Miracle-dz/UniFAR-FTUs huggingface dataset:
The Management and Stewardship of Medical Plastic Waste using Raman Spectroscopy to Sustain Circular Economy

Recycle and reuse of plastic material largely involved in medical acts is the subject of strict regulations and ethical guidelines. Different categories and types of plastics are used to date in medical units, mainly polypropylene and polyethylene, transparent or colored of high or low density. Regardless if they are contaminated or not with harmful pathogens, their disposal is an increasing problem with high costs for proper management and therefore their recycling must involve special management of waste. Here we prospect the possibility to implement new laser-based Raman technology for innovative sorting of residual medical plastic. Currently, medical plastic waste disposal attached enormous attention in the frame of circular economy for avoiding costs and pollution produced by the incineration procedures. To explore any potential added-value by-product resulted from medical waste we need to understand their structure and properties after medical use. Since medical grade plastics raised questions on their dissimilarity to ordinary polymers, we investigated their structural properties using Raman spectroscopy. Further, an algorithm based on plastic Raman signal could provide information on the type of plastics, potential microbial contaminants on used plastics, the influence of the used substances/drugs on the plastics molecular identity and finally, a database for rapid and cost-effective sorting for proper management. To achieve that, we investigated the influence on the pigments on the Raman signal of colored plastics material frequently used in medical environment.
Exploring the Influence of News Articles on Bitcoin Price with Machine Learning

In recent years, cryptocurrencies have become more and more popular around the world, and they are being accepted and used by more countries. Cryptocurrencies are decentralized, and they form an emerging market that is different from stocks. At present, there is already much work around the stock price prediction using news articles, but there are few papers on the cryptocurrency market. In this paper, we aim to research the effects of news articles on bitcoin prices. We extract features from news articles with both commonly used text feature extraction algorithms (e.g., N-Gram and TF-IDF) and SentiGraph, which is a novel text representation method we propose. SentiGraph takes advantages of sentiment analysis and transforms a news article into a graph. Compared with previous feature extraction methods, our experiment results show that this new approach is superior on the prediction accuracy, which also demonstrates the impacts of news articles on the bitcoin price.
Extending Instructional Support in an Algorithm Benchmarking System

OptimEx is an interactive system aimed at empirically comparing the outcomes of different optimization algorithms constructed to solve the same problem. In this paper, several extensions intended to improve the educational use of OptimEx are presented. Firstly, experiment setting has been enhanced, by means of both the automatic load of test cases provided by the instructor and the automatic load of the value ranges of test cases to be randomly generated. The presentation of results also was improved, by means of more refined statistics, the identification of test cases with the largest deviations from optimal results, or the possibility to present deviations as absolute or relative differences. Last but not least, the system allows measuring and comparing run times of the algorithms, using the same scheme of random generation of test cases.
Improved Selection of the Best Forwarding Candidate in 3MRP for VANETs

The design of different metrics in hop-by-hop routing protocols for vehicular ad hoc networks (VANETs) has been investigated with the clear objective to improve the performance of VANETs in terms of packet losses and average end-to-end delay. However, the best way of combining these metrics to compute a multiscore value for each candidate node to choose the best forwarding one, is an open issue yet. Based on a previous designed routing protocol called Multimedia Multimetric MapAware Routing Protocol (3MRP), a detailed analysis of the power mean function is done to determine which form of metrics combination will give the best results in terms of packet losses and average end-to-end delay. Extensive simulations have been conducted indicating that the geometric mean has obtained the lowest average percentage of packet losses.
Vision-Aided Localization For Ground Robots

In this paper, we focus on the problem of vision-based localization for ground robotic applications. In recent years, camera only or camera-IMU (inertial measurement unit) based localization methods are widely studied, in terms of theoretical properties, algorithm design, and real-world applications. However, we experimentally find that none of existing methods is able to perform high-precision and robust localization for ground robots in large-scale complicated 3D environments. To this end, in this paper, we propose a novel vision-based localization algorithm dedicatedly designed for ground robots, by fusing measurements from a camera, an IMU, and the wheel odometer. The first contribution of this paper is that we propose a novel algorithm for approximating the motion manifold for ground robots by parametric representation and performing pose integrating via IMU and wheel odometer measurements. Secondly, we propose a complete localization algorithm, by using a sliding-window based estimator. The estimator is designed based on iterative optimization to fuse measurements from multiple sensors on the proposed manifold representation. We show that, based on a variety of real-world experiments, the proposed algorithm outperforms a number of the state-of-the-art vision based localization algorithms by a significant margin, especially when deployed in large-scale complicated environments.
Wind Turbine Blade Icing Prediction Based on Deep Belief Network

