Miracle-dz/UniFAR-FTUs huggingface dataset:
A β-diketonate–europium(III) complex-based fluorescent probe for highly sensitive time-gated luminescence detection of copper and sulfide ions in living cells

We have developed a novel β-diketonate–europium(III) complex-based fluorescent probe, Eu3+-BHHCT-BPED, by introducing Cu2+-binding dipicolylamine (DPA)-containing N,N-bis(2-pyridylmethyl)ethanediamine (BPED) into a tetradentate β-diketone ligand 4,4′-bis(1′′,1′′,1′′,2′′,2′′,3′′,3′′-heptafluoro-4′′,6′′-hexanedione-6′′-yl)chlorosulfo-o-terphenyl (BHHCT). The probe was strongly fluorescent with a high quantum yield (57.6%) and a long luminescence lifetime (0.81 ms). Upon addition of Cu2+ ions, the fluorescence emission of Eu3+-BHHCT-BPED at 607 nm was selectively and efficiently quenched due to the coordination of Cu2+ ions with the BPED moiety. Furthermore, the resulting heterobimetallic complex Eu3+-BHHCT-BPED-Cu2+ displayed specific luminescence response to sulfide ions because a reversible fluorescence revival was induced by the formation of CuS. The “on–off–on” type signaling behavior of Eu3+-BHHCT-BPED towards Cu2+ and sulfide ions enabled the sensing of the two species with the detection limits of 3.7 nM and 0.19 μM, respectively. The time-gated luminescence imaging study has demonstrated the applicability of Eu3+-BHHCT-BPED for monitoring intracellular Cu2+ and sulfide ions with high sensitivity and selectivity.
Failure-Tolerant and Low-Latency Telecommand in Mega-Constellations: The Redundant Multi-Path Routing

In recent years, in order to provide worldwide broadband Internet access services, many mega-constellation projects have been proposed. Traditional ground-based and space-based telecommand systems only rely on a single path for data transmission, which is vulnerable once the path fails. This paper utilizes inter-satellite-links (ISLs) in the constellation network to reduce the risk of data transmission failure, and proposes the failure-tolerant and low-latency redundant multi-path routing algorithm (RMPR). RMPR transmits multiple data copies on multiple shortest edge-disjoint paths according to the optimal proportions with the minimum delay. Compared with traditional single-path routing and backup multi-path routing, RMPR ensures both reliability and timeliness in telecommand system.
Impact Evaluations of Engineering Programs Using Abet Student Outcomes

Engineering programs worldwide collect and report student learning outcomes data to conduct program evaluations for quality assurance and accreditation purposes. Accreditation agencies such as ABET typically mandate that at least two years of program evaluation data be provided and for institutions to show how this data has been used for continuous quality improvement. Engineering programs rarely evaluate interventions using multi-term student outcomes information over several years, since this quantitative data generally lacks accuracy and statistical power. The quality of outcomes data is affected by obsolete assessment methods and lack of digital access and technical analysis. In this study, we present essential elements of an authentic outcome based assessment model that used web-based software and embedded assessment technology to collect and report accurate cohort outcomes for credible multi-term evaluations. A non-experimental approach employing regression analyses were used to identify trends in student outcomes and evaluate the impact for three engineering programs. Detailed rubrics provide criteria to accurately classify multi-year student outcomes. The findings of this study present practical steps for engineering programs to effectively collect and report accurate cohort outcomes data and perform credible evaluations of program interventions based on multi-year outcomes data.
A reconfigurable circular patch antenna with shorting vias

A pattern-reconfigureable patch antenna is proposed. The basic configuration of the antenna is a circular patch with seven shorting pins placed along its diameter. By controlling four different PIN dioles, the proposed antenna can work under three modes, including two mutually orthogonal bidirectional modes and one nearly omnidirectional mode while the above two modes are excited simultaneously. All modes have achieved relatively high gain.
Surgical Action and Instrument Detection Based on Multiscale Information Fusion

