Miracle-dz/UniFAR-FTUs huggingface dataset:
Embedded-Based Object Matching and Robot Arm Control

We present an embedded-based robot arm grasp detection system. This system has two subsystems: an embedded vision subsystem and an embedded robot arm control subsystem. In the former, a template matching algorithm is processed into an Nvidia Jetson TX2 developer kit to detect objects. And, then, control a robot arm to grasp and place. Although embedded systems have some preferred benefits, such as: cost, weight, size and power consumption; the slow processing speed is a significant drawback. To deal with this problem, we propose methods to reduce the number of calculations on the measurement of similarity. After testing on 40 templates with 200 test images, the result shows that the average of execution time is up to 10x faster than the original. The average execution time on middle size templates, $(100 \sim 200) \times (100 \sim 200)$ pixels, obtains 0.176sec. In addition, the angle of objects is determined with a small angle interval: 1 degree.
Design and Gait Control of a Quadruped Robot with Low-Inertia Legs

This paper presents a novel quadruped robot, “Biodog II”, featuring low-inertia legs and coaxial transmission. Each leg has two degrees of freedoms (DOFs)-a knee and hip joint, which are actuated by proximal mounted DC motors, and a compliant ankle joint. Motor power can be transited to active joints efficiently and accurately by means of coaxial transmission. In particular, we employ a tendon-bone co-location structure on the leg to reduce stress and enhance strength. A Hopf oscillator-based Central Pattern Generator (CPG) is responsible for generating locomotion trajectories of different gaits. An embedded controller based on an ARM board is designed to command motors. Locomotion experiments including walk and trot gait test demonstrate the stability and mobility of Biodog II.
An Approximate Quadratic Programming for Efficient Bellman Equation Solution

This paper proposes an efficient algorithm which relies on quadratic programming for approximately solving the Bellman equation in reinforcement learning problem and guarantees to return optimal decision parameters. Through further applying universal approximation and fixed cardinality minimization techniques, the proposed algorithm in one hand expands the representation ability of basic linear value functions, on the other hand, it guarantees the convergence of the Bellman error. Experimental results on two canonical reinforcement learning scenarios demonstrate that the proposed algorithm achieves similar or better performance than the state-of-the-art algorithms, while reduces the computation time significantly and improves the robustness of the algorithm against state uncertainty.
Military Full-Duplex Radio Shield for Protection Against Adversary Receivers

This paper provides experimental results regarding an emerging physical-layer concept within the field of military communications, viz. the full-duplex ‘radio shield’, by building on the recently discovered full-duplex technology that allows an individual radio device to simultaneously transmit and receive (STAR) on the same spectrum. Its basic idea is to protect the surroundings of a radio device by broadcasting powerful jamming while successfully receiving tactical transmissions on overlapping frequencies. The jamming creates a protective dome of interference around such a military full-duplex radio (MFDR). The experimental results reported herein prove that the radio shield yields a large SINR advantage against interception, even though a fully practical full-duplex radio prototype with residual self-interference is used. Moreover, we show that the prototype radio shield is capable of preventing the control of improvised explosives or rogue drones while simultaneously receiving tactical signals. Therefore, the full-duplex radio shield can give armed forces a significant technical lead over an enemy by preventing it from using the frequency band for offensive purposes.
Generative Adversary Networks in Signal Generation Problems

The object of the research is a multilayer perceptron which is being considered as a base neural network for the generative adversary neural network. The goal of the study is to make a neural network and make observations about how it can possibly work on a problem with separating: linear separable, semi separable and not separable subsets. The methodology of the study is a representation of the mathematical model of the multilayer perceptron on a python programming language. The results of the study are represented on the figures 2 – 5. The future application of the results are relevant for the further analysis in high particle physics for the track identification problem. Conclusions on the research are mostly based on the results with the tasks of separating objects, the accuracy of the dividing subsets is important for the future basis analysis on the subsets.
A Study on Competitiveness of ICT Adoption and Entrepreneurship Orientation on SMEs in Indonesia

The increasing number of SMEs in Indonesia has become the government’s attention that determines the effect of the ICT Adoption and Entrepreneurial Orientation to SMEs Competitiveness. The purpose of this research is to examine the relationship of the ICT Adoption, Entrepreneurial Orientation and SME’s Competitiveness in Indonesia. The method used is quantitative method by data collection using questionnaire distribution to several SMEs from various business sectors. Data processes using multiple regression analysis performed using the SPSS version 2. Data collected using questionnaire distributed to SME’s owners that have adopted ICT and size of samples determined by Slovin technique. The result of research stated that SME facing difficulties to adopt ICT and more concerned to the sales quantities instead of product quality or market penetration, and also requires capital loan assistance to develop their businesses.
About Burst-Correction Capability of Block-Permutation LDPC Codes Ensembles

