Miracle-dz/UniFAR-FTUs huggingface dataset:
Neuromorphic Low-Power Inference on Memristive Crossbars With On-Chip Offset Calibration

Monolithic integration of silicon with nano-sized Redox-based resistive Random-Access Memory (ReRAM) devices opened the door to the creation of dense synaptic connections for bio-inspired neuromorphic circuits. One drawback of OxRAM based neuromorphic systems is the relatively low ON resistance of OxRAM synapses (in the range of just a few kilo-ohms). This requires relatively large currents (many micro amperes per synapse), and therefore imposes strong driving capability demands on peripheral circuitry, limiting scalability and low power operation. After learning, however, a read inference can be made low-power by applying very small amplitude read pulses, which require much smaller driving currents per synapse. Here we propose and experimentally demonstrate a technique to reduce the amplitude of read inference pulses in monolithic neuromorphic CMOS OxRAM-synaptic crossbar systems. Unfortunately, applying tiny read pulses is non-trivial due to the presence of random DC offset voltages. To overcome this, we propose finely calibrating DC offset voltages using a bulk-based three-stage on-chip calibration technique. In this work, we demonstrate spiking pattern recognition using STDP learning on a small $4\times 4$ proof-of-concept memristive crossbar, where on-chip offset calibration is implemented and inference pulse amplitude could be made as small as 2mV. A chip with pre-synaptic calibrated input neuron drivers and a $4\times 4~1$ T1R synapse crossbar was designed and fabricated in the CEA-LETI MAD200 technology, which uses monolithic integration of OxRAMs above ST130nm CMOS. Custom-made PCBs hosting the post-synaptic circuits and control FPGAs were used to test the chip in different experiments, including synapse characterization, template matching, and pattern recognition using STDP learning, and to demonstrate the use of on-chip offset-calibrated low-power amplifiers. According to our experiments, the minimum possible inference pulse amplitude is limited by offset voltage drifts and noise. We conclude the paper with some suggestions for future work in this direction.
Reliable Multi-Point Transmissions Over Directional MISO TWDP Fading Channels

In this paper, we consider wireless transmissions over directional multiple-input single-output (MISO) two wave with diffuse power (TWDP) fading channels, subject to interference over the same type of channel. TWDP fading is known to exhibit an outage behavior that can be even worse than Rayleigh fading and has experimentally been observed in a number of vehicular channel measurement campaigns. To enhance the reliability of multi-point transmissions over MISO TWDP fading channels, we propose a zero-forcing based coordinated beamforming scheme, for which we can analytically characterize and optimize the outage probability of the multi-point transmissions. Based on the developed outage expressions, we furthermore propose a greedy coordinated scheduling approach that attempts to maximize the achievable rate of the system. Finally, we propose a transmission rate adaptation scheme for fixed size packet transmissions, which supports reliable and at the same time resource efficient multi-point transmissions.
The Role of a Facilitator in Co-Design Applications for Exploratory Analysis in Domains of High Complexity: The Case of MAHiCGO

Background: Robust and useful tools for exploratory analysis in biosciences are still lagging behind the size and complexity of the biological datasets produced since the completion of the human genome in 2000. A possible reason is that developers are unlikely to understand domain and case-specific requirements of existing research questions. Methods: We formed a design team comprising a visualization expert, a Human Computer Interaction (HCI) researcher, bioinformatics domain experts, and the Principal Investigator (PI) as a ‘facilitator’ filling the communication gap between them. We implemented co-design methodology. Results: We identified the need for an interactive visual analytic tool for exploratory analysis of biological data. We describe the process of developing MAHiCGO, a novel tool for the simultaneous visualization of MA Gene Expression data, Hi-C data and Gene Ontology (GO) information in an interactive manner for the exploratory analysis of biological data. Conclusion: The key finding of this research to include a facilitator role in the co-design is useful in the evolving fields of design research and of bioinformatics, merging computational sciences with biosciences. The findings support in understanding new functional roles in the field of design, in particular design of computer applications in highly complex domains such as bioinformatics, and for highly complex tasks such as exploratory tasks. The findings also stress on the key role of visualization to expand user cognitive capabilities, and of co-design for constant engagement of domain experts in the creation process.
Research on Enhancement Method of Track Defect Sample Based on Deep Convolution Generative Adversarial Network

The scarcity of rail surface defect samples is the main factor that affects the direct application of deep learning methods to rail surface defect recognition. This paper proposes a method for enhancing orbital defect samples based on Deep Convolution Generating Adversarial Network (DCGAN). First, analyze the reasons for the disappearance of the gradient of the DCGAN generator network, and demonstrate from the mathematical deduction that DCGAN does not completely solve the problem of the disappearance of the gradient; then, by adding noise to the real image and the generated image at the same time to guide the learning process of the sample distribution, JS is realized (Jensen-Shannon) Divergence optimization; Finally, experimental demonstration. The experimental results show that this method effectively realizes the feature restoration of the defect samples in addition to improving the stability of the training process, provides sufficient and effective sample data for the application of deep learning in the detection of track defects, and has certain application value.
Neural-Network-Based Control for Discrete-Time Nonlinear Systems with Input Saturation Under Stochastic Communication Protocol

