Miracle-dz/UniFAR-FTUs huggingface dataset:
Asphalt Pavement Pothole Detection using Deep learning method based on YOLO Neural Network

There is an increasing need for assessment of national road condition. Currently, some automatic devices have been extensively applied to collect up-date data about road condition, such as the use of survey vehicle for collecting data—which make it faster and more accessible, and semi-automatically data processing that is useful for policy decision making. Yet, demand for more detail road data is continuously growing; thus, data improvement needs to perform, upgrading the existing solution. To date, stages on identification and classification of road damages are being conducted semi-manually based on images collected by survey vehicle; it is hindered due to the facts that this method is the cost-consuming process and may result in inconsistency. Therefore, this present work used YOLO with three different architecture configuration, i.e., Yolo v3, Yolo v3 Tiny, and Yolo v3 SPP, enabling us to create a more accurate assessment for detecting potholes on the road surface. The results showed the average mAP values for Yolo v3, Yolo v3 Tiny, and Yolo v3 SPP at 83.43%, 79.33%, and 88.93%. While the area measurement shows the accuracy of 64.45%, 53.26%, and 72.10% respectively. And it needs 0,04 second to detect each image. Conclusively, it shows a satisfying result in pothole detection; thus, this technique has a high opportunity to developed and implemented as a tool for road assessment.
Seismic Observation and Analysis Based on Three-Component Fiber Optic Seismometer

We demonstrate a three-component fiber optic seismometer for seismic observation, with no electronics in the sensor probe, but relying exclusively on optical fiber in the form of an unbalanced Michelson interferometer, with state-of-the-art performance characteristics. The fiber optic seismometer system is installed in the seismic observation cave at the Earthquake Administration Bureau of Jilin Province, in Changchun, Jilin, China, and has been operating without interruption for more than a year. Two minor earthquakes of different magnitudes recorded by the three-component fiber optic seismometer are analyzed and discussed in detail from the perspectives of time domain and frequency domain signal quality and fidelity. In addition, the recording results of the fiber optic seismometer are compared with those of the traditional electrical-mechanical seismometer co-located at the observation site. Actual observational results of the seismic events show that the three-component fiber optic seismometer can record the seismic waves clearly, with comparable performance as the conventional seismometer.
Energy Efficient Geocasting Based on Q-Learning for Wireless Sensor Networks

In this paper, we propose two energy efficient geocasting protocols based on Q-learning for wireless sensor networks (WSNs), called FERMA-QL and FER-MA-QL-E. We utilize the theorem of Fermat point to find Fermat points in geocasting, the node which is the closest to the Fermat points is selected as the relay nodes. Then, we establish the shared path among gateways, relay nodes and base station by Q-learning for data transmission. In FERMA-QL, the reward is given by the reciprocal of the distance between the received node and the destination node In FERMA-QL-E, the reward is given by the remaining energy of the received node divided by the distance between itself and the destination node. Sensors utilize the shared path to forward their data to achieve goal of reduce energy consumption. Simulation result shows that the proposed FERMA-QL and FERMA-QL-E can efficiently extend the life-time of the WSN.
A Resolution Enhancement Technique for Remote Monitoring of the Vital Signs of Multiple Subjects Using a 24 Ghz Bandwidth-Limited FMCW Radar

This study proposes a novel signal processing method for detecting the vital signs of multiple adjacent subjects using a 24 GHz frequency modulated continuous wave Doppler radar. Radar-based vital signs sensors have attracted significant attention because of their contactless and unobtrusive mode of measurement. However, limited-bandwidth, fixed-beam systems have been restricted to single subjects because a high resolution is required to detect the vital signs of multiple adjacent subjects. As the range resolution is determined by the frequency bandwidth, a novel method is proposed that doubles the effective frequency bandwidth by using a modified waveform. The proposed method can distinguish between two subjects sitting 40 cm apart, overcoming the 60 cm Rayleigh resolution for a frequency bandwidth of 250 MHz. The computational complexity of the proposed method is considerably low when compared with high-resolution algorithms such as the multiple signal classification algorithm. Furthermore, the method easily suppresses stationary clutter by using phase deviation. To validate the performance of the proposed method, experiments were conducted with two subjects lying side by side on a bed. The results indicate the excellent performance, with enhanced range and high detection accuracy. This method has many potential applications, including monitoring infants and sleep apnea patients.
Crone Controller Based Speed Control of Permanent Magnet Direct Current Motor

