Miracle-dz/UniFAR-FTUs huggingface dataset:
Building Transmission Backbone for Highway Vehicular Networks: Framework and Analysis

The highway vehicular ad hoc networks, where vehicles are wirelessly interconnected, rely on the multihop transmissions for end-to-end communications. This, however, is severely challenged by the unreliable wireless connections, signal attenuation, and channel contentions in the dynamic vehicular environment. To overcome the network dynamics, selecting appropriate relays for end-to-end connections is important. Different from the previous efforts ( e.g., clustering and cooperative downloading), this paper explores the existence of stable vehicles and propose building a stable multihop transmission backbone network in the highway vehicular ad hoc network. This paper is composed of three parts. First, by analyzing the real-world vehicle traffic traces, we observe that the large-size vehicles, e.g., trucks, are typically stable with low variations of mobility and stable channel condition of low signal attenuation; this makes their interconnections stable in both connection time and transmission rate. Second, by exploring the stable vehicles, we propose a distributed protocol to build a multihop backbone link for end-to-end transmissions, accordingly forming a two-tier network architecture in highway vehicular ad hoc networks. Finally, to show the resulting data performance, we develop a queueing analysis model to evaluate the end-to-end transmission delay and throughput. Using extensive simulations, we show that the proposed transmission backbone can significantly improve the reliability of multihop data transmissions with higher throughput, less transmission interruptions, and end-to-end delay.
Motion detection algorithm implemented on the ARM-based hardware

A technique of moving objects detection in a video stream was suggested and its ARM-processor based implementation was tested. The technique is based on a three-dimensional Markov chain representation of a visual sequence and the moving objects contour detection through the computed information amount in the elements of the image. To detect the object of interest through obtained contour points morphological processing was used. The algorithm was implemented on a 168 MHz, 128 Kb static SRAM memory Cortex-M4-core ARM controller. The 320×240 pixel frame processing time was 25 ms.
Feed Link Placement in Euclidean Graphs

We study the problem of connecting a new vertex p to a Euclidean graph G using k new edges,, called feed links which connect p to the boundary of the plane face f of G containing p . We aim to connect the vertex in such a way that the maximum dilation from p to a set of destination vertices in the graph is minimised, where we deﬁne dilation as the ratio between the distance in the graph and Euclidean distance. Previous work mainly focus on reducing the dilation to points on the boundary of f rather than points anywhere in G . We give two diﬀerent methods to solve this problem in polynomial time for a ﬁxed k . We show that the problem is NP-hard when k is not ﬁxed by a reduction from set cover. We give three diﬀerent approximation algorithms that place k feed links such that the maximal dilation is approximated. The ﬁrst algorithm gives a (1+ ε )-approximation for arbitrarily small ε , however, its running time is still exponential in k . The second gives a ( t + 1)-approximation when f is a t -spanner and runs in O ( mn 2 log n ) time, where m denotes the number of vertices on the boundary of f and n denotes the total number of vertices in the graph. The third approximation algorithm also has a running time of O ( mn 2 log n ) and is obtained after a comparison between dilation and distance in the context of feed link placement. Its approximation ratio depends on the diﬀerence in Euclidean distances between p and the destination vertices.
A model for quantifying performance degradation in virtual network function service chains

Virtual Network Functions (VNFs) can be chained and provisioned on demand, providing elasticity and dynamicity to the network. Due to the interdependencies between VNFs, resulting service chains may not work as expected, and because of that, it is crucial to determine which VNFs are having a negative impact on the service quality. In this paper, we introduce a model to quantify the guiltiness of a VNF on being a bottleneck in a service chain, which provides a metric that estimates the impact on processing delay. In addition, we propose an adaptive algorithm, based on linear regression and neural networks, to adjust the model parameters according to the environment particularities, such as the type and number of VNFs. We show through an experimental evaluation that the guiltiness metric faithfully characterizes end-service performance, by identifying up to 94% of the bottleneck VNFs in the analyzed scenarios. Also, we provide artifacts for researchers to reproduce our results in other scenarios.
Research on dynamic integrity measurement strategy based on measurement point distribution

Trusted computing technology is to further enhance the safety of the computer system by changing the traditional computer architecture. Integrity measurement is the key technology of trusted computing and the core and foundation of integrity protection system. In view of the fact that integrity measurement can't effectively avoid TOC-TOU security risks and overabundance of redundancy in the current trusted computing platform, a new dynamic integrity measurement strategy is proposed. The strategy takes the time point of the attack as the benchmark, and modifies the integrity measurement point distribution to measure the dynamic integrity of the measurement object in real time, so as to effectively grasp the integrity of the system operation. The analysis results show that the method not only has the static measurement ability, but also detect attack damage to the integrity of the system in the running process of the system, so as to improve the security of the system, and maintain the system flexibility and compatibility.
Local Positioning System Using UWB Range Measurements for an Unmanned Blimp

