Miracle-dz/UniFAR-FTUs huggingface dataset:
Climbing Trajectory Tracking Control of Unmanned Drone based on Total Energy Principle

In order to make the unmanned drone with energy limitation under the biasing condition still climb to the cruising altitude, a new height controller was designed inspired by the total energy control theory, and the influence of the speed deviation was introduced to adjust the elevator for the total energy distribution, so as to solve the coupling problem between longitudinal trajectory control and velocity control. Taking a certain type of target machine as the object, the simulation is carried out under two kinds of extreme biasing conditions. By comparing the simulation results of the traditional and improved control structure, it shows that the new controller is effective in practical engineering applications.
3D Reconstruction of Leg Bones from X-Ray Images using CNN-based Feature Analysis

3D reconstruction of subject specific bones from X-Ray images is an important issue in a variety of medical applications such as diagnosis. We propose a method to reconstruct 3D leg bones from 2D X-Ray images using feature analysis. First, bounding boxes are detected by Convolutional Neural Network(CNN). Feature points and feature ellipses are extracted from them. Such features are aligned with feature information of 3D bone model. Then, the boundary of X-Ray is detected from aligned boundary of 3D model. The 3D model is fine-tuned by adjusting the Statistical Shape Model parameter. We believe that this method makes 3D modeling easier by improving the automatic detection of feature information compared to the manual landmark input method.
Technologies for Personalized Ambient Intelligence based on 4 situational context awareness

Artificial Intelligence is growing very fast. Along with the development of artificial intelligence, Ambient intelligence is also developing, which can identify and transmit various activities such as people’s daily life and work. Ambient Intelligence has developed into convergence technology of Artificial Intelligence, Cloud Computing and Internet of Things. We will discuss each technology. In this paper, we propose Personalized Ambient Intelligence which focuses on personalization based on the necessary element technologies for Ambient Intelligence(AmI). The proposed Personalized Ambient Intelligence should consist of Ambient Intelligence environment, AmI Common Data Model, Context Classification, AmI Sensor Technologies and Ambient intelligence situation. Among them, Personalized Ambient Situation should be classified into 4 situation as Home, Business, Driving, Moving. Using Personalized Ambient Intelligence presented in this paper, personalized context awareness is likely to be possible, and prediction of the next context will also be possible. And Finally, we will look at a brief summary of our definition of Personalized Ambient Intelligence and the areas that can be used in the future and conclude with future research.
Towards a Novel Way to Predict Deficits After a Brain Lesion: A Stroke Example

Many studies have addressed the relations between different human brain regions and their role in cognitive, motor and sensory functions in patients that have suffered a brain lesion (stroke, traumatic brain injury, tissue removal). Nowadays, it is well established that the brain works as a network and the symptoms in a person are a combination of the direct impact of the lesion in a single region and its connectivity with other healthy brain regions. The aim of the present study is the development of a user-friendly desktop application to predict the induced cognitive deficits in patients who have suffered a brain lesion. The herein presented application is based on Neurosynth platform, and takes as an input a MRI mask that describes a lesion. Then our software exploits the knowledge that already exists in Neurosynth platform, so as to predict the potential deficits by grouping the Neurosynth's terms that have increased Z scores with our mask. In addition, we have embedded two types of visualization methods: One to present the slices of the brain mask and another to show the 3D volume of the mask into 3D semitransparent human brain. The added value of the presented application is that it may give us a clue about which mechanisms are probably affected by a lesion in a specific region, while in the future it could provide neurosurgeons with insightful knowledge helping them in the plannification of a forthcoming surgical procedure. The proposed software was tested on 7 stroke patients, predicting accurately the 91% of the measured deficits found during a neuropsychological assessment.
Leader Selection Algorithm and Its Verification Environment for Supporting Fault Tolerance

The naval combat system is a distributed system in which various subsystems are integrated and operated together. The integrated management system (IMS) is a software system for systematically and consistently managing the application software which control and operate various devices in such a combat system. Since the malfunction or failure of such an IMS can disable the entire combat system, the IMS is more important than other application software of the combat system. In this paper, we propose a method to guarantee the stable and correct operation of the combat system. To this end, we propose a redundancy scheme composed of one leader and several followers so as to tolerate the failure situation of the IMS. In addition, we propose a leader selection algorithm, which is a problem due to the redundancy scheme, to select a new leader when the leader fails and can no longer perform its role. To verify the validity of the study, we design and implement a prototype of IMS, verify the fault tolerance behavior of the system and verify the accuracy of the leader selection algorithm.
Method and Implementation of Target Tracking Based on Correlation Filter

