are presented and their characteristics are compared. During this research, vid2vid approach was selected and its effectiveness was examined. The process of collecting the corresponding dataset is described. Influence of various factors, such as training and segmentation parameters, on the model’s effectiveness was studied during the experiments and obtained results were analyzed. The conclusions are presented and the directions of further research are formulated.
EEG Activity Related to Decrease in Persistency of Gait Cycle Variability during Distracted Walking

Human gait cycle fluctuates during walking even with a constant walking speed. Such fluctuation, referred to as the gait cycle variability, is often quantified by measuring stride intervals, i.e., time intervals between one and the subsequent heel contacts of one of the feet. It has been known that gait cycle variability in healthy young people exhibits a positive persistency, which tends to be lost by aging and neurological diseases, including Parkinson's disease, suggesting that the positive persistency reflects endogenous stability of gait. We have reported previously that the degree of positive persistency decreased during distracted walking with smartphone-gaming induced cognitive load. Here, we hypothesized that the decrease in the positive persistency is caused by a change in the neural activity at supra-spinal level. To find evidence for this hypothesis, we measured electroencephalography (EEG) during walking with and without the cognitive load. As a result, though preliminary, amplitude of the EEG at gamma band increased at a timing prior to every heel contact in the cognitive load condition compared to the control condition. This preliminary result suggests that a supra-spinal gait control mechanism is involved in the gait cycle variability.
Particle Filter based Autonomous Underwater Vehicle Navigation System aided thru acoustic communication ranging

Autonomous Underwater Vehicles (AUVs) are platforms suitable for a wide variety of applications in the marine environment with economic and operational advantages. In these applications an AUV performs a given task as a mission. During the mission execution, the AUV will move around the environment following paths that allow it to fulfill the mission's objectives. To achieve this, a reliable Navigation System (NS) is required. In addition to this, the current operating concept includes the deployment of multiple AUVs on a given area, thus a communication system between vehicles is also required. In the underwater environment both navigation and communication systems deals with the particular characteristics of the medium that limits the use of conventional techniques. In this work, a complete NS for an AUV is presented. The developed NS is based on an inertial navigation scheme with velocity and position aiding. The position aiding takes advantage of the communication system onboard the vehicle, which avoids the use of additional positioning systems. The fundamentals of the applied solutions are described and experimental results and implementation details are provided. Also conclusions and future works are presented.
Financial Predictions based on Fusion Models-A Systematic Review

In this present economic era people want to generate money in terms of profit in a rapid time amount, which is proportional to their investment. This can be happened on making correct business decisions at right times. In general, the economy of any country is purely dependent on business transactions takes place all around the world. In all the sort of businesses the profit gained is proportional to investment, but one business platform which gives profit as multiples is the stock market. If a person selects the right stock - at right time, then the profits are earned. Hence, that the pair selection of the stock and a moment of corresponding stock plays a vital role in profit generation in stock market. The stock market is a very dynamic environment as it was very sensitive towards the each and every event occurred throughout the world. Even though it is very tough to estimate the next movement of any of the stock, many of the professionals are trying to predict the movements of stock market. They used several statistical methods, time series analysis models, machine learning forecasting techniques for the prediction task. The purpose of this study is to find out the suitable best model to predict the stock market, by studying various stock market prediction approaches, which combines different fields like data mining, machine learning and sentiment analysis. During the process of studying various techniques the common things noted are dataset to be formed with stock data and apply feasible preprocessing on it for making ready for further analysis. Apply machine learning algorithms and measure the prediction result as in terms performance measurements like accuracy, Mean Square Error etc. The final conclusions drawn from this survey are, forecasting is the very difficult task as the rapid changes in market movement takes place in stock market. And also for efficient and accurate predictions need to be considered various factors in real world. The efficient stock prediction act as great asset for several stock agencies and also used to overcome the problems of stock investors.
OWebSync: Seamless Synchronization of Distributed Web Clients

Many enterprise software services are adopting a fully web-based architecture for both internal line-of-business applications and for online customer-facing applications. Although wireless connections are becoming more ubiquitous and faster, mobile employees and customers are often offline due to expected or unexpected network disruptions. Nevertheless, continuous operation of the software is expected. This article presents OWebSync: a web-based middleware for data synchronization in interactive groupware with fast resynchronization of offline clients and continuous, interactive synchronization of online clients. To automatically resolve conflicts, OWebSync implements a fine-grained data synchronization model and leverages state-based Conflict-free Replicated Data Types. This middleware uses Merkle-trees embedded in the tree-structured data and virtual Merkle-tree levels to achieve the required interactive performance. Our comparative evaluation with available operation-based and delta-state-based middleware solutions shows that OWebSync is especially better in operating in and recovering from offline settings and network disruptions. In addition, OWebSync scales more efficiently over time, as it does not store version vectors or other meta-data for all past clients.
Towards Automated Structural Health Monitoring for Offshore Wind Piles