Aiming at the problem that wind turbine blade icing poses a great threat to the power generation performance and safe operation of wind turbine, a blade icing prediction method based on deep belief network (DBN) is proposed in this paper. Firstly, the invalid data in the original data are removed, and preprocessing such as labeling, averaging and normalization is performed. The resampling method is used to solve the problem of uneven distribution of positive and negative samples. According to the relationship model between icing and wind turbine performance, the features of wind speed and power related to icing fault are selected. Secondly, a four-layer DBN network consisting of two RBMs and one classifier is established. After the model parameters are set, the features and labels of the training set are placed in the DBN for training. Get the optimal model after multiple iterations. Finally, the test set is predicted and evaluated. Compared with SVM algorithm, this method has high prediction accuracy and strong stability, and has a significant effect on improving the efficiency of deicing system and reducing the risk of wind turbine operation.
Development of Joint Module with Two-speed Gear Transmission and Joint Lock Mechanism during Driving for Task Adaptable Robot

In order to achieve tasks in the real world environment, humanoid robots have motors and reduction drives optimized in relation to weight and size for providing the necessary torque and angle speed. Therefore, having torque or angle speed outside of the predicted range will usually cause the task to fail. In this research, we propose a joint module with a two-stage transmission mechanism during driving and a joint locking mechanism during non-driving so that the appropriate torque and joint speed can be attained during the task. By applying the joint module to a tricycle type robot and switching the driving state during the task execution, we were able to both reduce the motor load when lifting heavy objects at driving time and keep high rigidity of the joint at non-driving time.
IoT Based Smart Home Automation Enabled with Manual Mode Switch Control

The internet of things is the step towards the digitization of our real world. IoT has given us a platform where we can connect and control over radio waves through the internet. Many IoT based appliances are already available in the market with certain limitations. This paper proposed an IoT based home appliances controlled through internet as well as by the manual switches. We have made certain modifications to these existing electronic boards. The modified system adds extra features to the existing system while retaining its existing features. This proposed IoT based prototype offers a reliable system.
The use of Letrinhas in Elementary School: The results of a case study in Central Portugal

Co-created by students and faculty members of the Instituto Politécnico de Tomar (Portugal) and teachers from the Artur Gonçalves cluster of schools, Letrinhas is an education software designed to promote reading literacy. In order to analyse its impact on the promotion of reading as well as investigate its implications on school organisational and educational dynamics, a study has been conducted in various school clusters in Central Portugal. The study was undertaken with the collaboration of the training centre “Os Templários” which provided the teachers with appropriate training in the use of Letrinhas. This study involved 65 second- and third-grade students that were divided into two groups: one that used Letrinhas (experimental group) and another that used conventional methods (control group). The study revealed statistically significant differences in terms of fluency and accuracy, with the experimental group showing better results. Teacher expectations in relation to technology and its impact are positive and there is a favourable attitude towards its use. All teachers showed enthusiastic about using Letrinhas, recognising that this app promotes students' reading ability, autonomy and self-confidence. In terms of changes in organizational dynamics, the teachers have only referred to the investment on tablets.
Extracting Audio-Visual Features for Emotion Recognition Through Active Feature Selection

Research in automatic emotion recognition has seldom addressed the issue of computational resource utilisation. With the advent of ambient technology, which employs a variety of low-power, resource constrained devices, this issue is increasingly gaining interest. This is especially the case in the context of health and elderly care technologies, where interventions aim at maintaining the user’s independence as unobtrusively as possible. In this context, efforts are being made to model human social signals such as affects using low-cost technologies, which can aid health monitoring. This paper presents an Active Feature Selection (AFS) method using self-organized maps neural networks for emotion recognition in the wild. The AFS is used for feature subsets selection from three different feature sets: 62 out of 88 features were selected for eGeMAPs, 21 out of 988 for emobase, and 140 out of 2832 for LBPTOP features. The results show that the features subsets selected by AFS provide better results than the entire feature set and PCA dimensionality reduction method. The best improvement is observed on emobase features, followed by eGeMAPs. For visual features, nearly the same results are obtained with a significant reduction in dimensionality (only 5% of the full feature set is required for the same level of accuracy). The weighted score fusion results in an improvement, leading to 43.40% and 40.12% accuracies on the EmotiW 2018 validation and test datasets respectively.
On Theoretical Optimization of the Sensing Matrix for Sparse-Dictionary Signal Recovery

