Miracle-dz/UniFAR-FTUs huggingface dataset:
A feature-based performance evaluation model of WSN

Based on the issues in describing wireless sensor networks performance, a feature extraction enabled evaluation model of network performance is proposed. Firstly, performance indicators and characteristic factors are figured out from the basic three aspects. Secondly, mathematical models are established to evaluate the performance both qualitatively and quantitatively. Finally, the network simulations with OPNET, verify the validity and effectiveness of the models. This work focuses on scheme selection and parameter design of WSNs in practice.
Security and Privacy in IoT Systems: A Case Study of Healthcare Products

Internet of Things (IoT) is facilitated by heterogeneous technologies, which contribute to the providing of innovative services in a large number of application domains. The satisfaction of security and privacy requirements in this scenario are becoming a main challenge for IoT systems and their developers. Nevertheless, most works on IoT security and privacy requirements look at these requirements from a high level view. Hence, important aspects of security and privacy functionalities will be disregarded, causing wrong design decisions. To combat this problem, in our previous work, we summarized the most current documents related to security and privacy functionalities in the setting of IoT and provided a new taxonomy framework that organizes all aspects of security and privacy baselines, guidelines and recommendations. To give an understanding of how the framework can help to improve security and privacy of IoT products, and help to facilitate developing and designing secure and privacy-aware IoT systems, in this paper we delve deeper and demonstrate the usefulness of the framework by a case study of healthcare products, in combination with a recent security classification method.
Contactless blood perfusion assessment of the free flap in breast reconstruction surgery

Remote photoplethysmography proved useful as a non-contact method for estimating physiological data from different parts of the body. Recent studies have mainly focused on the face region for extraction of blood pulsation signal. In this paper, we have demonstrated the feasibility of non-contact remote photoplethysmography in the monitoring of free flap tissue. An experimental study was conducted, where video recordings of free flaps are obtained during breast reconstruction surgery for 8 patients. The hemoglobin absorption rate of the free flap is closely related to signal-to-noise ratio (SNR) values extracted from free flap pulsation signal during surgery. Obtained results show significantly lower SNR values when the free flap is disconnected from the blood supply compared to the SNR values when the flap is intact or after successful blood supply establishment. This method shows potential as a convenient, non-invasive and reliable tool in post-operative microvascular free flap monitoring.
Joint Learning of NNeXtVLAD, CNN and Context Gating for Micro-Video Venue Classification

Currently, micro-videos have grown explosively on various online social platforms. Accordingly, how to encode them to yield effective representation attracts our attention. NeXtVLAD is such an effective network that aggregates frame-level features into a compact supervector. However, the discriminant capability of such a supervector is still limited due to the lack of non-linear transformation and L2 normalization at the head and tail of original NeXtVLAD network, respectively. In order to address such problems, we propose an improved neural network architecture, normalized NeXtVLAD (NNeXtVLAD), which is extended with ReLU function and L2 normalization. In the light of such a new network, we build up an end-to-end framework which jointly learns NNeXtVLAD, CNN layer, and context gating for micro-video venue classification. Specifically, we first apply NNeXtVLAD layers as three-stream architecture to aggregate visual, acoustic, and textual features. We then pack and embed the aggregated features into CNN layer for enhancing the sparse concept-level representation. Finally, context gating is used to capture the interdependency among different network activations. Extensive experimental results on a real-world micro-video dataset exhibit that our proposed model significantly outperforms the state-of-the-art baselines in terms of both Micro-F1 and Macro-F1 scores.
Model Matching: A Novel Framework to use Clustering Strategy to Solve the Classification Problem

It is a common practice to handle labeled data with classifiers and unlabeled ones with clusterings. The traditional Bayesian network classifiers (BNC<inline-formula> <tex-math notation="LaTeX">$^{\mathcal {T}}\text{s}$ </tex-math></inline-formula>) learned from labeled training set <inline-formula> <tex-math notation="LaTeX">$\mathcal {T}$ </tex-math></inline-formula> directly map the unlabeled test instance into the network structure to calculate the conditional probability for the classification, which neglects the information hidden in the unlabeled data and will result in classification bias. To address this issue, we propose a novel learning framework, called model matching, that uses the “clustering” strategy to solve the classification problem. The labeled data is divided into several clusters according to the different class label to learn a set of BNC<inline-formula> <tex-math notation="LaTeX">$^{\mathcal {T}}\text{s}$ </tex-math></inline-formula> and a corresponding set of BNC<inline-formula> <tex-math notation="LaTeX">$^{p}\text{s}$ </tex-math></inline-formula> is built for each unlabeled test instance. To make a classification, the cross entropy method is applied to compare the structural similarity between BNC<inline-formula> <tex-math notation="LaTeX">$^{\mathcal {T}}$ </tex-math></inline-formula> and BNC<sup><italic>p</italic></sup>. The extensive experimental results on 46 datasets from the University of California at Irvine (UCI) machine learning repository demonstrate that for BNCs model matching helps improve the generalization performance and outperforms the several state-of-the-art classifiers like tree-augmented naive Bayes and Random forest.
Convolutional Neural Network for Resting State EEG Signal Analysis

