Miracle-dz/UniFAR-FTUs huggingface dataset:
Low-complexity decoders for non-binary turbo codes

Following the increasing interest in non-binary coding schemes, turbo codes over different Galois fields have started to be considered recently. While showing improved performance when compared to their binary counterparts, the decoding complexity of this family of codes remains a main obstacle to their adoption in practical applications. In this work, a new low-complexity variant of the Min-Log-MAP algorithm is proposed. Thanks to the introduction of a bubble sorter for the different metrics used in the Min-Log-MAP decoder, the number of required computations is significantly reduced. A reduction by a factor of 6 in the number of additions and compare-select operations can be achieved with only a minor impact on error rate performance. With the use of an appropriate quantization, the resulting decoder paves the way for a future hardware implementation.
Driver fatigue Assessment Based on the Feature Fusion and Transfer Learning of EEG and EMG

The methods for driving fatigue assessment based on multiple physiological signals have received extensive attention in recent years. In view of the limitations of individual differences leading to reduce the accuracy of fatigue assessment, this paper proposed a method for driving fatigue assessment based on the feature fusion and transfer learning of electroencephalogram(EEG) and electromyography(EMG). The EEG and cervical EMG signals from 20 healthy subjects were recruited synchronously using the simulated driving platform. The time-frequency analysis method was used to extract the fatigue characteristics of EEG signals and EMG signals. A driver fatigue assessment model based on feature fusion and transfer learning was constructed to achieve effective identification of different drivers' fatigue state. The results showed that the recognition rate of driving fatigue based on the feature fusion and transfer learning of EEG and EMG was higher than traditional support vector machine method, and it could improve the robustness of driving fatigue assessment in different subjects. Therefore, this paper provides a new method for practical application of driving fatigue assessment based on physiological signal.
Fault diagnosis of chillers using very deep convolutional network

A fault diagnosis and detection method for chillers based on a very deep convolutional network in conjunction with residual connections is proposed. The residual connections are applied to accelerate the training of the very deep convolutional neural network (DCNN) and improve the classification accuracy without increasing much computation burden. In addition, normalization and intermediate supervision are introduced to overcome the vanishing gradient problem. The strength of these allows us to faster the training process with easier initialization. The experimental data from ASHRAE RP-1043 is applied to validate the fault diagnosis method. The results demonstrate that the method achieves higher diagnostic accuracy compared to traditional methods based on support vector data description (SVDD) or principle component analysis (PCA).
Deep Learning for Sleep Stage Classification

Scoring of sleep stages plays an important role in the diagnosis of sleep-related diseases. Scoring by visual inspection is time-consuming and heavily depends on the experience of experts. Thus, there is an urgent need for an automatic sleep stage classification system. This paper proposes a novel compact convolutional neural network (C-CNN) using only single-channel EEG signal. Compared with traditional machine learning approaches based on hand-engineered features, our approach provides an end-to-end solution that requires almost no prior knowledge and preprocessing while achieving better performance. Experiments on the expanded Sleep-EDF database verified its effectiveness and efficiency. In addition, we notice the issue of class imbalance in sleep stages, and a class-imbalance metric, the balanced classification accuracy (BCA), is introduced. At the cost of a little drop in accuracy, which is still higher than existing classification methods, the introduction of class-imbalance weights can significantly increase the BCA metric and result in a higher recall for each sleep stage. This paper also proposes a recurrent neural network based on the attention mechanism and bidirectional long short-term memory (LSTM), which provides better performance than C-CNN but requires more training time.
A Decentralized Application for Secure Messaging in a Trustless Environment

