Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of IoT Network using Deep Learning-based Model for Anomaly Detection

Destructive cyber-attacks and cybercriminals are increasing with the increase in IoT (Internet of Things) devices globally. This ha/s led to the need for increase in security in IoT systems. Innovative and novel techniques are used by the intruders to accomplish malicious goals effectively through cyber-attacks. An Intrusion Detection System (IDS) is used for classification of attacks in IoT networks based on anomaly detection and machine learning techniques. Inefficiency is observed in the conventional machine learning models and intrusion detection techniques as the network technologies are unpredictable. Accurate identification of various anomalies is possible with deep learning models in several research segments. The input data along with its prominent characteristics may be categorized automatically for classification and anomaly detection using convolutional neural networks (CNN). Faster computations are enabled due to the performance efficiency of CNN. For IoT networks, an intrusion detection model based on anomaly detection is designed and developed in this paper. A multiclass classification framework is created initially using a CNN model. Further, 3D CNN is used for implementation of the proposed model. Various intrusion detection datasets from IoT networks are used for validation of the proposed CNN model. Pre-trained multiclass CNN model is used for implementation of multiclass and binary classification based on transfer learning. When compared to the conventional deep learning models, the proposed multiclass and binary classification framework has attained improved F1 score, recall, precision and accuracy.
Integration and Verification of IP Cores on SoC

As predicted by Moore's law, due to the shrinking transistor sizes, the density of ICs in a chip has increased exponentially and the VLSI trends have seen a sudden drift from computation-based cores to communication-based cores. As a result, the primary challenge in SoC design is to efficiently integrate all IP cores while maintaining their ability to perform their specific functions and communicate with one another, and the quality of integration must be verified at the SoC level, which confirms the same. The paper showcases the design of the various IP Cores such as interface controller block, PCIe block, Ethernet block, throttle and pipeline blocks, and various protocols used to transfer information between the cores via Network-on-Chip. It also explains the process of verification of the SoC design using Universal Verification Methodology(UVM). The IP Cores were integrated with no errors, zero undriven inputs ports and multi-driven ports. The developed test cases for verification were found to achieve a 100% pass after subsequent runs on the SoC. Additionally, the use of UVM helped to achieve a functional coverage of 88.35%, which is seen to be higher than coverage achieved in the absence of UVM.
Diagnosing Performance Issues in Microservices with Heterogeneous Data Source

Microservices architecture is vulnerable to performance issues due to its highly fine-grained decomposition of an application. To diagnose performance issues in microservices, existing works utilize system metrics as the specific indicator and do a lot of heavy computation such as building service dependency graphs during the diagnosing process.To improve the effectiveness and efficiency of issue diagnosing, we propose PDiagnose, a practical approach using multiple data sources including metrics, logs and traces jointly to diagnose performance issues in microservices systems. Through combining lightweight unsupervised anomaly detection algorithms and vote-based issue localization strategy, PDiagnose is application-agnostic and can localize root cause indicators accurately. Our evaluation on two public-available datasets shows that PDiagnose can achieve an overall recall of 84.8%, outperforming the best baseline approach. Meanwhile, the diagnosis duration of PDiagnose is also promising.
ODROM: Object Detection and Recognition supported by Ontologies and applied to Museums

In robotics, object detection in images or videos, obtained in real-time from sensors of robots can be used to support the implementation of service robot tasks (e.g., navigation, model its social behavior, recognize objects in a specific domain), usually accomplished in indoor environments. However, traditional deep learning based object detection techniques present limitations in such indoor environments, specifically related to the detection of small objects and the management of high density of multiple objects. Coupled with these limitations, for specific domains (e.g., hospitals, museums), it is important that the robot, apart from detecting objects, extracts and knows information of the targeted objects. Ontologies, as a part of the Semantic Web, are presented as a feasible option to formally represent the information related to the objects of a particular domain. In this context, this work proposes an object detection and recognition process based on a Deep Learning algorithm, object descriptors, and an ontology. ODROM, an Object Detection and Recognition algorithm supported by Ontologies and applied to Museums, is an implementation to validate the proposal. Experiments show that the usage of ontologies is a good way of desambiguating the detection, obtained with a and $\mathbf{mAP}{@}0.5=0.88$ and a $\mathbf{mAP}{@}[0.5:0.95]=61\%$.
Four-Port Multiband MIMO Filtenna with an Isolation Filter for Sub-6 GHz 5G Applications

