Miracle-dz/UniFAR-FTUs huggingface dataset:
Channel Impairment Analysis in Nano Communication Under CM-3 Scenario

Nano Communication (NC) is evolving as a new paradigm of Wireless Communication (WC) whose application ranges from security to medical, information communication to bioinformatics and body machine interface to consumer industry. But like any other WC system channel modelling of NC has been found as most challenging issue. Channel Model-3 (CM-3) is one of the channel models for NC among others (CM-1 to CM-4). However, in CM-3 model Poisson distribution has been used to estimate the path arrival time and number of arrival paths, for the calculation of channel impulse response (CIR) which is not suitable in real scenario. Therefore, in this paper CIR for NC under CM-3 has been presented using Negative Binomial distribution in place of Poisson distribution. Numerical discussions show the comparison of CIR under given distributions.
Binomial Galois Field Based Asynchronous Non-Adaptive Mode Energy Management System

In Delay Tolerant Networks (DTN), the inter-contact duration between the nodes is much greater than the contact duration. So, energy efficient neighbor discovery is an important characteristic of a energy constant network like DTN. In order to save energy, the nodes synchronize their interface wake-up and sleep using global clock but it is an energy hungry process. To avoid this energy hungriness many asynchronous protocols have been developed over the years based on the wake-up scheduling in order to enable energy efficient but timely neighbor discovery. In this paper, we propose an irreducible polynomial based asynchronous non-adaptive wake-up scheduling protocol that is distributed and energy efficient with high packet delivery ratio. All the nodes of the network follow the same scheduling pattern. Simulation results show that the proposed protocol in non-adaptive mode reduces the energy consumption by 33% in high load (60 packets/minute) compromising the delivery ratio by 8% and 38% in low load (6 packets/minute) compromising the delivery ratio by 11%.
Mapping the NGSI-LD Context Model on Top of a SPARQL Event Processing Architecture: Implementation Guidelines

NGSI-LD is an open specification released by ETSI which proposes an information model and an API for an easy to use and standard management of context information. The NGSI-LD information model is framed within an ontology and adopts JSON-LD as serialization format for context information. This paper presents an approach to the implementation of the NGSI-LD specification over a SPARQL Event Processing Architecture. This work is being developed within the European-Brasilian H2020 SWAMP project focused on implementing an Internet of Things platform providing services for smart water management in agriculture.
Binoclt: A New Binomial Classification Scheme for Long-Text Mining in Online Social Network

With the rapid development of information technology, articles with long-text content are very common among social networks. These articles generally focus on specific areas, whose contents can be further categorized into two kinds - articles spreading instant news or personal opinions, defined as ‘event’, and articles sharing interesting anecdotes, defined as ‘emotion’. Better classifying these large-scaled and fast-updated online articles brings great benefits to many relevant studies, such as topic detection and user behavior analysis. However, existing methods, such as deep neural network, require massive computing resources and long training time to classify articles. To overcome these shortcomings, we present an innovative online binomial classification scheme for long-text mining in social networks - Binoclt. First, we propose an online topic clustering algorithm based on keyword frequency to combine the extended TextRank with weighted titles and the improved GloVe methods. Then we define two kinds of features, namely Maximum Keyword Score (MKS) and Cluster Item Number (CIN), and use them to train our classifier to distinguish between articles as ‘events' or ‘emotion’. To verify the efficiency our classifier, we conduct extensive and comprehensive experiments using the real dataset from WeChat, which is the largest social networking platform in China. Then we compare Binoclt with other advanced neural network methods. The results show that our method contributes to a greater classification accuracy while it also reduces the complexity of work and shortens the training time.
Polar Code Decoder Framework

Polar codes gained large interest in the last years since they are the first channel codes that are proven to achieve channel capacity. Due to this property, Polar codes were recently adopted for the 5G standard. We present an industrial framework for the generation of Polar code decoders for highest data throughput. The framework automatically generates VHDL models ready for synthesis, placement and routing and corresponding simulation models to assess the communications performance. This framework enables Polar code decoder IP providers to give fast feedback to customers on communications and implementation performance. We demonstrate that this framework outperforms existing manually optimized decoders especially in terms of energy efficiency.
Identification of tibial stress fractures using therapeutic continuous ultrasound.