This work aimed at developing a CRONE Controller based Speed Control of Permanent Magnet Direct Current Motors in order to achieve a robust and more effective speed control. Torque is one of the fundamental factors that affect PMDC motor’s speed control, which generate uncertainty known as disturbance torque on shaft of the motor due to increase in temperature and decrease in frequency of the system. Proportional Integral Derivative (PID) controllers are commonly used in the speed control of PMDC motors, but PID controllers often do not fully reject external disturbances in the system. As such, the CRONE controller based speed control of PMDC motors was developed to address the limitations of PID control. To achieve this, a PMDC motor model with the following system response characteristics was adopted; settling time of 6.2s, rise time of 0.79s and overshot of 30.4%. A second generation CRONE controller was developed and then applied for speed control of the PMDC motor. The performance of the developed controller was then compared with that of a Proportional Integral Derivative – Particle Swamp Algorithm (PIDPSO) controller for speed control of PMDC motor. The result of the second generation CRONE controller for the speed control of PMDC motor obtained a settling time of 0.018s and overshoot of 0.0001%. The PID-PSO controller based speed control of the PMDC motor obtained a settling time and overshoot of 0.2s and 1.87% respectively. The effectiveness of CRONE controller was visualized in frequency domain, with the stability margin of 34.5dB and 900. Simulations were carried out using MATLAB 2016a.
Design of an Elastically Suspended Backpack with a Tunable Damper

Walking with loads is a common task in our daily life, inducing a significant increase in energy expenditure and leg force. Previous work has proved that an elastically suspended backpack could reduce the energy cost of load carriage. However, for various application situations and populations, people have different biomechanical requirements corresponding to the accelerative load force sequence on carriers versus step cycle. This paper proposes an elastically suspended backpack with a tunable damper regulating phase of load movement to further affect leg force in walking with load. Tunable electromagnetic damper adopts generator or motor modes, providing tunable positive or negative damping coefficient, respectively, and thus regulates the phase delay of load movement ranging from 0° to 360°. Examples of long-time walking, heavy load carriage task and patients with chondral abrasion in their knees are listed to demonstrate the mechanism of this tunable damped elastically suspended backpack.
A Research on the Fusion of Semantic Segment Network and SLAM

The development of traditional SLAM technology has gradually encountered bottlenecks in recent years. With deep learning, especially vision-based machine learning, a new and improved way of development has been found for SLAM named Semantic SLAM. The study of semantic SLAM has been a very hot topic in recent years. However, the related research is still in its infancy and is not systematic. In this paper, we will discuss the existing SLAM systems, semantic segmentation networks and semantic SLAM systems, introduce secondly the multiple effects of semantic network on localization, mapping and their applications, and finally put forward an idea of semantic Fusion.
Inapproximability of Clustering in Lp Metrics