Blockchain technology has been seeing widespread interest as a means to ensure the integrity, confidentiality and availability of data in a trustless environment. They are designed to protect data from both internal and external cyberattacks by utilizing the aggregated power of the network to resist malicious efforts. In this article, we will create our own decentralized messaging application utilizing the Ethereum Whisper protocol. Our application will be able to send encrypted messages both securely and anonymously. We will utilize the Ethereum platform to deploy our blockchain network. This application would be resistant to most suppression tactics due to its distributed nature and adaptability of its communication protocol.
Visualizing High-dimensional single-cell RNA-sequencing data through multiple Random Projections

Recent sequencing technology breakthroughs have resulted in a dramatic increase in the amount of available sequencing data, enabling major scientific advances in biology and medicine. Nowadays, sequencing transcriptome data of single cells (scRNA-seq) are growing rapidly, posing new challenges in their analysis, mostly due to their high dimensionality. In this paper, we study the problem of visualizing such high-dimensional scRNA-seq data. A new visualization scheme is presented based on a customized distance matrix retrieved by applying independently Nearest Neighbors search through multiple Random Projections. The proposed method is compared against well-known dimensionality reduction and visualization techniques showing its capabilities and performance.
Quantitative Identification of Technological Discontinuities

The aim of this paper is to develop and test the metrics to quantitatively identify technological discontinuities in a knowledge network. We first analyzed the various conceptual frameworks for defining such discontinuities and arrived at four metrics. We tested the four metrics: Metric 1 and 2 are the normalized versions of previously existing metrics and Metric 3 and 4 are newly developed from the innovation theories, by using a patent set representative of the magnetic information storage domain. The three representative patents associated with a well-known breakthrough technology in the domain, the giant magneto-resistance spin valve sensor, were selected based on qualitative studies, and the metrics were tested by how well each identifies the selected patents as top-ranked patents. The empirical results show that, first, global citation structure-based metrics clearly provide better performance in the identification of technological discontinuities than local citation count-based metrics which have not been shown as clearly before, second, non-continuous nodes on the major knowledge networks are not at all related to technological discontinuities, and, third, the two global metrics (Metric2: z-score of Persistence and Metric 4: z-score of Persistence times # of converging main paths) successfully identified the three selected patents as top-ranked patents out of over 30 000 patents.
Generating Interpretable Network Asset Clusters For Security Analytics

User-group or asset-group information in an enterprise network plays important roles in the detection of behavioral anomalies, particularly in peer-based analysis. While user peer group data is readily available, since it is maintained by enterprise IT for network security policy administration, asset peer group data is typically nonexistent. Therefore, a method to automatically create asset groups or clusters is desired. This is useful both in building the asset taxonomy for knowledge discovery and in asset-peer analysis for anomaly detection. This work presents a behavior-based, asset-clustering method by analyzing data from user-to-asset logon event records while leveraging the existing user peer group labels. Output asset clusters are stable, with interpretable cluster labels for operational consideration. We demonstrate the value of the derived asset clusters in peer analysis for anomaly detection.
Design and evaluation of visual SLAM method based on Realsense for mobile robots

In recent years, with the popularization and rapid development of various types of robots in production and life, robots have become one of the most promising areas of research in the 21st century. The study of autonomous mobile robots in unknown environments is very challenging, with simultaneous localization and mapping (SLAM) holding the key to the development of truly autonomous mobility. The acquisition of depth images containing distance information about local objects is a vital component of SLAM, so the use of depth cameras as a new type of sensor is increasingly common. In this study, the RealSense R200 depth camera was used as an external environment sensor that enables the simultaneous positioning of robots in an unknown environment and map construction. A visual SLAM method was designed and verified through a series of experiments. The results demonstrated that the constructed map reflects obstacle occupancy information with high accuracy.
Design and analysis of a parallel hybrid memory architecture for per-flow buffering in high-speed switches and routers

