Miracle-dz/UniFAR-FTUs huggingface dataset:
Power-sum Activated Neural Dynamics for Lower Limb Motion Intention Recognition

It can be found that the exact solution of the equations has quite similarity to the pursuing of the minimum error in the field of control. Therefore, many control problems can be explained from the perspective of solving equations. As a powerful computing tool, neural dynamics model has been widely applied in addressing time-varying equations and optimization problems because of its remarkable convergence and robustness. For neural dynamics model, the selection of activation function plays a significant role in its performance. In this paper, a special nonlinearly activated (i.e. power-sum activation function) neural dynamics model is exploited to the experimental simulation of intention recognition of lower limb motion. Compared with the linear activation function, the corresponding analyses of convergence and robustness are given respectively, demonstrating the superior characteristics of the neural dynamics model with power-sum activation function.
Closing the Skills Gap in the Era of Industrial Digitalisation

Manufacturing industry is in the midst of reshaping modern manufacturing by employing digital technologies due to its promising results. More and more companies are marching towards migrating their product offering and production infrastructure into Industry 4.0-compliant solutions. In this new era of industrial scenario, companies will need technicians and workers with digital skills mainly having abilities and competences in the Industry 4.0 Key Enabling Technologies (KET). However, lack of digital skills in the UK is considered perhaps the most serious obstacle to the practical implementation and investment in manufacturing digitalization technologies and is thus a major contributing factor to the UK productivity gap. Aiming to bridge the gap between the implementation of Industry 4.0 and educational organizations that focus on this field, the study presented here focuses on the development of a degree apprenticeship program in Digital technology solutions (DTS). The DTS program is developed in collaboration with leading UK industry partners to acquaint apprentices with appropriate knowledge that can help them to contribute towards digital transformation of their businesses
Recruitment Prediction using Machine Learning

Recruitment follows some major steps like attracting, selecting, appointing appropriate candidates for that position. There is ever-increasing focus of a functional recruitment. Every company invest lot of money and time for recruiting people to fill some specific positions and wasting resources for searching potential candidates. The total investment becomes loss if the selected candidates do not join in the company after completing this entire process. The main aim of this research is to predict joining efficient candidates on before resume selection and total process is to be done in an efficient way with minimal cost and minimal time. This prediction based on some relevant attributes of quantitative and qualitative aspects like age, gender, work experience, current salary and salary hikes etc., to predict the hiring candidates by applying various statistical measures on feature selection and using various machine learning algorithms to build the model. These results will help to predict the candidates who are joining in the end.
Design and Development of K-Band FMCW Radar for Nano-Drone Detection

Nano-drones, are insect-like size drones with a capability of intrusion to provide intelligence and potentially violate secure establishments and public privacy rights. Nano-drones are already an existing technology which is becoming more and more available, portable, affordable and easily operated. As such, they may soon become a plausible defence and security threat. This paper presents the design and development of a K-Band FMCW radar prototype for nano-drone detection. The FMCW radar prototype consists of connectorised components operating at a carrier frequency of 24 GHz and offer high parameter selection flexibility. Experiments have been carried out in order to evaluate the system performance. Results show that a small Arcade PICO Drone Nano Quadcopter (smaller than 5 cm) could be detected, and that its micro-Doppler signature could be extracted from data.
Polarization Conversion-Based Ambient Backscatter System

In Ambient Backscatter Communications (AmBC), a backscatter device communicates by modulating the ambient radio frequency (RF) signal impinging at its antenna. In many cases, the system setup is bi-static such that the receiver and the ambient signal source are separated in space. This configuration suffers from the direct path interference problem. The direct signal component can be several orders of magnitude stronger than the scattered one. This imposes a challenge for the receiver that needs to have high dynamic range in order not to lose the scattered signal component to the quantization noise. In this paper, we propose a novel AmBC system concept, in which a polarization conversion between the direct and scattered path is introduced at the backscatter device and exploited at the dual polarization based receiver antenna to mitigate the direct path interference. The proposed system is agnostic to the ambient signal source characteristics as long as it uses linearly polarized antennas. The backscatter device changes the polarization from linear to circular. The receiver antenna is a circularly polarized patch antenna with a 180°-hybrid to obtain the difference between the left- and right-hand polarized fields. Ideally, this receiver antenna and 180°-hybrid combination would completely remove linearly polarized direct path and reflected components. In this paper, we propose a robust design that can mitigate the direct path signal power more than 25 dB despite nonidealities in the antenna manufacturing.
Fast FBG sensor interrogation method based on silicon microring resonators