This paper focuses on robust target tracking under complex background. Base on correlation filter, dual filter is built by adding scale estimation to Minimum Output Sum of Square Error (MOSSE) where target transition and scale change can be estimated simultaneously. Meanwhile, Histogram of Gradient is used as feature descriptor to achieve stable tracking. Finally, the improved algorithm is transported and optimized to TI DSP multi-core platform. In the end, both quantitative and qualitative experiments are conducted to demonstrate the robustness and real-time performance of the method.
3D Human Pose Machine with a ToF Sensor using Pre-trained Convolutional Neural Networks

This paper proposes a new system for estimating 3D human pose with a ToF sensor. In the proposed system, a single low-cost ToF sensor captures depth data of human pose. Then, a new clean imaging converter for the ToF sensor transforms the depth data corrupted with sensor errors and zero values into a clean 3D image data. Finally, a deep learning predictor based on the convolutional neural networks pre-trained with millions of 2D image data of human pose, not 3D ones, estimates the 3D human pose from the clean 3D image data. Experimental results show that the proposed system shows the good performance comparable to the commercial system in terms of 3D human pose estimation accuracy.
Application of Sparse Representation Based on Novel K-SVD Algorithms in Mechanical Fault Diagnosis

In order to solve the problems of dictionary redundant, the low calculating speed and the influence, based on the theory of sparse representation, integrating the ideas of SVD (singular value decomposition) and sliding window. A sparse representation diagnosis method for bearing impact fault based on SWD-KSVD (sliding window denoising-K means singular value decomposition) is proposed. Firstly, the K-SVD algorithm commonly used in dictionary training of sparse representation is applied to learn a short section of time-domain signal containing impact and reconstruct this signal. Then, the best mode with only one impact was selected based on variance by sliding window. Finally, the sliding window inner product of the optimal mode is calculated on the whole fault signal to find the time when the impact occurs and reconstruct the impact, and fault characteristic extraction of rolling bearing would be realized. This mentioned algorithm only uses a shorter optimal mode and carries out optimal selection and correlation analysis, thus, the algorithm has a high reduction degree of impact morphology, a more accurate phase restoring, a faster speed of extracting fault feature, and a stronger anti-noise ability. Experiment shows that the proposed algorithm can effectively extract bearing impact fault features.
Global routing on rhomboidal tiles

We suggest a new model and algorithms for global routing in chip design. Traditional global routing covers the chip with a 3D grid graph. However, terminals are implicitly mapped to tile centers, any tile-internal wiring is largely ignored, hindering accurate congestion estimates, and the structure of the nets is altered. To overcome these deficiencies, we propose a new model that always considers exact pin and wire positions. We work with rhomboidal tiles and pack Steiner trees rather into the tiles than into a grid graph. We present a new algorithm for computing shortest paths with respect to tile prices, exploiting the rhomboidal shape of the tiles. We then solve the Steiner tree packing problem using the min-max resource sharing algorithm to approximately minimize the total wire length subject to wire density constraints. Our results are routes connecting to pin shapes which correlate well with detailed routing in terms of wire length and via count. Moreover, our rhomboidal model allows us to scale up the tile size and still produce reliable input for the detailed router. We demonstrate the benefits of this approach with experimental results on industrial chips.
Analysis of Driver's Steering Behavior for Lane Change Prediction

With the development of intelligent transportation systems and advanced driver assistance systems (ADAS), early prediction of driver's intentions is required. Among that, lane change is a complex driver conducted maneuver, thus, improper execution could lead to severe traffic accidents. Therefore, it is urgent to develop lane change prediction systems, especially which could work in the initial phase of lane change. Many previous studies have developed lane change detection algorithms, however, they use different criterions as onset of lane change: (1) the onset of lane change maneuver, (2) the time that vehicle crosses the lane line. This makes it hard to compare the earliest time where these systems could detect lane change reliably. Therefore, a systematically comparison of the predicting time in these algorithms is required, as well, parameters which could be used for early lane change detection need to be highlighted. In the present study, a driving simulator study of lane change was conducted, with 18 drivers involved. Data of vehicle's lateral position, steering wheel angle and velocity during both lane change and lane keep phase were collected. The results showed that, the time gap between the two criterions is approximately 3 s, thus, the earliest predicting time of these algorithms ranged from the onset of lane change maneuver to the first peak of steering wheel angle. In addition, a further analysis of drivers' steering behavior of lane changing showed that, means and variances of steering wheel velocity, in the phase up to 0.55 s before the onset of lane change maneuver, were significantly different from that in lane keep phase. This suggests steering wheel velocity a potential parameter for early lane change detection.
A Hierarchical Approach to Extract Application Logs with Visualization in a Containerized Environment