The detection of surgical actions and instruments plays a very important role in computer-assisted endoscopic surgery. However, organ deformation and narrow surgical field increase the task difficulty. Accordingly, the problems of the detection of surgical actions and instruments have not been solved yet. In this paper, we proposed a multiscale fusion feature pyramid network (MSF-FPN) to merge low-level semantic information and high-level semantic information. Firstly, the feature map effectively aggregates the information by the initial layer of the pyramid network, and then diverges after the cross-transmission of the feature information in the middle layer. Finally, a strong semantic feature map was obtained in the output layer. Experiments verified that the average precision of the proposed MSF-FPN on the public endoscopic surgeon action detection (ESAD) dataset is increased by 2.9% and 1.5% compared with the general FPN and path aggregation network (PANet), and the average precision on the proposed cataract-based object detection (COD) dataset is increased by 4.3% and 2.6%, respectively.
FEECA: Design Space Exploration for Low-Latency and Energy-Efficient Capsule Network Accelerators

In the past few years, Capsule Networks (CapsNets) have taken the spotlight compared to traditional convolutional neural networks (CNNs) for image classification. Unlike CNNs, CapsNets have the ability to learn the spatial relationship between features of the images. However, their complexity grows because of their heterogeneous capsule structure and the dynamic routing, which is an iterative algorithm to dynamically learn the coupling coefficients of two consecutive capsule layers. This necessitates specialized hardware accelerators for CapsNets. Moreover, a high-performance and energy-efficient design of CapsNet accelerators requires exploration of different design decisions (such as the size and configuration of the processing array and the structure of the processing elements). Toward this, we make the following key contributions: 1) FEECA, a novel methodology to explore the design space of the (micro)architectural parameters of a CapsNet hardware accelerator and 2) CapsAcc, the first specialized RTL-level hardware architecture to perform CapsNets inference with high performance and high energy efficiency. Our CapsAcc achieves significant performance improvement, compared to an optimized GPU implementation, due to its efficient implementation of key activation functions, such as squash and softmax, and an efficient data reuse for the dynamic routing. The FEECA methodology employs the Non-dominated Sorting Genetic Algorithm (NSGA-II) to explore the Pareto-optimal points with respect to area, performance, and energy consumption. This requires analytical modeling of the number of clock cycles required to perform each operation of the CapsNet inference and the memory accesses to enable a fast yet accurate design space exploration. We synthesized the complete accelerator architecture in a 45-nm CMOS technology using Synopsys design tools and evaluated it for the MNIST benchmark (as done by the original CapsNet paper from Google Brain’s team) and for a more complex data set, the German Traffic Sign Recognition Benchmark (GTSRB).
Development of Ionic Liquid Utilization in Biorefinery Process of Lignocellulosic Biomass

Ionic liquids have been utilized in various industries as the environment-friendly organic solvents. The tailor-made properties of ionic liquids to meet the requirements of specific tasks lead to the variety of applications in biorefinery. Ionic liquid applications have been developed and integrated in biorefinery of lignocellulosic biomass processing since last decade. Conversion of lignocellulosic biomass to sugars requires both efficient pretreatment and hydrolysis enzymes to produce biofuels and specialty chemicals, and ionic liquids were applied to improve the hydrolysis yields. Ionic liquid pretreatment is one of potential method due to its high efficiency to solubilize cellulose, and its recyclability. The high extractibility of ionic liquid as a solvent was described in many studies and became the new target for R&D sectors. However, the adverse effects of ionic liquids have been discovered bringing out controversy to be discussed. The present review is aimed to provide the insight of applications of ionic liquid and current improvement direction in biorefinery.
Third-Order Filtering Power Divider with Sharp Selectivity

