Miracle-dz/UniFAR-FTUs huggingface dataset:
Outage Performance and QoS Optimization in Full-Duplex System With Non-Linear Energy Harvesting Model

In order to overcome the problem that the traditional linear energy harvesting (EH) model is not practical, this paper presents a practical nonlinear EH model, which considers the sensitivity and saturation characteristics of the circuit. Furthermore, for purpose of solving the problem of spectrum requirement and energy consumption of wireless relays, the proposed model in this paper is used for studying the full-duplex (FD) decode-and-forward relay system. The expression of the outage probability of the system is deduced and the outage throughput of the system is calculated. According to different system quality of service (QoS) requirements, this paper further discusses the optimal scheme of non-linear EH based on outage performance and gives the closed expression of the optimal solution to minimize the outage probability of the FD system. And the optimal solution to maximize the outage throughput of the system is obtained based on the golden section method. The simulation results verify the correctness of the theoretical work, and the simulation results also show that the performance of the FD system under the proposed model is quite different from that of the traditional linear model.
Better Nutritional Status Is Positively Associated with mRNA Expression of SIRT1 in Community-Dwelling Older Adults in the Toledo Study for Healthy Aging.

Background The expression of certain genes involved in response to oxidative stress is likely related to aging-related outcomes, such as frailty in old age. Given nutrition's substantial impact in aging and age-related diseases, one of its mechanisms might be through influencing gene expression. Objective This study aimed to investigate the association between nutrition and the expression of 15 genes related to cellular response to stress in older community-dwelling individuals. Methods A nested case-control study of 350 participants (mean ± SEM age: 76.5 ± 6.9 y, 42.9% men, 22% frail according to Fried's criteria) was selected from the Toledo Study for Healthy Aging. Blood-derived RNA was retro-transcribed into complementary DNA. TaqMan Arrays were used for determining gene expression. The Mini Nutritional Assessment (MNA) and the PREDIMED (PREvención con DIeta MEDiterranea) questionnaire measured nutritional status and adherence to the Mediterranean diet (MD), respectively. Data were reweighed so that inference from linear and logistic regression models applied to the original sampling population. Results Higher MNA scores were associated with higher expressions of the gene coding for sirtuin-1 (SIRT1), regardless of age, sex, and Charlson comorbidity score (P = 0.04) and even after adjusting for frailty status (P = 0.016) and level of adherence to the MD (P = 0.04). Malnutrition risk and SIRT1 gene expression were inversely associated (P = 0.0045) independently of frailty status (P = 0.0045) and level of adherence to the MD (P = 0.0075). Conclusions In older individuals, improvement in nutritional status is positively associated with SIRT1 gene expression independently of frailty status or adherence to the MD. These findings may provide potential biomarkers and targets for health interventions among the elderly.
Dynamic Closest Point Identification and Estimation for Tumbling Target Capturing

A malfunctioned spacecraft is generally non-cooperative and uncontrolled. It is generally in the state of tumbling, bringing great challenge to capture and remove it. In order to perform on-orbital servicing task, it is necessary to determine the object on the target to be grasped. In this paper, we propose a method to identify the “Dynamic Closest Point” (denoted by DCP), an object on the target suitable to be grasped and always closest to the end-effector. The motion of the DCP is also estimated. An un-controlled space object is first modeled. Then, the “Dynamic Closest Point” is defined. Its existence is further proved and the determination process is detailed. Taking a practical malfunctioned spacecraft as the example, the motion characteristics of the DCP and a Fixed (on the target) Point are theoretically analyzed and compared from different motion types and rotation vectors. Finally, we develop a space robotic simulation system to verify the proposed method. Simulation results show that the capture efficiency is largely improved, compared with traditional capturing Fixed Point.
A Lightweight FourQ Primitive on ARM Cortex-M0

We describe the implementation of FourQ, an efficient Elliptic Curve Cryptographic (ECC) primitive with 128-bit security level, on 32-bit ARM Cortex-M0 microprocessor. It is the first time to implement FourQ curve on ARM Cortex-M0, the smallest processor available among whole series ARM productions and is suitable for embedded applications, such as wireless sensor networks (WSN) and Internet of Things (IoT). In finite field arithmetic, we propose a new strategy to improve the performance of Karatsuba multi-precision multiplicaion, which is the most significant operation for FourQ. In order to achieve high performance, we also utilize assembly code to achieve all the operations of field arithmetic. In curve arithmetic, we simplify the projective coordinates of original implementation of FourQ, and optimize the memory usage of point addition and point doubling, which makes it more applicable to embedded processor and corresponding applications. By combining the excellent properties of FourQ curve and optimized modification on ARM Cortex-M0, we show that the performance of scalar multiplication of FourQ is faster than exiting works on the same target microprocessor with the same security level. Experiments show that our scalar multiplication runs in under 1972k clock cycles on the Cortex-M0, improving on the speed record ukummer curve by 25.2% and Curve25519 by 45%.
Downlink Rate Analysis of Training-Based Massive MIMO Systems With Wireless Backhaul Networks