Proving hardness of approximation for min-sum objectives is an infamous challenge. For classic problems such as the Traveling Salesman problem, the Steiner tree problem, or the k-means and k-median problems, the best known inapproximability bounds for L-p metrics of dimension O(log n) remain well below 1.01. In this paper, we take a significant step to improve the hardness of approximation of the k-means problem in various L-p metrics, and more particularly on Manhattan (L-1), Euclidean (L-2), Hamming (L-0) and Chebyshev (L-infinity) metrics of dimension log n and above. We show that it is hard to approximate the k-means objective in O(log n) dimensional space: (1) To a factor of 3.94 in the L-infinity metric when centers have to be chosen from a discrete set of locations (i.e., the discrete case). This improves upon the result of Guruswami and Indyk (SODA'03) who proved hardness of approximation for a factor less than 1.01. (2) To a factor of 1.56 in the L-1 metric and to a factor of 1.17 in the L-2 metric, both in the discrete case. This improves upon the result of Trevisan (SICOMP'00) who proved hardness of approximation for a factor less than 1.01 in both the metrics. (3) To a factor of 1.07 in the L-2 metric, when centers can be placed at arbitrary locations, (i.e., the continuous case). This improves on a result of Lee-Schmidt-Wright (IPL'17) who proved hardness of approximation for a factor of 1.0013. We also obtain similar improvements over the state of the art hardness of approximation results for the k-median objective in various L-p metrics. Our hardness result given in (1) above, is under the standard NP is not equal to P assumption, whereas all the remaining results given above are under the Unique Games Conjecture (UGC). We can remove our reliance on UGC and prove standard NP-hardness for the above problems but for smaller approximation factors. Finally, we note that in order to obtain our result for the L-1 and L-infinity metrics in O(log n) dimensional space we introduce an embedding technique which combines the transcripts of certain communication protocols with the geometric realization of certain graphs.
Parallel pixel-decoding and deblocking filtering for HEVC

Continuous advancements in display devices have increased demands for video coding support for videos of over 2K high-resolution. Although the latest video coding standard, high efficiency video coding (HEVC) has nearly twice the coding efficiency than previous video coding standard, H.264/AVC, the increased computational complexity makes the real-time decoding of ultra-high definition (UHD) a challenging issue. In this paper, a data-level parallel processing method for deblocking filters is presented to improve the HEVC decoder speed. In addition, data-level parallel processing methods for pixel decoding are introduced and their performances with respect to slices and tiles are compared. The proposed hybrid parallelism decoder, which is used to decode pixels and deblock filters in parallel, achieved up to 2.17× speedup at UHD resolution compared with the sequential decoding time of HEVC reference software.
Quick Response in a Dual-channel Supply Chain with Strategic Consumers

We consider the quick response (QR) in a dual-channel supply chain in which a manufacturer sells products to strategic consumers through both its own direct channel and a retailer. The strategic consumers have the options to purchase early in the full-price period or delay the purchasing until the clearance period. With QR, the retailer could start a QR order and the manufacturer could start a QR production after the uncertain demand is realized. We develop a static game model to characterize the equilibrium decisions for each channel and investigate the impacts of QR on the decisions and profits of the manufacturer and the retailer. Through numerical analysis, we find QR may be beneficial to the retailer whereas either increases or decreases the manufacturer’s expected profit. We show that the dual-channel supply chain can achieve Pareto improvement with QR under certain conditions.
Green Fog Planning for Optimal Internet-of-Thing Task Scheduling

The incoming 5G technology is expected to proliferate tremendous internet-of-thing (IoT) services with real-time and mobility requirements, which are quite different from the legacy cloud services. Due to the centralized management relying on distant datacenters, cloud computing is short of satisfying the stringent IoT requirements, such as ultra-low latency, mobility, etc. Instead, distributed edge computing, such as fog computing has been coined as a promising approach and has received enormous attention in recent years. In this paper, to optimally provision the huge volume of IoT services with significant diversity, we propose to efficiently organize the leisure network devices in the network edge to form fog networks (fogs), which are then integrated with the cloud to provide storage and computing resources. Specifically, we propose two Integer Linear Programming (ILP) models to solve the fog planning issue under the integrated Cloud-Fog (iCloudFog) framework. In the first ILP model, the objective is to minimize the CAPEX cost caused by planning fogs and the OPEX cost caused by utilizing the planned fogs. In the second ILP model, the objective is to minimize the power consumption while maximizing the number of successfully provisioned IoT tasks on the planned fogs. The proposed ILP models are numerically evaluated by considering different IoT task requirements, such as real-time and mobility. The numerical results show that efficiently planned fogs can help to reduce the planning overhead while satisfying diverse IoT task requirements.
Experimental Performance Evaluation of Computer Vision for an Upper Limbs Rehabilitation Robot

