Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning to Decode Polar Codes with Quantized LLRs Passing

In this paper, a weighted successive cancellation (WSC) algorithm is proposed to improve the decoding performance of polar codes with the quantized log-likelihood ratio (LLR). The weights used in the WSC are automatically learned by a neural network (NN). A novel NN model and its simplified architecture are build to select the optimal weights of the WSC, and all-zero codewords can train the NN. Besides, we impose the constraints on weights to direct the learning process. The small number of trainable parameters lead to faster learning without performance loss. Simulation results show that the WSC algorithm is valid to various codewords and the trained weights make it outperform SC algorithm with the same quantization precision. Notably, the WSC with 3-bit quantization precision achieves a near floating point performance for short length.
Tumour Detection using Convolutional Neural Network on a Lightweight Multi-Core Device

Convolutional neural networks (CNN) have been the main driving force behind image classification and it is widely used. Large amounts of processing power and computation complexity is required to mimic our human brain as in the image classification. Such complexity may result in large bulky systems. A lack of such, while possible, may result in a rather limited use case and as such constrained functional implementation. One solution is to explore the use of Multicore System on Chips (MCSoC). CNN, however, were commonly built on Graphics Processing Units (GPU) based machine. In this paper, we reduce the overall size of a CNN while retaining a satisfactory level of accuracy so that it is better suited to be deployed in an MCSoC environment. We trained a CNN model that was validated on detecting malignant tumor cells. The results show significant boost in functionality in the form of faster inference times and smaller model parameter sizes, deploying neural networks in an environment that would have otherwise seemed less practical. Efficient inference networks on lightweight systems can serve as an inexpensive and physically small alternative to existing Artificial Intelligence (AI) systems that are generally costly, bulky and power hungry.
Theory and Design of a new Metamatrial Resonator for Millimeter wave Applications

In this paper, a new metamaterial resonator coupled to planar transmission lines for millimeter wave applications is proposed. The approach consists on modifing an uniplanar series resonator in order to form a close square loop inclusion which contains a distributed capacitance and inductance. The metamaterial behavior of the resonator is proved using the Nicholson-Ross-Weir algorithm. Moreover, An analytical equivalent-circuit model for the proposed structure is proposed and the resonant frequency is calculated and compared with simulated results. Indeed, we demonstrate that the new resonator presents many advantages over conventional split ring resonators and can be applied for millimeter wave applications.
Atrial Fibrillation Detection using Different Duration ECG Signals with SE-ResNet

Atrial fibrillation (AF) is the most common cardiac arrhythmia that has significant effects on associated morbidity and mortality. Therefore, early detection of AF by electrocardiogram (ECG) is substantial and necessary for effective treatments of AF. Here, we creatively propose a signal processing method for ECG signals and develop a 94-layer deep neural network to classify 4 rhythm signals using 8528 different duration single-lead ECGs from The PhysioNet Challenge 2017. And the model is trained and validated on different databases, respectively. We utilize F1 score to evaluate the performance of our method and obtain the mean F1 score of 85%. Finally, the experiment results demonstrate that our algorithm achieves excellent performance for AF and other arrhythmia detection and is also superior to existing algorithms.
Identifying Application-Layer DDoS Attacks Based on Request Rhythm Matrices

Application-layer distributed denial of service (AL-DDoS) attacks are becoming critical threats to websites because the stealth of AL-DDoS attacks makes many intrusion prevention systems ineffective. To detect AL-DDoS attacks aimed at websites, we propose a novel statistical model called the RM (rhythm matrix). Although the original features from the network layer are adopted, the access trajectory, including requested objects and corresponding dwell-time values, can be abstracted and accumulated into an RM. With an RM, we can almost losslessly compress complex features into a simple structure and characterize the user access behavior. We detect AL-DDoS attacks according to the increase of the abnormality degree in the RM and further identify malicious hosts based on change-rate outliers. In the experiments, we simulate three modes of AL-DDoS attacks with the latest popular DDoS attack tools: LOIC and HOIC. The results show that our method can detect these simulated attacks and identify the malicious hosts accurately and efficiently. For an AL-DDoS detection method, the ability to distinguish flash crowds is indispensable. We also demonstrate the excellent performance of our approach in distinguishing flash crowds from AL-DDoS attacks with two reconstructed public datasets.
2D Photogrammetry Image of Scoliosis Lenke Type Classification Using Deep Learning