STUDY DESIGN One-group discriminant analysis. OBJECTIVE To determine whether 1 MHz of continuous ultrasound can identify tibial stress fractures in subjects. BACKGROUND Stress fractures can lead to loss of function or to more serious nonunion fractures. Early diagnosis is important to reduce the risk of further injury and to assure a safe return to activity. Therapeutic ultrasound has been reported to be an accessible, less expensive alternative in diagnosing stress fractures compared with other diagnostic techniques. METHODS AND MEASURES Twenty-six subjects (12 men, 20.33 +/- 1.37 years; 14 women, 20.78 +/- 3.8 years) with unilateral tibia pain for less than 2 weeks volunteered to participate in the study. Continuous, 1 MHz ultrasound was applied to the uninvolved and involved tibias at 7 increasing intensities for 30 seconds each. Subjects completed a visual analog scale after the application of each intensity to assess the pain response to ultrasound. Results from the visual analog scale were compared to magnetic resonance imaging (MRI) findings to determine if continuous ultrasound could predict whether subjects had a normal MRI, increased bone remodeling, or advanced bone remodeling consistent with a stress fracture. RESULTS Discriminant analysis on the visual analog scale correctly classified subjects into 1 of 3 clinical classification groups in 42.31% of the cases. None of the subjects found to have a stress fracture by MRI were correctly identified by continuous ultrasound. This resulted in a predicted sensitivity of 0% and a predicted specificity of 100%. CONCLUSIONS A protocol using visual analog scores after the application of 1 MHz continuous ultrasound is not sensitive for identifying subjects with tibial stress fractures.
Compensation of Mutual Coupling for Two Dipoles Transmitting Antenna Array

A technique has been proposed for a more accurate estimation and compensation of mutual coupling in transmitting antenna array system. The compensation consists of the modification in feed voltage based upon the value of the mutual impedance found using FEKO. Pattern multiplication technique has been used to obtain the coupling-free azimuthal gain pattern with specific feed voltage. With the same feed voltage the pattern has been obtained in FEKO to observe the effect of mutual coupling. Finally, the coupled gain pattern has been obtained with the compensated feed voltage. All the three radiation patterns have been compared to see the effect of the coupling as well as the effectiveness of the compensation technique.
Prediction of the Visual Similarity Between Photos using Several Pre-Traind CNNs

We have measured the visual similarity between photos manually by making dozens of people classify similar photos. We proposed the method how to predict the unmeasured visual similarity between photos by making use of several 1000 class classifications to photos by pre-trained Convolutional Neural Networks (CNNs). We conducted a survey asking students to take photos of their lives that interest them. 200 photos were collected from 2012 to 2014. The visual similarity matrix P between these 200 photos was measured. The similarity matrix Q between them was easily obtained by CNNs. Divide 200 photos into two groups A and B. Assuming that only the visual similarity between photos included in A was measured. Minimizing Kullback - Leibler divergence between two Gaussian distributions with mean 0, covariance matrices P and Q, P could be restored from the visual similarity within A and Q. It was shown that restoration of P went quite well, if the photos included in A were similar to the photos included in B. On the other hand, it was shown that restoration of P went fairly well, otherwise.
OpenSnap: Collection of Globally Consistent Statistics in Software Defined Networks