This paper presents a novel microstrip filtering power divider, whose transmission property is featured by a third-order general Chebyshev bandpass response. A transmission zero near the right side of the passband greatly improves frequency selectivity. For demonstration, an example was fabricated and measured, whose center frequency is at 2.5 GHz and 3-dB bandwidth is 16%. The measured results agree well with the simulation ones. Suppression in the stopband is as high as 28 dB over 3th harmonic. In addition, the isolation between two output ports is greater than 17.5 dB over relatively wide frequency range. Compared with other counterparts, the proposed filtering power divider has advantages of sharp selectivity, wideband isolation and good out-of-band performance.
A Reinforcement Learning Assisted Eye-Driven Computer Game Employing a Decision Tree-Based Approach and CNN Classification

Human-Machine Interfaces employing biosignal-based inputs are hard to translate to real-life applications, in part because of the difficulty of developing generalized models to classify physiological events representing a user’s actions. In the proposed framework, an Electrooculography (EOG)-based game is operated through a pipeline of decision methods. These include a user-independent classification model of eye movements using a Convolutional Neural Network (CNN), which is fed with images created from signal windows, and an Ensemble of Utility Decision Networks (EUDN), which moderates the impact of oftentimes conflicting ocular events while enabling a more natural level of control over the interface. The CNN and the EUDN replace the normally used feature-based ocular event detection methods for EOG. Finally, a Reinforcement Learning-based game actuation approach simultaneously updates multiple $(State, Action)$ pairs for each rewarded outcome, intervenes to mitigate the consequences of wrongful game Commands, and can be used as part of a “shared-control” paradigm based on EOG. Results show a positive impact of Reinforcement Learning both in improving participants’ game performance as well as in reducing some of their subjective workload indicators.
Design of Key Modules in Multi-mode Multistandard Receiver RF Front-end Resilient to Out-of-band Blockers

This paper designs a key modules in multi-mode multi-standard receiver RF front-end resilient to out-of-band blockers, including broadband low-noise amplifier, mixer and output buffer circuit. The front-end is implemented in TSMC 0.18um CMOS technology, According to the simulation results, the front-end can work from 0.9GHz $\sim2.4$ GHz at 3.3V supply voltages, the NF of the circuit is less than 4.5dB, the VCG is greater than 15dB,S11 and S22 are both less than -10dB, and it achieves greater than 10dB BRR at 80MHz offset. The designed receiver RF Front-end tolerates 0 dBm blockers at 80 MHz offset with acceptable 8.99 dB NF, which meets the requirements of the front-end for RF receivers.
Analysis of Millimeter-Wave Channel Characteristics in Urban Microcell Environment Based on the SBR Method

Wireless communication at millimeter-wave bands has been a research hot spot in the latest years. In this paper, a typical urban microcell environment is simulated at 73 GHz in line-of-sight scenarios (LoS) and non-line-of-sight (NLoS) scenarios by the SBR method. First, the validity of the SBR method is verified by comparing the measurement and simulation results of path loss. Next, the propagation characteristics in LoS and NLoS scenarios are studied, such as power angle profiles and power delay angle profiles. Some characteristics of the millimeter-wave channel are revealed from these analyses.
Deep Reinforcement Learning-Based Traffic Sampling for Multiple Traffic Analyzers on Software-Defined Networks

Intrusion detection system (IDS) and deep packet inspection (DPI) are widely used to detect network attacks and anomalies, thereby enhancing cyber-security. Conventional traffic analyzers such as IDS have fixed locations and a limited capacity to perform DPI on large volumes of network traffic. Nowadays, software-defined networking (SDN) technology, which provides flexibility, elasticity, and programmability by decoupling the network control and data planes, makes it possible to capture entire or a certain portion of data traffic flows on SDN-capable switches and steer the captured network traffic to one of the traffic analyzers on the network. Therefore, how to sample network traffic and where to steer the sampled traffic among multiple traffic analyzers are critical problems facing cyber-security. Since there is a possibility that potentially useful information will be lost in not-captured traffic, deciding the sampling points and sampling rates of network traffic remains important. Additionally, after determining the sampling points and rates, sampled traffic must be sent to one of the multiple traffic analyzers for traffic inspection, which may incur additional network delivery overheads. We propose a less-intrusive traffic sampling mechanism for multiple traffic analyzers on an SDN-capable network using a deep deterministic policy gradient (DDPG), which is a representative deep reinforcement learning (DRL) algorithm for continuous action control. The proposed system learns sampling resource allocation policy under the uncertainty of flow distribution according to sampled traffic inspection results obtained from multiple traffic analyzers. Through extensive simulations and the SDN-based testbed experiments, we demonstrate that the proposed approach has a high probability of capturing malicious flows while maintaining a balanced load of multiple traffic analyzers and reducing flow monitoring overheads.
Multilayer Framework for Botnet Detection Using Machine Learning Algorithms