Rehabilitation robot for upper limbs can help physicians or therapists in conducting repetitive rehabilitation to patients. This robot is constructed by several electromechanical actuators, sensors, controllers, and the main computer. The patient holds a robot gripper in the left and right hands and moves the gripper according to a rehabilitation protocol determined by the physician who is in charge. A computer vision-based position sensor is designed to measure wrist position without using any marker. Moreover, a new algorithm is proposed to filter coordinate values measured by the video camera and extract the position estimate of the wrist. This sensor system estimates positions of the wrist using OpenPose library. The experimental performance was evaluated in terms of precision and speed for several resize values. Experimental results indicate that the pose-based computer vision algorithm can potentially be used for the upper limbs rehabilitation robot. Our finding stimulates further research to find more consistent values of the precision performance index.
A Task Offloading Scheme in Vehicular Fog and Cloud Computing System

Vehicular fog and cloud computing (VFCC) system, which provides huge computing power for processing numerous computation-intensive and delay sensitive tasks, is envisioned as an enabler for intelligent connected vehicles (ICVs). Although previous works have studied the optimal offloading scheme in the VFCC system, no existing work has considered the departure of vehicles that are processing tasks, i.e., the occupied vehicles. However, vehicles leaving the system with uncompleted tasks will affect the overall performance of the system. To solve the problem, in this paper, we study the optimal offloading scheme that considers the departure of occupied vehicles. We first formulate the task offloading problem as an semi-Markov decision process (SMDP). Then we design the value iteration algorithm for the SMDP to maximize the total long-term reward of the VFCC system. Finally, the numerical results demenstrate that the proposed offloading scheme can achieve higher system reward than the greedy scheme.
POL-SAR Image Classification Based on Modified Stacked Autoencoder Network and Data Distribution

This article proposes a novel autoencoder (AE) network based on the distribution of polarimetric synthetic aperture radar (POL-SAR) data matrix, called a mixture autoencoder (MAE). Through a detailed analysis of the data distribution POL-SAR data matrix, a normalization method is also presented in succession. The proposed MAE defines the data error term in the loss function according to the data distribution. It can be regarded as a process of unsupervised feature extraction designed specifically for POL-SAR data matrix. Then, a softmax classifier is trained with the help of data features and the corresponding label information. Next, a stacked MAE (SMAE) network is reasonably constructed by considering the data distribution among different layers. Finally, this article also presents a classification network through discarding the decoder process of the proposed SMAE and connecting with a softmax classifier. The SMAE is trained layer by layer using the unlabeled data. The softmax classifier is also trained with a small number of labeled pixels. With parameters obtained from the above-mentioned procedures as the initial parameters, the whole classification network is trained by the labeled pixels to get a well-trained model, which is used for predicting the corresponding label of the pixel in the data set. Three real POL-SAR data sets, including the AIR-SAR L-band data of Flevoland, The Netherlands, are used in the experiments. Compared with one classical algorithm and two related models with the similar structure, both the proposed methods show improvements in overall accuracy and efficiency as well as possess better adaptability of the parameter and preferable consistency with the classification performance.
Mueller-Weiss Syndrome: an Important but Under-recognised Cause of Foot Pain and Deformity

