Miracle-dz/UniFAR-FTUs huggingface dataset:
A Switchable Deep Learning Approach for In-Loop Filtering in Video Coding

Deep learning provides a great potential for in-loop filtering to improve both coding efficiency and subjective quality in video coding. State-of-the-art work focuses on network structure design and employs a single powerful network to solve all problems. In contrast, this paper proposes a deep learning based systematic approach that includes an effective Convolutional Neural Network (CNN) structure, a hierarchical training strategy, and a video codec oriented switchable mechanism. First, we propose a novel CNN structure, i.e., Squeeze-and-Excitation Filtering CNN (SEFCNN), as an optional in-loop filter. To capture the non-linear interaction between channels, the SEFCNN is comprised of two subnets, i.e., Feature EXtracting (FEX) subnet and Feature ENhancing (FEN) subnet. Then, we develop a hierarchical model training strategy to adapt the two subnets to different coding scenarios. For high-rate videos with small artifacts, we train a single global model using the FEX for all types of frames, whereas for low-rate videos with large artifacts, different models are trained using both FEX and FEN for different types of frames. Finally, we propose an adaptive enhancing mechanism which is switchable between the CNN-based and the conventional methods. We selectively apply the CNN model to some frames or some regions in a frame. Experimental results show that the proposed scheme outperforms state-of-the-art work in coding efficiency, while the computational complexity is acceptable after GPU acceleration.
Global Optimization Techniques in Camera-Robot Calibration

The need to relate measurements made by a camera to a different known coordinate system arises in many engineering applications. Historically, it appeared for the first time in the connection with cameras mounted on robotic systems. This problem is commonly known as hand-eye calibration. In this thesis, we study the problem of hand-eye calibration as well as a problem closely connected to it—the problem of robot-world calibration. The first objective of this work is to apply recent results in mathematical optimization to provide globally optimal solution to these problems. The second objective is to formulate and study these problems as minimization problems under some geometrically meaningful error measures using image measurements directly. We also study global optimizers in situation where image measurements are not available using the classical problem formulations. The solutions presented in this thesis are compared to existing methods and validated by both synthetic and real world data experiments. In the first part of the thesis, we survey the state of the art of the camera-robot calibration as well as of the main concepts of the geometrical computer vision. Further, we review several results in the globally optimization techniques and their application in the computer vision. Next, we formulate the problem of hand-eye calibration as a minimization problem under some geometrically meaningful error measures. We provide two solutions; the first solution employs a Structure-from-Motion approach and the Second Order Cone Programming optimization and the second one uses the Brand-and-Bound optimization strategy. Both solutions provide globally optimal minimizers and work with image measurements directly, instead of using them as a pre-step for explicitly calculating camera poses. Using a similar approach, we also formulate a minimization task for the robot-world calibration problem. This time, to solve the task we use the method of Linear Matrix Inequality relaxations. Further, we investigate the problem of hand-eye calibration in situations, where the information about the rotation of the robot is not known. This problem arises when the robot is not calibrated or the information from the robot is not available. We use the method of Gröbner basis to deal with this scenario. Finally, we revisit the classical formulation of the hand-eye and robot-world calibration. Using the method of Linear Matrix Inequality relaxations, we provide several global optimizers in situations, where image measurements are not available and the calibration has to be estimated from robot and camera poses only.
Novel Antenna Beamsteering for Wireless Applications

This thesis presents the author’s work on the design and development of novel antenna capable of electronic beamsteering1 for various wireless applications. Such antennas aim to change the direction of their radiation patterns and directivities, electronically and dynamically, in a fast and efficient manner without the need of mechanical aid. In particular, Metamaterials (MM) and, more specifically, Frequency Selective Surfaces (FSS), are introduced as base materials on novel beamsteering antenna designs. When these are structured as a transmitarray, i.e. a structure positioned in the near-field of a directive radiating element, such "artificial" materials allow to control the direction of the propagating electromagnetic wave providing the desired beamsteering capability. The innovative work developed in this Ph.D. yielded to the fabrication of two FSSinspired transmitarray antenna prototypes with Two-Dimensional (2D) beamsteering capability using a single Radio Frequency (RF) front-end. Following mathematical workout and proper model validation using a full-wave electromagnetic simulator, the transmitarrays have experimentally been characterised in terms of their beamsteering capability and finally tested under several real-case scenarios. The use transmitarrays in novel antenna beamsteering is attractive to many wireless applications, specially in the scope of the 5th Generation (of mobile network) (5G) and novel hybrid transmission schemes such as Spatial Diversity (SD) and Multiple-input Multiple-output (MIMO) with beamsteering. The reduction of the RF fronted burden associated to the use of a single radiation element, instead of many elements as used in other beamsteering techniques, the reduced implementation costs and the compact and light weight form factors, opens a new window for the deployment of transmitarray antennas in several top-notch wireless applications. This is considered to be a leap contribution to the antennas’ community with practical relevance to emerging industries in this field. 1Also spelled as “beam steering”. ix This page is intentionally left blank.
A case study on the sensitivity of downstream development to typhoon intensity and its initial location

