Miracle-dz/UniFAR-FTUs huggingface dataset:
Helical Long Period Fiber Grating Inscribed in Elliptical Core Polarization-Maintaining Fiber

A high-quality helical long period fiber grating (HLPFG) in elliptical core polarization-maintaining fiber (PMF) was experimentally demonstrated by using the hydrogen-oxygen flame heating technique. Such a single HLPFG with a length of 6.5 mm could be used to generate orbital angular momentum (OAM) modes, i.e., OAM<sub>±2</sub>. The secret of the successful generation of the OAM<sub>±2</sub> mode is that the elliptical core PMF has two-fold symmetry in the cross-section. The resonance dip of the coupling attenuation and wavelength was strongly dependent on the length and helical pitch of the HLPFG. The HLPFG also exhibited a higher strain sensitivity of 30.7 pm/<inline-formula> <tex-math notation="LaTeX">$\mu \varepsilon $ </tex-math></inline-formula>. The proposed method opens a new way to generate a higher-order OAM mode by using multiple-fold symmetry fiber.
Application of 3D hydrodynamic mathematical model in deep water channel of Binzhou port

Based on the three dimensional FVCOM model, hydrodynamics of Binzhou port sea area was simulated with and without the 50000 DWT channel project. The results show that, the implementation of the 50000 DWT channel project does not change the law of tidal current movement in large-scale sea area, and the change occurs near the project. Compared with the present situation, after the implementation of the project, in the channel shield section, the velocity increases and the cross flow decreases. It is helpful to reduce channel siltation.
A Post-Equalizer Based on Dual Self-Attention Network in UVLC System

The post-equalizer in the Underwater Visible Light Communication (UVLC) system can overcome the nonlinear distortion existing in the system. The existing nonlinear post-equalizer based on deep learning still has problems such as the number of data nodes has a great influence on the effect, the equalization effect decreases significantly when the data rate becomes higher and too complex a model leads to slow training time. In this paper, we propose a Dual Self-Attention Network (DSANet) as a post equalizer in the CAP modulated UVLC system. Experiments show that the DSANet-based post equalizer can achieve good equalization performance at different data rates; it shows strong robustness when the number of data nodes changes; its training speed is close to that of the plainest nonlinear post-equalizer.
A Hand-Held Non-Robotic Surgical Device to Compensate for Wire Length in Unpredicted Paths

This paper describes a surgical device that uses a compensation mechanism without motors and electronics to reduce the backlash in the end-effector. The device improves the precision and accuracy of surgical operations that involve unpredictable curved paths at a low cost. The device includes a flexible tube that moves along a curved path and a mechanism that compensates for the change in the length of the wire that actuates the end-effector as it follows an unpredictable path. The compensation mechanism minimizes the wire slack by utilizing a slider and spring mechanism that can maintain the pull wire length but can also move freely to extend the wire length as needed. We maximized the compensable wire length by optimizing the device driving parameters via simulation, verified the device performance experimentally, and built a fully operational prototype. The backlash of the device end-effector was reduced by approximately 38.3% compared to a device without compensation. Surgeons used the prototype with animal models in vivo and evaluated it as convenient and easy to manipulate. A hand-held non-robotic surgical device that compensates for wire length in unexpected curved paths to reduce end-effector backlash was demonstrated. The performance of the prototype was validated by driving, backlash, and hysteresis measurements, and in vivo animal experiments. This study is a significant step towards the development of a high-precision and low-cost surgical device for use in deep and narrow anatomies with unpredictable curved paths.
Application of Machine Learning Model and Hybrid Model in Retail Sales Forecast

