Miracle-dz/UniFAR-FTUs huggingface dataset:
In-Situ Self-Aligned NaCl-Solution Fluidic-Integrated Microwave Sensors for Industrial and Biomedical Applications

This work presents, for the first time, an in-situ self-aligned fluidic-integrated microwave sensor for characterizing NaCl contents in NaCl-aqueous solution based on a 16-GHz bandpass combline cavity resonator. The discrimination of the NaCl concentration is achievable by determining amplitude differences and resonant frequency translations between the incident and reflected microwave signals at the input terminal of the cavity resonator based on the capacitive loading effects of the comb structure inside the cavity introduced by the NaCl solution under test. Twelve NaCl-aqueous liquid mixture samples with different NaCl concentrations ranging from 0% to 20% (0 – 200 mg/mL), which are generally exploited in most industrial and biomedical applications, were prepared and encapsulated inside a Teflon tube performing as a fluidic channel. The Teflon tube is subsequently inserted into the cavity resonator through two small holes, fabricated through the sidewalls of the cavity, which can be used to automatically align the fluidic subsystem inside the combline resonator considerably easing the sensor setup. Based on at least five repeated measurements, the NaCl sensor can discriminate the NaCl content of as low as 1% with the measurement accuracy of higher than 96% and the maximum standard deviation of only 0.0578. There are several significant advantages achieved by the novel NaCl sensors, e.g. high accuracy, traceability and repeatability; ease of sensor setup and integration to actual industrial and biomedical systems enabling in-situ and real-time measurements; noninvasive and noncontaminative liquid solution characterization as well as superior sensor reusability due to a complete physical separation between the fluidic and microwave subsystems.
Flight Delay Prediciton at an Airport Using Maching Learning

The increasing demand for air transportation makes the problem of flight delays increasingly prominent. Accurate prediction of flight delays is of great significance to passengers' travel and the operation of airlines. In this paper, we establish a flight delay prediction model to predict the departure delay of Newark Liberty International Airport based on Catboost algorithm. The result shows that the prediction accuracy of our model can reach 0.77. In addition, we also analyze the contribution of each feature in the prediction by using SHAP.
Exploiting Non-conventional DVFS on GPUs: Application to Deep Learning

The use of Graphics Processing Units (GPUs) to accelerate Deep Neural Networks (DNNs) training and inference is already widely adopted, allowing for a significant increase in the performance of these applications. However, this increase in performance comes at the cost of a consequent increase in energy consumption. While several solutions have been proposed to perform Voltage-Frequency (V-F) scaling on GPUs, these are still one-dimensional, by simply adjusting frequency while relying on default voltage settings. To overcome this, this paper introduces a methodology to fully characterize the impact of non-conventional Dynamic Voltage and Frequency Scaling (DVFS) in GPUs. The proposed approach was applied to an AMD Vega 10 Frontier Edition GPU. When applying this non-conventional DVFS scheme to DNNs, the obtained results show that it is possible to safely decrease the GPU voltage, allowing for a significant reduction of the energy consumption (up to 38%) and the Energy-Delay Product (EDP) (up to 41%) on the training of CNN models, with no degradation of the networks accuracy.
Australian leaf-tailed geckos: phylogeny, a new genus, two new species and other new data

[Extract] Australia's rainforests and adjoining moist sclerophyll forests and heaths are well known for their high diversity and for many species confined to either single localities, or very narrow ranges. Leaf-tailed geckos from such forests well illustrate these characteristics. For nearly 200 years of discovery and description of Australia's reptiles, only two species of 'leaf-tails', Phyllurus platurus (Shaw, 1790) and P. cornutus (Ogilby, 1892) = Saltuarius cornutus (Ogilby, 1892), were known. Morphological studies since 1975 have resulted in the recognition of many new species and the genus Saltuarius Couper, Covacevich & Moritz, 1993 - P. caudiannulatus Covacevich, 1975; P. isis Couper, Covacevich & Moritz, 1993; P. nepthys Couper, Covacevich & Moritz, 1993; P. ossa Couper, Covacevich & Moritz, 1993; S. salebrosus (Covacevich, 1975); Saltuarius occultus Couper, Covacevich &Moritz, 1993 and S. swaini (Wells & Wellington, 1985). Eight of the 12 presently known species have narrow distributions with four confined to single localities.
A Review and Analysis of Automatic Optical Inspection and Quality Monitoring Methods in Electronics Industry