A botnet is a malware program that a hacker remotely controls called a botmaster. Botnet can perform massive cyber-attacks such as DDOS, SPAM, click-fraud, information, and identity stealing. The botnet also can avoid being detected by a security system. The traditional method of detecting botnets commonly used signature-based analysis unable to detect unseen botnets. The behavior-based analysis seems like a promising solution to the current trends of botnets that keep evolving. This paper proposes a multilayer framework for botnet detection using machine learning algorithms that consist of a filtering module and classification module to detect the botnet’s command and control server. We highlighted several criteria for our framework, such as it must be structure-independent, protocol-independent, and able to detect botnet in encapsulated technique. We used behavior-based analysis through flow-based features that analyzed the packet header by aggregating it to a 1-s time. This type of analysis enables detection if the packet is encapsulated, such as using a VPN tunnel. We also extend the experiment using different time intervals, but a 1-s time interval shows the most impressive results. The result shows that our botnet detection method can detect up to 92% of the f-score, and the lowest false-negative rate was 1.5%.
Design of a high-speed anti blocking wideband receiver front-end

In electronic warfare, when the receiver receives the external strong electronic blocking signal, the output power of the internal amplifier of the receiver is too large or deep saturated, which results in the instantaneous blind area of the receiver. The traditional anti blocking method is to manually set the digital attenuator in the receiver, which makes the amplifier in the receiver work in the linear area. However, the response time is too long, and the receiver cannot work normally in this process. In this paper, a high-speed anti blocking wideband receiver front-end is introduced, which can automatically suppress the external strong blocking signal effectively, so as to protect the normal operation of the receiver. The high-speed anti blocking wideband receiver has the advantages of small front-end size, wide working frequency band, fast anti blocking response, which can be widely used in electronic countermeasures.
Design of Broadband Matching Method for Square-law Detection Circuit of Microwave Imaging

Presents a broadband matching circuit designs for a square-law detector with a bandwidth of 2GHz to 6GHz, which is used to solve the problem of correlation circle distortion. The problem is caused by the mismatching of the input impedance of the detector in a microwave imaging system with a bandwidth of 4GHz. The matching process starts from the theoretical analysis of Smith circle graph and impedance matching. LTC5535 chip, ADS (Advanced Design System) software, and Smith Chart tool are used to simulate and optimize the schematic diagram of a broadband square-law detector. The ideal component is replaced by the component in Murata library to complete layout design and joint simulation. The simulation results show that the broadband square-law detector has good impedance matching within the operating frequency range, and the input insertion loss is less than −10dB, which has certain practical value.
Joint Equalization of Linear and Nonlinear Impairments for PAM4 Short-Reach Direct Detection Systems