Scoliosis is a spine deformity where 40% to 80% of victims are among adolescence. The current clinical approach uses radiographic images to determine the type of spine deformity. However, the adolescence is in the risk of cancer due to high exposure to radiation. A non-invasive approach using 2D-photogrammetric approach has been introduced and proven to be the best tool to classify scoliosis Lenke type. Convolution Neural Network (CNN) has been examined to classify either Lenke type 1 or other types of Lenke. Experiments were conducted to determine the relationship between different sizes of an input image and the best number of layers to the Lenke classification accuracy. Forty-five (45) numbers of 2D-photogrammetric images have been collected for these experiments and the results show that the three (3) convolve layers with the filter size 3x3 achieve higher accuracy compared to other filter sizes. It also shows that the image size is crucial to achieving higher accuracy and our investigation indicates that 180x180 input size leads to the high accuracy of 84.6%.
Device-Enhanced MEC: Multi-Access Edge Computing (MEC) Aided by End Device Computation and Caching: A Survey

Multi-access edge computing (MEC) has recently been proposed to aid mobile end devices in providing compute- and data-intensive services with low latency. Growing service demands by the end devices may overwhelm MEC installations, while cost constraints limit the increases of the installed MEC computing and data storage capacities. At the same time, the ever increasing computation capabilities and storage capacities of mobile end devices are valuable resources that can be utilized to enhance the MEC. This article comprehensively surveys the topic area of device-enhanced MEC, i.e., mechanisms that jointly utilize the resources of the community of end devices and the installed MEC to provide services to end devices. We classify the device-enhanced MEC mechanisms into mechanisms for computation offloading and mechanisms for caching. We further subclassify the offloading and caching mechanisms according to the targeted performance goals, which include throughput maximization, latency minimization, energy conservation, utility maximization, and enhanced security. We identify the main limitations of the existing device-enhanced MEC mechanisms and outline future research directions.
Privacy-Preserving Collaborative Model Learning Scheme for E-Healthcare

With the advances of data mining and the pervasiveness of cloud computing, online medical diagnosis service has been extensively applied in e-heathcare field, and brought great conveniences to people’s life. However, due to the insufficient data sharing among healthcare centers under the security and privacy concerns of medical information, the flourish of online medical diagnosis service still faces many severe challenges including diagnostic accuracy issues. In this paper, in order to address the security issues and improve the accuracy of online medical diagnosis service, we propose a new privacy-preserving collaborative model learning scheme with skyline computation, called PCML. With PCML, healthcare centers can securely learn a global diagnosis model with their local diagnosis models in the assistance of cloud, and the sensitive medical data of each healthcare center is well protected. Specifically, with a secure multi-party vector comparison algorithm (SMVC), all local diagnosis models are encrypted by their owners before being sent to the cloud, and can be directly operated without decryption. Detailed security analysis shows that PCML can resist security threats in the semi-honest model. Moreover, PCML is implemented with medical datasets from UCI machine learning repository, and extensive simulation results demonstrate that PCML is efficient and can be implemented effectively.
Topological Higher-Order Neuron Model Based on Homology-Continuity Principle

Recently, deep neural networks (DNNs) have been applied in various areas in computer vision. However, due to the simple neuron configuration, the existing DNNs rely heavily on the diversity of the data distribution and have poor generalization performance. In addition, the excessive network complexity also leads to low learning efficiency. To solve above problems, a topological higher-order neuron model was proposed, which is modeled according to the point-set topology in high-dimensional space. The proposed neuron is determined by the adaptively changed center and the direction weights with strong generalization ability. The high-dimensional feature space of the samples is constructed by a category-based covering learning method, thus achieving an optimal coverage of each category. Experiments on several different classical datasets demonstrate the generalization of the proposed neuron model, which provides a new approach for the further development of DNNs and can be widely used in computer vision.
Social Robots as Cyber-Physical Actors in Entertainment and Education

For a number of years robots have been widely used in industry mainly for assembly tasks and other automated processes. Advances in robot hardware and software have significantly improved the capabilities of robots and have led researchers to investigate the potential uses of robots in other fields where more autonomy, intelligence and higher levels of interaction with humans are required. Two of these fields are education and entertainment. As robotic platforms have become more accessible and more affordable not only to researchers but also for the general public, social robots are increasingly being introduced in the classroom and the theater for educational and entertainment purposes respectively. The present paper is a short survey on the use of social robots as theatrical actors, and presents an outline of the authors' preliminary work on this field, aiming at employing this approach in special education.
The Differential Cell Count