In this paper, an adaptive dynamic programming (ADP) strategy is investigated for discrete-time nonlinear systems with unknown nonlinear dynamics subject to input saturation. To save the communication resources between the controller and the actuators, stochastic communication protocols (SCPs) are adopted to schedule the control signal, and therefore the closed-loop system is essentially a protocol-induced switching system. A neural network (NN)-based identifier with a robust term is exploited for approximating the unknown nonlinear system, and a set of switch-based updating rules with an additional tunable parameter of NN weights are developed with the help of the gradient descent. By virtue of a novel Lyapunov function, a sufficient condition is proposed to achieve the stability of both system identification errors and the update dynamics of NN weights. Then, a value iterative ADP algorithm in an offline way is proposed to solve the optimal control of protocol-induced switching systems with saturation constraints, and the convergence is profoundly discussed in light of mathematical induction. Furthermore, an actor-critic NN scheme is developed to approximate the control law and the proposed performance index function in the framework of ADP, and the stability of the closed-loop system is analyzed in view of the Lyapunov theory. Finally, the numerical simulation results are presented to demonstrate the effectiveness of the proposed control scheme.
A Triplet Nonlocal Neural Network With Dual-Anchor Triplet Loss for High-Resolution Remote Sensing Image Retrieval

Conventional deep-learning-based retrieval models are generally trained under the framework of scene classification with cross-entropy loss, this way focuses only on the output probability corresponding to the label of input samples, while ignoring the predictive information of other categories, which makes the retrieval accuracy susceptible to the intraclass difference of the image samples. And conventional methods often used fixed-size convolution kernels that only consider the local area with fixed sizes, thus largely ignoring the global information. In response to the above problems, this article constructs a triplet nonlocal neural network (T-NLNN) model that combines deep metric learning and nonlocal operation. The proposed T-NLNN follows the three-branch network design, with shared weights in each branch. We evaluate T-NLNN on three public high-resolution remote sensing datasets, and the experimental results suggest that T-NLNN has discriminative feature learning ability and outperforms other existing algorithms. In addition, we propose a dual-anchor triplet loss function to facilitate the utilization of information in the input samples. The experimental results prove that the proposed dual-anchor triplet loss function works better than the traditional triplet loss function on all datasets.
A Transfer Learning Approach for Identification of Distracted Driving

In recent years, the number of motor vehicle traffic crashes is rising around the world. According to the United States National Highway Traffic Safety Administration (NHTSA), the main cause of road fatalities and injuries is distracted drivers. Driver distraction is a specific sort of driver interference on the roadway. In this case, a deep learning-based system can detect and distinguish the source of distractions in real-time, to avoid traffic accidents and make better transport safety. This paper attempts to develop the system using transfer learning and fine-tuning methods with different model architectures. Various pre-trained weights were used with fine-tuning to improve accuracy and implemented using Mobile Net, VGG16, and ResNet50 models. Finally, the results illustrate that transfer learning on Mobile Net with frozen layers is the best model out of three models getting 99% (495 out of 500) of accuracy on the test dataset images.
A Variable Frequency Bidirectional CLLC Resonant Converter with Variable Capacitor

Isolated bidirectional CLLC resonant converter generally operates at a resonant frequency to ensure desired active power transmission ratio, high efficiency and stable voltage gain. However, its actual resonant frequency usually deviated from the operating frequency is not constant due to parameter deviations of the resonant components and variations of the operation power and temperature, which may change its voltage conversion gain and transmission efficiency. This paper proposes a novel variable frequency isolated bidirectional resonant converter with variable capacitor clamp auxiliary circuits. DC voltage bias on ceramic capacitors are applied and the resonant capacitance is changed by adjusting duty cycles of the auxiliary switches, which are almost lossless. DC voltage bias characteristics of ceramic capacitors and output characteristics of the converter are researched. Experimental results obtained from a prototype verify the theoretical analysis of the proposed novel bidirectional CLLC resonant converter.
Improved CNN-Based Path Planning for Stairs Climbing in Autonomous UAV with LiDAR Sensor