Compressive Sensing (CS) is a new paradigm for the efficient acquisition of signals that have sparse representation in a certain domain. Traditionally, CS has provided numerous methods for signal recovery over an orthonormal basis. However, modern applications have sparked the emergence of related methods for signals not sparse in an orthonormal basis but in some arbitrary, perhaps highly overcomplete, dictionary, particularly due to their potential to generate different kinds of sparse representation of signals. Here, we first propose the Signal space Subspace Pursuit (SSSP) algorithm, and then we derive a low bound on the number of measurements required. The algorithm has low computational complexity and provides high recovery accuracy.
Real-Time Sentiment Analysis of 2019 Election Tweets using Word2vec and Random Forest Model

Sentiment analysis of social media data consists of attitudes, assessments, and emotions which can be considered a way human think. Understanding and classifying the large collection of documents into positive and negative aspects are a very difficult task. Social networks such as Twitter, Facebook, and Instagram provide a platform in order to gather information about people’s sentiments and opinions. Considering the fact that people spend hours daily on social media and share their opinion on various different topics helps us analyze sentiments better. More and more companies are using social media tools to provide various services and interact with customers. Sentiment Analysis (SA) classifies the polarity of given tweets to positive and negative tweets in order to understand the sentiments of the public. This paper aims to perform sentiment analysis of real-time 2019 election twitter data using the feature selection model word2vec and the machine learning algorithm random forest for sentiment classification. Word2vec with Random Forest improves the accuracy of sentiment analysis significantly compared to traditional methods such as BOW and TF-IDF. Word2vec improves the quality of features by considering contextual semantics of words in a text hence improving the accuracy of machine learning and sentiment analysis.
Design of a 3-DOF Linkage-Driven Underactuated Finger for Multiple Grasping

This paper presents a 3-DOF linkage-driven underactuated finger for achieving several different grasp modes. This finger mechanism is constructed by stacking one five-bar mechanism over one double parallelogram. This special architecture allows for installing all of the actuators on the base. A 2-finger robotic gripper having three actuators is developed for performing self-adaptive power grasping, parallel precision grasping, grasping during contact with the environment, and other grasping tasks through changing the orientation of the distal phalanx actively. The performance of the gripper is confirmed through both simulation and practical grasping experiment.
Data Fusion Optimization Analysis of Wireless Sensor Networks Based on Joint DS Evidence Theory and Matrix Analysis

In view of the energy Limitation and data redundancy of wireless sensor networks (WSN), in order to better coordinate WSN computing resources and make rational use of its energy, this paper uses DS evidence theory and matrix analysis to jointly improve WSN consumption and efficient use of redundant data. On the one hand, the DS evidence theory is used to fuse redundant data of WSN; on the other hand, with the idea of matrix analysis, the process of DS evidence theory fusion is accelerated. Simulation experiments show that this joint mode not only improves the energy utilization of WSN, but also optimizes the transmission data quality of WSN.
Sparse-3D Lidar Outdoor Map-Based Autonomous Vehicle Localization

Difficulties in capturing unique structures in the outdoor environment hinders the map-based Autonomous Vehicles (AV) localization performance. Accordingly, this necessitates the use of high resolution sensors to capture more information from the environment. However, this approach is costly and limits the mass deployment of AV. To overcome this drawback, in this paper, we propose a novel outdoor map-based localization method for Autonomous Vehicles in urban environments using sparse 3D lidar scan data. In the proposed method, a Point-to-Distribution (P2D) formulation of the Normal Distributions Transform (NDT) approach is applied in a Monte Carlo Localization (MCL) framework. The formulation improves the measurement model of localization by taking individual lidar point measurements into consideration. Additionally, to apply the localization to scalable outdoor environments, a flexible and efficient map structure is implemented. The experimental results indicate that the proposed approach significantly improves the localization and its robustness in outdoor AV environments, especially with limited sparse lidar data.
Detecting layered structures of partially occluded objects for bin picking

When robots engage in bin picking of multiple objects, a failure in grasping partially occluded objects may occur because other objects may overlap the desired ones. Therefore, the layered structure of objects needs to be detected, and the picking order needs to be established. In this paper, we propose a new dataset that evaluates not only the area of objects but also the layered structures of objects. In this dataset, three tasks are targeted: object detection, semantic segmentation, and segmentation of occluded areas for bin picking of multiple objects. The dataset, called “the Amazon Robotics Challenge (ARC) Multi-task Dataset” contains 1,500 RGB images and depth images, including all scenes containing bounding box labels, semantic segmentation labels, and occluded area labels. This enables representing the layered structure of overlapped objects with a tree structure. A benchmark of the ARC multi-task dataset demonstrated that occluded areas could be segmented using a Mask regional convolutional neural network (R-CNN) and that layered structures of objects could be predicted. Our dataset is available at the following URL:http://mprg.jp/research/arc_dataset_2017_e.
Power Allocation Schemes for Implant Nodes in Cooperative Wireless Body Area Networks