Differential cell counts have provided extensive data through more than a century of laboratory hematology. Routine bench morphology count continues to play a role in analyses of blood and other tissues such as bone marrow, but for many purposes it is being replaced by other tech- nologies that provide greater precision and consistency. This discussion of the differential cell count traces the development of electronic analysis and looks at the recent impact of monoclonal antibody-based flow cytometry. The introduction of the extended differential count has led to a rapid increase in electronic analysis, with resultant increased accuracy, lower cost, and more efficient turn- around times. Moreover, the extended differential is leading to acquisition of effective data on complex specimens and is paving the way for new parameters in the field. Lab. WBC differential count has become widely accepted and used by clinicians, and is generally considered to yield clini- cally useful information in health and disease. As a laboratory test, the differential cell count is unlike almost all other tests performed in the clinical laboratory. It provides a pathology report on the morphological appear- ance of blood cells and on the frequency in which they appear, not in a descriptive manner but in a highly stylized, quantitative format. It is called a "differential" count because all enumerated cells are brought into a common mathemati- cal context, ie, a frequency distribution for the different cell types present. The total number of cells counted may vary, but in most clinical situations it is 100, and specific cell types are commonly expressed as percentages of the total count. Thus there is within this context a particular distribution of cell types for healthy, normal individuals, and there are dis- tribution patterns associated with disease states.
Realtime 2-D DOA Estimation using Phase-Difference Projection (PDP)

Estimating the direction of arrival (DOA) information of a signal is important for communications, localization and navigation systems. Time-delay based methods are popular DOA algorithms that can estimate DOA with a minimal number of receivers. Time delay can be measured with subsample accuracy using phase-difference based methods. Phase-wrapping represents a major challenge for time delay estimation that occurs when inter-sensor spacing is large. Several methods exist for phase-unwrapping; the most successful methods are those search methods, which are time-consuming and do not lend themselves to theoretical analysis. In this paper, we present a phase-difference projection (PDP) method for DOA estimation which is capable of delivering more accurate results with reduced computational complexity. The proposed method has been tested and compared with several benchmark algorithms in both simulations and experiments. The results show that, at a signal-to-noise ratio (SNR) of -18 dB, using the proposed PDP algorithm, the percentage of the DOA estimates with errors smaller than <5° is 54%, and it reaches 100% at SNR =-7dB. This performance is not matched by the benchmark methods. For the utility test, we implemented this algorithm to realize an ultrasound-based air-mouse and it achieves satisfactory user experiences when using Google Maps, or playing some interactive games.
Detecting False Data Injection Attacks in Linear Parameter Varying Cyber-Physical Systems

In this paper, we investigate the process of detection of False Data Injection (FDI) in a Linear Parameter Varying (LPV) cyber-physical system (CPS). We design a model based FDI detector capable of detecting false data injections on output measurements and scheduling variables. To improve the detection accuracy of FDI attacks, the attack detector design uses the performance metric H_ to maximize the detection capability of the detector module to effectively detect FDI attacks. On the other hand, it uses the $H_{\infty}$ metric to minimize the effect of disturbance on the detector module given an unreliable network. We assume that the network unreliability comes from packet dropout that we modeled as Bernoulli process. The FDI attack detector is designed such that H_ and $H_{\infty}$ performance metrics are maintained despite packet dropout. Based on stochastic stability, we define a set of sufficient Linear Matrix Inequalities (LMI) that we solve as a multi-objective optimization problem to obtain the detector gain. The obtained detector gain is used for estimating the current system state and current output measurement using the system input, manipulated measurements and manipulated scheduling variables. Then, the output of the detector is compared with the actual sensor measurement. The resulting residual signal carries the information about the FDI attack.The proposed approach is tested and validated on a two-tank system. The evaluation results demonstrate that the proposed detector is able to detect FDI attacks.
3D Millimeter Wave Screening for Metallic Surface Defect Detection

A 3D surface reconstruction method based on the unwrapped 2D phase grid of an 80 GHz synthetic aperture radar scan is presented. The introduction is followed by the explanation of the used 2D phase unwrapping algorithm. The results show the visualization of the radar scan which enables the detection of defects which are less than 10 mm in diameter and 30 µm in depth. This underlines the potential of mmWave radar scans for high quality monitoring systems.
Towards Platform Specific Energy Estimation for Executable Domain-Specific Modeling Languages

Energy consumption is becoming a major subject when designing, developing and running programs. Most developers code and run their applications in an energy oblivious manner, mostly because of a lack of energy-related knowledge about their system. This problem also exists in the realm of executable domain-specific modeling languages, where end-users create models conforming to a given meta-model and execute them with little knowledge about their operational semantic and related energy consumption. In this work, we propose a domain-specific language for decorating meta-models of executable languages with platform-specific energy estimation formulas. We also extend the GEMOC execution engine to dynamically perform energy estimations on any executable model conforming to the decorated meta-model. The energy estimation model defined can then be easily adapted to other models and platforms, without requiring any measurement tooling or knowledge from the end-user.
Eigenmode Solution for TE-Wave Propagation Through Anisotropic Metamaterial Particle 3-D Arrays via Dipole Approximation