The problem of maximal correctable error burst length estimation is considered for the probabilistic ensemble of block-permutation low-density parity-check codes. It is noted that this task has polynomial running time. For the number of probabilistic ensembles the simulations are performed showing that the correctable burst length grows linearly with the block size of the block-permutation construction, and with high probability equals to the block size minus one.
Planning for NFV Implementation Strategy in LTE Network Model in Indonesia

The rapid development of the telecommunications world is offset by the growth of data traffic which is very significant forcing operators to always provide quality services that can be relied on by customers. Telecommunication network operators must have a flexible, scalable and inexpensive network in operation. But the conventional networks they have today have many limitations. Amongs them are inflexible and unscalable in the sense that the existing telecommunications equipment is a proprietary vendor, where software and hardware are integrated in one device. Operators are impossible to integrate with other vendor devices and difficult to upgrade capacity. Network Function Virtualization (NFV) is widely discussed as an effective new way to overcome these challenges by utilizing virtualization technology that offers new ways of designing, implementing and managing telecommunications networks. The main idea of NFV is to separate the physical network device from the functions that run on it. The problem related to the use of NFV in cellular operator networks especially those in Indonesia is that the system is still new and not yet mature, NFV networks still have various problems. This paper will discuss what network architecture is suitable & which devices can be implemented as NFV. So it is expected to conclude that the most suitable NFV solution to be implemented in Indonesia, refers to the condition of the network and cellular telecommunications services in Indonesia.
Designing Tunable Notch Filters on Dielectric Resonators

Electrically tunable filters are very important for the success of a programmable radio concept. Designing microwave filters, whose parameters can be changed programmatically, is a problem. This article is devoted to the design and study of the parameters of electrically tunable filters on dielectric resonators, which can be used, among other things, to reject powerful interference at the input of a receiving device.
Air-Filled Substrate Integrated Waveguide Fed Magneto-Electric Dipole Antenna Array for Millimeter-Wave Applications

A substrate integrated magneto-electric (ME) dipole antenna array with an improved radiation efficiency is presented in the 28-GHz band. A novel full-corporate air-filled substrate integrated waveguide (SIW) feed network with low losses is designed and realized by utilizing the low-cost printed circuit board (PCB) facilities. Different from most previous millimeter-wave planar arrays consisting of <inline-formula> <tex-math notation="LaTeX">$2 \times 2$ </tex-math></inline-formula> sub-arrays, an SIW fed sub-array with a larger size of <inline-formula> <tex-math notation="LaTeX">$2 \times 4$ </tex-math></inline-formula> is investigated to simplify the configuration of the major portion of the array feed network, which is of importance to the successful realization of the air-filled SIW feed network. An <inline-formula> <tex-math notation="LaTeX">$8 \times 8$ </tex-math></inline-formula> array prototype with a four-layered geometry is fabricated and measured to verify the design. The array has an impedance bandwidth of 25.9%, a gain up to 26 dBi and stable radiation patterns over the operating band. Benefitted from the absence of the significant dielectric loss in the feed network, a high antenna efficiency of 80% is also achieved. The proposed design provides an effective method to improve the achievable gain and efficiency characteristics of the substrate integrated arrays with a large size that are attractive for millimeter-wave wireless applications.
A Shapelet Dictionary Learning Algorithm for Time Series Classification

Shapelet algorithm is a powerful time series classification method because it can extract discriminative subsequence from time series. Most existing Shapelet-based algorithm focus on searching Shapelet in training dataset, which brings two drawbacks about computationally expensive and low generalization ability. To solve the above drawbacks, this paper proposes a new algorithm named Shapelet Dictionary Learning (SDL), which can discover Shapelet in a generative way, not in a searching way like common Shapelet algorithm. The SDL algorithm optimizes the model of Dictionary Learning to generate a group of Shapelets, which has higher generalization ability. Compared with the baselines on 45 datasets, the proposed SDL algorithm achieves a significant improvement on the accuracy of time series classification.
Active Hyperspectral Mid-Infrared Imaging Based on Widely Tunable QCL Laser