The network switches and routers require both high-speed and large-capacity packet buffers. However, existing packet buffer architectures have the problems of speed scaling and flow number scaling limitations. To address the two problems simultaneously, this paper proposes a parallel hybrid SRAM/DRAM architecture for per-flow buffering in high-speed switches and routers. Tail SRAM and head SRAM are used to apply per-flow buffering for packet aggregation, so that the middle DRAM is accessed in a larger granularity and the DRAM's bandwidth utilization is improved. To mitigate the flow number scaling limitation, a dynamic memory allocation with hard timeout (DMA-HT) memory management algorithm is designed. The key idea of DMA-HT is that the memory space is dynamically allocated for the newly arrived flows, and a hard timeout is assigned for each queue. After a specific period of time, the memory space is freed, so that the SRAM space is efficiently utilized by the most recently active flows. A queuing system is used to model the proposed method, and theoretical analysis is performed to optimize the timeout value. With the derived formulas, multiple performance parameters are quantitatively analyzed, and the optimal timeout can be obtained. Both numerical results and simulations show that the proposed architecture can and reduce packet loss rate and average delay significantly compared with previous solutions with the same SRAM capacity.
Analysis of Machine Learning Techniques for Anomaly Detection in the Internet of Things

A major challenge faced in the Internet of Things (IoT) is discovering issues that can occur in it, such as anomalies in the network or within the IoT devices. The nature of IoT hinders the identification of issues because of the huge number of devices and amounts of data generated. The aim of this paper is to investigate machine learning for effectively identifying anomalies in an IoT environment. We evaluated several state-of-the-art techniques which can identify, in real-time, when anomalies have occurred, allowing users to make alterations to the IoT network to eliminate the anomalies. Our results offer practitioners a valuable reference about which techniques might be more appropriate for their usage scenarios.
DeepLab-Based Spatial Feature Extraction for Hyperspectral Image Classification

Recently, deep learning has been used for hyperspectral image classification (HSIC) due to its powerful feature learning and classification ability. In this letter, a novel deep learning-based framework based on DeepLab is proposed for HSIC. Inspired by the excellent performance of DeepLab in semantic segmentation, the proposed framework applies DeepLab to excavate spatial features of the hyperspectral image (HSI) pixel to pixel. It breaks through the limitation of patch-wise feature learning in the most of existing deep learning methods used in HSIC. More importantly, it can extract features at multiple scales and effectively avoid the reduction of spatial resolution. Furthermore, to improve the HSIC performance, the spatial features extracted by DeepLab and the spectral features are fused by a weighted fusion method, then the fused features are input into support vector machine for final classification. Experimental results on two public HSI data sets demonstrate that the proposed framework outperformed the traditional methods and the existing deep learning-based methods, especially for small-scale classes.
Object‐based land‐cover classification for the Phoenix metropolitan area: optimization vs. transportability

An object‐based approach was utilized in the development of two urban land‐cover classification schemes on high‐resolution (0.6 m), true‐colour aerial photography of the Phoenix metropolitan area, USA. An initial classification scheme was heavily weighted by standard nearest‐neighbour (SNN) functions generated by samples from each of the classes, which produced an enhanced accuracy (84%). A second classification was developed from the initial classification scheme in which SNN functions were transformed into a fuzzy‐rule set, creating a product transportable to different areas of the same imagery, or for land‐cover change detection with similar imagery. A comprehensive accuracy assessment revealed a slightly lower overall accuracy (79%) for the rule‐based classification. We conclude that the transportable classification scheme is satisfactory for general land‐cover analyses; yet classification accuracy can be enhanced at site‐specific venues with the incorporation of nearest‐neighbour functions using class samples.
Discrete- Time ZND Stabilization Control of the 4th-Order Hyper-Chaotic Lu System with One Input via Four-Instant ZeaD Formulas

