Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Method for Fast Path Planning of Underwater Vehicle in the Shallow Sea Environment

In view of the problem that the optimal solution quality of classic path planning methods for underwater vehicle is not good enough to guarantee that there has the best path in the complex environment of shallow coastal waters. A new fast path planning method for complex shallow sea environment is proposed in this paper. The method uses inner and extra loop search strategies to ensure that the planned path is globally optimal. The simulation results show that the new method can find an optimal path rapidly between the starting point and the destination point under the premise of avoiding reefs and other risks. Considering the voyage constraints, energy constraints, exposure risks and special shallow sea working environment comprehensively, the new method is more suitable for the fast path planning of underwater vehicle than the traditional A* algorithm.
ProSign+ A Cloud-Based Platform Supporting Inclusiveness in Public Communication

Today, the deaf community represents a population of more than 70 million individuals around the globe. Sign language is recognized as one of the most popular means of communication for the deaf. The Convention on the Rights of Persons with Disabilities recognizes and promotes the use of sign languages and establishes that their use is critical to ensuring access to information and services, both in everyday life and during emergencies. However, most public information and service providers do not comply with the goal of providing information and services through speech-to-sign language interpreters. In this paper we leverage the results of our previous work and propose ProSign+, a cloud-based speech-to- sign language platform conceived as an intermediary service for any legal entity which is expected to provide inclusive access to information and services also for deaf people (e.g., public transportation companies, municipalities, hospitals, schools and higher education institutions). The idea is to foster a cost-effective transformation of public service providers towards the goal of leaving no one behind, which is central to the Agenda 2030 for Sustainable Development, adopted by all United Nations Member States in 2015.
Small-Area and Low-Power FPGA-Based Multipliers using Approximate Elementary Modules

Approximate multiplier design is an effective technique to improve hardware performance at the cost of accuracy loss. The current approximate multipliers are mostly ASIC-based and are dedicated for one particular application. In contrast, FPGA has been an attractive choice for many applications, because of its high performance, reconfigurability, and fast development. This paper presents a novel methodology for designing approximate multipliers by employing the FPGA-based fabrics. The area and latency are significantly reduced by cutting the carry propagation path in the multiplier. Moreover, we explore higher-order multipliers on architectural space by using our proposed small-size approximate multipliers as elementary modules. For different accuracy requirements, eight configurations for approximate 8 × 8 multiplier are discussed. In terms of mean relative error distance (MRED), the accuracy loss of the proposed 8 × 8 multiplier is low as 0.17%. Compared with the exact multiplier, our proposed design can reduce area by 43.66% and power by 20.36%. The critical path latency reduction is up to 27.66%. The proposed multiplier design has a better accuracy-hardware tradeoff than other designs with com-parable accuracy.
An AODV-Based Communication-Efficient Secure Routing Protocol for Large Scale Ad-Hoc Networks

This paper proposes a secure routing protocol based on an ad-hoc on-demand distance vector (AODV) that successfully achieves both security and communication efficiency. Although many studies have previously discussed such a secure protocol, the conventional methods significantly degrade the communication efficiency because of large packets and complex communication procedures. The proposed method allows the intermediate node to generate a route reply (RREP). This is impossible in conventional methods due to the restriction of digital signatures in route requests (RREQ). In the proposed method, each intermediate node holds a packet received from a specific node in the past, and appends the held packet to the RREQ of another node and generates its own signed RREP. This procedure guarantees that the third party holds the route to the destination. It is shown that the proposed method outperforms conventional secure protocols in terms of network load.
Systematic Modeling of Electrostatics, Transport, and Statistical Variability Effects of Interface Traps in End-of-the-Roadmap III–V MOSFETs

Thanks to their superior transport properties, indium gallium arsenide (InGaAs) metal–oxide–semiconductor field-effect transistors (MOSFETs) constitute an alternative to conventional silicon MOSFETs for digital applications at ultrascaled nodes. The successful integration of this technology is challenged mainly by the high defect density in the gate oxide and at the interface with the semiconductor channel, which degrades the electrostatics and could limit the potential benefits over Si. In this work, we: 1) establish a systematic modeling approach to evaluate the performance degradation due to interface traps in terms of electrostatics and transport of InGaAs dual-gate ultrathin body (DG-UTB) FETs and 2) investigate the effects of random interface-trap concentration as another roadblock to the scaling of the technology, due to statistical variability of the threshold voltage. Variability is assessed with a Technology CAD (TCAD) simulator calibrated against multi-subband Monte Carlo (MSMC) simulations. The modeling approach overcomes the TCAD limitations when dealing with ultrathin channels (i.e., below 5 nm) without altering crucial geometrical parameters that would compromise the dependability of the variability analysis. Our results indicate that interface-trap fluctuation becomes comparable with the other variability sources dominating the total variability when shrinking the device dimensions, thus contrasting the trend of reduced variability with scaling. This, in turn, implies that interface and border traps may strongly limit the benefits of InGaAs over Silicon if not effectively reduced by gate process optimization.
Low-Latency Cooperative Computation Offloading for Mobile Edge Computing