A fast FBG sensor interrogation method based on silicon on insulator (SOI) microring resonators is reported. The simple and effective FBG sensor interrogation scheme is based on the detection of the signal intensity at the drop port of a microring resonator, and its interrogation speed depends only on the photodetector and the front end electronics. The performance of the FBG interrogation method is evaluated in terms of minimum detectable wavelength shift and dynamic strain resolution and compared with a commercial FBG interrogator.
Position Control of an Underwater Biomimetic Vehicle-Manipulator System via Reinforcement Learning

This paper addresses a position control method of an underwater biomimetic vehicle-manipulator system (UBVMS) through reinforcement learning. The system description of the UBVMS with undulating fins is given. Considering the force/torqu generated by undulating fins and hydrodynamic force/torqu, the dynamic model of this UBVMS is established. The position control problem is modeled into a continuous-state, continuous-action Markov decision process (MDP) with a deterministic state transition algorithm based on the dynamic model. To solve this MDP, a reinforcement learning method is presented, which is based on the deep deterministic policy gradient (DDPG) theorem. The simulations of the position control in 5 cases are shown in the end.
Utilization of Iron Lathe Waste and Post-Tensioned Steel Reinforcement in Lightweight Concrete Ribbed Slabs

The ribbed slab unit system of lightweight concrete provided a lighter and stiffer than the equivalent conventional slab system. Ten ribbed slabs were tested under two-point load as simply supported up to the failure. In this work, the lightweight concrete was produced by omitting the coarse aggregate and increasing the fine materials, approaching to a desired strength- to-density ratio. All the slabs have the same area of the cross-section and the steel reinforcement ratio. The studied parameters were a ratio of the rib depth to the overall section depth, volumetric fraction ratio of iron lathe waste, and use post-tensioned steel wires as an additional tension reinforcement. A mechanical drag technique was used to apply the initial prestress force in the post-tension system. The results showed that the increase in the rib depth to the overall section depth ratio increased the ultimate load capacity by (54.17%) up to certain limits. Also, the use of iron lathe waste improved the load-carrying capacity by about (40.54%). It was found that the better ratio of the rib depth to the overall section depth was approached to (0.835), and the ratio of iron lathe waste of (2%) was suitable without the occurrence of balling and agglomeration. On the other hand, the use of lightweight concrete as a prestressed concrete led to a clear improvement in the structural performance of the slabs. The load factor that represents the ratio of ultimate strength to the service load was found approximately equal to (4).
Any Publicity is Good Publicity: Positive, Negative and Neutral Tweets Can All Become Trends

Anecdotal evidence suggests that Twitter trends are characterised by highly polarised tweets. However, specific experiments intended to measure the correlation between the emergence of a trend and the overall sentiment expressed on it have been few and limited. Thus, we have launched an investigation to ascertain the nature of the relationship between trends and strength of sentiment. As a testbed for our experimentation, we have retrieved a large collection of tweets related to the COVID19 pandemic, in the particular context of the UK Government briefings broadcasted in the media. Our results highlight the presence of a significant percentage of trends with a nearly neutral sentiment. Indeed, there does not seem to be an apparent correlation between trends and polarity.
Fault-Tolerant Control for Load Frequency Control System via a Fault Observer

To guarantee the stability and reliability of the multi-area interconnected power system under sensor failures, this paper presented a fault observer based active sensor fault-tolerant control scheme for load frequency control in power system. A fault observer is firstly introduced in order to estimate the sensor failures in different control areas. Then, based on the estimation of the fault, an distributed integral-type sliding mode fault-tolerant control scheme is presented to ensure that the frequency of each subsystem in the multi-area interconnected power system can run stably within the allowable range. Finally, the simulation experiment by applying different sensor faults in a three-area interconnected power system was operated, and the simulation results prove the effectiveness and the desired performance of the presented distributed active fault-tolerant control scheme.
Optical MEMS Accelerometer Based on Waveguide Bragg Grating Integrated with Crab-Leg Beam

Design and analysis of high-sensitivity optical MEMS accelerometer based on waveguide Bragg grating integrated with Crab-leg beam for low frequency applications is presented in this paper. Crab-leg beam are used to suspend the proof-mass to decrease spring stiffness with low device footprint. A waveguide Bragg grating is positioned on the shin of crab-leg to maximize strain sensitivity. To compensate for the temperature dependence of Bragg spectral properties, a reference grating positioned on the proof-mass is used. Due to excellent linear response, high-sensitivity of 184 pm/g and low fundamental frequency, the proposed accelerometer is suitable for microgravity and seismometry applications.
Road Semantic Segmentation Oriented Dataset for Autonomous Driving