Unmanned blimps are a safe and reliable alternative to conventional drones when flying above people. On-board real-time tracking of their pose and velocities is a necessary step toward autonomous navigation. There is a need for an easily deployable technology that is able to accurately and robustly estimate the pose and velocities of a blimp in 6 DOF, as well as unexpected applied forces and torques, in an uncontrolled environment. We present two multiplicative extended Kalman filters using ultrawideband radio sensors and a gyroscope to address this challenge. One filter is updated using a dynamics model of the blimp, whereas the other uses a constant speed model. We describe a set of experiments in which these estimators have been implemented on an embedded flight controller. They were tested and compared in accuracy and robustness in a hardware-in-loop simulation as well as on a real blimp. This approach can be generalized to any lighter than air robot to track it with the necessary accuracy, precision, and robustness to allow autonomous navigation.
Cloud denial of service detection by dendritic cell mechanism

The term cloud computing is not new anymore in computing technology. This form of computing technology previously considered only as marketing term, but today cloud computing not only provides innovative improvements in resource utilization but it also creates a new opportunities in data protection mechanisms where the advancement of intrusion detection technologies are blooming rapidly. From the perspective of security, cloud computing also introduces concerns about data protection and intrusion detection mechanism especially cloud computing are exposed to Denial of Service (DoS) attacks. This paper aims to provide DoS detection mechanism for cloud computing environment. As a result, we provide an experiment to examine the capability of the proposed system. The result shows that the proposed system was able to detect DoS attacks that conducted during the experiment with 94.4% detection rate. We conclude the paper with a discussion on the results, then we include together with a graphical summary of the experiment's result.
z-TORCH: An Automated NFV Orchestration and Monitoring Solution

Autonomous management and orchestration (MANO) of virtualized resources and services, especially in large-scale network function virtualization (NFV) environments, is a big challenge owing to the stringent delay and performance requirements expected of a variety of network services. The quality-of-decisions (QoD) of a MANO system depends on the quality and timeliness of the information received from the underlying monitoring system. The data generated by monitoring systems is a significant contributor to the network and processing load of MANO systems, impacting thus their performance. This raises a unique challenge: how to jointly optimize the QoD of MANO systems while at the same minimizing their monitoring loads at runtime? This is the main focus of this paper. In this context, we propose a novel automated NFV orchestration solution, namely z-TORCH (zero Touch Orchestration) that jointly optimizes the orchestration and monitoring processes by exploiting machine-learning-based techniques. The objective is to enhance the QoD of MANO systems achieving a near-optimal placement of virtualized network functions at minimum monitoring costs.
Effective retrieval of related documents based on spelling correction to improve information retrieval system

Due to the increasing daily of users documentation in the global communications network, the management, and control of its information in it have been challenged. How to extract useful knowledge in an Internet heterogeneous environment is essential. Information retrieval techniques can retrieve user information needs in a large amount of data. Search engines are the first selection of users to find information. In this search, Web crawler plays a key role in search engines. A web crawler is a script that routinely scans the web. In this paper, a information retrieval method will be presented using a combination of vector space modeling and language statistical model to improve the retrieve of related documents. The proposed approach is based on the structural similarity of the document corpus. Measuring the similarity of the input term is based on the shortest path between each term and the next term. In this method, the number of nodes, edges and links is calculated. Finally, the similarity using the path feature and cosine similarity is measured. Available documents the proposed method by the crawler of the web is compiled from various Wikipedia pages. The results of the conceptual retrieved of documents show that 84% accuracy, 88% recall and average precision of 56% have improved compared to other methods.
Adaptive tracking control of quadrotor UAV system with input constraints

In this paper, sliding mode control(SMC) and adaptive tracking control are given to perform the position and attitude control of quadrotor unmanned aerial vehicle(UAV) model. For the double closed-loop control system, SMC is used in the inner loop position subsystem control. In the inner loop subsystem, the virtual attitude angles of attitude subsystem tracking control are calculated. The adaptive tracking control is applied in the outer loop attitude subsystem control with input saturation constraints. Subsequently, the auxiliary design system is introduced to analyze the impact of input constraint, and its states are used for adaptive tracking control design. Then, the internal uncertainties and the external disturbances will be observed and compensated by extended state observer (ESO). Finally, simulation studies are presented to illustrate the effectiveness of the proposed SMC and adaptive tracking control.
A lightweight multi-factor mobile user authentication scheme