Mueller-Weiss syndrome, or spontaneous osteonecrosis of the tarsal navicular bone in adults, is an important but under-recognised idiopathic foot condition that causes chronic mid-and hind-foot pain. Progressive deformity leading to advanced mid-tarsal osteoarthritis occurs when the disease is not recognised in its early stages. We present a case series of Mueller-Weiss syndrome from two regional hospitals in Hong Kong to illustrate the key radiographic features as well as computed tomography and magnetic resonance imaging findings accompanied by a review of the current literature, with the aim of increasing awareness of this important but under-recognised cause of foot pain in adults
Multivariate Probit Models for Interval-Censored Failure Time Data

Survival analysis is an important branch of statistics that analyzes the time to event data. The events of interest can be death, disease occurrence, the failure of a machine part, etc. One important feature of this type of data is censoring: information on time to event is not observed exactly due to loss to follow-up or non-occurrence of interested event before the trial ends. Censored data are commonly observed in clinical trials and epidemiological studies, since monitoring a person’s health over time after treatment is often required in medical or health studies. In this dissertation we focus on studying multivariate interval-censored data, a special type of survival data. By saying multivariate interval-censored data, we mean that there are multiple failure time events of interest, and these failure times are known only to lie within certain intervals instead of being observed exactly. These events of interest can be associated because of sharing some common characteristics. Multivariate intervalcensored data draw more and more attention in epidemiological, social-behavioral and medical studies, in which subjects are examined multiple times and several events of interest are tested at the observation times. There are some existing methods available in literatures for analyzing multivariate interval-censored failure time data. Various models were developed for regression analysis. However, due to the complicated correlation structure between events, analyzing such type of survival data is much more difficult and new efficient methodologies are needed. Chapter 1 of this dissertation illustrates the important concepts of interval-censored data with several real data examples. A literature review of existing regression models
Designs of rectangular-shaped planar Inverted-F antennas at mobile operating frequencies with different ground plane techniques

This article presents the designs of planar inverted-F antennas (PIFAs) at frequencies of 0.835 GHz, 0.9 GHz, 1.8 GHz, 1.9 GHz, 2 GHz, and 2.6 GHz. Initially, the designs of rectangular-shaped PIFAs are determined through the parametric studies concerning the dimensions of the antenna’s patch length, shorting plate, ground plane, and substrate. Afterward, rectangular-shaped slots are introduced into radiating element of two antennas that operate at a lower frequency range of less than 1 GHz, to tune the resonant frequency to the respective 0.835 GHz and 0.9 GHz. Different configurations of partial or full ground plane are implemented to improve the reflection coefficient, S11 performance to be below -10 dB in both simulation and measurement. The proposed six PIFAs have gain that are greater than 2 dB with the nearly omnidirectional radiation patterns. All the designs and analyses are performed using the CST Microwave Studio utilizing Rogers 4003C substrate.
The Interaction and Degeneracy of Mixed Solutions for Derivative Nonlinear Schrödinger Equation

The mixed solutions of the derivative nonlinear Schrodinger equation from the trivial seed (zero solution) are derived by using the determinant representation. By adjusting the interaction and degeneracy of mixed solutions, it is possible to obtain different types of solutions: phase solutions, breather solutions, phase-breather solutions and rogue waves.
Resolvent Estimates in (weighted) Lp spaces for the Stokes Operator in Lipschitz Domains

Recently, by Z. Shen, resolvent estimates for the Stokes operator were established in Lp(Ω) when Ω is a Lipschitz domain in Rd, with d≥3 and |1/p-1/2|l1/(2d)+e. This result implies that the Stokes operator generates a bounded analytic semigroup in Lp(Ω) in the case that Ω is a three-dimensional Lipschitz domain and 3/2-elpl3+e. To fully understand the work of Z. Shen, a lot of background information is needed. In this thesis the resolvent estimates are studied in detail in the case d=3. In the end the results of Shen are extended to resolvent estimates in Lp(w,Ω), where Ω is a three-dimensional Lipschitz domain, |1/p-1/2|l1/6, and w∈A2p/3∩RH3/(3-p) is a weight function that belongs to an intersection of a Muckenhoupt weight class and satisfies a reverse Holder inequality.