Electronics industry is one of the fastest evolving, innovative, and most competitive industries. In order to meet the high consumption demands on electronics components, quality standards of the products must be well-maintained. Automatic optical inspection (AOI) is one of the non-destructive techniques used in quality inspection of various products. This technique is considered robust and can replace human inspectors who are subjected to dull and fatigue in performing inspection tasks. A fully automated optical inspection system consists of hardware and software setups. Hardware setup include image sensor and illumination settings and is responsible to acquire the digital image, while the software part implements an inspection algorithm to extract the features of the acquired images and classify them into defected and non-defected based on the user requirements. A sorting mechanism can be used to separate the defective products from the good ones. This article provides a comprehensive review of the various AOI systems used in electronics, micro-electronics, and opto-electronics industries. In this review the defects of the commonly inspected electronic components, such as semiconductor wafers, flat panel displays, printed circuit boards and light emitting diodes, are first explained. Hardware setups used in acquiring images are then discussed in terms of the camera and lighting source selection and configuration. The inspection algorithms used for detecting the defects in the electronic components are discussed in terms of the preprocessing, feature extraction and classification tools used for this purpose. Recent articles that used deep learning algorithms are also reviewed. The article concludes by highlighting the current trends and possible future research directions.
Generation of Safety and Liveness Complaint Automata from Goal Model Specifications

One of the main limitations of the goal model approach to formal requirement specification is the lack of representation of temporal constraints. Existing works in this domain have transformed goal models into state machines with the only motive of model checking them against temporal properties. The generated state machines could contain invalid state sequences that violate some property. In this paper, we aim to go one step further and generate a Kripke Transition System which is compliant with respect to a given set of temporal properties. We introduce the Safety and Liveness Compliance (SLC) framework which incorporates a compliance assurance mechanism within the model transformation process itself. This assurance mechanism ensures that the generated Kripke Transition System does not generate any counter-examples when checked against the predefined safety and liveness properties. We also present a qualitative comparison of our proposed SLC framework with the other related works.
A CMOS Current Starved VCO for Energy Harvesting applications

This work presents a current starved voltage-controlled oscillator based on standard 0. 13μm CMOS process. Integrated within a power management circuit, the proposed oscillator provides an average periodic signal with a frequency of 84.81kHz. Additionally, to assure a stable periodic signal from the environmental instability a voltage reference is designed. The proposed architecture makes full use of subthreshold and deep triode MOSFETs to avoid the employment of any passive component. A voltage reference exhibits an output reference of 258.34mV in response to a 1.0 - 3.2V voltage supply. It shows a line sensitivity of 0.49%/V at 27°C. The output of the architecture leads to a robust time-control local oscillator, which can be employed in power management circuit for energy harvesting systems and be used on wireless sensor networks and implantable medical devices.
Distributed SDN Based Network State Aware Architecture for Flying Ad-hoc Network

Flying networks are resource constraints while the nature of nodes' mobility is very dynamic and unpredicted. Therefore, these networks are very prone to link failure and performance degradation. By considering the existing limitations, this work proposes a new approach consists of proactive and reactive network failure mitigation techniques that have been named as a hybrid approach. In the proposed architecture, the SDN controllers are distributed where each one controls its local domain nodes. The controller node continuously monitors the network state information and proactively adjusts the near-future changes to the topology. Each local domain also contains a sink node that directly connects to the controller. The sink node is used to forward the network state information to the controller and keep the controller defined flow rules for local domain nodes. The sink node can also request a new path in case of any link failure or any topology updates cause by nodes' movement. Besides, a distributed routing protocol also runs on domain nodes to establish connectivity toward the sink node.
AND Flash Array Based on Charge Trap Flash for Implementation of Convolutional Neural Networks

Various memory devices have been proposed for implementing synapse devices in neuromorphic systems. In this letter, an AND flash array based on charge trap flash (CTF) memory was proposed. CTF-based synapse devices are particularly suitable for off-chip learning applications because they have excellent reliability and stable multi-level operation characteristics. In addition, we proposed a method to implement convolutional neural networks in the proposed array, and performed system-level simulation using the characteristics of the fabricated device. Finally, we investigated the accuracy degradation of the neuromorphic system related to data retention and proposed a multiple cell mapping scheme to address this degradation issue.
Deep Learning-Based Computational Color Constancy With Convoluted Mixture of Deep Experts (CMoDE) Fusion Technique