Massive multiple-input multiple-output (MIMO) systems with wireless backhaul networks have been considered as a candidate technology for 5G. Despite their potential, these systems still require improvement in areas such as resource allocation and interference management. In this paper, we derive a closed-form lower bound ergodic downlink rate expression for training-based multicell massive MIMO systems with full duplex wireless backhaul networks. We also propose a wireless backhaul link selection method, in which only certain access points will be activated and only certain users will use wireless backhaul links. The results show that the wireless backhaul with a link selection method can improve the downlink rate compared to other methods. The downlink rate of the minimum rate users can be significantly improved by prioritizing the wireless backhaul links for the minimum rate users. Power analysis shows that there is concave relationship between access point transmit power and median downlink rate. The results also show that the optimum access point transmit power is much lower than the standard transmit power from the users and base station. Additional investigation shows that users are more likely to use backhaul links when they are located farther from the base station. We also demonstrate that as the number of spatial multiplexed users increases, the performance improvement provided by wireless backhaul system decreases.
An Experimental Comparison of ROS-compatible Stereo Visual SLAM Methods for Planetary Rovers

Ego-motion estimation and awareness of the surroundings are crucial tasks to perform for an exploration vehicle. Visual SLAM techniques allow to fulfill these needs by extracting meaningful information from images: changes in the appearance of the environments are used to simultaneously estimate both its structure and the relative motion of the camera. While tipically heavyweight and computationally complex, thanks to the most recent advances in embedded platform architecture, most of the available open source Visual SLAM software can be run online on ARM processors. This, together with miniaturization and lower power consumption, opens great scenarios for autonomous navigation of mobile robots. This paper covers the implementation of most of the available open source Visual Stereo SLAM on a nVidia Jetson TX2 platform highlighting critical information such as pose estimation accuracy, capability of loop closure, CPU and memory usage. The measurement setup is mounted on the MORPHEUS (Mars Operative Rover of Padova Engineering University Students) rover and LiDAR data is used as reference for performance evaluation.
Novel hands‐free near‐infrared fluorescence navigation and simultaneous combined imaging for elevation of vascularized lymph node flap

To the Editor, We read with great interest the article entitled “Alternative vascular constructs of lymph node flap transfer,” by Nicoli et al. In this article, the authors described the anatomical basis in detail for lymph node flap transfers. The authors are to be congratulated for their excellent work. In addition to their work, we would like to offer a modification of intraoperative navigation for successful lymph node flap transfers. Intraoperative navigation using indocyanine green (ICG) fluorescence has been standardized in a wide range of fields in surgery. Near‐infrared (NIR) fluorescence visualizes the vascularization or lymphatic drainage with a hand‐held camera device. This technique is useful for intraoperative or outpatient use, but the hand‐held camera is a burden on surgeons during an operation. Since a novel NIR fluorescence imaging system with an extendable arm offers hands‐ free handling and stabilized imaging, this system can be applied to ICG‐guided navigation surgery with an advantage. An NIR fluorescence imaging system with an extendable arm (LIGHTVISION; Shimadzu, Kyoto, Japan; Figure 1) was set over the surgical field at a distance of 50‐70 cm. After injection of ICG contrast, the imaging system visualized ICG enhancement with an optical image. The simultaneously combined image of ICG enhancement and the optical image was displayed on a monitor. Watching the monitor, all the surgeons performed an operation without handling the camera. Using this NIR fluorescence imaging system, surgeons can simultaneously watch the combined image of ICG enhancement and an optical image and easily detect the margin of ICG enhancement. This is useful for safe elevation of lymph node flap or resection of liver or any other navigation surgery. Furthermore, a hands‐free imaging eliminates a burden on surgeons, and a sharp image is suitable for review and education. Although it can be difficult to use this system at an office or a bedside, this novel imaging system can be applied to navigation surgery with preferable advantage.
Quality of Information as an indicator of Trust in the Internet of Things