Using time series data to predict future sales changes of products is of great significance to every retailing company in terms of management and planning of resources. In order to find an effective method to improve the accuracy of sales forecasting of retail goods which strongly influenced by season and holiday, this paper analyzes the feasibility of traditional time series model, hybrid models based on time series model and machine learning model, and machine learning model in predicting Walmart sales. The Prophet model which decomposes trend, season, and holiday and the machine learning model-lightGBM model- are used to train and test Walmart supermarket sales data from 2011-01-29 to 2016-06-19, and use data from 2016-06-19 to 2016-08-14 to make prediction and empirical analysis. The results suggest that the Root Mean Square Error (RMSE) of the Prophet model and the LightLGB model are 0.694 and 0.617, respectively, indicating that the machine learning model performs well in the sales forecast of retail stores. This provides a new idea for retailers to forecast sales by category and region.
Inter-Regional Learning Curve Model of Renewables in the Context of Technology Spillover

Through a large number of studies on different forms of learning curve model, there has obtained an argument that renewable technological change can be affected by a broad range of factors. Among them, technology spillover (or knowledge spillover) is not to be ignored when identifying underlying mechanism on inducing renewable technological change. From the standpoint of technology spillover, an interregional learning curve model is presented in this paper, which not only provides an alternative approach of learning curve formulation capable of reflecting spillover effect on cost reduction, but also supports to estimate the impact of local-regional learning experience on the overall region. The case study of li-ion battery is demonstrated to validate the proposed learning curve model, in which the impact of the learning experience of li-ion battery at different region on the global level is also discussed.
Octave-Tuning Dual-Core Folded VCO Leveraging a Triple-Mode Switch-Less Tertiary Magnetic Loop

To satisfy the requirements for multi-standard frequency generation, mode-switching oscillators are developed to increase the frequency tuning range without incurring considerable phase noise penalty. However, this improvement in the tuning range is often limited by the parasitics of the circuitry for mode selection and tuning. To address this challenge, this work presents a dual-core octave-tuning VCO using a switch-less tertiary magnetic coupling loop. The design employs the benefits of transformer-switching without incurring switch loss and quality factor degradation and also the benefits of mode switching without the parasitics from the mode selection and tuning network. The VCO employs a non-constant <inline-formula> <tex-math notation="LaTeX">$RC$ </tex-math></inline-formula> scaling for the capacitors’ bank, across the tuning bits, and positive feedback dc-coupled inductive-degenerated output buffer to maximize the tuning range. Implemented in 22 nm fully depleted silicon-on-insulator (FDSOI), the dual-core VCO achieves a 72% tuning at 8–17 GHz and a figure-of-merit-tuning (<inline-formula> <tex-math notation="LaTeX">$\text {FoM}_{T}$ </tex-math></inline-formula>) of 208.8 dBc/Hz at 11 GHz. The chip operates from a 0.45 V supply with a power consumption of 17–33 mW and a core area of 0.39 mm<sup>2</sup>. To the best of our knowledge, the VCO achieves the highest <inline-formula> <tex-math notation="LaTeX">$\text {FoM}_{T}$ </tex-math></inline-formula> and lowest supply for designs over 10 GHz.
Revisiting HyperDimensional Learning for FPGA and Low-Power Architectures

Today’s applications are using machine learning algorithms to analyze the data collected from a swarm of devices on the Internet of Things (IoT). However, most existing learning algorithms are overcomplex to enable real-time learning on IoT devices with limited resources and computing power. Recently, Hyperdimensional computing (HDC) is introduced as an alternative computing paradigm for enabling efficient and robust learning. HDC emulates the cognitive task by representing the values as patterns of neural activity in high-dimensional space. HDC first encodes all data points to high-dimensional vectors. It then efficiently performs the learning task using a well-defined set of operations. Existing HDC solutions have two main issues that hinder their deployments on low-power embedded devices: (i) the encoding module is costly, dominating 80% of the entire training performance, (ii) the HDC model size and the computation cost grow significantly with the number of classes in online inference.In this paper, we proposed a novel architecture, LookHD, which enables real-time HDC learning on low-power edge devices. LookHD exploits computation reuse to memorize the encoding module and simplify its computation with single memory access. LookHD also address the inference scalability by exploiting HDC governing mathematics that compresses the HDC trained model into a single hypervector. We present how the proposed architecture can be implemented on the existing low power architectures: ARM processor and FPGA design. We evaluate the efficiency of the proposed approach on a wide range of practical classification problems such as activity recognition, face recognition, and speech recognition. Our evaluations show that LookHD can achieve, on average, $ 28.3\times$ faster and $ 97.4\times$ more energy-efficient training as compared to the state-of-the-art HDC implemented on the FPGA. Similarly, in the inference, LookHD is $ 2.2\times$ faster, $ 4.1\times$ more energy-efficient, and has $ 6.3\times$ smaller model size than the same state-of-the-art algorithms.
Malware Variant Detection Based on Decomposed Deep Convolutional Network