Thermal imaging and the recent availability of widely tunable infrared QCL lasers (Quantum Cascade Laser) allow us to propose an active hyperspectral imaging system operating in mid-infrared (MIR) band to obtain simultaneously large amounts of spatial and spectral information on the samples.In order to evaluate more precisely the capacities of the active hyperspectral imaging, we propose a system composed of four powerful QCL tunable lasers (in order to cover 3 – 5 μm and 7 – 11 μm wavelengths) and three cameras: a visible and near-infrared (NIR) range, a bolometer for 7 – 13 μm range and an InSb cooled camera for 3 – 5 μm range. We present the algorithm for image acquisition, image and data processing. Finally, we present and discuss some preliminary results using this system to characterize plant leaves under controlled growing conditions.
Secondary analysis of diabetes and psychological distress in American Indian women from the California Health Interview Survey (CHIS)

Secondary Analysis of Diabetes and Psychological Distress in American Indian Women from the California Health Interview Survey (CHIS) Since European settlers arrived to the United States (U.S.), American Indians (AI) have been separate and unequal members of society. After a long history of discrimination, ethnocide, genocide, and distrust, the AI have become a population with severe disparities, having the highest rates of diabetes, depression, suicide, tuberculosis, and alcoholism than any other minority or majority population in the U.S. The author's purpose for conducting this study was to explore a possible relationship between depression or psychological distress and diabetes in AI women. AI women are the most under studied group in the country; therefore, a secondary analysis of the large established California Health Interview Survey (CHIS) was done. The sample used 1,110 self-identified AI women's data. Even though the literature shows that the AI have the highest rates of diabetes and depression of any population, this analysis revealed no statistically significant relationship between the two diseases. It did reveal many limitations and implications associated with the use of such large databases for the AI woman. Among the limitations were the survey itself, its administration to the AI population, guidelines for self-identifying as AI, and the researcher's limited access to the data. The implications of this study are significant. Large databases provide the basis for social and political decisions such as 3 allocation of federal dollars for health care. Healthcare and health care services are designed according to the health burden of specific populations. If these databases are in error, or not representative of the true population, healthcare decisions will not reflect the true health care needs of the population. The inadequacies of large databases results in less funding, leading to less quality health care, and an increase in AI health disparities. Further research is needed to determine the actual health burden depression and diabetes place on the AI woman. 5 DEDICATION This dissertation is dedicated to two very special people who showed me " the way " the first 20 years of my life. My great-grandmother – Minnie Myers " Mo-Mo " (Choctaw) and my grandfather – Ivy Cowling (Chickasaw). Thank you for loving, accepting, and guiding me regardless of my 'half-breed' status. I also dedicate this work to all of those who are caught between two worlds, not quite accepted and not quite rejected by both. May you find your …
Weld Defect Detection Based on Deep Learning Method

Welding is an important joining technology but the defects in welds wreck the quality of the product evidently. Due to the variety of weld defects’ characteristics, weld defect detection is a complex task in industry. In this paper, we try to explore a possible solution for weld defect detection and a novel image-based approach is proposed using small X-ray image data sets. An image-processing based data augmentation approach and a WGAN based data augmentation approach are applied to deal with imbalanced image sets. Then we train two deep convolutional neural networks (CNNs) on the augmented image sets using feature-extraction based transfer learning techniques. The two trained CNNs are combined to classify defects through a multi-model ensemble framework, aiming at lower false detection rate. Both of the experiments on augmented images and real world defect images achieve satisfying accuracy, which substantiates the possibility that the proposed approach is promising for weld defect detection.
An Analysis about the Security of the Current Open Source Cloud Platform

Cloud computing is convenience, flexibility, costeffective, making it the rapid development. Cloud computing business have been developed by many famous IT companies such as Microsoft, Google, Amazon, and we call it Cloud Service Provider (CSP). Many small companies also want to deploy their services on the cloud for reducing their costs. However, one issue that can't be ignored is that security needs to be analyzed before the cloud platform deployment. The current consensus is that security of cloud is the biggest obstacle to its development. Current cloud security issues are mainly concentrated in the theoretical stage, manifested in what cloud platform should have to ensure security; each cloud platform has its own implementation, and does not disclose its security implementation mechanisms. Therefore, for companies who want to deploy their own private cloud platform, open source cloud platform is a good choice. This paper focus on the mainstream open source cloud platform (OpenStack, CloudStack, Eucalyptus), and make an analysis of the security to the user as a reference when they choosing the open source cloud platform.
Dynamic Mobility Load Balancing for 5G Small-Cell Networks Based on Utility Functions

