Reviewerly/exHarmony huggingface dataset:
Automated Three-Dimensional Image Based Localization of Blastomeres for Single Cell Surgical Applications

Single biological cell surgery requires accurate localization of cell structures and organelles within the cell to permit automated processing. This study proposes an image based system to localize blastomeres in 3-dimensions (3D), in preparation of embryo orientation and ablation. Optical section images along the z-axis (z-stacks) are used to localize the blastomeres using computer vision, and further reconstruction and relocalization are achieved by clustering the 3D data along the z-axis. Two 3D reconstruction algorithms were investigated. The first proposed system processes each z-stack image using local phase coherence maps (LPC) and circle Hough transforms to accurately locate the centroid of the blastomeres while the second system generates and localizes the 3D data, in real time, using the optical flow of the image feed as the z-stacks are acquired. Experimental results demonstrate accurate localization of the blastomeres.
Study on Collaborative Task Assignment of Sphere Multi-Robot based on Group Intelligence Algorithm

With the development of science and technology, many complex problems cannot be completed efficiently by a single robot and require multiple robots to work together. For complex task scenarios with multiple robots, the multi-robot task allocation problem is the key to coordinating robots to work efficiently. In this paper, for the application scenario of multi-robot collaborative inspection task allocation, the task allocation problem is first mathematically modelled using the multi-robot problem model, and simulations based on the resource balance search algorithm and the genetic population intelligence algorithm are applied respectively. Incorporating constraints in the population intelligence genetic algorithm, which transforms robot power constraints into distance constraints for research, allows for targeted simulation solutions for practical multi-robot collaborative detection. The results show that the genetic algorithm based on population intelligence can solve the problem well and minimise the total cost of multi-ball robot clustering, enhance the optimised search capability of the algorithm, improve the rationality of multi-robot task allocation and increase the efficiency of task completion.
An Intelligent Planning Method For The Multi-Rotor Manipulation Robot With Reinforcement Learning

In this paper, an intelligent planning method based on proximal policy optimization algorithm for the multi-rotor aerial manipulation robot is presented. This method can not only avoid the complexity of the dynamic analysis and modeling but also the large disturbance of the manipulator to the robot’s body produced by the independent kinematic planning method. The disadvantage of kinematic planning method in aerial manipulation is given. The detailed structure of the adopted training and simulation environment is introduced. A deep reinforcement learning formulation is proposed to deal with the aerial manipulation of the robot. The particulars of setup in training and simulation are illustrated and the practical training and a series tests are carried out. The results of simulation proved the feasibility of this intelligent planning method and its advantages in real-time planning or replanning to enhance the stability of the robot in manipulation.
Application of A star algorithm in amphibious hull cleaning robot

Ships sailing in the sea for a long time, the bottom of the ship will be attached to many creatures, resulting in slower and slower sailing speed, there is a certain security risks. This paper studies an amphibious hull cleaning robot, which can clean the bottom of the ship and store the bottom condition for professional inspection. Thus, the cost of ship cleaning and maintenance can be reduced and the safety of personnel can be improved. In this paper, A-Star algorithm is proposed to solve the path planning problem of the robot.
Adaptive Hardware Architecture for Neural-Network-on-Chip

Neural networks are beneficial for several applications in classification and regression problems. Hardware implementation of a neural network is a challenge in terms of making adaptive architecture to fit different applications. Network-on-Chip (NoC) is an efficient method in power and bandwidth to make communication between several nodes. This paper proposes an adaptive neural network based on NoC. NoC consists of routers and Process Elements (PEs), and each router is connected to its PE which consists of m nodes. These nodes are used to construct layers for a neural network. A configuration packet is used to show the number of layers and of nodes per layer. The proposed method can assign multiple routers to represent a layer based on the required configuration. Thus, the proposed method supports the construction of different layers with the adaptability of different nodes configuration (desired number) appropriate for the target application. The proposed method is implemented on FPGA Altera 10 GX, and it achieves an accuracy of 98.18% with the MNIST dataset. It has been also tested using multiple datasets. The results show the proposed method has comparable resource utilization with the traditional method.
Service-Oriented Architecture based Approach for Secure Video Transmission in Next Generation Networks