Aiming at the existing malware variants detection method based on deep convolutional neural networks (DCNN) has the problem of large computational resource consumption, a decomposing deep neural network (Dec-DCNN) is proposed for optimization. The computational cost was reduced by using single value decomposition (SVD) to split the pre-trained standard convolution operation into two simpler convolution operations. And the optimized network after decomposition does no need to be retrained, which can reduce the number of parameters and calculations while maintaining the detection accuracy of the pre-trained model. Experimental results show that the Dec-DCNN had a detection time of only 46% of DCNN with an overall detection accuracy of 98.5%. At the same time, compared with the current mainstream malicious variant detection model, Dec-DCNN has a stronger ability to express texture features of malware images.
VoIP Traffic Detection in Tunneled and Anonymous Networks Using Deep Learning

Network management is facing a great challenge to analyze and identify encrypted network traffic with specific applications and protocols. A significant number of network users applying different encryption techniques to network applications and services to hide the true nature of the network communication. These challenges attract the network community to improve network security and enhance network service quality. Network managers need novel techniques to cope with the failure and shortcomings of the port-based and payload-based classification methods of encrypted network traffic due to emergent security technologies. Mainly, the famous network hopping mechanisms used to make network traffic unknown and anonymous are VPN (virtual private network) and TOR (Onion Router). This paper presents a novel scheme to unveil encrypted network traffic and easily identify the tunneled and anonymous network traffic. The proposed identification scheme uses the highly desirable deep learning techniques to easily and efficiently identify the anonymous network traffic and extract the Voice over IP (VoIP) and Non VoIP ones within encrypted traffic flows. Finally, the captured traffic has been classified into four different categories, i-e. , VPN VoIP, VPN Non-VoIP, TOR VoIP, and TOR Non-VoIP. The experimental results show that our identification engine is extremely robust to VPN and TOR network traffic.
COVER CROPS IN THE OFF-SEASON IN THE WEED MANAGEMENT AT NOTILLAGE AREA

Cover crops can provide suppression of weeds and together with chemical control make the proper management of weeds in agricultural areas. The objective of this study was to evaluate the effect of cover crop cultivation during the off-season on weed development in a no-tillage area. The experimental design was in randomized blocks scheme with six treatments and four replications. The treatments were: fallow (control), millet (Pennisetum glaucum) + crotalaria (Crotalaria spectabilis + C. juncea + C. ochroleuca), millet + pigeon pea (Cajanus cajans), millet + Urochloa ruziziensis, millet + Urochloa ruziziensis + pigeon pea and millet + buckwheat (Fagopyrum esculentum. The evaluations were done at 30, 75 and 225 days after sowing of the cover crops (DAS). The main growth weed species in the area were Cenchrus echinathus, Euphorbia heterophylla and Digitaria insularis. Fallow treatment showed greater number of weed species with density of 184 plants m, 9.0 species and with 527.8 g m of dry matter mass at 225 DAS. In all treatments verified reduction in the density and mass of weeds dry matter compared to the fallow, with average of 30 plants m, 5.8 species and 7.9 g m at 225 DAS, respectively. The use of cover crops is an important strategy for weed control in agricultural areas.
Internet of Things Applications, Security Challenges, Attacks, Intrusion Detection, and Future Visions: A Systematic Review