Convolutional neural network (CNN) has a wide range of applications in the field of computer vision, but it is rarely used to process raw electroencephalograph (EEG) signals. On the one hand, raw EEG signal is the timing signal of multiple electrodes, so it’s impossible to extract local features like a picture using convolutional filters. On the other hand, EEG data collection is rather cumbersome, so the amount of EEG data is much smaller than some large datasets such as ImageNet, which leads to deeper network more prone to overfitting on EEG datasets. In this paper, we propose a method for processing resting state EEG signals, which uses Fourier transform to convert time-series EEG signals into frequency-domain signals, and construct a CNN model according to the characteristics of EEG signal. We applied this method to personal identification and classification of gender and age. For personal identification, we achieved an accuracy of 95.5% on our dataset, and the accuracy of gender and age classification was 62.7% and 51.2% respectively.
An Improved High Playload Steganography Mechanism Using Hybrid Edge Detector

This paper presents an improved version of high payload steganography mechanism using hybrid edge detector. The proposed steganpgraphic method aims to improve the efficiency of cover media in terms of embedding capacity and stego image quality. The property of human visual system is followed here i.e. more data is embedded in edge areas as compared to less data embedding in smooth areas. The experimental results prove the efficacy of the proposed method in comparison with the investigated methods in this paper.
Context-Aware Confidence Sets for Fine-Grained Product Recognition

We present a new approach for fine-grained classification of retail products, which learns and exploits statistical context information about likely product arrangements on shelves, incorporates visual hierarchies across brands, and returns recognition results as “confidence sets” that are guaranteed to contain the true class at a given confidence level. Our system consists of three important components: 1) a nested hierarchy of product classes are automatically constructed based on visual similarities, 2) a confidence set predictor is trained based on class posteriors by using coarse-to-fine binary classifiers to discriminate each nested cluster of the hierarchy from the remainder of classes and a Bayesian network (BN) model that encodes the joint distribution of classifier scores with the fine-level class variable, and 3) n hidden Markov model (HMM) is trained with nested hidden states from the class hierarchy to model spatial transition across the nodes of product class hierarchy and resolve errors in the context-free confidence set results. Novel aspects of the proposed method include 1) combining confidence sets and context information via a HMM, 2) applying this concept to fine grained recognition of products arranged in retail shelves, and 3) presenting experimental results on four large datasets, collected from actual retail stores. We compare our approach with existing confidence set approaches and state-of-the-art convolutional neural networks classifiers including SENet-154, DenseNet-161, B-CNN, and Inception-Resnet-v2. Our approach performs comparably or better than state-of-the-art deep classifiers and exhibits high accuracy for relatively small confidence set sizes.
Optimization of Attachment Place of Two Photovoltaic Systems to Power Line with Distributed Load

Distributed generation based on photovoltaic systems is of interest for scientists and specialists, since it has an impact on power losses in distribution networks and voltage deviations at consumers, due to the change in power flows from busbars. The article analyzes the effect of connecting two photovoltaic systems to a power line with a distributed load in terms of power losses. As the objects of research, the systems of power supply to poultry farms were analyzed. Two methods have been proposed for determining the optimal power and places of connection of photovoltaic systems from the obtained analytical expressions for minimum electric power losses in a power line with distributed load, and based on the developed nomogram making it possible to determine approximately different sets of distances and powers of photovoltaic systems.
A Hierarchical Attention Fused Descriptor for 3D Point Matching

Motivated by recent successes on learning 3D feature representations, we present a Siamese network to generate representative 3D descriptors for 3D point matching in point cloud registration. Our system, dubbed HAF-Net, consists of feature extraction module, hierarchical feature reweighting and recalibration module (HRR), as well as feature aggregation and compression module. The HRR module is proposed to adaptively integrate multi-level features through learning, acting as a hierarchical attention fusion mechanism. The learnable feature pooling technique VLAD is extended into our aggregation module, which is further utilized to extract principal components of features and compress them into a low dimensional feature vector. To train our model, we amass a large dataset for 3D point matching. The dataset is composed of matched and unmatched point block pairs, which are automatically searched from existing reconstruction datasets with known poses. The experiments demonstrate that the proposed HAF-Net not only outperforms other state-of-the-art approaches in 3D feature representation but also has a good generalization ability in various tasks and datasets.
Tile-based Streaming of 8K Omnidirectional Video: Subjective and Objective QoE Evaluation