This paper presents a lightweight multi-factor mobile user authentication scheme. It combines the biometrics of users and information of smart phones to realize mutual authentication between users and remote servers. This algorithm uses a lightweight encryption algorithm to achieve quick authentication. Compared with the previous biological features, our scheme does not save the biological feature template. So there is no disclosure of the users biological characteristics. The solution also enables off-line updating of biometrics and passwords, reducing not only the traffic but also the security threats posed by open channels. This scheme can better resist the currently known attacks, and has superior performance.
Interferometric radar compressive sensing imaging with direct downsampling

An experiment of direct downsampling on echo signals from a moving train by a Ku-band interferometric noise radar was successfully conducted and high-resolution ISAR images were reconstructed through compressed sensing (CS). The radar transmitted stepped-frequency chaotic noise signals (SF-CNSs) covering a total bandwidth of 4.02GHz by 20 subpulses with a frequency step of 200MHz and each subpulse is of 220MHz bandwidth. Benefitted from the random characteristics of the transmitted waveforms, just simple uniform downsampling is required for applying CS to reconstruct radar images. In the Experiment, 10MHz and 1MHz sampling rates were used far less than the Nyquist rate required for recovering a signal of 220MHz bandwidth by ordinary approach. From the obtained interferometric phases (InPhes) along the train body, we also observed the micro-motion (m-M) phenomenon of the train which was reported previously.
Two types of social grooming methods depending on the trade-off between the number and strength of social relationships

Humans use various social bonding methods known as social grooming, e.g. face to face communication, greetings, phone and social networking sites (SNS). SNS have drastically decreased time and distance constraints of social grooming. In this paper, I show that two types of social grooming (elaborate social grooming and lightweight social grooming) were discovered in a model constructed by 13 communication datasets including face to face, SNS and Chacma baboons. The separation of social grooming methods is caused by a difference in the trade-off between the number and strength of social relationships. The trade-off of elaborate social grooming is weaker than the trade-off of lightweight social grooming. On the other hand, the time and effort of elaborate methods are higher than those of lightweight methods. Additionally, my model connects social grooming behaviour and social relationship forms with these trade-offs. By analysing the model, I show that individuals tend to use elaborate social grooming to reinforce a few close relationships (e.g. face to face and Chacma baboons). By contrast, people tend to use lightweight social grooming to maintain many weak relationships (e.g. SNS). Humans with lightweight methods who live in significantly complex societies use various types of social grooming to effectively construct social relationships.
Tack and deformation based sensorised gripping using conductive hot melt adhesive

The development of a universal method for grasping objects of varying morphology, material and size in unstructured environments remains an unsolved challenge. There is no one ‘universal’ solution which can function with low positional precision, can grasp a variety of objects and materials and has easily inter-gratable sensing to allow simple feedback mechanisms to be implemented. This paper works towards this goal, detailing a grasping method utilising the tack force of thermoplastics to enable grasping using a conductive hot melt adhesive (CHMA). CHMA is developed to be conductive and pressure sensitive by including conductive carbon black particles. The material has controllable tackiness and deformation; this combined effect enables high tack forces to be achieved enabling successful grasping of a wide range of objects. Temperature and indentation force primarily control the tack force generated; a theoretical model relating these is given in this paper. This integrated pressure sensing ability enables a feedback system to be implemented to optimise the time taken to grasp and minimise the force required to grasp the object. This feedback system has been integrated to enable a variety of objects to be picked — ranging from highly soft fabric, to PTFE and a soft ball, all of which were tested and analysed for single point pick and place.
Tumor-derived granzyme B-expressing neutrophils acquire antitumor potential after lipid A treatment

Neutrophils are known to possess both pro- and anti-tumor properties, a feature that could be related to the diversity and plasticity of these cells. Here we explored the hypothesis that under an appropriate environment and stimuli, neutrophils could induce an effective response against tumor cells. In a rat and mouse models, we show that a substantial amount of colon tumor associated-neutrophils (TAN) expressed the cytolytic enzyme granzyme B, which is absent in spleen or blood circulating neutrophils. This TAN population was also found into tumors of patients with colon cancer. Tumor neutrophil infiltration was correlated with an increase of chemokines known to attract neutrophils in both rat models and patients. These cells were involved in a Lipid A analog-mediated colon tumor regression. Mechanistically, treating the rats with the Lipid A analog triggered granzyme B release from neutrophils in tumor cell vicinity, which was correlated to tumor regression. Alteration of granzyme B function in tumor cells decreased the cytotoxic effect of Lipid A in rat and mouse models. Granzyme B expression in neutrophils could be induced by the lipid A analog but also by some of the cytokines that were detected in the tumor microenvironment. These results identify a subpopulation of neutrophils expressing granzyme B that can act as a key player of lipid A-mediated colon cancer regression in rat and mouse models and the molecular mechanisms involved may provide novel approaches for human therapeutic intervention.
Method to increase stability of the scale factor of a precision pendulum accelerometer with digital feedback

