Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-time Optimal Resource Allocation in Multiuser Mobile Edge Computing in Digital Twin Applications with Deep Reinforcement Learning : (Invited Paper)

We investigate the optimal resource allocation of mobile edge computing (MEC) with multiple Internet-of-Thing (IoT) devices in digital twin applications. Based on Markov decision process and model-free deep reinforcement learning (DRL) approach, we propose double deep RL-based online computation offloading method to implement the deep neural network that learns from interactions to solve the computation offloading and transmission latency problem in the dynamic MEC-aided IoT environments. In particular, we design an adaptive method for continuous action-state spaces to minimize the completion time and total energy consumption of the IoT devices for stochastic computation offloading task. The proposed real-time Lyapunov optimization and DRL algorithms achieve a low computational complexity and optimal processing time. Simulation results demonstrate that the proposed method can achieve near-optimal control performance with an enhanced energy consumption and significantly minimize the computation time.
A Neural Network Processing Unit Using Adiabatic Quantum-Flux-Parametron Superconducting Technology

Adiabatic quantum-flux-parametron (AQFP) is a new superconducting technology developed in recent years. It achieves the highest energy efficiency among the superconducting logic families and has excellent potential for implementing large-scale systems. Convolutional Neural Network (CNN) is one of the representative algorithms of deep learning and has been widely used in many fields, such as computer vision, in recent years. Thus, with the development of neural networks, AQFP will have a broad application prospect in high-performance computing and deep learning. In this study, we propose an AQFP-based processing unit for convolutional computation in CNN. We generate a circuit of the proposed processing unit using AQFP integrated circuit design methodology that contains logic synthesis, placement and routing, simulation and verification. The designed circuit is fabricated by the AIST 10kA/cm2 high-speed standard process (HSTP).
Development of One-legged Robot with Structural Joint Stops

Human and animal joints have range-of-motion limits. Our research group has realized dynamic motions by a golf swing robot and a humanoid robot by utilizing nonlinear reaction forces generated at both ends of the range of motion. In this study, we developed a one-legged robot consisting of a hip joint and a knee joint with a joint stop mechanism at each joint that utilizes both ends of each joint's range of motion, and established the dynamic model of the robot including the joint stop mechanism. The simulation results to verify the mechanism's effectiveness show that a more stable landing motion can be obtained by using the reaction torque generated by the mechanism. The landing motion experiment was also performed by the robot prototype with the mechanism mounted.
A High-Performance Optical Waveguide Sensor for Curvature Sensing of Enveloped Soft Actuators

Curvature sensing is extremely significant for soft actuators. Many flexible resistive sensors based on conductive powder and liquid are developed to realize the curvature sensing of actuators. However, they have low sensitivity and linearity, high hysteresis, and are easily damaged. In order to address these problems, we promoted a method to achieve the curvature sensing of an enveloped soft actuator by embedding a flexible optical waveguide sensor into the actuator. In this paper, we design and fabricate a high-performance optical waveguide sensor with a square core cross-section (1.5 mm), and the effect of the cross-section size of the optical waveguide core on the performance of the sensor is studied for the first time. Then, realizing the proprioception of the enveloped actuator based on the high-performance sensor. Three conclusions are drawn from the experimental results: 1) In handcrafted conditions, sensors with the smaller core cross-section size have better performance in bending, stretching, and pressing deformations. 2) Sensors with a smallest square core cross-section (1.5 mm width) fabricated in this work can be stretched to 130% and undergo pressure at 50 N (about 400 KPa), which is better than many optical sensors. In addition, they have high linearity, low hysteresis, and a small dead area (less than 10%) in stretching response. 3) The enveloped soft actuator with curvature sensing is stable and has good linearity; the root mean square error of the fitted curve between the curvature of the actuator and the power loss of the sensor is about 0.57.
Improved Bump Detection and Defect Identification for HBMs using Refined Machine Learning Approach

The 2D-3D metrology is a critical step for in-line inspection and off-line failure analysis. Due to lack of relevant data and complexity of embedded components, identifying and segmenting defects such as voids, pad misalignments in 2D and 3D voxel data has been a challenge in the semiconductor industry. Addressing this problem has the potential to further improve fault detection in this field significantly. This work follows our previously published works in EPTC 2020, ECTC 2021, introducing a cost-effective and non-destructive approach using deep learning and 3D x-ray microscopy. In particular, we apply our 3D object detection and Semi-Supervised Learning (SSL) image segmentation on High Bandwidth memory and logic bumps (HBMs). This paper introduces new detection and segmentation methods that overcomes issues in the current data such as data imbalance or defective bumps. We applied better 2D-3D detection strategy and loss and activation functions for 3D semicon data. We describe the data features, our new approach on 2D-3D scanned data, methods developed to perform better object detection and segmentation to classify each pixel into individual categories such as solders, voids, Cu-Pillars, and Cu-Pad. We analyze in-depth observations from our new models and discuss the benefits and improvements of our revised approach.
Nonlocal entanglement analysis using quantum dot and microcavity coupled system