This paper constructs a system based on cooperative communication and computation to minimize the execution and transmission delay under the constraint of the total energy consumption. In the system, the device can choose the amplifyand-forward scheme or decode-and-forward scheme to offload tasks to edge server according to the distance between devices, and the cooperative communication and computation are considered. An optimization problem is modeled on the proposed system, and then the NP-hardness of the problem is proved. A heuristic algorithm, named GHA, is proposed to produce an approximate solution for the optimization problem. After that, a dynamic programming algorithm, named DPA, is also proposed to generate exact solution and verify the performance of GHA for small sized problems. Numerical results demonstrate that GHA can provide nearly optimal solutions, and the gap between the approximate solution and the exact one is kept in [2.0%, 3.0%].
A Versatile Acceleration Framework for Machine Learning Algorithms

Current Machine Learning (ML) accelerators are based on custom designs targeted to specific algorithms, like the NPU coprocessor for neural networks. We propose a Versatile Acceleration Framework based on a key concept called Performance Semantics, which is an abstraction on data-level parallel behaviors for execution kernels nested in loops. It includes a Versatile Accelerator Pipeline and a software library of common Performance Semantics. The ML programmers only need to invoke the library functions in their algorithms and get accelerated transparently. We implement our framework on FPGA with an embedded ARM CPU, and test it with a set of popular ML algorithms. The results show that our framework can successfully cover the computation kernels in these ML algorithms, and achieves enormous performance speedup ranging from 15x to 40x over ARM CPU and 2.x to 12.x over x86 CPU.
Analysis and Investigation of Properties of Algebraic Geometric Codes

Linear block noise-proof codes constructed according to algebraic curves (algebraic geometric codes) are considered, their design properties are evaluated, algorithms of construction and decoding are studied. The energy efficiency of the transmission of discrete messages by M-ary orthogonal signals in the application of algebraic geometric codes is studied, the achievable energy gain from the use of noise-immune coding is estimated.
Fabrication of Capacitive Micromachined Ultrasonic Transducers for Underwater Applications using Thermocompression Wafer-Bonder

Capacitive Micromachined Ultrasonic Transducers (CMUTs) are the prospective alternative to the traditional piezoelectric ultrasonic transducers. CMUTs are essentially parallel plate capacitors produced using Microelectromechanical Systems (MEMS) technology. The production of CMUTs is broken down into sacrificial underetching and wafer bonding methods. The sacrificial release-based techniques are complex and require several adjustments in terms of optimizing fabrication steps and material selections. Further, the sacrificial release-based processes need ultimate control over the gap height and membrane thickness. On the contrary, the wafer bonding fabrication processes are not only simpler than the sacrificial release methods but also provide a very good parametric control over the membrane thickness and gap height. Besides its advantages, the wafer bonding methods are very sensitive to contamination and surface roughness. The surface roughness problems are addressed by either using the costly Silicon-on-Insulator (SOI) wafers or by using complex Chemical Mechanical Polishing (CMP) method. This article presents a simple and economical CMUTs wafer bonding fabrication method. A thermocompression based metal bonded technique is adopted to successfully fabricate low frequency CMUTs to be used for underwater applications.
An Asynchronous Algorithm for Optimizing the Communication Performance

Iterative applications such as stencil problem are the most important and time-consuming computing core for numerical simulations and scientific applications. Iterative applications often require high frequency iterations. The dependency computations between steps lead to frequent data exchanges. Existing large-scale clusters tend to have long memory access latency and network latency. Long latency results in higher communication or data movement costs than computating costs. Therefore, the main performance bottleneck of stencil algorithm on large-scale clusters is how to improve the iterative frequency. This paper proposes a new asynchronous algorithm which could reduce the number of data exchanges among processes of high-frequency iterative applications. The asynchronous algorithm consists of multiple rounds and communication only occurs after each round. The input data comes from the asynchronous communication, resulting in inaccurate but speculative computations. The speculative computations could improve accuracy and speed up convergence. In addition, the speculative computations can automatically correct the precision which is caused by asynchronous communication. The proposed algorithm has been tested on a stencil-based parallel computation and compared with a BSP implementation of the same application. The asynchronous algorithm can effectively reduce the number of data exchanges at the expense of higher computation overhead and larger message size, performance can be improved up to 2.8x.
Prediction of Short-Imminent Heavy Rainfall Based on ECMWF Model