Omnidirectional video (ODV) streaming applications are becoming increasingly popular. They enable a highly immersive experience as the user can freely choose her/his field of view within the 360-degree environment. Current deployments are fairly simple but viewport-agnostic which inevitably results in high storage/bandwidth requirements and low Quality of Experience (QoE). A promising solution is referred to as tile-based streaming which allows to have higher quality within the user’s viewport while quality outside the user’s viewport could be lower. However, empirical QoE assessment studies in this domain are still rare. Thus, this paper investigates the impact of different tile-based streaming approaches and configurations on the QoE of ODV. We present the results of a lab-based subjective evaluation in which participants evaluated 8K omnidirectional video QoE as influenced by different (i) tile-based streaming approaches (full vs. partial delivery), (ii) content types (static vs. moving camera), and (iii) tile encoding quality levels determined by different quantization parameters. Our experimental setup is characterized by high reproducibility since relevant media delivery aspects (including the user’s head movements and dynamic tile quality adaptation) are already rendered into the respective processed video sequences. Additionally, we performed a complementary objective evaluation of the different test sequences focusing on bandwidth efficiency and objective quality metrics. The results are presented in this paper and discussed in detail which confirm that tile-based streaming of ODV improves visual quality while reducing bandwidth requirements.
High-Speed On-Chip Mixing by Microvortex Generated by Controlling Local Jet Flow Using Dual Membrane Pumps

Robot integrated microfluidic chip is a key technology for microscale applications. Recently, the technology has been applied to on-chip mixing, which mix solutions on a microfluidic chip because it is a promising tool to analyze not only the chemical reaction with the small sample volume, but also the response of cells to environmental changes. However, these conventional mixing methods require the mixing time of millisecond-order due to the difficulty of mixing in the laminar condition of a microchannel whose Reynolds number tends to be low. In this letter, we propose a high-speed on-chip mixing by the microvortex generated by controlling local jet flow using dual-membrane pumps. First, we confirmed that vortex was successfully generated within 20 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>s by the local jet flow. The velocity and Reynolds number were analytically estimated as approximately 20 m/s and <inline-formula><tex-math notation="LaTeX">${\text{1.6}} \times \text{10}^{\text{3}}$</tex-math></inline-formula>, respectively. Second, we evaluated the response time of the mixing using the microvortex. We mixed 200-nm nanobead suspension and the DI water in the velocity of main flow of 1 m/s. By measuring the intensity at the certain observation area, we confirmed that our method successfully mixed solutions and the mixing time was approximately 500 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>s, whose speed has not been achieved by conventional robot integrated on-chip mixers. Moreover, we confirmed that our system can control the concentration of mixed flow by controlling flow rate ratio of sample and sheath flow. From these results, we confirmed that we achieved high-speed on-demand on-chip mixing by the microvortex.
Training and limited feedback strategies for fading channels

A practical challenge in wireless communication systems is acquisition of channel state information at the transmitter and the receiver. In this thesis, we address the problem of training and limited-rate feedback optimization for fading channels. In a wideband (multi-carrier) channel, given limited coherence time and power, only a finite number of channel coefficients (each corresponding to a coherence band, or sub-channel) can be estimated. The achievable rate of such systems is therefore governed by the coherence time. A threshold based on-off transmission scheme, which requires only limited rate feedback, is assumed. For both single-user and a multiple access wideband channels, we optimize the training length and power, number of sub-channels probed, and the feedback threshold. For a correlated fading channel, we propose an adaptive training power control scheme, which can be implemented with limited rate channel state feedback (CSF). With this scheme, training power is reduced (increased) when the channel is faded (good). At low signal to noise ratios, or with fast fading, this scheme is shown to provide substantial gains. Next we consider the design of a limited CSF scheme for multi-carrier channels in which sub-channels randomly take on either a good or bad state. The problem is formulated as a vector quantization problem and performance bounds are obtained using rate-distortion theory. The results are then applied to Rayleigh fading sub-channels, which are reduced to two state sub-channels by comparing channel gains with a threshold. We also study the reliability of block codes over memoryless channels in which a fraction of received symbols are noiselessly fed back to the transmitter. We give an upper bound on the error exponent for this channel. In addition, for additive white Gaussian noise channels, we propose a coding scheme that exploits the partial feedback and achieves an error exponent that is close to the upper bound. Finally, we study the trade-off between CSF and receiver state feedback (RSF) over a multi-carrier block Rayleigh fading channel. RSF refers to the information about the decoder state, which can be used to improve reliability (e.g., through re-transmissions). Given a fixed forward rate we divide the feedback between CSF and RSF to maximize the error exponent. It is shown that the optimal trade-off depends critically on the coherence time.
Spectrum-Sharing Method for Co-Existence Between 5G OFDM-Based System and Fixed Service