Various neural network (NN)-based equalizers have been proposed as effective digital signal processing (DSP) tools for short-reach optical direct detection (DD) communication systems. However, they are mainly designed for solving nonlinear problems due to the intrinsic nonlinear activation function. In this letter, we propose novel DSP schemes to deal with both linear and nonlinear impairments in short-reach DD optical links, including cascade feedforward NN (FNN)/cascade recurrent NN (RNN)-based equalizers, and combination of feedforward equalization (FFE) and traditional non-cascade FNN/RNN-based equalizers. A 50-Gb/s pulse amplitude modulation (PAM)-4 directly modulated laser (DML)-based system is experimentally carried out and the bit-error-rate (BER) performance of the proposed DSP algorithms are compared. Experimental results show that cascade FNN/RNN and FNN/RNN-FFE could help improve the BER performance compared with traditional non-cascade FNN/RNN, since linear impairments are taken into account by either FFE or the cascade structure. With the help of the joint optimization of both linear and nonlinear effects through the network training process, cascade FNN/RNN shows great superiority over other DSP schemes. We also investigate on the impact of decision feedback equalization (DFE) concatenated with FNN/RNN. Compared with using FNN/RNN followed by FFE, no significant BER improvement can be found using DFE in place of FFE.
A Real-time Calibration Method of Visibility Function for Passive Millimeter Wave Imaging

This paper presents a real-time calibration method for the visibility function of millimeter wave passive imaging, which can effectively correct the multiplicative and additive errors of the visibility function in real time. In this method, the phase shifter is used to adjust the phase of the local oscillator periodically, the visibility functions corresponding to different local oscillator phases are established into equations, and then the visibility functions are estimated by the least square method. After that, the real-time calibration algorithm of visibility function is simulated by MATLAB, and verified by the 256 channel passive millimeter wave imaging (PMMW) system prototype developed by Beihang University, which proves the correctness of the method. Experimental results show that the results obtained by the visibility real-time calibration method can be directly used in the reconstruction of brightness temperature image.
Marvell ThunderX3: Next-Generation Arm-Based Server Processor

Marvell ThunderX3 is the third-generation Arm-based server processor from Marvell. This article describes the architecture of ThunderX3, going into details of core microarchitecture. ThunderX3 is distinctive among mainstream processors in supporting four SMT threads. The article describes the threading microarchitecture of ThunderX3 and highlights threading benefits. The article also touches on the on-chip interconnect and L3-cache and the power management subsystem. Initial benchmark results from silicon show 30%+ single thread and up to 3X socket level performance gains over the prior generation resulting in industry-leading single thread and socket level performance.
Scalable and De-centralized Battery Management System for Parallel Operation of Multiple Battery Packs

Large-scale energy storage applications require multiple lithium-ion battery packs operating in parallel. Such applications comprise of renewable energy storage systems, battery packs for large-scale automobiles such as electric trucks, tanks, armoured vehicles, diesel-electric submarines, etc. The current technology to enable parallel operation of multiple battery packs is quite hardware intensive. It requires a separate pack management system operating as a master and battery management systems in each of the battery packs configured as slaves. This significantly affects the scalability of such systems as the number of battery packs that can be connected in parallel is completely dependent on the capacity of the master. This paper presents an alternative approach through a decentralized pack management system. The proposed approach does not require the master-slave configuration of battery packs and does not require any centralised hardware to manage the battery packs. Instead, this methodology enables the individual battery packs to communicate with each other independently thereby enabling decentralised pack management. Thus, there is no theoretical limit to the number of packs that can be operated in parallel and this allows the system to be capable of scaling up to meet virtually any size, small, medium or large.
Hydrochloric acid regeneration in hydrometallurgical processes: a review

Hydrochloric acid regeneration has become a vital stage in most chloride process flowsheets. Pyrohydrolysis has been the traditional method of regenerating hydrochloric acid from a variety of chloride solutions, including magnesium, iron and nickel brines, however, its high thermal requirements renders it uneconomic in most processing flowsheets. Hydrochloric acid being an expensive acid, it is vital to the continued development of chloride processes to develop an economically favourable and environmentally sound method to recycle the lixiviant back to the process. This paper seeks to review the latest research in this area, including the use of electrowinning, solvent extraction, metal sulphate salt crystallisation and hydrolytic distillation to regenerate leaching lixiviants. Metal sulphate salt crystallisation is the most promising method included in this review; with significant research having been conducted to date and favourable economics having been shown. Industrial application of this method depends on careful design and operational control due to sensitive parameter control being needed.