Short-imminent heavy rainfall is a weather phenomenon with large rainfall intensity in a short period of time, and its effective prediction can minimize the damage caused by short-imminent heavy rainfall. Fujian province is a region with short-imminent heavy rainfall. The accurate identification of short-imminent heavy rainfall has been the key research goal of Fujian provincial meteorological researchers. In this paper, based on ECMWF numerical forecast products and ground observed rainfall data, RBF interpolation is adopted to achieve data assimilation, and data are divided according to the ground observed rainfall data. Due to the unbalanced distribution of samples, unlike the traditional oversampling method, this paper uses the Synthetic Minority Oversampling Technique (SMOTE) to generate short-imminent heavy rainfall data, combined with Focal loss and DBNs to construct the shortimminent heavy rainfall classification model FL_DBNs. The experimental results show that the TS value of FL_DBNs is increased by 47.8% in the cumulative rainfall of 1-30mm, and the PO and ETS are greatly improved. Accumulated rainfall of more than 30mm has reached a certain classification requirements, and solved the problem that it is difficult for ECMWF to predict rainfall above 30mm, which has certain guiding significance for short-imminent heavy rainfall classification.
Case Report: Pigmented Villinodular Synovitis of Both Knee Joints

The condition started before two years as simple intermittent swelling, pain and discomfort of the left knee joint responded to rest and simple analgesics. In the last 2 months the pain increased and both knees undergo sudden fulminant swelling. Left one was more than right. The condition aggravated with loss of function and inability to walk with intermittent locking. The provisional diagnosis by primary medical unit was Osteoarthritis treated by NSAID, exercises and physiotherapy but there was no response and the condition worsens and continued in deterioration without precise diagnosis till she visits our hospital.
Indoor 3D Semantic Robot VSLAM Based on Mask Regional Convolutional Neural Network

During the construction of indoor environmental semantic maps by robot Vision SLAM (VSLAM), there exist some problems such as low label classification accuracy and low precision under the situation of sparse feature points. In this case, this paper proposes an indoor three-dimensional semantic VSLAM algorithm based on Mask Regional Convolutional Neural Network (RCNN). Firstly, an Oriented FAST and a Rotated BRIEF (ORB) algorithms are used to extract image feature points. Secondly, a Random Sample Consensus (RANSAC) algorithm is employed to eliminate mismatched points and estimate camera position-pose changes. Then, a Mask RCNN algorithm is applied to make partial adjustments to its hyper parameter. A self-made data set is used to transfer learning, fulfilling real-time target detection and instance segmentation of a scene. A three-dimensional semantic map is constructed in combination with VSLAM algorithm. The semantic information in the environment not only improves the accuracy of VSLAM construction and positioning, but also reduces the impact of object movement on the construction by marking movable objects. Meanwhile, the VSLAM algorithm is used to calculate the positional constraints between objects and improve the accuracy of semantic understanding. Finally, by comparing with other methods, it demonstrates that this method is more correct and effective. It was also verified that the proposed method can accurately interpret the semantic information in environment for the construction of three-dimensional semantic maps.
Towards Deep Object Detection Techniques for Phoneme Recognition

The use of cutting edge object detection techniques to build an accurate phoneme sequence recognition system for English and Arabic languages is investigated in this study. Recently, numerous techniques have been proposed for object detection in daily life applications using deep learning. In this paper, we propose the use of object detection techniques in speech processing tasks. We selected two state-of-the-art object detectors, namely YOLO and CenterNet, based on a trade-off between detection accuracy and speed. We tackled the problem of phoneme sequence recognition using three systems: the domain transfer learning system (DTS) from image to speech, intra-language transfer leaning system (IaTS) between speech corpora within the same language (English to English), and inter-language transfer learning system (IeTS) between speech corpora from dissimilar languages (English to Arabic). For English phoneme recognition, the Texas Instruments/Massachusetts Institute of Technology (TIMIT) corpus is used to evaluate the performance of the proposed systems. Our IaTS based on the CenterNet detector achieves the best results using the test core set of TIMIT with 15.89% phone error rate (PER). For Arabic phoneme recognition, the best performance, with 7.58% PER, was achieved using the CenterNet. These results show the effectiveness of using object detection techniques in phoneme recognition tasks. Furthermore, based on the findings of this study, speech processing tasks may be treated as object detection tasks.
Game-Based Attack Defense Model to Provide Security for Relay Selection in 5G Mobile Networks