The past decade has seen a rise in complexity and scale of software systems, particularly with the emerging of the Internet of Thing consisting of large scale and heterogeneous entities which results in difficulties in providing trustworthy services. To overcome such challenges, providing high quality information for IoT service provider as well as monitoring trust relationship of end-users toward the services are paramount. Such trust relationships are user-oriented and subjective phenomenon that hook on specific quality of data. Following this catalyst, we propose a mechanism to evaluate quality of information (QoI) for streaming data from sensor device; then use the QoI evaluation score as an indicator of trust. Concepts and assessment methodology for QoI along with a trust monitoring system are described. We also develop a framework that classifies streaming of data based on semantic context and generate QoI score as a relevant input for a trust monitoring component. This framework enables a dynamic trust management in the context of IoT for both end-users and services that empowers service providers to deliver trustworthy and high quality IoT services. Challenges encountered during implementation and contribution in standardization are discussed. We believe this paper offers better understanding on QoI and its importance in trust evaluation in IoT applications; also provides detailed implementation of the QoI and Trust components for real-world applications and services.
Region-Wise Super-Resolution Algorithm Based On the Viewpoint Distribution

Recently, super-resolution techniques have been energetically studied for the purpose of reusing the low resolution image contents. Although a lot of approaches to achieve the appropriate super-resolution have been proposed such as non-linear filtering, total variation regularization, deep learning etc., the characteristic of the viewpoint distribution of the observer has not been effectively utilized. Because applying super-resolution to unimportant regions in an image may hinder the observer’s attention to seeing the display, it leads to a low subjective evaluation. This paper proposes the region-wise super-resolution algorithm based on the view-point distribution of observer. However, we cannot obtain the viewpoint distribution map for an image without the pre-experiment using the device such as eye mark recorder, therefore, the saliency map is utilized in this paper. Numerical examples show that the proposed algorithm using saliency map achieves a higher subjective evaluation than the previous study based on the non-linear filtering based super-resolution. Furthermore, in numerical examples, the proposed algorithm using the saliency map is shown to give the similar results of the algorithm using the viewpoint distribution map obtained by the pre-experiment using eye mark recorder.
A New Method of Image Steganography Using 7th Bit of a Pixel as Indicator by Introducing the Successive Temporary Pixel in the Gray Scale Image

As the internet has become the medium for transferring the sensitive information, the security of the transferred message has become the utmost priority. Image steganography has emerged out as the eminent tool of information hiding that ensures the security of the transmitted data. Image files provide high capacity, and their frequency of availability over the internet is also high. In this paper, a method of image coding is proposed that hides the information along a selected pixel and on the next value of the selected pixel, that is, pixel + 1. One bit is hidden at the selected pixel, and the second bit is hidden on the pixel +1 value. On the basis of the 7th bit of the pixels of an image, a mathematical function is applied at the 7th bit of the pixels, which generates a temporary variable (pixel + 1). The 7th bit of the selected pixel and 7th bit of pixel + 1 are used for information hiding and extraction. On the basis of a combination of these two values, two bits of the message can be hidden on each pixel. After implementation, the efficiency of the method is checked on the basis of parameters like PSNR and MSE, and then comparison with some already proposed techniques was done. This proposed image steganography showed interesting, promising results when compared with other existing techniques.
Acies: A Privacy-Preserving System for Edge-Based Classification

In this paper, we propose Acies, a differential privacy based privacy-preserving classification system for edge computing to secure the classification models offloaded to edge devices. Acies supports popular classifiers such as Nearest Neighborhood, Support Vector Machine and Sparse Representation Classifier with a variety of feature selection methods. According to our evaluation on different datasets, classification models with Acies can be private and remain high utility. Acies achieves reliable privacy protection under reconstruction attacks with minimal impact on classification accuracy (2%—5%) only. Acies outperforms the naive input dataset perturbation methods by up to 30% higher classification accuracy when the privacy requirements of the applications is high (∊ is less than 2).
Simulation of muscle-powered jumping with hardware-in-the-loop ground interaction

We developed a novel reverse haptic interface to augment forward dynamic simulations with real-world contact forces. In contrast with traditional haptics, in which a realworld user drives an interaction with a simulated environment, reverse haptics allows a simulated mechanism to probe the realworld environment through a force-sensing robotic manipulator. This method can implicitly extend computer models of biomechanics and robotic control with complex ground interactions. A 3- DoF manipulator and a biologically inspired musculoskeletal model were developed to test jumping performance on a diverse range of real-world substrates. Jumps were of similar height despite differences in material properties and no active muscle control. Muscle power was lower at the hip, yet total muscle work was higher, against compliant surfaces compared to stiff surfaces. Through reverse haptics, the forces of actuation, inertia and contacts could be measured simultaneously to reveal how intrinsic muscle properties may compensate for substrate dynamics.
Personalized LSTM Based Matrix Factorization for Online QoS Prediction

Quality of Service (QoS) prediction is an important task in services computing, which has been extensively investigated in the past decade. Many time-aware QoS prediction approaches have been proposed and achieved encouraging prediction performance. However, they did not provide effective model updating mechanisms, and thus have to periodically retrain the whole models to deal with the newly coming data. How to timely update the prediction model to precisely predict missing QoS values of candidate services becomes an urgent issue. In this paper, we propose a novel personalized LSTM based matrix factorization approach for online QoS prediction. Our approach can capture the dynamic latent representations of multiple users and services, and the prediction model can be timely updated to deal with the new data. Experiments conducted on a real-world dataset show that our approach outperforms several state-of-the-art approaches in online prediction performance.
Toward End-to-End Control for UAV Autonomous Landing via Deep Reinforcement Learning