There are various datasets for segmentation such as KITTI, CamVid, Cityscapes, ApolloScape, Mapillary Vistas, etc. These datasets try to include as many classes as possible, and classify objects in pixels across all regions of an image. However, the greater the number of classes, the more difficult it is to learn through deep learning. In particular, from an autonomous driving point of view, it is more important to understand the road situation than to detect the sky or buildings. This paper proposes a Road Semantic Segmentation Oriented (RSSO) Dataset. This dataset divides the various markers into eight classes, including lanes on the road. ApolloScape’s lane segmentation dataset is also provided by labeling only road markers. However, RSSO dataset has the advantage of finding drivable regions by including asphalt in the class. We expect the proposed dataset to be useful for a variety of autonomous driving applications.
Multi-features visual odometry for indoor mapping of UAV

In the near-decade, Visual SLAM (Simultaneous Localization and Mapping) system is becoming more and more important for the navigation of unmanned aerial vehicle (UAV) system, because it is effective to replace positioning devices such as GPS (Global Position System) in the indoor scenes. However, there are still challenges when the camera working in the low-texture scenes. The visual SLAM algorithm based on a single feature is difficult to obtain enough features. The positioning accuracy and robustness of the whole system will be reduced or even cannot work properly. Targeting at these issues, we present the MF-VO: an optimization-based multi-features visual odometry. The algorithm extracts both the point feature and the line feature in the image frame. It enhances the robustness of the SLAM system in low-texture scenes and makes the system more robust. A more accurate visual pose can be obtained by minimizing the reprojection error of local points and lines. Additionally, our algorithm is validated on the EuRoC MAV datasets. MF-VO has proven to be effective in improving UAV robustness and accuracy compared to another advanced visual odometry method.
MGDM Transmission of 2×28-Gb/s OOK signals Operating in 0.8-μm Region over 2.2 km of SSMF

We demonstrate the transmission of 2×28-Gb/s MGDM OOK signals operating at 852.6 nm over 2.2-km long SSMF. Only two photodetectors are used for the detection of these two MGDM signals with the help of a multiple-input single-output equalizer.
Ge-Free Ring-Core Fibers: New Opportunities for Short-Reach WDM-MDM Systems

For achieving low intrinsic losses of LP-mode-groups in radially-single-mode ring-core fibers, we propose to adopt a Germanium-free (pure-silica/fluorine-doped) ring-core. Calculated effective-index-differences, group-velocity-dispersions, differential-group-delays, and effective-areas suggest that these fibers are suitable for short-reach WDM-MDM systems.
Feature Enhancement Based Text Sentiment Classification using Deep Learning Model

Text sentiment classification is a significant task in the recent years to understand the opinions and thoughts hidden in the text to enhance more productivity in e-commerce websites and also in the social media. Here we integrate deep learning models to analyze the text sentiments. In this paper, Convolutional Recurrent Neural Network (CRNN) method for text sentiment analysis is proposed. The proposed CRNN is a combination of different layers used to extract the features from the text dataset. During training CRNN is able to learn the features set of the text sentiment dataset. The performance of the proposed approach is evaluated on text sentiments of publically available movie review (MR) dataset. Results show that the proposed method outperforms the traditional deep learning techniques
Micro-Spatial Electricity Load Forecasting Using Clustering Technique

Low growth of electricity load forecast eliminates cost opportunity of electricity sale due to unserviceable load demands. Meanwhile, if it is exorbitant, it will cause over-investment and incriminate investment cost. Existing method of sector load is simplified and easy to implement. However, the accuracy tends to bias over one area of which data is limited and dynamic service area. Besides, the results of its forecast is macro-based, which means it is unable to show load centers in micro grids and failed to locate the distribution station. Therefore, we need micro-spatial load forecasting. By using micro-spatial load forecast, the extrapolated areas are grouped into grids. Clustering analysis is used for grouping the grids. It generates similarity matrix of similar data group. Clustering involves factors causing load growth at each grid; geography, demography, socio- economic, and electricity load per sector. Results of every cluster consist of different regional characteristics, which later the load growth is projected as to obtain more accurate forecast.
Feasibility of Utilizing Air Gapped Toroidal Magnetic Cores for Detecting Pulse Wave in Radial Artery