5G mobile networks promise universal communication environment and aims at providing higher bandwidth, increased communication and networking capabilities, and extensive signal coverage by using multiple communication technologies including Device-to-Device (D-to-D). This paradigm, will allow scalable and ubiquitous connectivity for large-scale mobile networks where a huge number of heterogeneous devices with limited resources will cooperate to enhance communication efficiency in terms of link reliability, spectral efficiency, system capacity, and transmission range. However, owing to its decentralized nature, cooperative D-to-D communication could be vulnerable to attacks initiated on relay nodes. Consequently, a source node has the interest to select the more protected relay to ensure the security of its traffic. Nevertheless, an improvement in the protection level has a counterpart cost that must be sustained by the device. To address this trade-off as well as the interaction between the attacker and the source device, we propose a dynamic game theoretic based approach to model and analyze this problem as a cost model. The utility function of the proposed non-cooperative game is based on the concepts of return on protection and return on attack which illustrate the gain of selecting a relay for transmitting a data packet by a source node and the reward of the attacker to perform an attack to compromise the transmitted data. Moreover, we discuss and analyze Nash equilibrium convergence of this attack-defense model and we propose an heuristic algorithm that can determine the equilibrium state in a limited number of running stages. Finally, we perform simulation work to show the effectiveness of the game model in assessing the behavior of the source node and the attacker and its ability to reach equilibrium within a finite number of steps.
A novel method to study the property of the interface between silicone rubber and fiber reinforced plastic

In this paper, a four-electrode system is developed to determine the interface resistivity of the specially made specimens containing an interface between silicone rubber (SR) film and a fiber reinforced plastics (FRP) substrate. Both simulation and experiment show that the shield electrodes can successfully shield a considerably part of the bulk current. With the shield electrodes, the current flowing through the interface can be obtained and the interface resistivity is calculated. The influence of liquid permeation of SR as well as the recovery process on the interface resistivity is studied. The results show that interface resistivity is a sensitive electrical parameter reflecting the interface status and the newly-proposed test method can provide a means of evaluating the interface between the SR housing and FRP rod in a composite insulator.
Using Multi-Feature Fusion for Detecting Freezing of Gait Episodes in Patients with Parkinson's Disease

This paper proposes a model for detecting Freezing of Gait (FoG) episodes in patients with Parkinson's Disease (PD) using multi-feature fusion. The proposed approach applies two schemes for feature extraction. The first one is time-domain statistical feature engineering and the second one is spectrogram-based time-frequency analysis by Convolutional Neural Network (CNN) feature learning. The two extracted feature sets are fused with applying Principal Component Analysis (PCA) algorithm for dimensionality reduction. Benchmark dataset of three tri-axial accelerometer sensors for patients with PD is tested in both principle-axes and angular-axes. Moreover, performance of the proposed approach is characterized on experiments considering several Machine Learning (ML) algorithms. Experimental results show that using multi-feature fusion with PCA dimensionality reduction outperforms using typical single feature sets. The significance of this study is to highlight the impact of using multi-feature fusion on the performance of FoG episodes detection.
Transformer Insulation Degree of Polymerization Estimation through Adaptive Neuro Fuzzy Inference System Approach

The useful life of a power transformer is linked to the integrity of its solid (cellulosic) insulation. The insulation deterioration is time dependent and is influenced by varying temperatures, moisture, oxygen, oil conditions and loading profiles. Accordingly, once the solid insulation state is compromised, its dielectric and mechanical properties are not recoverable. Thus, longevity in service of a power transformer can only be achieved by incessant monitoring and assessing the credibility of its insulation system. The tensile strength of the solid insulation can be indicated by determining the degree of polymerization of the paper. Consequently, the degree of polymerization is a key indicator of the insulation status that correlates well with the transformer remnant life. Since degree of polymerization measurement is an intrusive test involving transformer disassembling, utilities have opted to the use of furans concentration in the oil as a way of determining the degree of polymerization of transformer solid insulation. However, these furans and degree of polymerization correlations are based on mathematical models. This paper introduces an adaptive neuro fuzzy model to estimate the degree of polymerization of a mineral oil-immersed power transformer based on furan content and CO2/CO gas concentration ratio in the insulation system. Practical data from numerous power transformers of different lifespans subjected to different operating regimes have been used to validate the accuracy and credibility of the established adaptive neuro fuzzy model. Compared to the conventional models, the results show that the proposed model is effective in estimating the degree of polymerization of transformer solid insulation.
The Development of Intelligent Models for Health Classification

This paper focuses on development intelligent health classification models for individuals with C4.5 techniques, which collected physical fitness data of those who exercise in Ratchaburi province. A total of dataset were used for creating models 376 records, and 19 attributes of personal information consists of age, sex, weight, height, pulse, upper blood, lower blood, weight bike, hand womenorce, leg stretch, triceps, biceps, suprailiac, subscapular, leg, grip, womenlex, lung capacity, $\mathrm{O}^{2}$ and two classes. The classification is divided into five classes, including the best health, good, normal, low and very low. In our experiment, the researcher divided the data set into two groups: training and testing and designed the test using 10-fold cross-validation method. The accuracy rate of C4.5 shown 100%.