The paper considers a method to compensate for the error in the scale factor of a quartz pendulum accelerometer caused by the pendulum unbalancing at accelerations over 20 g. The method allows for performing compensation by implementing algorithms programmed in the digital feedback amplifier without structural alterations to the accelerometer mechanical part. The proposed technical solutions can be applied in any type of pendulum accelerometers. The effectiveness of the proposed algorithm is confirmed by the results of experimental research.
A hybrid recommender system using multi layer perceptron neural network

Recommender systems try to personalized preferences, obviously this predicting based on previous users taste. Many RS suffer from Cold-start problem, it pertains to the issue which the system cannot invoke any reasoning for users whom they have not yet accumulated proper information. In this paper, we will propose a hybrid recommender system based on collaborative filtering (CF) techniques and content-based filtering (CBF) which combined by artificial neural network (ANN) to be an appropriate model for both cold-start and ordinary users. In our proposed method, Mutual Information techniques help us to select the best property to make our model. The comparison is performed on MovieLens and Netflix datasets. Our results show that our prediction results are more accurate than other methods with the same dataset.
2′-deoxy-2′-[18F] fluoro-D-glucose positron emission tomography, diffusion-weighted magnetic resonance imaging, and choline spectroscopy to predict the activity of cetuximab in tumor xenografts derived from patients with squamous cell carcinoma of the head and neck

We investigated changes on 2′-deoxy-2′-[18F]fluoro-D-glucose positron emission tomography (18FDG-PET), diffusion-weighted magnetic resonance imaging (DW-MRI), and choline spectroscopy as early markers of cetuximab activity in squamous cell carcinoma of the head and neck (SCCHN). SCCHN patient-derived tumor xenografts models were selected based on their cetuximab sensitivity. Three models were resistant to cetuximab and two were sensitive (one was highly sensitive and the other one was moderately sensitive). Cetuximab was infused on day 0 and 7. Maximal standardized uptake values (SUVmax), apparent diffusion coefficient (ADC), and total choline pool were measured at baseline and at day 8. To investigate the possible clinical relevance of our pre-clinical findings, we also studied the SUVmax and ADC modifications induced by cetuximab in five patients. Cetuximab induced a significant decrease in SUVmax and an increase in ADC at day 8 compared to baseline in the most cetuximab-sensitive model but not in the other models. At day 8, in one resistant model, SUVmax was decreased compared to baseline and was significantly lower than the controls. Choline spectroscopy was not able to predict cetuximab activity. The five patients treated with cetuximab had a 18FDG-PET partial response. One patient had a partial response according to RECISTv1.1. Interestingly, this last had also an increase in ADC value above 25%. Our preclinical data support the use of PDTX to investigate imaging techniques to detect early treatment response. Our pre-clinical and clinical data suggest that DW-MRI and 18FDG-PET should be further investigated to predict cetuximab activity.
Lane line detection by using Hough Transform

In this study, a mixed approach is proposed to increase the detection success of lane-line especially for heavily shaded road images. These kinds of studies are the basis of lane departure warning and lane keeping assistance systems. Accurate and automatic detection is an important feature for reducing the accident risk and increasing driving safety. In most of the existing studies carried out on this subject, it has been observed that MATLAB software is widely used. In this study, the application was developed by using two different software in LabVIEW which is a graphical development platform. The text-based source codes developed in MATLAB has been integrated into the LabVIEW platform using the “Mathscript Node” tool for Hough Transformation (HT). By combining the superior features of both platforms, a stronger user interface in terms of visual objects compared with the MATLAB GUI with satisfactory analytical abilities was get. This mixed approach provides the software developers to choose the most appropriate syntax in a single platform. The proposed method has been tested on a limited number (4) of images, especially for heavily shaded images selected from dataset of the Carnegie Mellon University Robotics Institute Vision and Autonomous Systems Center and up to 100% success has been achieved in images exposed to high disturbance.
Passive geolocation using phase difference rate for electronic support systems

In this research, the developed passive geolocation technique using phase difference of rate is presented for electronic support systems. Traditionally, triangulation method depending on platform transposition is used for localization. This method requires a long period of time to achieve high accuracy. Therefore a new generation triangulation algorithm proposed in [2] and named phase difference of rate ranging is developed and analyzed with different frequencies, noise levels and distances between antennas.