The autonomous landing of an unmanned aerial vehicle (UAV) is still an open problem. Previous work focused on the use of hand-crafted geometric features and sensor-data fusion for identifying a fiducial marker and guide the UAV toward it. In this article we propose a method based on deep reinforcement learning that only requires low-resolution images coming from a down looking camera in order to drive the vehicle. The proposed approach is based on a hierarchy of Deep Q-Networks (DQNs) that are used as high-end control policy for the navigation in different phases. We implemented various technical solutions, such as the combination of vanilla and double DQNs trained using a form of prioritized buffer replay that separates experiences in multiple containers. The optimal control policy is learned without any human supervision, providing the agent with a sparse reward feedback indicating the success or failure of the landing. The results show that the quadrotor can autonomously land on a large variety of simulated environments and with relevant noise, proving that the underline DQNs are able to generalise effectively on unseen scenarios. Furthermore, it was proved that in some conditions the network outperformed human pilots.
Coordinated Standoff Tracking of a Ground Moving Target and the Phase Separation Problem

We extend existing results regarding the kinematics stability of the cooperative standoff tracking of a stationary target by a single UAV as well as a team of UAVs based on a guidance vector field. Furthermore, we examine the so called phase separation problem and by treating simultaneously the convergence toward the desired standoff radius, heading and angular difference between the tracking vehicles, we arrive at guidance laws that are globally asymptotically stable and at the same time reveal a tradeoff between overshoot and speed of convergence to the loitering circle. Simulations are performed to verify the results and demonstrate the feasibility of the proposed guidance laws.
Distributed RGBD Camera Network for 3D Human Pose Estimation and Action Recognition

Skeleton based human action recognition has recently attracted a lot of attention in the research community. 3D skeleton data is becoming easier to access due to the evolution of new depth sensors like Kinect v2. However, the performance of the depth sensors is subjected to viewpoint variations and occlusions. In this paper, we propose a novel distributed sensor data fusion method to address this problem. The information weighted consensus filter(ICF) is introduced to fuse the skeleton data, so as to get more precise joint positions. To demonstrate the proposed idea, we capture the human action sequences in different views and compare the recognition accuracy between the fused and the raw data, and prove that the fused data can help improve recognition performance.
A Reflection-Aware Unified Modeling and Linearization Approach for Power Amplifier Under Mismatch and Mutual Coupling

The behavior of a power amplifier (PA) is substantially affected by the output mismatch and mutual coupling in modern compact transmitters. To date, different works in the literature address the problem of coupling and mismatch separately, which are limited in terms of performance with a higher level of mismatch and mutual coupling. This paper identifies the reflection at the PA–antenna interface to be an effective parameter to unify the problem of mismatch and mutual coupling. Thus, a reflection-aware PA modeling and linearization method is proposed to compensate the adverse effect of mismatch and/or mutual coupling. The performance of the proposed method is investigated with a class AB and a Doherty PA under wide range of output mismatch and/or mutual coupling conditions with a 20-MHz WCDMA signal. The proposed method shows robust performance with an average adjacent channel power ratio better than 45 dBc. Such robust linearization performance under diverse output mismatch and mutual coupling conditions is highly desirable for modern and future communication systems that are subject to undergo rapid fluctuations under antenna matching and cross-coupling conditions.
Multirate Partitioning Interface for Enhanced Stability of Power Hardware-in-the-Loop Real-Time Simulation

Power hardware-in-the-loop (PHIL) technology allows for the testing of physical equipment including power electronic converters in a simulation environment that closely mirrors the reality of the electric power grid. A challenge involved in PHIL systems is the design of the interface between the digital network model and physical equipment as this has a significant influence on stability of the real-time simulation. While a small time step supports stability and accuracy, its lower limit is given by the real-time constraint. The proposed multirate partitioning (MRP) interface addresses this issue by using a rather small time step at the hardware–software interconnection and then employing a staged adaptation of the time step within the digital model in accordance with the real-time constraint. Thus, multiple rates are used. The Nyquist stability criterion confirms enhanced stability and bandwidth by the MRP compared with single-rate (SR) counterparts. Moreover, a PHIL test of two parallel photovoltaic converters feeding a low-voltage network reveals different behaviors depending on the interface. Using the MRP, the waveforms more closely track the real-world curves of active and reactive power with the relative accuracy increasing with the speed of the transients. Informed decision making regarding the integration of renewables in the grid is so supported.