In this study, the sensitivity of downstream baroclinic development to a tropical cyclone's (TC's) intensity and its initial location relative to the upstream trough during the extratropical transition (ET) of Typhoon Malakas were examined. The downstream development intensity at the mid‐latitudes was mainly determined by two factors under the effect of ET, i.e. the strength of transporting low potential vorticity (PV) air downstream by the TC outflow and the strength of the PV gradient being disturbed in the mid‐latitude jet. The intensified TC had a stronger upper‐level outflow, and the intensified circulations associated with it could disturb the mid‐latitude PV gradient more strongly. Therefore, downstream development intensified, i.e. the trough‐ridge became more pronounced, and the jet became stronger. Downstream development seemed to be more sensitive to the TC location than its intensity. The TC, being closer to the trough, interacted with the mid‐latitude flow more strongly because the trough advanced and strengthened the process that the TC outflow transported low PV air to the mid‐latitudes in this situation. Moreover, the TC disturbed the PV gradient earlier and more strongly. The two effects intensified the downstream development. If the TC were weakened or moved away from the upstream trough, the outcome would be the opposite.
RAD‐EI: A routing attack detection scheme for edge‐based Internet of Things environment

Internet of Things (IoT) offers various types of application services in different domains, such as “smart infrastructure, health‐care, critical infrastructure, and intelligent transportation system. ” The name edge computing signifies a corner or edge in a network at which traffic enters or exits from the network. In edge computing, the data analysis task happens very close to the IoT smart sensors and devices. Edge computing can also speed up the analysis process, which allows decision makers to take action within a short duration of time. However, edge‐based IoT environment has several security and privacy issues similar to those for the cloud‐based IoT environment. Various types of attacks, such as “replay, man‐in‐the middle, impersonation, password guessing, routing attack, and other denial of service attacks” may be possible in edge‐based IoT environment. The routing attacker nodes have the capability to deviate and disrupt the normal flow of traffic. These malicious nodes do not send packets (messages) to the edge node and only send packets to its neighbor collaborator attacker nodes. Therefore, in the presence of such kind of routing attack, edge node does not get the information or sometimes it gets the partial information. This further affects the overall performance of communication of edge‐based IoT environment. In the presence of such an attack, the “throughput of the network” decreases, “end‐to‐end delay” increases, “packet delivery ratio” decreases, and other parameters also get affected. Consequently, it is important to provide solution for such kind of attack. In this paper, we design an intrusion detection scheme for the detection of routing attack in edge‐based IoT environment called as RAD‐EI. We simulate RAD‐EI using the widely used “NS2 simulator” to measure different network parameters. Furthermore, we provide the security analysis of RAD‐EI to prove its resilience against routing attacks. RAD‐EI accomplishes around 95.0% “detection rate” and 1.23% “false positive rate” that are notably better than other related existing schemes. In addition, RAD‐EI is efficient in terms of computation and communication costs. As a result, RAD‐EI is a good match for some critical and sensitive applications, such as smart security and surveillance system.
Modeling and Simulation of Non-ideal Buck Converter in CCM

The circuit average equivalent model of non-ideal Buck converters in CCM was established on the basis of energy conservation average method and time averaging equivalent circuit method, which is considering the influence of the every element’s equivalent parasitic parameters and inductance current ripples existing in actual converters, and the influence of the output current oscillation for the circuit performance. Analysis and simulation by Matlab were implemented. It could be seen that the new model is intuitive and has clear physical meaning. Therefore, it can reflect the actual operation of the system more accurately.
On the Energy Consumption of Quantum-resistant Cryptographic Software Implementations Suitable for Wireless Sensor Networks

For an effective protection of the communication in Wireless Sensor Networks (WSN) facing e.g. threats by quantum computers in the near future, it is necessary to examine the applicability of quantum-resistant mechanisms in this field. It is the aim of this article to survey possible candidate schemes utilizable on sensor nodes and to compare the energy consumption of a selection of freely-available software implementations using a WSN-ready Texas Instruments CC1350 LaunchPad ARM® Cortex®-M3 microcontroller board.
Video annotation by crowd workers with privacy-preserving local disclosure