In the human and computer vision, color constancy is the ability to perceive the true color of objects in spite of changing illumination conditions. Color constancy is remarkably benefitting human and computer vision issues such as human tracking, object and human detection and scene understanding. Traditional color constancy approaches based on the gray world assumption fall short of performing a universal predictor, but recent color constancy methods have greatly progressed with the introduction of convolutional neural networks (CNNs). Yet, shallow CNN-based methods face learning capability limitations. Accordingly, this article proposes a novel color constancy method that uses a multi-stream deep neural network (MSDNN)-based convoluted mixture of deep experts (CMoDE) fusion technique in performing deep learning and estimating local illumination. In the proposed method, the CMoDE fusion technique is used to extract and learn spatial and spectral features in an image space. The proposed method distinctively piles up layers both in series and in parallel, selects and concatenates effective paths in the CMoDE-based DCNN, as opposed to previous works where residual networks stack multiple layers linearly and concatenate multiple paths. As a result, the proposed CMoDE-based DCNN brings significant progress towards efficiency of using computing resources, as well as accuracy of estimating illuminants. In the experiments, Shi’s Reprocessed, gray-ball and NUS-8 Camera datasets are used to prove illumination and camera invariants. The experimental results establish that this new method surpasses its conventional counterparts.
Amplified Methanol Sensitivity in Reduced Graphene Oxide FET Using Appropriate Gate Electrostatic

The current study concerns a technique to amplify the gas sensitivity of reduced graphene oxide (rGO)-field-effect transistor (FET) sensor for the detection of very low concentration gaseous species. rGO channel exhibits tunable carrier density with ambipolar transport in FET structure by the change of electrostatic potential through the gate terminal. At low carrier density of channel, the exchange of carriers due to surface adsorption and desorption is significant that eventually enhances the sensitivity of the rGO-FET sensor at a typical gate voltage (<inline-formula> <tex-math notation="LaTeX">${V}_{GS}$ </tex-math></inline-formula>). The back-gated rGO-FET sensor was fabricated on <inline-formula> <tex-math notation="LaTeX">${p}$ </tex-math></inline-formula>-Si substrate with 90-nm-thick SiO<sub>2</sub> layer. The defects, disorders, and morphology of single-layer (major) rGO were characterized with the spectroscopic and microscopic study. The modulation of ambient sensitivity of rGO film was tested in the exposure of different ambient like dry air, argon, oxygen, and a test vapor methanol. 1.95% methanol (at 150 ppm) sensitivity at <inline-formula> <tex-math notation="LaTeX">${V}_{GS}= 0$ </tex-math></inline-formula> V was amplified up to 127.93% at <inline-formula> <tex-math notation="LaTeX">${V}_{GS}= 5$ </tex-math></inline-formula> V and 82.38% at <inline-formula> <tex-math notation="LaTeX">${V}_{GS}= 13$ </tex-math></inline-formula> V where the temperature and drain-to-source voltage (<inline-formula> <tex-math notation="LaTeX">${V}_{DS}$ </tex-math></inline-formula>) were 100 °C and 1.4 mV, respectively.
Service Chaining Offloading Decision in the EdgeAI: A Deep Reinforcement Learning Approach

Many mission critical devices are increasing with upcoming 5G network to fulfill a low latency for a real time network service on smart factory, autonomous vehicle, etc. Distributed cloud computing system also has a key role to execute the various mobile devices, because, an edge computing is the nearest from the mobile devices to provide low latency and computation energy consumption. In this paper, we consider the autonomous vehicles with video live streaming services. Especially, the vehicles require a low transmission delay as within 10 ms. To reduce a latency with low energy consumption, we propose a service chaining offloading decision with a deep reinforcement learning. We split tasks of the vehicle per service function blocks which have their own role. So it can do partial offloading and user association in a On-Device Edge of the vehicle and in the SBS at the same time. We can get results that service chaining offloading decision gives more optimal energy consumption with low-latency to autonomous vehicle users.
Development of photoreversible covalent inhibitors for protein tyrosine phosphatases.

Reversible protein tyrosine phosphorylation is a key determinant in eukaryotic signaling pathways. The level of phosphorylation is balanced by the opposing activities o f protein tyrosine kinases (PTKs) and protein tyrosine phosphatases (PTPs). Although a large number o f PTPs have been identified, elucidating the in-vivo function of any one specific PTP is still a difficult task due to the lack of specific PTP agents. This dissertation describes the development o f photo-reversible and covalent inhibitors for specific PTPs. These inhibitors are a-haloacetophenone derivatives and inactive PTPs by alkylating the conserved active site catalytic cysteine residue. The inactivated PTPs can be conveniently reactivated by irradiation with UV light. Further, this class of PTP inhibitors can permeate human cells and affect tyrosine phosphorylation levels in certain proteins. Initially, the a-haloacetophenone inhibitors tested possessed similar inhibition potency towards different PTPs. Previous results indicated that PTP IB inhibition by cinnamates could be improved by attaching peptide based specificity elements. First, a variety of a-haloacetophenones were tested for SHP-1(ASH2) and PTP IB inhibition.
Recent Advances in High Frequency Modeling by Means of Domain Confinement and Nested Kriging