Video transmission is one of the many applications that are supported by the evolving technology of Next Generation Networks (NGNs). However, secure video transmission is known to be one of the most challenging tasks to manage in NGNs. Majority of the currently existing infrastructures were not built to effectively handle this cumber some task which involves unpredictable daily increasing video traffic. Upgrading the currently existing infrastructures may cost twice as much as the initial implementation. Hence a better approach for handling video transmission securely is needed while delivering improved Quality of Service (QOS), Quality of Experience (QOE) and also maintaining a fast reliable network. In this paper, we present a Service Oriented Architecture (SOA) based approach for effectively securing video transmission in NGNs.
Video Conference System in Mixed Reality Using a Hololens

The mixed reality conference system proposed in this paper is a robust, real-time video conference application software that makes up for the simple interaction and lack of immersion and realism of traditional video conference, which realizes the entire process of holographic video conference from client to cloud to the client.This paper mainly focuses on designing and implementing a video conference system based on AI segmentation technology and mixed reality.Several mixed reality conference system components are discussed, including data collection, data transmission, processing, and mixed reality presentation.The data layer is mainly used for data collection, integration, and video and audio codecs.The network layer uses Web-RTC to realize peer-to-peer data communication.The data processing layer is the core part of the system, mainly for human video matting and human-computer interaction, which is the key to realizing mixed reality conferences and improving the interactive experience.The presentation layer explicitly includes the login interface of the mixed reality conference system, the presentation of real-time matting of human subjects, and the presentation objects.With the mixed reality conference system, conference participants in different places can see each other in real-time in their mixed reality scene and share presentation content and 3D models based on mixed reality technology to have a more interactive and immersive experience.
Feature Fusion Transformer Network for Natural Language Inference

Natural Language Inference (NLI) is a branch of Natural Language Processing (NLP) whose main task is to determine the relationship between a sentence pair (two sentences). Early attempts were made to use Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to solve such problems, butit was difficult to achieve ideal results. Until the last few years, satisfactory results have been achieved using various methods based on attention mechanisms. Attention mechanism has unique advantages in a wide range of receptive fields, but it is difficult to extract local structure features effectively, while neural networks represented by CNN are good at local feature extraction. Therefore, we first verified the above point by experiments, then proposed the inference network models Feature Fusion Transformer Network (FFTN). The model is improved based on the Transformer model structure, retaining the non-local feature extraction advantages of the self-attention mechanism, combining the convolution method to enhance the feature attention of the local domain, and efficiently combining local and non-local features. Finally, excellent accuracy performance is achieved on the SNLI and MultiNLI datasets.
Quantum noise and squeezing in semiconductor nanolasers and nanoLEDs

On-chip optical interconnects require light-sources with reduced energy cost, small footprint and low noise. We show that semiconductor nanolasers and nanoLEDs may feature squeezed intensity noise by exploiting novel cavity designs based on extreme dielectric confinement. NanoLEDs may be advanta-geous as compared to nanolasers.
Automatic Recognition Method of Aviation Thin Cable Characters Based on Rotating Monocular Camera

In order to solve the difficulty of manual recognizing the characters printed on thin aviation cables, an automatic recognition method by rotating a monocular camera is presented., two indexes that reflect the completeness and centralizer of characters are designed to automatically search an appropriate image of aviation cable captured by the rotated camera. Then, an optimal image-stitching method is proposed by finding the peak point of ‘coincidence of black pixels’, which improve the quality of character image. Moreover, based on the equal-spaced and straight-line distribution of cable characters, the projection algorithm is optimized, and a character extraction algorithm considering the black pixel’s density and degree of centering is developed. Finally, a-multi SVM classifier is designed to achieve highly accurate recognition of confusing characters. The experimental results demonstrate the effectiveness of the recognition method and algorithm.
Low Power Neural Network Accelerators Using Collaborative Weight Tuning and Shared Shift-Add optimization

Accelerators for DNN inference in embedded applications using fixed-point arithmetic are attractive from the perspectives of hardware complexity and power consumption. While techniques have been proposed for DNN inference with constrained values, these are typically accompanied by loss of inferencing accuracy. We propose instead an inferencing architecture predicated on tuning of weights with minimal impact on accuracy to facilitate sharing of shift and add operations across different weight computations in a multiplier-less manner. The highly nonlinear relationship between the distribution of ones and zeros in the binary encoding of multiplier weights is exploited to share shift-add operations. A systolic array architecture supporting such a computation paradigm is developed. Experimental results on hardware savings, power and latency tradeoffs are presented and demonstrate the benefits of the proposed scheme.
Fault Mode Analysis of Neural Network-based Object Detection on GPUs with Neutron Irradiation Test