Unmanned aerial vehicles (UAV) technology has been an innovative advancement in the scientific environment over recent years. In this paper, we propose an approach that facilitates UAVs with a monocular camera combined with light detection and range (LiDAR) sensor to navigate autonomously for stairs climbing in completely unknown, GPS-denied indoor environments. The suggested approach utilizes a state-of-the-art CNN model for the task. We suggest a novel approach utilizing the video feed derived from the UAV front camera to determine the next maneuver in the deep neural network model. The process is viewed as a classification activity, where the deep neural network model classifies the image as a stair or no-stair and LiDAR sensor data are used for distance calculation. The training is performed from a dataset of images obtained from multiple stairs. We show the effectiveness of the proposed device in indoor stairs scenarios in real-time.
Practical Design Method of Distributed-Element Reflectionless Bandstop Filters

A practical reflectionless bandstop filter topology is presented in this paper. One unique feature of the presented distributed-element reflectionless filter circuit compared to others is that it only requires to design and tune $N+1$ pairs of coupled lines for an Nth-order filter. Closed-form design equations for a second-order filter have been provided, and they can be expanded for higher-order filters. For verifying the presented filter topology and the design method, a reflectionless bandstop filter has been designed, fabricated, and measured. The measurement shows that the fabricated filter has the return loss larger than 20 dB in the frequency range of interest, with having the predefined transmission response.
Artificial Neural Networks based Multi-Objective Design Approach for Single-phase Inverters

Power decoupling functions of single-phase inverters are designed to minimise the total capacitance. This however might result in suboptimal design of the inverters because of complex tradeoff between inverter power density and efficiency. In this paper, an artificial neural network (ANN) based multi-objective design approach is proposed to first analyse the tradeoff and then optimise the design of a single-phase inverter with power decoupling function. The main contributions of this paper include 1) developing a systematic power loss and volume models by considering the dominant design and performance space parameters; 2) implementing the mathematical model to generate the training data set and develop an ANN based design approach; 3) comparing the results with a numerical models-based design method. The proposed design approach is implemented in MATLAB/Simulink. The simulation results show that the proposed ANN based design approach reduces the repeated usage of mathematical models, achieves optimal design and minimises the computational burden.
Single-Stage Flyback AC-DC Converter with Primary-Side Controlled DCM/CCM Operations

In this paper, primary side regulation (PSR) technology for single-stage flyback converter in discontinuous conduction mode (DCM) and continuous conduction mode (CCM) operations is presented. The output voltage and current are estimated by sensing the auxiliary winding voltage and the power switch current without the secondary-side output feedback circuits. In this paper, the algorithm used for PSR allows the magnetizing current to be operated in DCM and CCM. Therefore, the PSR flyback converter is suitable for wide input voltage range and wide output power range applications. Finally, the control algorithm is implemented with the digital control to achieve high power factor with 90-264 Vrms universal input voltage and 24 V/96 W output voltage and rated output power. Experimental results show the power factor is higher than 0.93 and the output voltage control accuracy and output current accuracy are within 6.25% and 8%, respectively under the universal input voltage range conditions. The highest system efficiency is 88.3%.
Spiral-Coupled-Line-Based Wilkinson Power Divider

A spiral-coupled-line-based <italic>Ka</italic>-band (26.5–40 GHz) CMOS Wilkinson power divider with a shunt capacitor (C) at the input and a shunt RC (or C) between the outputs is reported. The spiral coupled line, i.e., two noninverting spiral transmission lines (TLs) with magnetic coupling, has a positive mutual inductance. This leads to a significant TL-length reduction (from about <inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula>/7 to <inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula>/12) for a specific TL series inductance. At 33 GHz, the power divider achieves prominent <inline-formula> <tex-math notation="LaTeX">$S_{11}$ </tex-math></inline-formula> of −21.5, <inline-formula> <tex-math notation="LaTeX">$S_{22}$ </tex-math></inline-formula> of −22.1, <inline-formula> <tex-math notation="LaTeX">$S_{33}$ </tex-math></inline-formula> of −22.1, <inline-formula> <tex-math notation="LaTeX">$S_{32}$ </tex-math></inline-formula> of −26.1, <inline-formula> <tex-math notation="LaTeX">$S_{21}$ </tex-math></inline-formula> of −4.063, and <inline-formula> <tex-math notation="LaTeX">$S_{31}$ </tex-math></inline-formula> of −4.052 dB. The corresponding amplitude imbalance (AI) and phase difference (PD) are −0.011 dB and −0.336°, respectively. Moreover, <inline-formula> <tex-math notation="LaTeX">$S_{11}$ </tex-math></inline-formula> is smaller than −10 dB for 0–45.3 GHz, corresponding to 137.3% fractional bandwidth (FBW). <inline-formula> <tex-math notation="LaTeX">$S_{32}$ </tex-math></inline-formula> is smaller than −10 dB for 24.1–40.2 GHz, covering the <italic>Ka</italic>-band and corresponding to 48.8% FBW. The power divider occupies a normalized area of <inline-formula> <tex-math notation="LaTeX">$1.2\times 10^{-4}\lambda _{0}^{2}$ </tex-math></inline-formula>, one of the smallest normalized chip areas ever reported for millimeter-wave power dividers.
Detachable Web-Based Learning Framework to Overcome Immature ICT Infrastructure Toward Smart Education