Capturing and monitoring the global state of the network in a software defined network (SDN) is crucial for efficient routing, performance monitoring, Quality of Service (QoS) assurance, etc. The two major existing approaches for statistics collection in SDN are polling-based and event-based. Due to the asynchronous nature of the network, statistics collected through polling have inconsistencies and are not suitable for capturing the consistent global state of the network. On the other hand, event-based monitoring schemes may give sparse information about the network. Globally consistent state detection is well studied for asynchronous systems. However, current SDN standards such as OpenFlow do not support any functionality to collect globally consistent statistics. In this paper, we propose “OpenSnap”, an algorithm to determine the globally consistent state of the system. To support OpenSnap, we extend the OpenFlow protocol by adding a new action. The experimental results show that the statistics collected at the SDN controller using the proposed OpenSnap algorithm are always consistent.
CBCT Image Feature Enhancement for Endodontic Therapy

Root of tooth is an important part of the body; it not only holds the tooth in jawbone but also keeps the jawbone healthy. The only living part of the structure inside the root is the pulp. The bacterial infection in pulp leads to losing of teeth which means you lose the root. Hence, accurate evaluation of the root canal anatomy is required to keep your teeth healthy. For that endodontic therapy is the best treatment. In endodontic therapy, clear understanding of the root features is required. In this proposed work, CBCT image enchantment is done to get the features for endodontic therapy. The algorithm includes multi-scale retinex method to enhance the CBCT dental image without losing the important medical information of the original image. Multi-scale retinex offers a fair tradeoff between good dynamic ranges with rendition of contrast. The outputs have been compared with the histogram equalization and CLAHE method by calculating the peak signal to noise ratio (PSNR) and structure of similarity index values (SSIM) values. The results have been encouraging; moreover the proposed method can be implemented in the treatment, planning and follow up of the endodontic therapy.
Nubolic Simulation of AMS Systems with Data Flow and Discrete Event Models

This paper deals with the performance verification of analog/mixed-signal (AMS) systems by a mixed symbolic/numeric (nubolic) simulation. The approach is to piggyback the symbolic simulation via code-instrumentation on the existing, numeric SystemC AMS simulator. This permits the combination of symbolic and numeric simulation ("nubolic simulation"). The particular focus in the paper is the handling of the symbolic discrete-event process activations. This permits the symbolic simulation of digital parts of AMS systems modeled by discrete event processes. The approach is demonstrated by the symbolic simulation of a dual-charge-pump PLL of an IEEE 802.15. 4 RF transceiver that includes an asynchronous digital counter as a frequency divider.
Network Processor Load Balancing for High-Speed Links

While transmission rates already achieve speeds beyond 40 Gb/s, today’s network processors are only slowly approaching 10 Gb/s. In this paper we present a load-balancing scheme that enables system designers to bridge the performance gap using multiple slower NPs in parallel to serve high-speed links. The proposed scheme works in a flowpreserving manner to ensure in-sequence packet delivery as well as local validity of connection state information, while avoiding inter-processor communication. The effectiveness of the algorithms is evaluated by simulation with extrapolated workloads, and the impact of specific parameters on system performance is the subject of a factor-relevance analysis.
Stability and Bifurcation Analysis of Delayed Neural Network Using Harmonic Balance Approach

Neural networks are commonly used to model brain functions and to resolve a variety of information that the brain receives. The earlier methods of centre manifold and multiple scales of order reduction of a delayed nonlinear system offer a serious challenge in analyzing Hopf bifurcation of a general delayed system. To overcome this challenge a standard procedure called Harmonic balance approach is used to analyze the stability and bifurcations of limit cycles. This paper analysed the bifurcation and stability for Hopfield network employing a two delays neural network. Using Harmonic balance approach, we have carried out Hopf bifurcation and stability analysis to obtain the periodic solutions. The stability of the bifurcated periodic solutions is examined along with the theoretical analysis. Nyquist criterion has been used to determine stability of the model. The critical values under which Hopf bifurcation occurs is determined. The analysis reveals that the stability of the solutions is guaranteed only inside the interval from 0.6 to 1.1. Results of mathematical simulation of frequency, phase and waveform with different phase angles are also presented.
QBLK: Towards Fully Exploiting the Parallelism of Open-Channel SSDs