In this paper, a body area network (BAN) consisting of source and destination with multiple amplify and forward nodes called relays in between them are considered and the power allocation to the relays is investigated. This investigation has been done for Implant nodes considering two scenarios. In the first scenario, the objective is to maximize the effective signal to noise ratio (SNR) of the source-relay and the relay-destination links under the total relay power constraint. In the second scenario, the objective is to maximize the total power allocated for a given data rate. These objectives are aimed to show their results by means of error rate. Finally the results are correlated with respect to direct transmission and then concluded.
Low Complexity Frequency Monitoring Filter for Fast Exon Prediction Sequence Analysis

Over the last few years, the application of Digital Signal Processing (DSP) techniques for genomic sequence analysis has received great interest. Indeed, among its applications in genomic analysis, it has been demonstrated that DSP can be used to detect protein coding regions (exons) among non-coding regions in a DNA sequence. The period-3 behavior exhibited by exons is one of its features that has been exploited in several developed algorithms for exon prediction. Identification of this periodicity in genomic sequences can be done by using different methods such as the well-known Fast Fourier Transform (FFT) and the Goertzel algorithm for complexity reduction in which the reduction of computational time is a great challenge in genomic analysis. Therefore, this paper presents a novel one frequency analysis by using half of the arithmetic complexity of the Goertzel algorithm for gene prediction. Compared to the Intel®’s FFT (MKL) optimized function, the Goertzel’s (IPP) and the dedicated Goertzel compiled function with ICC on Xeon CPU (24 cores), the proposed method conserves the same accuracy provided by the referenced methods which will manifest a speedup of 3000, 10 and 2 compared to MKL FFT, IPP Goertzel and the dedicated Goertzel with ICC, respectively.
Efficient Environment Guided Approach for Exploration of Complex Environments

Remote inspection of a complex environment is a difficult, time consuming task for human operators to perform. The need to manually avoid obstacles whilst considering other performance factors i.e. time taken, joint effort and information gained represents significant challenges to continuous operation. This paper proposes an autonomous robotic solution for exploration of an unknown, complex environment using a high DoF robot arm with an eye in hand depth sensor. The main contribution of this work is a new strategy to find the next best view by evaluating frontier regions of the map to maximise coverage, in contrast to many current approaches which densely sample joint or workspace configurations of the robot. Multiple utility functions were evaluated that showed different behaviours. Our results indicated that the presented algorithm can explore an arbitrary environment efficiently while optimising various performance criteria based on the utility function chosen, application constraints and the desires of the user.
Perception System Design for Low-Cost Commercial Ground Robots: Sensor Configurations, Calibration, Localization and Mapping

For commercially successful ground robots, high degree of autonomy, low manufacturing and maintenance cost, as well as minimized deployment limitations in different environments are essential attributes. To deliver an ‘anywhere deployable’ product, it is impractical to rely on one single sensor or one single piece of algorithm to overcome all related challenges. Instead, the entire robotic system should be dedicated designed, including the choices of sensors, processors, algorithm integration for various functionality, and so on.This paper presents our design of perception system for commercial ground robots, which is able to operate in most common environments. The designed system is equipped with low-cost sensors and processors. The first key contribution of this paper is the design of the robotic sensory system, which includes a monocular camera, a 2D laser range finder (LRF), wheel encoders, and an inertial measurement unit (IMU). Our sensory system can be built at a cost of as low as $100. Furthermore, the selected sensors provide complementary characteristics for perception of both robot ego-motion and its surrounding environments, which are the prerequisites for ‘anywhere’ deployment. The second key contribution of this paper is that a complete set of technologies is proposed based on our sensor systems, including sensor calibration (factory calibration and online calibration), localization (environmental exploring and re-localization), as well as mapping. The proposed methodology includes both efficient engineering implementation and theoretical novelty for high performance systems. Experimental results from our robotic testing platform and off-the-shelf commercial robots are presented. These results demonstrate that the proposed system can be deployed in various environmental conditions without performance compromise.
Responsive Joint Attention in Human-Robot Interaction

Joint attention has been shown to be not only crucial for human-human interaction but also human-robot interaction. Joint attention can help to make cooperation more efficient, support disambiguation in instances of uncertainty and make interactions appear more natural and familiar. In this paper, we present an autonomous gaze system that uses multimodal perception capabilities to model responsive joint attention mechanisms. We investigate the effects of our system on people’s perception of a robot within a problem-solving task. Results from a user study suggest that responsive joint attention mechanisms evoke higher perceived feelings of social presence on scales that regard the direction of the robot’s perception.