This study investigates the co-existence of fifth generation (5G) mobile communication systems and fixed service (FS) in the 28-GHz band through the utilization and modification of an existing spectrum-sharing method known as the advanced minimum coupling loss (A-MCL) model. The proposed model is based on the power spectral density (PSD) overlap between the 5G orthogonal frequency-division multiplexing (OFDM)-based system and the FS. Spectrum-sharing studies typically need 5G parameters, such as the spectrum emission mask (SEM); however, no such information is available for the new system to achieve accurate results. The proposed model is suitable for spectrum-sharing studies between 5G and other wireless systems without the need for the 5G SEM. Moreover, the existing model is implemented in a new application (i.e., 5G) in the 28-GHz band with different 5G bandwidths. Furthermore, the FS parameters and its frequency allocation are selected based on the Canadian standards to obtain preliminary results for the co-existence between the 5G system and the FS. Results show that co-existence is feasible when certain distances are applied, especially with higher 5G bandwidths (such as 0.5 and 1 GHz) when the 5G system acts as an interferer. In addition, the antenna position plays a major role in reducing the required separation distances between the victim receiver and the interfering transmitter. This model can be used for any future mobile generation such as the sixth generation (6G) mobile system if its PSD is known. This study is concurrent with the worldwide spectrum-sharing studies requested by the International Telecommunication Union for WRC-19.
Design and Simulation of Quadrature Branch-Line Coupler for S Band Applications

Quadrature couplers constructed with the two coupled transmission lines that are set close together for energy transfers between the two lines. The design of branch-line coupler has been carried out for the band of 2.4GHz to 3.3 GHz. The applications of this band are in communication satellites, weather radar etc. Modified branch line coupler with compact size and wide bandwidth has been designed for replacing conventional quarter wave Branch Line Coupler presented in this work. The closed form of equations has been used for the coupler design. The simulation has been done in AWR software using RT Duroid substrate. The simulated results show the S parameter characteristics i.e. S11 & S22 are below -20dB while S21 & S31 are exactly at -3 dB. 90° phase difference between port 2 and port 3 is observed at the bandwidth ranges from 2.4 GHz to 3.3 GHz. The EM Simulation has been done to get the accurate results and verified with the AWR schematic. The theoretical analysis and practical results for designing of simple structure of branch line coupler has described thoroughly.
A Dynamic Privacy-Preserving Key Management Protocol for V2G in Social Internet of Things

With the smart grid (SG) and the social Internet of Things (SIoT), an electric vehicle operator can use reliable, flexible, and efficient charging service with vehicle-to-grid (V2G). However, open channels can be vulnerable to various attacks by a malicious adversary. Therefore, secure mutual authentication for V2G has become essential, and numerous related protocols have been proposed. In 2018, Shen et al. proposed a privacy-preserving and lightweight key agreement protocol for V2G in SIoT to ensure security. However, we demonstrate that their protocol does not withstand impersonation, privileged-insider, and offline password guessing attacks, and it does not also guarantee secure mutual authentication, session key security, and perfect forward secrecy. Therefore, this paper proposes a dynamic privacy-preserving and lightweight key agreement protocol for V2G in SIoT to resolve the security weaknesses of Shen et al.’s protocol. The proposed protocol resists several attacks including impersonation, offline password guessing, man-in-the-middle, replay, and trace attacks, ensures anonymity, perfect forward secrecy, session key security, and secure mutual authentication. We evaluate the security of the proposed protocol using formal security analysis under the broadly-accepted real-or-random (ROR) model, secure mutual authentication proof using the widely-accepted Burrows-Abadi-Needham (BAN) logic, informal (non-mathematical) security analysis, and also the formal security verification using the broadly-accepted automated validation of Internet security protocols and applications (AVISPA) tool. We then compare computation costs, and security and functionality features of the proposed protocol with related protocols. Overall, the proposed protocol provides superior security, and it can be efficiently deployed to practical SIoT-based V2G environment.
Interconnected Observers for a Powered Two-Wheeled Vehicles: Both Lateral and Longitudinal Dynamics Estimation

The paper focuses on the accurate estimation of the powered two wheelers vehicle states, including both the longitudinal and lateral dynamics. The examination of road crashes statistic reveals that loses of control is responsible for the most motorcycle accidents. Motivated by the need of observers to acquire certain states used in safety and control systems to prevent possible dangerous situation, this work investigates the design of an interconnected observers. First, the linear parameter varying (LPV) of the two-sub models of the PTWv motion are transformed into a Takagi-Sugeno (TS) form. Secondly, the observer convergence study is based on Lyapunov theory associated with the Input to State Practical Stability (ISpS) to guaranty boundedness of the state estimation errors. Further, sufficient conditions are given in terms of linear matrix inequalities (LMIs). Finally, observer performances are tested and compared to the motorcycle model states and several simulation cases are provided to highlight the effectiveness of the suggested method using motorcycle Simulator Software BikeSim©.