By exposing physical channels to host software, Open-Channel SSD shows great potential in future high performance storage systems. However, the existing scheme fails to achieve acceptable performance under heavy workloads. The main reasons reside not only in its single-buffer architecture, more importantly, but also in its line-based physical address management. Besides, the lock of address mapping table is also a performance burden under heavy workloads. We propose QBLK, an open source driver which tries to better exploit the parallelism of Open-Channel SSDs. Particularly, QBLK adopts four key techniques, namely (1) Multi-queue based buffering, (2) Per-channel based address management, (3) Lock-free address mapping, and (4) Fine-grained draining. Experimental results show that QBLK achieves up to 97.4% bandwidth improvement compared with the state-of-the-art PBLK scheme.
Proposal and Evaluation of A Method for Automatically Classifying Images of Agricultural Work and Animals Acquired with Motion Sensor Cameras

We have developed a field monitoring system (http://kansatu.net) using network-connected cameras and sensors installed in apple trees to support the hands-on agriculture curriculum of an elementary school. The system enables to collects and stores a variety of images taken by network-connected cameras equipped with infrared motion sensors. Agricultural works and animals are photographed in these images. The system has been in place seven years since 2011, during which time, there have been years in which more than approximately 50,000 images were collected. However, these data have not been used effectively to learn agricultural works. On the other side, in recent years, damage to agricultural crops by wild animals has been increasing. From these backgrounds, our ultimate goal is to utilize the images captured by motion sensor-equipped network cameras to develop countermeasures to prevent wild animals from damaging agricultural crops and to educate and cultivate future farmers. In this study, we proposed a method that employs image analysis technology and the date and time of image capture to automatically classify images acquired by the motion sensor network cameras by type of agricultural work and animal. We also developed a method for automatically classifying the type of agricultural work. We evaluated the accuracy of the developed method by comparing the results of automated classification with the results of manual classification. The recall of the proposed method exceeded 90% for all three types of agricultural work tested, which was equal to or greater than the classification accuracy achieved with manual classification.
Prevention and Mitigation of DNS based DDoS attacks in SDN Environment

Denial-of-Service attack (DoS attack) is an attack on network in which an attacker tries to disrupt the availability of network resources by overwhelming the target network with attack packets. In DoS attack it is typically done using a single source, and in a Distributed Denial-of-Service attack (DDoS attack), like the name suggests, multiple sources are used to flood the incoming traffic of victim. Typically, such attacks use vulnerabilities of Domain Name System (DNS) protocol and IP spoofing to disrupt the normal functioning of service provider or Internet user. The attacks involving DNS, or attacks exploiting vulnerabilities of DNS are known as DNS based DDOS attacks. Many of the proposed DNS based DDoS solutions try to prevent/mitigate such attacks using some intelligent non-“network layer” (typically application layer) protocols. Utilizing the flexibility and programmability aspects of Software Defined Networks (SDN), via this proposed doctoral research it is intended to make underlying network intelligent enough so as to prevent DNS based DDoS attacks.
Research on the Improved Data Processing Method for Foot-Mounted Inertial Pedestrian Positioning System

Foot-mounted Inertial Pedestrian Position System (FIPPS) plays an important role for indoor position application. It can be used in the environment without GNSS such as firefighting and military. However, the accumulating position error produced by the MIMU measurement noise, makes the system for long time position impossible. Zero Velocity Update (ZUPT) is a proposed algorithm to reduce position error for FIPPS. For ZUPT, Kalman filter is used to estimate and compensate the position error. However, because the heading misalignment angle cannot be observed by ZUPT, part of the position error caused by heading misalignment angle cannot be compensated. According to the problem above, this paper shows the improving FIPPS position algorithms we proposed in recent years, including: a) Adaptive Gradient Descent Fast-Initial Alignment Algorithm for solving the problem of the inaccuracy initial attitude resulting in the position error; b) the FR-data algorithm for solving the stable problem when the pedestrian walking fast; c) Adaptive Inertial/Magnetometer Positioning Algorithm and Improved Attitude Algorithm for improving the heading misalignment angle observability; d) Dual-foot positioning algorithm based on Adaptive Inequality Constraints Kalman filter for correcting the position error. Finally, performance of the FIPPS improve algorithm is tested using MTi-G710 MIMU.
Optimization of Autonomous Vehicles Movement in Urban Intersection Management System