Development of modern high-frequency components and circuits is heavily based on full-wave electromagnetic (EM) simulation tools. Some phenomena, although important from the point of view of the system performance, e.g., EM cross-coupling effects, feed radiation in antenna arrays, substrate anisotropy, cannot be adequately accounted for using simpler means such as equivalent network representations. Consequently, the involvement of EM analysis, especially for tuning of geometry parameters, has become imperative in high-frequency electronics. Notwithstanding, excessive computational costs associated with massive full-wave simulations required by these procedures and even more by tasks such as uncertainty quantification or multi-criterial optimization, constitute a practical bottleneck. Repetitive evaluations of a structure can be facilitated by the use of fast replacement models (surrogates). Among available methods, approximation models are by far the most popular due to their flexibility and accessibility. Unfortunately, surrogate modeling of high-frequency structures is hindered by the curse of dimensionality and nonlinearity of system responses, primarily frequency characteristics. The recently proposed performance-driven techniques attempt to address this issue by appropriate confinement of the model domain to focus the modeling process only on the relevant part of the parameter space, i.e., containing the designs that are of high quality from the point of view the assumed performance figures. The nested kriging framework is perhaps the most advanced of these methods and allows for constructing reliable surrogates over broad ranges of the system parameters and operating conditions. This article summarizes the recent developments of the technique, including the basic formulation and several advancements aiming at the improvement of the surrogate predictive power or lowering the computational cost of training data acquisition. These include the incorporation of sensitivity data, as well as dimensionality reduction through principal component analysis. The problem of uniform data sampling in confined domains is also discussed. Our considerations are comprehensively illustrated using several examples of antennas and microwave circuits.
Identification of Critical Nodes for Enhanced Network Defense in MANET-IoT Networks

In heterogeneous environments, the Internet of Things (IoT) combined with mobile ad hoc network (MANET), i.e., MANET-IoT network, becomes more attractive to end users and economically successful. However, the introduction of MANET potentially makes the system more prone to attacks due to its lack of centralized management, weak connectivity, and resource constraints. To enhance the network robustness to intentional attacks, the critical nodes of the MANET-IoT network should be firstly identified and then protected. Most of the existing methods for identifying critical nodes usually focus on static networks or a single topology snapshot in dynamic networks without considering the correlation between topology snapshots, which cannot effectively deal with the dynamic changes in the topology of MANET-IoT networks. In this paper, a dynamic critical node identification (DCNI) method is proposed. First, we propose a comprehensive metric to measure the node importance in the topology snapshot. Then, we introduce a sliding time window to filter out the topology snapshots which have a close correlation with the current snapshot, and fuse the importance values of the same node in different topology snapshots. Finally, the critical nodes are selected based on the ranking result of fused importance. Thereafter, the port hopping mechanism could be applied to the critical nodes for enhancing network defense capability. The simulation results show that the proposed method is more effective in identifying critical nodes than existing static methods in MANET-IoT networks, and the port hopping mechanism can improve network defense significantly to denial of service (DoS) attacks.
A Review of Object Detection Techniques

Object detection has been known as the core of computer vision and attracted much research attention in recent years especially because of its close relationship with video analysis and image understanding. According to the abundant research on object detection, many traditional object detection methods have been proposed. This paper introduces some famous traditional methods, which are based on SIFT, HOG, SURF, and ORB. However, due to the characteristics of large amount of computation and simple training structure, the traditional detection method has low detection speed. With the fast rise of deeper learning, stronger devices are implemented to address the problems that exist in conventional architectures. In the architecture of the network, training and optimization functions etc., these models are special. In this paper, we review the frameworks for object detection based on deep learning. We begin our review with the methods based on Convolutional Neural Networks. Then typical methods of object detection and some helpful modification to improve detection performance are introduced. Moreover, the methods based on YOLO and SSD are introduced. In fact, despite of the same basis of algorithm or features, the performance and features of different methods are various. Thus in this paper we analyze the features and architecture of each method. This also offers some research to equate various approaches and draw some concrete conclusions. In this paper, we also introduce some typical datasets used for testing or training the object detection model. This paper made a systematic classification and summary in the object detection field, which can be meaningful and useful for the scholars who started to learn about it.
Analysis and Compensation of Latencies in NTS-secured NTP Time Synchronization

Many time synchronization services use the Network Time Protocol (NTP), which resides in the upper OSI layers and is thus usually implemented in software. However, software-related runtimes reduce the synchronization accuracy and are further influenced by the cryptographic protection of time messages using the Network Time Security protocol (NTS). This paper examines these runtimes and shows the effects on popular NTP implementations. After separating these latencies into their components, we present different approaches for compensation by modifying the NTP timestamps. For non-correctable latencies, this paper provides mitigation strategies to improve the synchronicity.