In this paper, a four-port multiple-input-multiple-output (MIMO) filtenna (antenna with filter) system with isolation enhancing band-reject filter (BRF) is presented. The filtenna system is designed by integrating a multi-functional filter (MFF) in the antenna’s feed line. The MFF has two modes of operation. In its 1st mode, the filtenna system acts as a dualband antenna in the frequency range of 2.3-4.5 GHz and 5.3-5.5 GHz. While, in the 2nd mode, it acts as a single band antenna in the frequency range 3.3-4.1 GHz. To improve the isolation between the antenna elements, an open stub line (OSL) based BRF has been incorporated in the ground plane of the filtenna system. The overall design is integrated into a Rogers RO 4003C substrate (εr=3.55, tanδ=0.0031) of dimension 100×65×1.52 mm3. The designed antenna system is suitable for sub-6 GHz fifth-generation (5G) systems.
Implementation of the Risk-based Approach Methodology in Information Security Management Systems

Currently, most companies systematically face challenges related to the loss of significant confidential information, including legally significant information, such as personal data of customers. To solve the problem of maintaining the confidentiality, integrity and availability of information, companies are increasingly using the methodology laid down in the basis of the international standard ISO / IEC 27001. Information security risk management is a process of continuous monitoring and systematic analysis of the internal and external environment of the IT environment, associated with the further adoption and implementation of management decisions aimed at reducing the likelihood of an unfavorable result and minimizing possible threats to business caused by the loss of manageability of information that is important for the organization. The article considers the problems and approaches to the development, practical implementation, and methodology of risk management based on the international standard ISO 31000 in the modern information security management system.
Fashion Landmark Detection via Deep Residual Spatial Attention Network

Fashion landmark detection is challenging due to the large spatial variances of the landmarks and the scale variations of the clothing images. Therefore, the fashion landmark detection model requires two abilities for accurately locating the coordinates of the landmarks. One is to autonomously focus on the task-related features of clothing images to adapt to the diverse spatial distribution of the fashion landmarks. The other is to extract features that contain multi-scale context information to deal with the scale variations. For these purposes, first, we propose the Direction-aware Spatial Attention Module(DASM), which embeds the direction-aware information into spatial attention to capture global contexts and help the network enhance features. And we integrate our DSAM with a bottleneck to building a Spatial Attention ResBlock(SARB). Based on the SARB, we establish a residual-style network for the feature extraction. Then, two feature fusion operations are performed for encoding multi-scale contexts, which are the feature refinement in a top-to-down way and the multi-scale feature aggregation. We name the proposed model the Deep Residual Spatial Attention Network. We demonstrate the effectiveness of our proposed method by the experimental results on two benchmark datasets, which show the proposed fashion landmark detection network outperforms the state-of-the-art methods.
Humidity Capability of Enhancement Mode GaN High Electron Mobility Transistors

In this work, the humidity performance of three enhancement mode GaN HEMTs with different chip designs and packages from different manufacturers were investigated by a High Humidity, High Temperature, Reverse Bias (H3 TRB) test. The devices were tested until end of life (EoL) with a few devices even surviving the end of the test after passing 6000 test hours. Some of the devices provided a precursor of the catastrophic failure, while others just failed suddenly. There were little changes in the characteristics obtained at intermediate measurements. The Weibull analysis of the failures shows a consistent and clear wear-out behaviour. These results document an excellent humidity capability of the tested GaN devices even outperforming usual silicon devices, although GaN devices exhibit smaller insulation distances and higher electric fields. However, during the initial phase of the test conductive anodic filaments (CAF) formed in the PCB adapters and compromised the leakage current readings such that the adapters had to be removed to be able to continue the test. In other words, the weakest spot is no longer the semiconductor device itself, but has moved to other components in the circuitry.
CIV: Information Volume of Complex Mass Function

Dempster-Shafer evidence theory is an efficient theory for dealing with uncertainty which has been applied in many fields. As an extension of evidence theory, complex evidence theory is effective for handling the uncertainty of complex-valued information. However, the existing uncertainty measurements in complex evidence theory only consider the first-order uncertainty of a complex mass function (CMF) but ignore the higher-order uncertainty. Hence, in this paper, a novel information volume of complex mass function (CIV) is proposed, which is a complex-valued extension of information volume of mass function (MIV). Besides, the real part of CIV is compatible with the MIV and Shannon entropy. The properties of CIV are illustrated by examples. In addition, based on CIV, a complex evidence combination algorithm is proposed and applied in target recognition. The results show that the proposed algorithm can combine CMFs and recognize targets with correctness and high efficiency.
Layered-Manufacturing of Fiber-Reinforced Composites

In this paper, we present a rapid manufacturing process for the layered fabrication of polymer-based composite parts using short discontinuous fibers as reinforcements. This process uses a UV-laser-based system for the selective solidification of the composite liquid. The primary components of the prototype are: (1) fiber-resin mixing subsystem, (2) composite-liquid deposition subsystem, (3) liquid leveling subsystem, and (4) laser-light delivery subsystem. Axiomatic Design Theory was used to validate the design selected for the experimental embodiment of the process. Extensive microscopic examination of the layered composite parts verified that the prototype system can yield comparable layer quality, in terms of accuracy and uniformity, to that of pure-resin parts made by a photopolymer-based commercial system. Furthermore, mechanical testing of these composite specimens showed up to 60 percent improvement in modulus over the unreinforced layered specimens.
Science and technology fields and higher education institutions with mathematically trained contributors: Metadata analysis of IEEE papers