Neural network-based (NN-based) object detection algorithms are often implemented and executed on Graph Pro-cessing Units (GPUs). Understanding the reliability and fault modes of NN-based object detection is indispensable to improve and guarantee system reliability. In this work, we measure and analyze the fault modes of NN-based object detection running on GPUs using a quasi-monoenergetic neutron beam. Experimental results show that there are burst fault modes that repeat the same silent data corruption (SDC) errors and induce variant SDC errors. While the repetitive burst-mode errors of 56&#x0025; probably originate from upsets in NN model parameters, the root cause of the remaining variant burst errors of 44&#x0025; is unknown.
Fuzzy Inference Speed Enhancement for Low Budget Computers using Hash Indices

Fuzzy inference is a powerful tool used in many fields of science nowadays, including medical science. However, for applications where the number of fuzzy rules is very large, the increased computational complexity for systems with limited resources (such as low budget computers and embedded systems) can result in a very slow operation. In this paper, a new method is proposed to accelerate the operation of Fuzzy Inference Systems that is faster than the conventional sequential procedure, primarily for such computer systems.
Accumulation of adversarial examples for underwater visual object tracking

Recent studies show that small perturbations in video frames could misguide the deep learning-based visual object trackers. In this paper, we first attempt to generate an accumulation of adversarial examples for underwater VOT (Visual object tracking). This is the first attempt at underwater VOT attack. The data used are the URPC (Underwater Robot Picking Contest) in 2017 and 2018, and the fish2 subset of the VOT2019(Visual Object Tracking Challenge in 2019). We generate adversarial examples by minimizing the L2 total loss function which we designed. Experiments show that this attack method can achieve an effective attack, which can reduce the success rate of at least 48.6% of the DaSiamRPN(Distractor-aware SiamRPN) trackers.
Fabry disease automatic recognition through image processing

Image processing algorithms are widely used nowadays, finding employment in the photographic field, in facial recognition, up to clinical applications. Diagnostic processes are based on digital images obtained using radiological tools capable of representing regions of the human body on 2D or 3D matrices. Image processing techniques are fundamental in order to obtain good results in terms of resolution, contrast and for a correct reconstruction of the image. Histological examinations are important for diagnosing pathologies too, analysing microscopic images of biological tissue. This is the case with Fabry disease, whose symptoms and complications can be severe (as kidney damage, heart attacks and strokes) with important effects on the quality of life of those affected. This work shows image processing algorithms as useful diagnostic support tools, capable to segment cellular tissue and to identify glycolipid bodies, typical of Fabry disease, in cellular tissue digital optical microscopic images. The obtained results are the number of intracellular accumulation structures with an estimated error lower than 10%, and the ratio of the areas of cellular tissue and glycosphingolipid accumulations, which constitute quantitative parameters for the diagnosis. Tools execution times are very low, from few seconds up to 10 minutes, with a substantial time saving compared to manual analysis. Also a mobile version of the algorithms is developed, as important application in the field of telepathology.
Sentiment Analysis of South African News Company

This paper reports on using RStudio packages to conduct a text and sentiment analysis of tweets from SABCNews. To download selected tweets, the twitteR package in R was used. A 10-day period was to collect data (tweets). In this regard, a set of 1000 tweets written in English were analysed from the @SABCNews. This analysis was performed to classify each tweet as positive, negative or neutral and to what degree; thus, learn more about the audience, transform every interaction into an actionable strategy for success, and shape the company’s business strategy. The tweeting pattern is measured using Hashtag and Favorite Count, Source of Tweet, and Count of Tweet compared to Favorite Count and Retweet Count. Based on trust, joy, surprise, anticipation, fear, sadness, disgust, and anger, it can be observed that most tweets expressed trust and, joy; with no tweet expressing anger. Furthermore, the Twitter sentiment analysis assigned for each tweet showed that a rapid change in sentiments for each tweet occurs late at night before individuals sleep and early morning before they go to their daily duties. While these results are positive for SABCNews; however, they emanate from only English language tweets. Therefore, future work will focus on sentiments expressed in other South African languages and compare views from other news agencies.
Research on Ship Abnormal Behavior Detection Method Based on Graph Neural Network