Amid the recent disease outbreaks that have been spreading across the world, the education systems in every country have witnessed a dramatic transformation. In particular, the situation has promoted online learning on an unprecedented scale, with classes being held virtually on digital platforms. This vital transformation is a major challenge in developing countries where information infrastructure for remote learning is lacking. Inspired by these observations, this study first investigated the obstacles in the way of the deployment of smart education systems (SESs) in developing countries from a technical perspective. Consequently, a detachable SES framework, named vSmartEdu, is proposed. The framework is based on a hybrid online/offline web-service model, which adopts a service-based architecture (SBA) design concept to develop smart classrooms. In particular, the online mode is activated for a web-based classroom if an Internet service is available. In contrast, the offline-version of the system is available in offline mode in packaged form, and is utilized when the Internet is not available in a local classroom. Finally, a prototype was deployed to collect feedback from learners and educators at various educational levels. The trial implementation and survey results concretely validate the feasibility and advantages of the proposed solution.
How to Prevent Cross-texting in Mobile Messengers Using Honorifics Features in Korean Text

‘Cross-texting’ refers to accidentally sending a message to an unintended person. As mobile phone messenger use becomes more active, such mistakes occur frequently. The functions provided by messenger are generally a post solution, and it is difficult for users to find mistakes in advance. In this paper, we proposed a model that analyzes the honorifics features of sentences to determine whether the sentences in the dialogue are cross-texting. By extracting the politeness and completeness features from the sentence, and modeling a specific user’s dialogue, it determines whether the target sentence is suitable for the dialogue. This method can provide a hint to easily determine whether the sentence that the user writes is cross-texting, only with the previous chats. As a result of experimenting on a cross-texting dataset created using an actual messenger conversation corpus, cross-texting was detected with 94% accuracy.
End-to-End Delay Analysis in Aerial-Terrestrial Heterogeneous Networks

Recently, the use of unmanned aerial vehicles (UAVs) or airship platforms to improve the performance of cellular networks has received growing interests. It is expected that the next generation cellular networks will enable the low-latency and high-reliability communications with the support of aerial-terrestrial heterogeneous networks (HetNets) composed of flying and ground base stations (BS). In addition, with the emergence of various delay-sensitive applications (e.g., autonomous vehicles), the end-to-end delay becomes a crucial quality of service (QoS) metric. However, how to design an efficient and delay-guaranteed aerial-terrestrial HetNets remains an open research challenge. In this paper, we develop an analytical approach to characterize the end-to-end delay performance of aerial-terrestrial HetNets by using network calculus. A stochastic model that takes into account the successful transmission and resource utilization in the radio access network is proposed to evaluate the delay performance within a target delay-violation probability. In addition, the effect of background traffic in the core network are carefully examined to present a tight upper bound for the end-to-end delay. Simulation results validate the proposed analysis under different network settings such as available bandwidth, the number of UAVs, and the trajectory of UAVs. Also, it is revealed that the trajectory of UAVs plays a more significant role in the delay performance compared to the number of UAVs.
Multi-objective Evolutionary Algorithm based on Ensemble of Initializations for Overlapping Community Detection

Recently, developing efficient multi-objective evolutionary algorithms (MOEAs) for detect communities in complex networks has received immense recognition. Employing MOEAs for community detection provide a set of Pareto-optimal solutions (PS) where each solution in PS represent different network partition. In literature, majority of existing approaches proposed for detecting community structures focus on obtaining disjoint communities (each node belongs to a unique community). However, communities in real-world networks often overlap with each other and thus it is essential to develop algorithms for overlapping community detection. Hence, in this paper, we propose a MOEA based on ensemble of initializations for overlapping community detection (OCD-enMOEA). In OCD-enMOEA, we employ an ensemble of initialization procedure, where the individuals are encoded by an indirect representation based on permutation. Then, a decoding procedure based on community fitness is employed to transform the individuals into communities. The community fitness metrics adopted for decoding is associated with a resolution parameter $\alpha$, which allows exploration of community structures at different levels of granularity. However, the community fitness metric is very sensitive to the parameter $\alpha$ and adapting the parameter to achieve better performance is very difficult. Hence, in this paper, we employ a set of values for the parameter $\alpha$ as an ensemble and transform the individuals into communities. To validate the performance of proposed OCD-enMOEA, we have employed five real-world networks and compared with six baseline algorithms to detect overlapping communities. The experimental results demonstrate that the proposed method exhibit better performance in comparison with the baseline algorithms.