Internet of Things (IoT) technology is prospering and entering every part of our lives, be it education, home, vehicles, or healthcare. With the increase in the number of connected devices, several challenges are also coming up with IoT technology: heterogeneity, scalability, quality of service, security requirements, and many more. Security management takes a back seat in IoT because of cost, size, and power. It poses a significant risk as lack of security makes users skeptical towards using IoT devices. This, in turn, makes IoT vulnerable to security attacks, ultimately causing enormous financial and reputational losses. It makes up for an urgent need to assess present security risks and discuss the upcoming challenges to be ready to face the same. The undertaken study is a multi-fold survey of different security issues present in IoT layers: perception layer, network layer, support layer, application layer, with further focus on Distributed Denial of Service (DDoS) attacks. DDoS attacks are significant threats for the cyber world because of their potential to bring down the victims. Different types of DDoS attacks, DDoS attacks in IoT devices, impacts of DDoS attacks, and solutions for mitigation are discussed in detail. The presented review work compares Intrusion Detection and Prevention models for mitigating DDoS attacks and focuses on Intrusion Detection models. Furthermore, the classification of Intrusion Detection Systems, different anomaly detection techniques, different Intrusion Detection System models based on datasets, various machine learning and deep learning techniques for data pre-processing and malware detection has been discussed. In the end, a broader perspective has been envisioned while discussing research challenges, its proposed solutions, and future visions.
Revising Security Protocols against Network Attacks based on Event-B

Verifying that a security protocol is immune to a given kind of network attack is usually an important but challenging task. Even we know a security protocol is vulnerable to some kind of attacks, revising it to disable that attack is also a non-trivial work. This paper proposes a generic framework based on Event-B and Rodin platform to guide the revising of a security protocol so that an known successful attack can no longer take place upon the revised protocol. The attack scenario is reflected with an abstract model and is developed to capture the behaviours from both the protocol and the attacker. The vulnerability of the protocol to the attack is verified by showing that the model is a refinement of the abstract model. Then the revision of the protocol can be modeled by first separating the protocol behavior from the model and refining it solely. The security of the revised protocol can be verified by showing that the composed model with the attack is no longer a refinement of the abstract model. We demonstrated this approach on the revision of the 4-way handshake protocol of Wi-Fi Protected Access2(WPA2) that is exposed to Key Reinstallation Attacks(KRACK).
Structure design of ankle rehabilitation robot

According to the motion law and structural characteristics of the ankle joint and the research on the ankle rehabilitation training method, aiming at the problems of the traditional ankle rehabilitation training mode is simple, the treatment effect is not obvious, and the training period is long, a kind of ankle rehabilitation robot structure is proposed. The device mainly uses hydraulic system to make the height difference between the front and back of the foot plate, and adopts the design concept of slide track determination, so that the ankle can reach a reasonable range of motion, and can meet the requirements of ankle rehabilitation treatment. Firstly, the motion law of ankle joint is analyzed, and the structure of ankle rehabilitation robot is designed to meet the basic functions of ankle rehabilitation training requirements, and the feasibility of the design scheme is verified.
The Gain Matrix in Input-to-Output Stability Analysis with Applications to Interconnected Energy Systems

Applications of local input-to-output stability theorem mainly focus on isolated power systems. This paper aims at giving a try to extend the local input-to-output stability based stability criterion to integrated energy system, narrowing down on how to construct the linear gain matrix in the interconnected energy system. By modify multiregional inoperability input-output model, a formula of the linear gain matrix is derived, with a simple energy system as the case study.
Co-Inference Discriminative Tracking Through Multi-Task Siamese Network