We propose an optimal nonlocal entanglement analysis protocol for a two-electron system exploiting the quantum dot and microcavity coupled system, resorting to the projective measurement on the entangled photon pair. The maximally entangled states can, in principle, been distinguished by two nonlocal parties without destroying them, which will improve the efficiency of long-distance quantum communication.
Design of Quality-of-Experience Criteria for Resource Allocation Toward 6G Wireless Networks: A Review and New Directions

With the evolution of mobile terminals and the development of user demands, the existing mobile system is facing with serious challenges, such as the nearly saturated spectrum, limited processing capability of mobile terminals, and the high complexity of emerging technologies. To deal with these challenges, 6G is attracting extensive attentions. In this paper, we firstly investigate the potential technologies for 6G from the view of network design and resource management, then highlight the potential challenges for resource allocation, imposed for satisfying multi-demands in future hyper-heterogeneous networks with massive different devices and applications. In addition, the existing criteria for resource allocation are summarized for their common forms and limitations when dealing with multi-demands. Considering the limitations of the existing criteria, a novel quality-of-experience (QoE)-aware criterion on demand side is introduced toward efficient resource allocation in 6G, including its modeling, the potential application forms and typical scenarios, the future directions and challenges.
Fuzzy-logic based PID-N Controller for Automatic Generation Control

In this study, Fuzzy-logic based Proportional-Integral-Derivative controller with filter (F - PID- N) is recommended in the Load Frequency Control loop to regulate the frequency of a two-area, multi-source power-system. Since, now-a-days the load demand increases exponentially, renewable energy sources are chosen as the best alternatives to meet the desired load demand. Hence here solar unit is added in both the areas with the conventional hydro-thermal system and the whole system is considered for the study of dynamic stability. The above controller is proved to be superior by comparing it's result with that of PID controller. The controller gains are obtained through the efficient Wild-Goat-Algorithm (WGA). For the conduction of the experiment, 1 % load variation is inserted in the area-1 of the system taken for the study. Further Capacitive Energy Storage device is used as the energy storage system (ESS) to reduce the frequency and tie-line power excursion. The robustness of the proposed controller is proved by varying the system parameters.
Firefly Optimization with Bidirectional Gated Recurrent Unit for COVID-19 Diagnosis on Chest Radiographs

The epidemic of coronavirus disease 2019 (COVID-19) has caused an ever-growing demand for treatment, testing, and diagnosis. Chest x-rays are a fast and low-cost test that can detect COVID19 but chest imaging is not a first-line test for COVID19 because of lower diagnosis performance and confounding with other viral pneumonia. Current studies using deep learning (DL) might assist in overcoming these issues as convolution neural networks (CNN) have illustrated higher performance of COVID19 diagnoses at the earlier phase. This study develops a new Firefly Optimization with Bidirectional Gated Recurrent Unit (FFO-BGRU) for COVID19 diagnoses on Chest Radiographs. The main intention of the FFO-BGRU technique lies in the recognition and classification of COVID-19 on Chest X-ray images. At the initial stage, the presented FFO-BGRU technique applies Wiener filtering (WF) technique for noise removal process. Followed, the hyperparameter tuning process takes place by using FFO algorithm and SqueezeNet architecture is applied for feature extraction. Lastly, the BGRU model is applied for COVID19 recognition and classification. A wide range of simulations were performed to demonstrate the betterment of the FFO-BGRU model. The comprehensive comparison study highlighted the improved outcomes of the FFO-BGRU algorithm over other recent approaches.
C2C12 Differentiation and Orientation Approach Towards Actuation Improvement for Biohybrid Robots

The weak maturity and poor alignment of myotube significantly limit the contractility of C2C12 mouse cell line, which is an important source of engineered skeletal muscle tissue powering biohybrid robots. Currently, substrate material, biochemical factor, topological clue and so on are proved to improve the maturing status and oriented growth of myoblasts, and co-stimulation methods stand a good chance of synergetic enhancement. Therefore, this paper invests C2C12 myogenic differentiation under triple-stimulation: soft substrate, coating and micro-groove structure. The impacts of these three factors are evaluated by the viability, orientation rate and fusion rate of C2C12 myotube, while the viability, obtained by CCK-8 test, is the basic assessment of cell proliferation; the orientation rate and the fusion rate indicates the potential for controllable contractility and the maturity of myotube, and both are analyzed and calculated from fluorescence image processing. After 7 days differentiational culture of C2C12 cell, with a GelMA substrate coated with 10 μg/cm2 fibronectin, the highest viability reaches 78.1%, and the wider (150 μm) and the shallower (25 μm) micro-groove scaffold provide more advantageous support for C2C12 differentiation and orientation. The results of this study are a referable protocol for unidirectional differentiation of C2C12 cell line based biohybrid actuator.
Advances of Gate Stack Technology in MOSFETs