Recently, the shortage of mathematically trained individuals who can integrate mathematical knowledge and skills with other types of knowledge and skills and thereby contribute to innovation has become apparent. To find clues for solving this problem, we identify scientific and technology fields (STFs) that require mathematically trained contributors and identify higher education institutions (HEIs) that provide students an opportunity to develop the mathematical knowledge needed in the STFs. We perform this by analyzing a large amount of metadata of papers in IEEE journals, which is easily accessible via the Scopus API. The metadata contains author's affiliation information, which is useful for identifying HEIs with a mathematical faculty. For example, if an author's affiliation information contains terms related to mathematics such as “Department of Mathematics” or “Dept. of Math.,” the author can be considered to have had advanced mathematical training. We demonstrate that 1) the contributions of mathematically trained individuals to research fields covered by published IEEE papers have continued to increase, 2) their contributions are particularly important to micro-STFs such as information theory, reliability, fuzzy systems, and neural networks, and 3) the degree of their contributions to the various STF-focused journals differs between HEIs.
Detection of Cyber-Physical Attacks Aiming at Multi Transmission Line Congestions Using Dynamic State-Estimation

Recent cyberattacks on the U.S. power grid in 2018 and shutdown of substations in Ukraine due to cyberattacks in 2015 proved how vulnerable power systems are. Consequently, it is critical to develop tools to study these cyberattacks and propose detection strategies to protect power systems. In this paper, a dynamic state-estimation approach using alternate recursive least-square (ARLS) estimation will be used to detect cyberat-tacks targeting multiple transmission lines. The attack model is considered as a cyber-physical attack that allows targeted false data injections (FDIs) on selected load measurements on the system to overflow multiple transmission lines. The performance of ARLS estimation is evaluated using single and multiple attacks on targeted lines in IEEE 118-bus benchmark. Performance measures are used to evaluate the accuracy and detection rate of the proposed dynamic state-estimation technique.
Analytical Inverse Kinematics for Franka Emika Panda – a Geometrical Solver for 7-DOF Manipulators with Unconventional Design

This paper presents an analytical inverse kinematic solution for the 7-DOF Franka Emika Panda robot with joint offsets. Contrary to the common belief that this problem has been thoroughly studied, there is no published work that discusses the existence of multiple solutions from a pure geometrical perspective. By investigating the geometry of the robot, the proposed solution reveals 8 different joint configurations for a given end effector pose and fixed redundant parameter, and a non-iterative algorithm is designed to consistently obtain one of them during motion control without random switching. The solver is tested in terms of accuracy and computational speed, and data show that accurate results can be obtained within a few microseconds, making it highly preferable for online trajectory planning and optimisation. The presented methodology can be extended to other 7-DOF anthropomorphic manipulators.
An OpenAI-OpenDSS framework for reinforcement learning on distribution-level microgrids

This paper introduces a Python framework built upon open-source software for electric grids (OpenDSS) and deep learning (Open AI) for the study of further applications of reinforcement learning on distribution grid networks. This paper describes the framework and applies it to a 13-bus, grid-tied microgrid system, training and using a reinforcement learning agent to optimally control capacitor banks to maintain system voltage under changing loads. The performance of the agent is then compared to known optimal control, as well as to the performance of a capacitor controller built-in to OpenDSS, and a supervised learning-trained neural network.
Improved GPU Implementations of the Pair-HMM Forward Algorithm for DNA Sequence Alignment

With the rise of Next-Generation Sequencing (NGS) technology, clinical sequencing services become more accessible but are also facing new challenges. The surging demand motivates developments of more efficient algorithms for computational genomics and their hardware acceleration. In this work, we use GPU to accelerate the DNA variant calling and its related alignment problem. The Pair-Hidden Markov Model (Pair-HMM) is one of the most popular and compute-intensive models used in variant calling. As a critical part of the Pair-HMM, the forward algorithm is not only a computational but data-intensive algorithm. Multiple previous works have been done in efforts to accelerate the computation of the forward algorithm by the massive parallelization of the workload. In this paper, we bring advanced GPU implementations with various optimizations, such as efficient host-device communication, task parallelization, pipelining, and memory management, to tackle this challenging task. Our design has shown a speedup of 783X comparing to the Java baseline on Intel single-core CPU, 31.88X to the C++ baseline on IBM Power8 multicore CPU, and 1.53X - 2.21X to the previous state-of-the-art GPU implementations over various genomics datasets.
Impact Of Environmental Conditions On Fingerprint Systems Performance