With the growing number of cars in the world, there are corresponding problems that society needs to solve. In metropolises, intersections are generators of traffic jams and traffic accidents. The introduction of unmanned autonomous vehicles is one of the solutions, however, it is necessary to optimize their movement. In this paper, authors propose a model of the functioning of the intersection management system. The aim of the system is to organize a conflict-free, safe and optimal traversal of unmanned vehicles within the framework of the intelligent transport system of a smart city. The model assumes that at each intersection there are objects of transport infrastructure responsible for building routes at the intersection. To assess the feasibility of the presented model, the authors developed a software simulator of a network of urban intersections and conducted experiments. Based on simulation experiments, the use of the developed model can significantly reduce the time spent by unmanned vehicles to overcome the intersection compared to a traffic lights.
Evaluation of the Analytical Performance of the Xpert MTB/RIF Assay

ABSTRACT We performed the first studies of analytic sensitivity, analytic specificity, and dynamic range for the new Xpert MTB/RIF assay, a nucleic acid amplification-based diagnostic system that detects Mycobacterium tuberculosis and rifampin (RIF) resistance in under 2 h. The sensitivity of the assay was tested with 79 phylogenetically and geographically diverse M. tuberculosis isolates, including 42 drug-susceptible isolates and 37 RIF-resistant isolates containing 13 different rpoB mutations or mutation combinations. The specificity of the assay was tested with 89 nontuberculosis bacteria, fungi, and viruses. The Xpert MTB/RIF assay correctly identified all 79 M. tuberculosis isolates and correctly excluded all 89 nontuberculosis isolates. RIF resistance was correctly identified in all 37 resistant isolates and in none of the 42 susceptible isolates. Dynamic range was assessed by adding 102 to 107 CFU of M. tuberculosis into M. tuberculosis-negative sputum samples. The assay showed a log-linear relationship between cycle threshold and input CFU over the entire concentration range. Resistance detection in the presence of different mixtures of RIF-resistant and RIF-susceptible DNA was assessed. Resistance detection was dependent on the particular mutation and required between 65% and 100% mutant DNA to be present in the sample for 95% certainty of resistance detection. Finally, we studied whether assay specificity could be affected by cross-contaminating amplicons generated by the GenoType MTBDRplus assay. M. tuberculosis was not detected until at least 108 copies of an MTBDRplus amplicon were spiked into 1 ml of sputum, suggesting that false-positive results would be unlikely to occur.
Subway Passenger Flow Forecasting With Multi-Station and External Factors

With the rapid development of urban rail transit, more and more people choose to travel by subway. Therefore, accurate passenger flow forecasting is of great significance for passengers and municipal construction and contributes to smart city services. In this paper, we propose a multi-type attention-based network to forecast the subway passenger flow with multi-station and external factors. The proposed network has different types of attention mechanisms to adaptively extract relevant features, including multi-station, external factors, and historical data. In addition, the hierarchical attention mechanism is used to model the hierarchical relationship between subway lines and stations. In addition, the embedding method is applied to better combine the different kinds of data. The experiments on real subway passenger flow data in a city in China demonstrate that our method outperforms five baseline methods. Moreover, our method can visualize the impact of different stations and other factors on traffic, which plays an important role in passenger travel and subway dispatch.