Kumavat, Apeksha Dipak M.S.E.C. E. , Purdue University, December 2016. Video Annotation By Crowd Workers With Privacy-preserving Local Disclosure . Major Professor: Alexander J. Quinn. Advancements in computer vision are still not reliable enough for detecting video content including humans and their actions. Microtask crowdsourcing on task markets such as Amazon Mechnical Turk and Upwork can bring humans into the loop. However, engaging crowd workers to annotate non-public video footage risks revealing the identities of people in the video who may have a right to anonymity. This thesis demonstrates how we can engage untrusted crowd workers to detect behaviors and objects, while robustly concealing the identities of all faces. We developed a web-based system that presents obfuscated videos to crowd workers, and provides them with a mechanism to test their hypotheses about what behaviors and/or objects might be present in the videos. Our system, called Fovea, works by initially applying a heavy median blur to the videos. This guarantees privacy but impedes recognition of other content of interest. An algorithm was developed as a part of this thesis to calculate the radius of a safe-to-reveal region around a pixel. It was implemented into an interactive system that allows workers watching the blurred videos to selectively reveal small regions by clicking. We compared two approaches for local disclosure of information—foveated mode and keyhole mode—together with a non-interactive blur-only mode as a control. The results showed that both modes led to superior recognition of actions while keeping the odds of correct face recognition close to that of the control.
Research on Facial Expression Recognition Technology Based on D-S Ttheory

This paper proposed a kind of facial expression recognition technology based on the D-S theory of information fusion,in the background of affective computing theory,moreover designed and implemented the system IFFER.In the part of facial expression recognition,the training for the classification uses JAFFE expression library.Using color match and brightness match,this method divides face image into the eye and mouth images,then identifies facial expression respectively by trained eye SVM classifier and mouth SVM classifier,in the end,fuses the identification using D-S theory.The experimental results show that recognizing the two parts of image,in terms of training or recognition,the data dimension has greatly reduced and increased efficiency.In recognition rate,the fusion of the results has more improved than the pre-fusion.
Thyroid nodules classification and diagnosis in ultrasound images using fine‐tuning deep convolutional neural network

Ultrasonography AKA diagnostic sonography is a noninvasive imaging technique that allows the analysis of an organic structure, thanks to the ultrasonic waves. It is a valuable diagnosis method and is also seen as the evidence‐based diagnostic method for thyroid nodules. The diagnosis, however, is visually made by the practitioner. The automatic discrimination of benign and malignant nodules would be very useful to report Thyroid Imaging Reporting. In this paper, we propose a fine‐tuning approach based on deep learning using a Convolutional Neural Network model named resNet‐50. This approach allows improving the effectiveness of the classification of thyroid nodules in ultrasound images. Experiments have been conducted on 814 ultrasound images and the results show that our proposed approach dramatically improves the accuracy of the classification of thyroid nodules and outperforms The VGG‐19 model.
Pay as How You Behave: A Truthful Incentive Mechanism for Mobile Crowdsensing

Mobile crowdsensing (MCS) is widely applied in large-scale distributed networks for collecting sensing data from workers. In an MCS system, workers are recruited to complete tasks for data requesters, and they will get profits. Accordingly, how to establish an effective incentive mechanism has become an important issue to consider. Since workers are naturally selfish, they try to maximize individual benefits while minimize costs. In this article, we propose a truthful incentive mechanism which pays for the workers by the workers’ performance in the task just completed and the reputation. For each worker, through the future prediction function, we get the reputation of the worker by utilizing the previous performances. In the proposed scheme, partial payment for the workers is distributed depending on workers’ reputation. The final payment is based on punishments and rewards according to the performances. Moreover, data accuracy and response time are introduced to evaluate the worker performance in the task. It can be demonstrated that the mechanism provides continuous incentives to workers compared to the single ex-ante and ex-post pricing schemes. The experimental results show that our mechanism is effective.
Energy Efficiency in Wireless Sensor Networks, Through Data Compression

Wireless sensor networks are an ever-growing market in today’s society; Smart homes with wirelessly connected and controllable devices, continuous monitoring in environmental, health care and industrial settings, and automated, self-adjusting systems in homes and factories are all growing technologies that are built using wireless sensor networks. In many of these systems, energy consumption can be a major concern; Most devices in the network rely on small batteries as their energy source. Because of this, any energy usage optimization can lead to greatly increased lifetime. Often, radio communication between microcontrollers and other devices is the biggest energy drain in the system. Compression of sensor data before transfer between nodes can be used to reduce the energy consumed by the radio transmissions. By first compressing the data, it is possible to reduce the size of the message transmitted over radio, which in turn will lead to less energy consumed. However, energy is required to perform the compression and decompression. It can be important to have a good idea of when compression may be beneficial for the overall energy efficiency of a wireless sensor network, and when it might be better to send the data without compression. In this thesis we will look at typical properties of wireless sensor networks, wireless transfer protocols for sensor networks, and sensor data. We will analyze the effects of compressing sensor data before sending it over the network, and perform experiments and measurements in a simple wireless sensor network, to assess the effects data compression can have on the energy consumption in wireless sensor networks.
Achievable Ergodic Secrecy Rate in Bursty Interference Channels With Opportunistic User Scheduling