In this study, the hyper-chaotic 4th-order Lu system stabilization (or say, stabilizing) problem is solved based on Zhang neural dynamics (ZND) method as well as four-instant Zhang et al discretization (ZeaD) formulas. Firstly, the controller for hyper-chaotic single-input 4th-order Lu system stabilizing is developed by using ZND method. Then, by discretizing the continuous ZND stabilizing controller, the discrete-time ZND stabilizing models are obtained, and the four-instant ZeaD formulas are adopted during this process. Finally, computer experiments are performed to check the accuracy and the effectiveness of our discrete-time ZND stabilizing models for hyper-chaotic 4th-order Lu system stabilizing.
Projected Force-Admittance Control for Compliant Bimanual Tasks

Bimanual manipulation is fundamental for humanoid robots. It has gained a lot of attention in robotics research as a key ability towards versatile behavior. To achieve such behaviors in real-world tasks, bimanual controllers must be stable and simple to implement. On the other hand, admittance and impedance control frameworks are well-known for their efficiency in robot's manipulation tasks which require compliant motions e. g. for physical human-robot interactions. Based on these frameworks, we propose a new control framework, the Projected Force-Admittance Control (PFAC), for compliant bimanual manipulation tasks. By analyzing the load distribution in bimanual tasks using grasp mapping technique, the controller uses the projected constraint force, which, together with the actuation force given by the PI controller, are fed into an admittance control framework, and finally provides the virtual target pose to an impedance controller that can be modeled as a mass-spring-damper system. With this control strategy, we ensure motion synchronization and target force regulation under external perturbations and/or while tracking a trajectory. We demonstrate the stability and usability of the controller in several experiments with the humanoids robot ARMAR-6. Combining it with movement primitives approaches such as Dynamic Movement Primitive (DMP), a variety of compliant bimanual tasks are implemented and evaluated.
A multi-task learning based approach to biomedical entity relation extraction

Automatic extraction of high-quality biomedical entity relations from biomedical texts plays an important role in biomedical text mining. Currently, existing methods generally focus on training a single task model for a specific task (e.g., drug-drug interaction extraction, protein-protein interaction extraction), ignoring the correlation among multiple tasks. To solve the problem, we used neural network-based multi-task learning method to explore the correlation among multiple biomedical relation extraction tasks. In our study, we constructed a fully-shared model (FSM) and a shared-private model (SPM) and further proposed an attention-based main-auxiliary model (Att-MAM). Experimental results on five public biomedical relation extraction datasets show that the multi-task learning can effectively learn the shared information among multiple tasks and obtain better performance than the single task method.
Review and Analysis of Instrumentation Amplifier for IoT Applications

Instrumentation amplifiers (IAs) are essential circuit blocks for many precision sensor readouts. Particularly, for IoT applications, IAs used in the sensing nodes should be of lowcost (no trimming) and energy-efficient, which is however still a challenge. As a design guidance, this paper reviewed the major IA topologies and circuit techniques proposed in the last two decades. Different trade-offs, including gain accuracy, energy consumption, noise, etc. are discussed when designing IAs for IoT applications. Meanwhile, the startup issue, which wastes a lot energy while is not considered in most IA design is raised in this paper.
Asynchronous Event-Triggered Output Feedback Control for CPS Under Data Injection Attacks

This paper is concerned with the problem of event-triggered dynamic output feedback control for continuous-time cyber-physical systems under data injection attacks. The resulting criterion provide sufficient conditions to ensure the asymptotic stability of the closed-loop system subject to compromising both measurement and control channels. Event-triggered mechanisms are implemented at both sensor and controller nodes asynchronously in order to alleviate the number of control actions against attacks and reduce communication burden. Moreover, we present a co-design procedure to simultaneously synthesize feedback laws and event-triggering parameters. Finally, numerical examples are included to show effectiveness of the proposed method.
Optimal PID Controller Design of DC-DC Buck Converter using Whale Optimization Algorithm

This paper presents a novel design method to determine optimal proportional-integral-derivative (PID) controller parameters of a DC-DC buck converter using the whale optimization algorithm (WOA). The simplicity of the proposed algorithm provides fast and high quality tuning of optimum PID controller parameters effectively. The performance of the proposed WOA-based PID controller is validated using a time domain performance index. From simulation results, the proposed method compared to the genetic algorithm was found more efficient in improving the transient response of Buck converter.
Novel CPW-Fed UWB Antenna for X-Band Applications