Because of the existing maritime intelligent supervision, ship abnormal behavior detection ignores the interdependence between different time series of ships and different abnormal behavior characteristics, which leads to poor real-time performance and limited detection accuracy of abnormal detection. This paper explores the mechanism of ship abnormal behavior monitoring and detection based on graph attention network. This mechanism uses a sliding window to generate fixed data input, uses graph attention network to capture the dependence between ship behavior characteristics, and determines the threshold of anomaly according to the sea environment set in this paper. In order to complete the online real-time detection of ship abnormal behavior with multivariate and multidimensional time series, improve the accuracy of ship abnormal behavior detection and reduce the blind area of VTS (Vessel Traffic Service) system monitoring.
Classification of Ultrasound Breast Images Using Fused Ensemble of Deep Learning Classifiers

Ultrasound (US) imaging is an affordable, radiation-free screening that has been successfully used for early stage breast cancer screening. Deep learning-based classifiers are currently being used to classify breast cancer. Deep learning requires large amount of dataset for training. However, currently available databases of breast cancer US images are small and the images have tumors of different sizes. Therefore, the deep learning-based classifiers are unable to provide good generalization. To address these challenges, we propose a fusion of three models namely transfer learning, multi-scale and autoencoder. Transfer learning model is based on VGG16 and is used to overcome the issue of limited data. Convolutional autoencoders extract features that can represent even noisy images. We propose a novel multi-scale deep learning model to address learning of US images with tumors of various sizes and shapes. These three models are trained independently and then their classification outputs are fused using differential evolution (DE) algorithm to get the final classification results. The proposed novel fused ensemble of deep learning-based classifiers is evaluated using two publicly available US datasets. Transfer learning, autoencoder, and multi-scale models individually achieve an accuracy of 88%, 85%, and 89% respectively. The fusion of the outputs of the three models using DE algorithm provides a classification accuracy with an accuracy of 93%. The source code available at https://github.com/EbrahimAli1989/Breast-Cancer-classification-.git.
Research on Path Planning and Motion Control Technology for Mobile Robot Doing Inspection

A hybrid path planning algorithm that improves the fusion of the A* algorithm and the dynamic window algorithm is proposed to address the problem that the path of the mobile robot is not optimal and cannot avoid temporary obstacles during inspection operations in complex environments. The kinematic analysis of the mobile inspection robot is carried out firstly, and the relationship between the robot running speed and robot posture is derived, and a motion control scheme is proposed. Secondly, the robot's environment map is constructed, and the problem that the traditional A* algorithm has more inflection points in the path planning process is pointed out. Then, based on the 8 directional neighbourhood search strategy, a 16 directional neighbourhood search strategy is proposed, and the dynamic window algorithm is incorporated in order to make the robot avoid temporary obstacles in the actual operation process. The experimental results show that the fused algorithm can not only avoid temporary obstacles effectively, but also reach the target point accurately.
Traffic Monitoring Using Intelligent Video Sensors to Support the Urban Mobility Planning

The vehicular traffic is an extremely topical issue given its influence on the economic, social and environmental system. The mobility of people and goods is a significant factor of dynamism, but the congestive phenomena, caused by the greater vehicular flows, can be reflected in negative terms on the quality of citizens life. Smart management of transport networks is necessary and this, also and above all, means a traffic monitoring on the road. The systematic collection of traffic data is important for the safety/security related to the traffic phenomena, but also in support of urban mobility planning, for a more efficient management of infrastructures, to ensure appropriate traffic safety levels and the best possible levels of service. There are now monitoring tools, such as those based on video sensors, which can facilitate the task of transportation planners and managers and which can also provide opportunities as those relating to monitoring the security of citizens with respect to acts of vandalism or violence (security). In the first part, the paper proposes a synthetic framework on technologies used for traffic monitoring, with a description of the sensors and variables likely to be captured. Attention is then focused on the monitoring technologies using video sensors, bringing out the greater wealth of information that can derive from it and the positive effects for the purposes of traffic planning. Finally, a proposal for a methodological approach is put forward to configure a continuous monitoring system based on a distribution of permanent video sensors on the urban road network, useful for the effective management of mobility phenomena and for planning activities.
Interior Deformation and Failure of a Short Composite Sandwich Beam under Three-Point Bending

Composite sandwich plates and beams are increasingly employed as an engineering material in structures such as airplanes, ships, bridges, and vehicles because of their superb strength to weight ratio. Understanding a sandwich structure’s failure mechanism is a prerequisite for a safety design. In this paper, we employ a new experimental technique called DVSP (Digital Volumetric Speckle Photography) to map the interior deformation of a short composite sandwich beam under three-point bending. 3D displacement fields and shear strain fields of 5 transverse and 4 longitudinal sections of the beam are mapped quantitatively in detail as a function of increasing load. The beam fails in delamination of the interface between the face sheet and core.