Simulation plays an important role in the development, testing and evaluation of new robotic applications, reducing implementation time, cost and risk. In this paper we show a digital twin simulation model of an inspection ROV which is capable of performing structural health monitoring by automated creation of a map of an offshore wind monopile. The data is compared to a known reference model. The digital twin simulation model is extended by a physical sensor data input device to bridge the gap between simulation and testing in water.
Robotic Arm Combined with the Visual Images in a Transparent Object Recognition

This paper presents the transparent object to recognition by robot-mounted camera to provide multiple pick and place functions. Through coordinate transformation the transparent object can be found by the LabViewsystem integration of a robotic arm. There are four kinds of transparent bottles to demonstrate the vision recognition and motions of pick and place with various positions by the embedded system.
Unobtrusive notification based on auditory changes in ambient music

Notifications are an indispensable aspect of the user experience with smart devices. However, the increased usage of smart devices yielded proportional increase in notifications. Research has shown that increased notifications may reduce the efficiency of users during tasks, as they are interrupted, and their attention is drawn toward the notification. Therefore, our work investigates a novel notification method based on ambient music, which is background music that was found to unobtrusively motivate users and increase their focus during work. Accordingly, we designed a preliminary study to investigate increased music loudness and duration of increase as basic parameters for designing unobtrusive ambient music-based notifications. Based on extracted insights from our previous study, we follow with a deeper study comparing user impressions and usability of ambient-music notifications with those of common notification. Our results show that most of the participants were able to recognize our notifications, while having minimal effect on their concentration during the task. We discuss some challenges in designing notifications based on ambient music, especially about user preferences of sound loudness and its relation to task type. Lastly, we provide our conclusion and discuss future research direction.
An Anti-Attack Trust Mechanism Based on Collaborative Spectrum Sensing for Underwater Acoustic Sensor Networks

The main method for long-distance underwater communication is underwater acoustic communication(UAC). The bandwidth of UAC channel is narrow and the frequency band resources are scarce. Therefore, it is important to improve the frequency band utilization of UAC system. Cognitive underwater acoustic (CUA) technology is an important method. CUA network can share spectrum resources with the primary network. Spectrum sensing (SS) technology is the premise of realizing CUA. Therefore, improving the accuracy of spectral sensing is the main purpose of this paper. However, the realization of underwater SS technology still faces many difficulties. First, underwater energy supplies are scarce, making it difficult to apply complex algorithms. Second, and more seriously, CUA network can sometimes be attacked and exploited by hostile forces, which will not only lead to data leakage, but also greatly affect the accuracy of SS. In order to improve the utilization of underwater spectrum and avoid attack, an underwater spectrum sensing model based on the two-threshold energy detection method and K of M fusion decision method is established. Then, the trust mechanism based on beta function and XOR operation are proposed to combat individual attack and multi-user joint attack (MUJA) respectively. Finally, simulation result shows the effectiveness of these methods.
Beam-Reconfigurable Antenna Based on Vector Modulator and Rotman Lens on LTCC

A beam-switching array is designed using a <inline-formula> <tex-math notation="LaTeX">$4\times 6$ </tex-math></inline-formula> Rotman lens as a beam-forming network to switch the beam towards −30°, −10°, 10°, and 30°. Six substrate integrated waveguide fed <inline-formula> <tex-math notation="LaTeX">$1\times 4$ </tex-math></inline-formula> microstrip patch arrays are used as the radiating elements. The beam-switching array is designed on the LTCC substrate to operate at 71–76 GHz. In this work, the feasibility of implementing a beam-switching network with a vector modulator (VM) integrated circuit as switching element is studied for the first time. The measured radiation pattern of the beam-switching array with the VM is in line with the simulated radiation pattern. The measured peak realized gain of the beam port 1, 2, 3, and 4 with 3-dB backoff gain of the VM are 17, 17, 17.8, 16.9 dBi at 75 GHz, respectively. In addition, the work demonstrates the possibility of beam-reconfigurability in beam-switching array by simultaneous excitation of the beam ports in optimum phase using the VM. The simultaneous excitation of the adjacent two ports, 1-2, 2-3, and 3-4, steer the beam towards −19°, 0°, and 19°, respectively, which improves the beam cross-over level. With various combinations of simultaneous excitation, the half power beam-width of the beam-switching array is varied from 17.8° to 75°.
Investigation of Approaches to Computerized Evaluation of Objects of Art

Modern methods of image analysis are expensive, inaccessible to the public and do not provide a complete description of artistic works, which significantly reduces the possibility of their use for novice artists who need a high-quality initial assessment at the beginning of their career. This paper will review modern image processing algorithms and identify the characteristics of algorithms that are most suitable for evaluating works of art.