Deployment of small cells was introduced to support high data rate services and expand macro cell coverage for the envisioned 5G networks. A small cell network, which has a smaller size, along with the user equipment (UE) mobility, frequently undergoes unbalanced load status. Consequently, the network performance is affected in terms of throughput, increasing handover failure rate, and possibly higher link failure rate. Hence, load balancing has become an important part of recent researches on small cell networks. Mobility Load Balancing (MLB) involves load transfer from an overloaded small cell to under-loaded neighbouring small cells for the more load-balanced network. This transfer is performed by adjusting the handover parameters of the UEs according to the load situations of the small cells in the vicinity. However, inaccurate adjustment of parameters may lead to inefficient usage of network resources or degrade the Quality of Service (QoS). In this paper, we introduce a Utility-based Mobility Load Balancing algorithm (UMLB) and a new term named load balancing efficiency factor (LBEF). The UMLB algorithm considers the operator utility and the user utility for the MLB-based handover process. While LBEF is proposed to order the overloaded cells properly for the MLB algorithm operation. The simulation results show that the UMLB minimizes standard deviation with a higher average-UE data rate when compared to existing load balancing algorithms. Therefore, a well-balanced network is achieved.
A Hybrid Multi-objective Algorithm for Energy-Efficient Scheduling Considering Machine Maintenance*

The aim of this paper is to address the energy-efficient scheduling problems in the hybrid flow shop problem (HFSP) with machine maintenance. For this aim, the mixed integer programming model (MIPM) considering energy consumption and machine maintenance is proposed firstly. Given the NP-hard nature of the model, a hybrid multi-objective algorithm (HMOA) combining an improved algorithm based on the Non-Dominated Sorting Genetic Algorithm (NSGA-II-imp) with a heuristic algorithm is developed to solve MIPM. The NSGA-II-imp designs a new elite remained strategy for the purpose of balancing two conflict objectives and a heuristic algorithm is mainly designed to add machine maintenance during production scheduling. Finally, the proposed algorithm is compared to GA, SA-GA, NSGA-II separately based on extensive numerical experiments. Results show the effectiveness of the algorithm in this paper.
Indoor Positioning for Dementia in Smart Homes Based on Wearable Device

In order to track the trajectory of the elderly with dementia in indoor environment, it is important to constantly to monitor the position of the elderly with dementia. Therefore, we proposed an indoor positioning method based on hand free device –smart bracelet. The challenge of the indoor positioning basing on smart bracelet is that the Bluetooth signal is not only subject to indoor environments, but also affected by the motion of hand. Indoor items, body shading and other factors will cause the signal diffraction, reflection or even loss. For this challenge, we use median filter to reprocess the original signal and integrated the RSSI features with the features of the wrist's gestures. After that we use random forest to establish the position model and adopt the finite state machine(FSM) to calibrate the result of the classify model. It's proved that the proposed system has good robustness and can accurately locate the elderly in the indoor environment.
Key-Based Cookie-Less Session Management Framework for Application Layer Security

The goal of this study is to extend the guarantees provided by the secure transmission protocols such as Secure Sockets Layer (SSL) or Transport Layer Security (TLS) and apply them to the application layer. This paper proposes a comprehensive scheme that allows the unification of multiple security mechanisms, thereby removing the burden of authentication, mutual authentication, continuous authentication, and session management from the application development life-cycle. The proposed scheme will allow creation of high-level security mechanisms such as access control and group authentication on top of the extended security provisions. This scheme effectively eliminates the need for session cookies, session tokens and any similar technique currently in use. Hence reducing the attack surface and nullifying a vast group of attack vectors.
A Firefly-Cuckoo Search Algorithm for Optimizing the Beam Patterns of the Random Antenna Arrays

In this paper, a firefly-cuckoo search algorithm (FCSA) is proposed to solve the beam pattern optimization problem of the random antenna arrays (RAA). FCSA introduces the Levy flight mechanism of the cuckoo search algorithm as the global search operator to improve the performance of the conventional firefly algorithm. Several simulations are conducted to evaluate the proposed approach and the results show that FCSA has better performance in terms of the accuracy and the convergence rate for reducing the maximum sidelobe level (SLL) of the RAA.