Information in modern systems has a mechanism to record events typically known as a log. Reviewing logs provides an extract of system behavior to developers and system admins. Although in recent years, a variety of log mining approaches are available still users find it challenging to integrate the log parser with existing systems.In this paper, we have focused on log files associated with different application services and used a hierarchical approach to structure the log and then to convert them into a JSON (JavaScript Object Notation) format. Finally, we presented a visualization of parsed logs onto a UI-tool having with recent fashion on microservices-based deployment and evaluate its performance capability in terms of multi-input streams. We have used Python for implementing the algorithm and docker as a container engine. The outcomes of our study will be extremely valuable for the re-engineering of event analysis.
Evaluation Method of Timbre Thickness for Speech Based on CNN

In order to evaluate the thickness of speech timbre, this paper uses the spectrogram as the data input, and uses caffe model to study the thickness evaluation of speech timbre with AlexNet and GoogleNet as the neural network architecture respectively. The experimental results show that CNN has high accuracy in the field of speech timbre evaluation, and can effectively identify the thickness of speech timbre.
ML-based Power Seat Control system

We propose the power seat control system based on a machine learning approach from passengers’ control actions. More specifically, the system adjusts seat in cars by referring to the trained model on behalf of passengers. If they manually adjust the car seat, the system re-trains the inference model. Whenever manual adjustment happens, the model update is repeated. The system can be used in any area of adjustment system such as a power seat and a power mirror. Consequently, the proposed system can improve passenger’s safety and satisfaction in driving.
Evaluation of Quantitative Features and Convolutional Neural Networks for Nodule Identification in Thyroid Thermographies

Thyroid anomalies have high prevalence and their early identification is crucial for a more effective treatment. Thermograms can be used in this process, since nodules tend to be more vascularized, resulting in a diferent behaviour delated to temperatures on the skin surface over them. This work presents a methodology for determining thyroid nodules. We evaluate features that would allow to segment possibly nodular regions in thermographs. Convolutional neural networks (CNN) are used to classify these regions, identifying which ones refer to nodules. The good results of CNN in the classification (with more than 92% of accuracy), show that the viability of the proposed methodology depends on the success of the segmentation.
Handwritten Marathi Character Image Recognition using Convolutional Neural Network

With the advent of digitization these days, there has been an upper leverage of machines to perform the tasks that are troublesome for humans to do manually. In earlier decades, most of the valuable information were demonstrated with the help of pen and paper and were stored in physical form. Hence there is an imminent need to develop the techniques that can extract, search and understand these handwritten chronicles. The meaningful information is now present in multiple scripts which humans can comprehend but computers cannot. Various machine learning algorithms are being developed to make the computer "learn" and image recognition is like the icing on the cake. This paper presents a convolutional neural network (CNN) approach to make computer recognize the diversified Marathi language characters. The code is implemented using twelve layer filters and results are interpreted using the machine learning Tensorflow library developed by Google.
Recruitment of Indigenous Australians with linguistic and numeric disadvantages

Recruitment challenges for mining corporations operating in Australia have intensified with the increasing global demand for mineral resources and the 1993 Native Title legislation compelling negotiated land use agreements. A finite labour pool, further compressed by an ageing and retiring workforce, competition for labour, a poor industry image as well as a requirement for applicants to possess particular educational and vocational competencies has not been offset by greater Indigenous participation, despite training provisions being a feature of land use agreements. This paper presents an analysis of a novel recruitment technique that is devoid of the need for English literacy and numeracy skills, for Indigenous people with expectations to be employed in the extensive mining operations at Nhulunbuy on the Gove Peninsula of the Northern Territory (NT) of Australia. Predicating the scheme design was a comprehensive literature reporting that the English literacy and numeracy skills likely to be held by Australian Indigenous people would preclude them from usefully participating in standard Western recruitment procedures. Analyses reveal that the scheme is a robust predictor of sustainable employment. This leads to a line of reasoning that discriminatory recruitment practices can be substituted with alternative methods to identify human work related potential and, subsequently, address economic challenges and social dislocation of marginalised Indigenous groups.
Obstacle Detection for Power Transmission Line Based on Deep Learning

Power line inspection robots often work in the mountains, surrounded by trees and other background disturbances, while the field illumination changes significantly. The distance between the camera and the obstacles has a great influence on the scale changes of the obstacles in the image space. Therefore, accurate obstacle detection is very difficult. The deep learning methods such as Single Shot Multi-Box Detector (SSD) algorithm can be insensitive to illumination and scale changes in complex background with a much high accurate detection. But the amount of parameters of the SSD are so huge that make it very difficult to transplant to embedded systems. Aiming at the problem of stable and accurate detection of obstacles and easy to transplant to embedded systems, this paper proposes a method which significantly reduces the model parameters and improves the detection speed. In this method, the 23 convolution layers of the original SSD are simplified to 7 layers, and the Batch Normalization layer is added after each convolution layer to normalize the convolutional data. The result shows that the algorithm not only ensures the detection accuracy, but also greatly reduces the parameter quantities of the model and improves the detection speed significantly from 4.5 fps of original SSD to 15 fps of simplified SSD on Jetson TX2(an embedded system). Compared with some classical computer vision based detection algorithms, the method is more adaptable to complex environments.
Extracting Promising Topics on Smart Manufacturing Based on Latent Dirichlet Allocation (LDA)