A very compact coplanar waveguide (CPW)-fed ultra-wide band (UWB) printed antenna (PMA) is presented. The proposed antenna is supposed to cover the UWB range from 7 GHz to 10 GHz, with return loss values below −10 dB in the whole frequency range, for X-band applications, often used for dense satellite communications. The 30 $\pmb{\mathrm{x}\ 35\ \mathrm{x}\ 1.6\ \mathrm{mm}^{3}}$ antenna is mounted on a dielectric substrate named FR-4 with thickness $\pmb{\mathrm{h}=1.6}\mathbf{mm}$, relative permittivity $\varepsilon \mathrm{r}=4.3$ and loss tangent of 0.025. All the design and simulated results are performed using CST Microwave Studio, 3D Electromagnetic Simulator software. The results show broad bandwidth and good omnidirectional radiation patterns in the passband, with a very compact size. The satisfactory overall performance with such a simple structure and small size makes the proposed antenna attractive for use in ultra-wide band (UWB) systems, especially for X-band applications.
A Dual-Path Open-Loop CMOS Slew-Rate Controlled Output Driver with low PVT Variation

A dual-path open-loop slew-rate controlled CMOS driver is presented. The proposed output driver incorporates a delay-locked loop (DLL) to minimize the slew-rate variation over process, voltage and temperature (PVT). A dual-path open-loop structure is introduced to cancel the high-frequency components of the output signal. Simulation using the Global Foundry $0.18 \mu \mathrm{m}$ CMOS process shows that the driver achieves less than 0.66V/ns slew-rate variation operating at 500 Mbps over 16 corners, corresponding to 56% reduction compared with that of a conventional output driver.
Optimization of a Scalable and Adaptable Diversity/Multiplexing Trade-Off in 28 GHz Systems

This paper investigates the optimization of the feedback information required by the adaptation of MIMO techniques in a large-scale environment. The main aim is to propose three optimization techniques and compare its BER performances in a 28 GHz system. It is shown that the reduction of the feedback amount leads to a BER increase. However the "keep" approach based method can guarantee near optimal BER performances while ensuring a remarkable feedback’s reduction.
Research and Design on Lightweight Automotive Ethernet FQTSS Protocol

In view of the problem that the current automotive Ethernet AVB protocol stack cannot fully adapt to the vehicle network environment, optimization about the FQTSS protocol in Ethernet AVB is focused on in this paper. Considering the status of current automotive Ethernet AVB protocols stack is conservative, a lightweight Ethernet AVB platform is designed and implemented in this paper. With reference to the relevant standards and specifications given by the AVnu Alliance, a hardware test platform based on STM32 is built. Then, combined with the improved LwIP protocol stack and μC/OS-II operating system, a solution about low-cost, low-overhead, low-latency lightweight automotive Ethernet FQTSS protocol is proposed and implemented. Finally, some necessary test is executed to verify the feasibility of the solution.
Protocol for Self-Redemption of Free Parking in Smart Shopping Malls

In many shopping malls in metropolitan cities, a customer is entitled to free parking after spending a certain amount in the shopping mall. To redeem free parking, the customer presents the sales receipts to a staff member in a service counter and this staff member processes these sales receipts (e.g., checks the date and calculates the total amount of spending) for redeeming free parking. In this paper, we propose a self-redemption protocol by which customers can redeem free parking by themselves. This protocol applies digital signature and QR code to transform the sales information from human-readable form to machine-readable and non-repudiated form, such that redemption can be handled by a low-cost computer called redemption machine. This self-redemption protocol gives win-win outcomes to both the shopping malls (which enhance the malls and save manpower cost) and the customers (who enjoy better convenience and smaller waiting time).