The feasibility study of utilization of air gapped toroidal magnetic cores for monitoring changing volume of pulsating blood in artery is shown in this paper. The changing impedance of conductive path of blood is represented by a single solid core copper wire, connected to a closed circuit with potentiometer. Commercially available toroidal magnetic cores with two different shapes of custom implemented air gaps and desired number of windings were used to induce current in copper wire. The theoretical expectation of the possibility to focus most of the reluctance of emerging magnetic field into the air gap was experimented. The location of conductive path relative to the implemented air gap was determined to be critical and the best outcome was gained when the copper wire was passing through the center of toroidal core instead of the gap itself. Also, the shape of the air gap was determined to acquire important role related to the location of the conductive path – as the flux fringing is appearing not to significantly affect the magnitude of induced current. The potentiality of monitoring pulse wave by positioning the air gap of toroidal core noninvasively onto the location of radial artery in forearm was concluded not to be feasible with the used setup. The results of on-desk measurements were validated with finite element method simulation of magnetic flux density distribution.
The impact of first metatarsophalangeal angle on the gait features measured by an in-shoe motion sensor

For proper walking, metatarsophalangeal joints play a crucial role during gait. In this study, we specify characteristic features observed in the foot motion signals measured by an in-shoe motion sensor that are significantly affected by the first metatarsophalangeal angle (FMTPA). To validate these specified features, we conduct a classification test for high and low FMTPA subjects by using them. Fifty subjects (23 males and 27 females) participated in our study, and their FMTPAs were measured by digital photography. The subjects with FMTPA above and below 20 degrees were categorized as the high and low groups, respectively, on the basis of the FMTPA distribution in all subjects. The perspective features that have a level of significance of p < 0.05 were specified by comparing the foot motion of the subjects in both groups. The two groups were classified by support vector machine learning. From the results, most significant differences are observed in the coronal plane during pre-swing. By applying the specified features as a predictor, 82.1% of strides are correctly classified in the leave-one-subject-out cross-validation test, which suggests the specified foot motion features intrinsically reflect the gait change induced by FMTPA.
Deep Learning Model for Shield Tunneling Advance Rate Prediction in Mixed Ground Condition Considering Past Operations

The advance rate (AR) is a significant parameter in shield tunneling construction, which has a major impact on construction efficiency. From a practical perspective, it’s helpful to establish a predictive model of the AR, which takes into account the instantaneous parameters as well as the past operations. However, for shield tunneling in mixed ground conditions, most researches focused on the average values of AR per ring and neglect the influence of past operations. This article presents a long short-term memory (LSTM) recurrent neural network model, which was developed for the slurry shield tunneling in a mixed ground of round gravel and mudstone in Nanning metro. A temporal aggregated random forest is employed to rank the importance of the explanatory features. The model performances in different ground conditions are investigated. The results show that the LSTM model can be effectively implemented for the AR prediction. A high correlation is observed between predicted and measured AR with a correlation coefficient ( $R^{2}$ ) of 0.93. The LSTM based AR predictive model is compared with the random forest (RF) model, the deep feedforward network (DFN) model, and the support vector regression (SVR) model. The comparison shows that the LSTM model has the best performances compared to other models. With one-fourth features, we can achieve a 95% prediction accuracy measured by the $R^{2}$ in the proposed model.
Comparison of the Use of Wavelet Transform and Short-Time Fourier Transform for the Study of Geomagnetically Induced Current in the Autotransformer Neutral

A large amount of data has been recorded and accumulated by the registration system of geomagnetic disturbances in the Kola power grid for many years (from 2011 to the present). The measuring devices of this system are installed in dead-grounded neutrals of autotransformers at a number of substations in the North-West of Russia (in the Murmansk region and Karelia). The article compares the analysis of the current in the autotransformer neutral using the short-time Fourier transform (STFT) and the wavelet transform (WT). A discussion of the spectrograms and scalograms of both data processing methods is given on the example of the geomagnetic storm analysis that occurred on September 7–8, 2017. The geomagnetic storm was significant and the value of the geomagnetically induced current (GIC) reached 78 A these days. During the research, the most appropriate method for analysing the GIC was selected - continuous wavelet transform.
A New Method for Detecting Leaks in MEMS Accelerometers at Wafer-Level

This paper presents a novel method for detecting leaks in wafer-level packages. The hardware setup relies on fully electrical stimuli and measurement, which enables a precise estimation of the device’s damping and therefore cavity pressure. This is achieved by measuring the real time movement of the seismic mass via the C-V-curve of the device, which provides distinct advantages over determining the cavity pressure by resonance frequency measurements. While there is no feasible method of determining the transfer function for highly damped devices without significant loss of precision and speed, with this new approach presented here the precision is unaffected by higher damping values. Additionally a test time reduction of 6 to 13 times compared to the current method is achieved. This will help lowering the costs associated with MEMS testing by a significant amount.