Although smart manufacturing (SM) has attracted enormous attention, it is ambiguous how to realize it due to lack of practical evidence and academic knowledge on technological components. Accordingly, it is required to explore knowledge landscape to investigate promising technologies. For this purpose, this study extracts 35 topics discussed in abstracts in previous literatures by employing Latent Dirichlet Allocation. The analysis results unveil big data, product information management, cyber-physical system, cloud manufacturing platform, and industrial Internet of things are identified as promising topics. It is also noteworthy that SM needs a unified vision because topics are diverged rather than converged.
OFDM Based Range and Doppler Estimation by Multivariate Frequencies

Fusion of Radar Intelligent Sensing and Wireless Communication has now been a demanding platform for autonomous vehicles and modern intelligent transportation networks. In this paper, Orthogonal Frequency Division Multiplexing (OFDM) waveform design for data transmission and radar processing is proposed i.e. target parameters are efficiently estimated. Doppler measurement is improved by analyzing the multivariate frequencies of all the subcarriers of received echo with efficient FFT and the maximum likelihood (ML) estimation algorithm. Furthermore, the performance of the radar algorithms is analyzed on MATLAB. Theoretical analysis and experimental results reveal that the derived methods can attain the Doppler accuracy and achieve a high resolution-velocity estimation of moving targets.
Graph-based Community Detection in Social Network

Nowadays, online social networking sites have become very popular around the world. Among them, twitter is one of the most useful networks for many users. Online users want to share their emotional states and attitudes about various features and events by using these networks. Community detection is a key aspect of research field until now. Likewise, we have still a crucial problem for detecting the communities in an efficient and effective way. So, we will propose the framework to find the communities by using the graphs. In previous studies, researchers discovered the communities in undirected networks. This system will discover the communities in directed network. Sentiment community detection in twitter network may be beneficial for detecting like-minded and densely connected users. In this system, while community detection will use link partitioning method and sentiment analysis will use three classifiers for reducing neutral values of tweets and for calculating sentiment values of emoticon tweets. We expect this framework will be a systematic framework for detecting the communities with sentiments of tweets using graphs in directed network.
MQTLS: Toward Secure MQTT Communication with an Untrusted Broker

The publisher-subscriber (pub/sub) paradigm is one of the promising communication models to meet the requirements such as interoperability and support of heterogeneity for the Internet-of-Things (IoT). Message Queueing Telemetry Transport(MQTT), one of the protocols based on the pub/sub model, is widely used in practice with public brokers by Eclipse Mosquitto or HiveMQ. A broker in the pub/sub model, however, is intrinsically a man-in-the-middle between clients (i.e., publishers and subscribers); thus, a broker can read and alter all the messages delivered from publishers to subscribers. Therefore,both publishers and subscribers should not exchange confidential messages via an untrusted broker or should use it in-between them at risk.We propose MQTLS, an MQTT-aware secure communication protocol among publishers, subscribers, and brokers, while restricting brokers not to read any messages from publishers, ex-cept topics that need for message delivery. The MQTLS protocol is secure, based on our novel security definition called client-to-broker-to-client (CBC) security semantics for the pub/sub model. Our OpenSSL-based MQTLS proof-of-concept shows that MQTLS increases the delay time at the initial setup dueto asymmetric signature/verification. However the delay time becomes marginal – only 4.06% at the high-end device and 10.04% at the low-end device compared with the standard TLS protocol– after the key is distributed.
Performance Evaluation of Deep Neural Networks in Detecting Loop Closure of Visual SLAM

This paper concerns the problem of Loop Closure Detection (LCD) of visual Simultaneous Localization and Mapping (SLAM). The LCD is a crucial model to reduce the accumulative error in visual SLAM. The traditional LCD methods use hand-crafted features, which ignore useful information. We propose a LCD method based on Convolutional Neural Networks (CNNs) without any manual intervention for visual features. We compare and analyze several popular deep neural networks models for LCD. Two open datasets has been used to evaluate the performance of LCD in terms of mean-per-class accuracy. The results show that deep neural networks are feasible for LCD and the ResNet50 network outperforms the other deep neural networks.