In this digital era, an increasing number of semiconductor manufacturing companies have developed thinner and cheaper circuit components, such as diodes, MOSFETs, and FinFETs. At the same time, gate stack technology is also introduced in MOSFETs to help solve the problem posed by short channel effects. There are a large number of researches on different MOSFETs using gate stack technology, therefore, this paper is a literature review of three different kinds of gate stacked MOSFETs and their behavior against short channel effects. The MOSFETs discussed in this paper are investigated in 2017, 2018 and 2020, and the ability of weakening short channel effects is also analyzed. Drain-induced barrier lowering (DIBL), threshold voltage roll-off, and subthreshold swing are three common short channel effects, and though the improvement in gate stack technology, they are suppressed in some degree. Gate stack technology has been developing new possibilities and it is expected that gate stack MOSFETs are much more powerful in confronting short channel effects and they will play a key role in different fields in the future.
Deep Learning Methods for Real-time Detection and Analysis of Wagner Ulcer Classification System

At present, the ubiquity method to diagnose the severity of diabetic feet (DF) depends on professional podiatrists. However, in most cases, professional podiatrists have a heavy workload, especially in underdeveloped and developing countries and regions, and there are often insufficient podiatrists to meet the rapidly growing treatment needs of DF patients. It is necessary to develop a medical system that assists in diagnosing DF in order to reduce part of the workload for podiatrists and to provide timely relevant information to patients with DF. In this paper, we have developed a system that can classify and locate Wagner ulcers of diabetic foot in real-time. First, we proposed a dataset of 2688 diabetic feet with annotations. Then, in order to enable the system to detect diabetic foot ulcers in real time and accurately, this paper is based on the YOLOv3 algorithm coupled with image fusion, label smoothing, and variant learning rate mode technologies to improve the robustness and predictive accuracy of the original algorithm. Finally, the refinements on YOLOv3 was used as the optimal algorithm in this paper to deploy into Android smartphone to predict the classes and localization of the diabetic foot with real-time. The experimental results validate that the improved YOLOv3 algorithm achieves a mAP of 91.95%, and meets the needs of real-time detection and analysis of diabetic foot Wagner Ulcer on mobile devices, such as smart phones. This work has the potential to lead to a paradigm shift for clinical treatment of the DF in the future, to provide an effective healthcare solution for DF tissue analysis and healing status.
Automatic Detection of Cassava Leaf Disease using Transfer Learning Model

Africa is the world's leading producer of cassava crops. It is also extensively distributed in Asia and Latin America. Cassava is produced and exported in large quantities from Thailand, which is the world's top producer of it. It may grow and function well in poor soil conditions. Cassava may be grown successfully in any climate. In addition to the nutrients present in rice, it has various health benefits. It was a significant source of calories and carbs. Cassava production has been reduced since 2016 owing to cassava disease. Cassava disease is the principal cause of decreased cassava yield and a direct influence on farmer income. A lot of techniques were employed in the recent years but there is scope for improvement in performance parameters. In this research, a pre-training model based on deep learning is used to detect cassava leaf disease. The illness is classified into five categories Cassava Bacterial Blight (CBB), Cassava Brown Steak Disease (CBSD), Cassava Green Mite (CGM), Cassava Mosaic Disease (CMD) and Healthy. The deep learning model used in this study is densenet169. The model is implemented using a collection of 21397 images having 5 classes of cassava leaf. The model has been evaluated in terms of performance parameters such as loss, accuracy, specificity, sensitivity.
Mobility-aware Multi-Access Edge Computing for Multiplayer Augmented and Virtual Reality Gaming

Augmented Reality (AR) and Virtual Reality (VR) games are some of the emerging use cases of 5G in the area of ultra-Reliable and Low Latency Communications (uRLLC). A multiplayer AR/VR game broadly consists of compute-intensive tasks which convert the raw data generated from sensory sources such as wearables, smartphones, etc., to action data such as location, orientation, intention, etc., and services that process the action data. Services generate a common response to all players by taking action data as input. The total response time must be as low as 20 milliseconds for a good user experience and to prevent motion sickness. While considering these aspects, the multiplayer game must be scalable, and users should be able to move. Multi-access edge computing (MEC) helps to improve performance by partially/fully offloading such tasks from mobile devices and latency-sensitive services from the cloud to a server at the edge called the MEC host. We propose, for the first time, an online mobility-aware heuristic in a Multi-access Edge Computing Network (MEN) to reduce the response time, specifically the Game Frame Time (GFT), consistently, for an improved Quality of Experience (QoE), for such games. This is done by jointly offloading tasks and placing services, and migrating both whenever required. Additionally, for improved response, the network is partitioned into regions, and a service instance is placed on a MEC host, called the Region Coordinator (RC), in each region, in a decentralized manner. When a new player joins, an old player leaves, or old players move, the number of players and their mobility patterns change in a particular region. This may require allocating or moving tasks from one MEC host to another and migrating services to a new RC. While tasks and services are migrated, the associated state and data must be moved to the destination MEC host. Our experiments demonstrate that the standard deviation for the mean GFT is 0 ms in the best case and 9.26 ms in the worst case, providing a uniform user experience, even when mobility is as high as 50% (it means 50% of the players are moving). When there is mobility, the GFT increases by 28.29% in the best case and 37.18% in the worst case, compared to a no-mobility scenario. We also demonstrate that, given computing power, there is a tradeoff between responsiveness and GFT.
Deep Reference Frame Interpolation based Inter Prediction Enhancement for Versatile Video Coding