Biometrics testing has for objective to determine the performance of a biometric system in order to guarantee security and user experience requirements. Providing trust in biometric systems is a key for many manufacturers. The performance is usually measured through the computation of matching scores between legitimate and impostor samples from a given database. Different bias in particular those linked to the environmental conditions can modify the performance of a biometric system. In this paper, we study the impact of acquisition conditions on fingerprint systems considering at the same time the quality and accuracy. We defined an own-made database controlling the acquisition conditions and we observe the behavior of three different matchers on these biometric data. Experimental results allow us to quantity their impact on performance and draw conclusions for testing biometric systems.
Signal-to-noise performance of a fiber optic subcarrier link using an HBT optoelectronic up-converter

The signal-to-noise performance of a fiber optic microwave subcarrier link using an HBT as an optoelectronic up-converter in the millimeter-wave band has been experimentally investigated for the first time. This paper shows that the HBT not only has a high photodetection ability in direct photodetection but also has a high conversion ratio between a microwave subcarrier of an optical signal and a millimeter-wave carrier. The link signal-to-noise ratio of an HBT up-converter at IF=3.2 GHz/RF=30 GHz is 11 dB and 21 dB higher than that of a HBT direct photodetector and a PIN photodetector at 30 GHz, respectively. The HBT optoelectronic up-converter has the function of a photodetector, an IF amplifier and a mixer. <<ETX>>
Highly Integrated 200 kW SiC Three-Phase Dual-Active-Bridge Converter with 3D-Printed Fluid Coolers

This work features a highly efficient high power density dc-dc converter based on a soft switching capable, dual-active-bridge topology. Moreover, to reduce switching losses at high frequencies and consequently minimize the size of the passive components, modern 1200 V silicon carbide half bridge modules are used. Furthermore, to decrease the size of the cooling apparatus, compact 3D-printed fluid coolers are utilized. These measures that has been stated previously resulted in a highly integrated active part of the converter with a total box volume of 8 dm3 including the semiconductor switches, capacitors and sensors for a 200 kW unit. The first prototype shows a power density capability of 6.48 kW/dm3 at a maximum power output of 201 kW. The design of this converter and its advanced cooling concept as well as in-operation measurements will be presented.
An enhanced microwave rectifying circuit using HSMS‐282

A novel single diode rectifying circuit at 2.45 GHz is presented. An inductor is series‐connected with an HSMS‐282 Schottky diode to enhance the rectification performance. The imaginary part of the complex impedance of the diode is taken into consideration. Simulations and measurements show that the dynamic range of the load is widened with good radio frequency to direct current conversion efficiency (RF‐to‐DC Eff.). A conversion efficiency over 70% is demonstrated in this novel design. © 2009 Wiley Periodicals, Inc. Microwave Opt Technol Lett 51: 1151–1153, 2009; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.24237
A Platform for Deploying Multi-agent Deep Reinforcement Learning in Microgrid Distributed Control

Distributed control strategies have been attracted significant attention due to numerous advantages over traditional centralized control strategies. The development of deep reinforcement learning method provides a novel approach to control grid without knowing the system's parameters. The training and validating process with grid simulation as environment have been supported by several toolboxes. In this paper, a platform based on redis NoSQL database is proposed to the deploy the multi-agent system of deep reinforcement learning algorithms for control microgrid in a distributed manner. The accuracy of agent implementation under realistic condition with physical communication network can be evaluated with the proposed platform. The distributed control in islanded DC microgrid using Deep Deterministic Policy Gradient is introduced as an use case to show the operation of the platform.
Efficient Table-Based Polynomial on FPGA

Field Programmable Gate Arrays (FPGAs) are gaining popularity in the context of scientific computing due to the recent advances of High-Level Synthesis (HLS) toolchains for customised hardware implementations combined with the increase in computing capabilities of modern FPGAs. As a result, developers are able to implement more complex scientific workloads which often require the evaluation of univariate numerical functions. In this study, we propose a methodology for table-based polynomial interpolation aiming at producing area-efficient implementations of such functions on FPGAs achieving the same accuracy and at similar performance as direct implementations. We also provide a rigorous error analysis to guarantee the correctness of the results. Our methodology covers the forecast of resource utilisation of the polynomial interpolator and, based on the characteristics of the function, guides the developer to the most area-efficient FPGA implementation. Our experiments show that in the case of a radiation spectrum of a Black Body application based on evaluating Planck’s Law, it is possible to reduce resource utilisation by up to 90% when compared to direct implementations not using table-based methods. Moreover, when only the kernels are considered, our method uses up to two orders of magnitude fewer resources with no performance penalties. Based on previous more theoretical works, our study investigates practical applications of table-based methods in the context of high performance and scientific computing where it is used to implement common but more complex functions than the elementary functions widely studied in the related literature.