The wavenumbers for an obliquely propagating transverse electromagnetic mode via a 3-D array of anisotropic scatterers are theoretically studied and numerically derived through a consistent method based on the dipole approximation. First, it is assumed that the anisotropic particle can be replaced with a set of dipole moments, and, in terms of its polarizabilities and the array interaction coefficients, the dispersion equation of the system is efficiently extracted. Then, the eigenmodes are rapidly retrieved using a computational non-linear equation solver scheme. The proposed technique is applied to two well-known anisotropic metamaterial scatterers, and the desired wavenumbers are accurately evaluated and successfully validated for various angles of propagation.
Methods of Assessing the Effectiveness of Network Content Processing Systems for Detecting Malicious Information9 Taking into Account the Elimination of Uncertainty in the Semantic Content of Information Objects

The paper presents a mathematical model of the process of changing the state of indicators (features) of harmful information to be detected as part of the functioning of network content processing systems. The model is based on Markov chains and takes into account the probability-time mechanism for changing the state of the functioning process of systems of this class in dynamics. On the basis of the model, a technique of multi-criteria dynamic evaluation of the effectiveness of network content processing systems is proposed. It is based on the analysis of current deviations of the indicator values of harmful information in dependence on the requirements to them. Also it uses and eliminates the uncertainty of the semantic content of information objects. The methods (technique) makes it possible to take into account transient processes by introducing controls of the structure, parameters and modes of functioning of network content processing systems under conditions of various types of influences.
A Ditherless 2.4GHz High Resolution LC DCO

This paper presents an LC-based digitally controlled oscillator (DCO) with an enhanced frequency resolution. Tuning range of the circuit is 2.2GHz to 2.98GHz with a fine resolution of 100Hz. The DCO exhibits a phase noise of -115.3 dBc/Hz at 1MHz frequency offset and a Figure of Merit (FoM) of -181 dBc/Hz. The circuit consumes 1.25mA current and designed with 65-nm eight-metal CMOS technology with an operating supply voltage of 1.0 V.
Wide-Angle and Ultra-Wideband Absorption in One-Dimensional Superconductor Photonic Crystals With Quasi-Periodic Sequences

In this paper, the properties of wide-angle and ultra-wideband (UWB) absorption of one-dimensional (1-D) superconductor photonic crystals (SPCs) in the terahertz (THz) region are theoretically analyzed by transfer matrix method (TMM). We principally study the absorption features in SPCs with different arrangements by contrast where their external factors and dielectric types are consistent. The arrangements of SPCs adopt two construction rules of generous Fibonacci sequence (GFS) and Cantor-like sequence (CLS). In addition, the influences of the recursive numbers, external factors and polarization-depend properties on the proposed structures are also considered. We introduce multifractal theory to describe the absorption trends and self-similarity of quasi-periodic structural system subtly specific to the variation of recursive numbers. Moreover, the reason for angle-insensitive absorption is also investigated via the interference field theory. The calculated results demonstrate that the UWB absorption can sustain over 80 degrees within THz region for transverse magnetic (TM) wave at high temperature in the proposed SPCs, which provides theoretical guidance for the design of the high-performance THz absorber.
User Clustering and Scheduling in UAV Systems Exploiting Channel Correlation

Unmanned aerial vehicle (UAV) communication system equipped with multiple input and multiple output (MIMO) antennas has been recognized as an efficient technique for obtaining high spectral efficiency. To improve the system capacity in UAV communication scenario, this paper investigates the user clustering and downlink user scheduling strategy. We first develop a channel correlation based K-means algorithm for user clustering. Furthermore, to diminish intergroup interference, a greedy user scheduling algorithm driven by the K-means clustering strategy, is devised to appropriately select the users with low-correlated channels to schedule at the same time slot. Simulation results are provided to demonstrate the superior performance of the proposed scheduling algorithm over conventional method. It is observed that the presented K-means aided algorithm can achieve near maximum average sum rate with much lower computational complexity.
A Review of Stereo-Photogrammetry Method for 3-D Reconstruction in Computer Vision

3-D reconstruction is one big concern since the first days of computer vision (CV) development. By going through the history of 3-D reconstruction, we show the contribution of this field into society and the necessity of improving the system’s complexity, speed and accuracy. Although various algorithms and methods have been introduced, stereo-photogrammetry that applies the binocular vision principle is the closest to human vision system. Originated from the motivation of improving the stereo-photogrammetry and applying for robot manipulation, the method is reviewed with all three known-to-date categorizes local, global and semi-global. In addition, it is advisable to rectify the stereo images before matching to find depth and reconstruct 3-D models. Therefore, some of the rectification methods are demonstrated. Finally, we discussed our findings and future work relates to stereo-photogrammetry.