This paper studies secure transmissions in a bursty interference channel constructed by opportunistic user scheduling. To improve the physical layer security, we propose a signal-to-noise-plus-interference ratio (SINR) based opportunistic transmission scheme and mathematically analyze the ergodic secrecy rate per transmitter-receiver pair. In this scheme, a subset among $K$ transmitter-receiver pairs is selected opportunistically, but each transmitter determines its activation state based on limited feedback of the SINR from its designated receiver only, without any information from other transmitters. For comparison, we also analyze the ergodic secrecy rates per transmitter-receiver pair for two benchmark schemes: non-opportunistic transmission and random transmission. Using derived analytical expressions, the achievable ergodic secrecy rates per transmitter-receiver pair are obtained by optimizing the activation probability for each scheme. To solve the non-convex optimization problem, we first show that the objective function is a sum of quasi-concave functions. Then, leveraging this fact, we propose a two-level iterative algorithm based on the damped Newton method. Furthermore, asymptotic behaviors of the achievable ergodic secrecy rate per transmitter-receiver pair are analyzed to offer insights behind the mathematical expressions. Our analytical and numerical results exhibit the gain of the proposed SINR based opportunistic transmission scheme compared to the benchmark schemes.
Comparison of machine-learning algorithms for classification of VPN network traffic flow using time-related features

ABSTRACT Network traffic classification and characterisation is playing an increasingly vital role in understanding and solving security-related issues in internet-based applications. The priority of research studies in this area has focused on characterisation of network traffic based on various layers of communication protocols as outlined in the TCP/IP stack and even further expanded to concentrate on specific application-layer protocols. Virtual Private Networks (VPNs) have become one of the most popular remote access communication methods among users over the public internet and other Internet Protocol (IP)-based networks. VPNs are governed by IP Security, which is a suite of protocols used for tunnelling the already encrypted IP traffic, to guarantee secure remote access to servers. In this paper, we propose and develop a framework to classify VPN or non-VPN network traffic using time-related features. Our focus is on classification of network traffic which is encrypted, tunnelled through a VPN, and the one which is normally encrypted (non-VPN transmission), using machine-learning techniques on data sets of time-related features. Six classification models: logistic regression, support vector machine, Naïve Bayes, k-nearest neighbour and ensemble methods – the Random Forest (RF) classifier and Gradient Boosting Tree (GBT) classifiers – are compared, and recommendations of optimised RF and GBT models over other models are provided in terms of high accuracy and low overfitting. Features which contributed to achieve 90% accuracy in each category were also identified.
Using Network Traces to Generate Models for Automatic Network Application Protocols Diagnostics

Network diagnostics is a time-consuming activity that requires an administrator with good knowledge of network principles and technologies. Even if some network errors have been resolved in the past, the administrator must spend considerable time removing these errors when they reoccur. This article presents an automated tool to learn the expected behavior of network protocols and possible variations. The created model can be used to automate the diagnostic process. The model presents a finite automaton containing protocol behavior for different situations. Diagnostics of unknown communication is performed by checking the created model and searching for error states and their descriptions. We have also created a proof-of-concept tool that demonstrates the practical potential of this approach.
A Review of Evaluation and Selection of Open Source Software in Electronic Medical Record

The valuation and choice of unsuitable open source software in electronic medical record (OSS-EMR) groups negatively affects the functions of organization and business processes. This study provides an insight into the assessment and choice of OSS-EMR groups in two ways. Firstly, quality characteristics are determined to evaluate OSS-EMR sets according to current frameworks and quality models. Secondly, the capabilities of appropriate choose methods for resolving OSSEMR groups problems are discussed on the basis of Multi-criteria assessment and select issues are considered for choosing the best OSS-EMR. The obtained results can be attributed to the following: (1) quality characteristics considered for the evaluation of OSS-EMR groups; (2) the gap in the assessment criteria used in EMR and other issues; and Multi-Criteria or Multi-Attribute Decision Making techniques used in framework for evaluating and selecting OSS in health informatics as part of a recommended solution