In video coding, bi-directional inter prediction aims to remove temporal redundancy via two-sided previously coded frames as reference. High-quality reference frames are essential to reduce the prediction residuals and improve coding efficiency performance. In this paper, we propose a deep learning-based reference frame interpolation method to enhance bi-prediction by introducing a synthetic frame to reference picture lists. Specifically, reconstructed frames are fed into a well-designed interpolation and filtering network to synthesize a picture which can be regarded as an additional reference of to-be-coded frame. Then, the picture is inserted at the appropriate place in reference picture lists to provide a more reliable reference for subsequent motion estimation and motion compensation. Experimental results show that the proposed method achieves 2.03%/6.96%/6.40% coding efficiency improvements for Y/U/V components under random access configuration, when compared with the VVC reference software VTM-15.0.
Influence Factors of Resignation Intention of Primary-level Employees in China Logistics Companies

Attracting and retaining the personnel skills of logistics are important issues in the company's operation, especially with the high turnover rate of primary-level employees. Therefore, the company must be reducing the rate of turnover in order to maintain the company's competitiveness. This paper explored the hypothesis that the influence factors of the high turnover rate in the primary-level employees, and verifies the signature of the hypothesis by regression analysis. A questionnaire survey on companies providing logistics services for e-commerce enterprises in Hubei Province of China was conducted for this study. The results show that content operation processes and tiring work are important factors in the resignation intention of primary-level employees in the logistics industry. The logistics companies must provide learning and training of skills of logistics, and improve the environment of working in order to reduce the turnover rate of employees.
Classification of Schizophrenia Patients According to DNA Methylation Data Based on Meta-learning

The question for understanding the correlation between genetic expression and diagnosis of schizophrenia patients has always been challenging. The DNA methylation can provide an effective approach to solve the problem. However, limited by the size of datasets and the high dimensionality, conventional feature selection and pattern recognition methods cause a severe overfitting phenomenon. In this paper, the DNA methylation data are analyzed to filter out the most important gene points for classification. To alleviate the negative overfitting impacts the optimization-based meta-learning i.e., the MAML algorithm, is developed to solve the multi-classification and feature selection problems. The proposed method combining MAML with attention-based mechanism is capable of conducting feature selection with fast generalization and quick adaptation for small samples. The major work lies in that the proposed model is pretrained with TCGA which can be tailored to our circumstance with priori information. To test the efficiency of the proposed algorithm, the DNA methylation data are used including homicidal schizophrenics and violent schizophrenics from some authoritative institute. Our method achieves a recognition accuracy of 91.89% (homicidal schizophrenics, schizophrenics without violent behaviors, and normal people) and 67.96% (schizophrenics with violent behaviors, schizophrenics without violent behaviors, and normal people}) respectively, which is a great improvement compared to the best-known results based on the traditional method with 82.52% and 63.76%. Furthermore, 1000 significant feature gene points are selected from more than 400 thousand feature points.
Verifying the Independence of Anterior and Mediolateral Margin of Gait Stability Indices

Gait stability indices are used for the quantitative assessment of individual fall risk. The margin of stability (MoS) is a popular gait stability index. This index computes the fall risks in the anterior and mediolateral directions; however, the relationships between the anterior and mediolateral MoSs have not been studied. We analyzed the MoS values in these two directions and several gait parameters using canonical correlation analysis, and obtained the linear relationships between these two groups. The gait kinematic data of 300 strides of 60 elderlies from an open database were used for this analysis. Our analysis verified that the MoS values in the anterior and mediolateral directions exhibited a weak relationship. The anterior posture became stable as the anterior gait speed decreased or the cadence increased. Furthermore, gait motions with greater step widths exhibited higher mediolateral MoS values, indicating greater stability. These findings indicate that the anterior and mediolateral postural stabilities are independently related to different gait parameters, which can aid in understanding stable walking strategies.