In essence, visual tracking is a matching problem without any prior information about a class-agnostic object. By leveraging large scale off-line training data, recent trackers based on Siamese networks usually expect to pre-learn underlying similarity functions before a tracking task even begins. Consequently, they lack discriminative and adaptive powers. To address the issues, we propose a multi-stage co-inference tracker (named MSCI) via a multi-task Siamese network, in which a complicated tracking task is divided into three complementary sub-tasks (i.e., classification, regression and detection). Firstly, we design a novel multi-task loss function to end-to-end train the multi-task Siamese network via jointly learning from three sub-tasks. The multi-task Siamese network contains three parallel yet collaborative output layers, which correspond to three key components of our tracker (i.e., classifier, regressor and residual learning based detector). By sharing representations within the components, we not only improve each component’s generalization performance, but also enhance our tracker’s discriminative power. Then, we design a co-inference approach to effectively fuse the complementary components. As a result, our tracker can avoid the pitfalls of purely single components and get reliably observations to improve its adaptive power. Comprehensive experiments on OTB2013, OTB2015 and VOT2016 validate the effectiveness and robustness of our MSCI tracker.
Accurate Detection of Heart Rate and Blood Oxygen Saturation in Reflective Photoplethysmography

In recent years, the demand for wrist wearable devices to monitor continuously critical physiological parameters in real time that are limited by designated hospital monitoring equipment is steadily increasing. In the medical field, one of the main issues that wearable devices could sufficiently address is the pervasive monitoring of vital signs and the corresponding health status assessment of the rapidly growing elderly population in real time. Main advantages in the adoption of wearable devices for the real time monitoring are the significant decrease of the cost both for the health system and subsequently the patient as well as the dramatic decrease of the waiting time in the hospital emergency rooms.Reflectance pulse oximetry being the right mode to be used at the wrist for measurements such as Heart Rate (HR), Peripheral Capillary Oxygen Saturation (SpO2) and Respiratory Rate (RR) imposes many technical challenges with its excessive sensitivity to all types of entailed artifacts due to arm/hand/body motions to be amongst the major ones.This work introduces a low-power wrist wearable device comprising a Photoplethysmography (PPG) array sensor special extraction algorithms to estimate HR and SpO2 parameters and a Multiple Linear Regression model, which after training performs considerable reduction of the imposed Motion Artifacts (Mas) thus enabling more accurate reading outputs.
Reliability Analysis and Cumulative Impact Assessment of Forest Ecosystem Services: A Case Study of Jilin Province, China

Forests are an important source of ecosystem services, and they are a common link between economic value and environmental protection. Thus, it is of great significance to study forest ecosystem services. In previous studies, scholars have focused on the evaluation results of a single service component, but they did not consider the credibility of the data, which consisted of the cumulative effects of multiple components on forest ecosystem services. The current study examined the forest management plan for Jilin Province and used forest resource inventory data from China. Reliability analysis and cumulative impact assessment were used to overcome the problem of cumulative changes and interactions between services in order to make the evaluation results more objective and reasonable. The results showed that the resulting two values from the evaluation of forest ecosystem services had no obvious cumulative effect, and the evaluation value for overall reliability was 0.774, which confirms that the results have a certain credibility. The results of this study considered the cumulative effect of forest ecosystem services on the value evaluation. The total value of the service was equal to the sum of each service value, which might be primarily related to the data format and sample size used in this study. The innovation of this research lies in the consideration of data reliability and the cumulative influence of the services on the value evaluation process, which makes up for the shortcomings of previous service value evaluation methods. However, the limitation of this study is that multiple reference groups and control groups were not set up to verify that the inconsistency in the evaluation results was indeed due to the data format and sample size. Future research will focus on comparative studies of data types and sample sizes. Furthermore, future research will identify important service mechanisms and discuss the establishment of a forest ecological benefit compensation system to fundamentally solve the problems of power and mechanism associated with forest management, especially with respect to public welfare forests, in order to achieve a positive impact on the development of sustainable forestry in China.