Miracle-dz/UniFAR-FTUs huggingface dataset:
Hexagonal Grid Graph as a Basis for Adaptive Sampling of Ocean Gradients using AUVs

In this paper we present an adaptive sampling strategy for Autonomous Underwater Vehicles (AUVs) seeking to explore and map ocean gradients using a hexagonal grid as its potential path. The laborious and deterministic method of pre-programming trajectories has gradually been replaced by intelligent and adaptive sensing strategies. These both allows more effective use of sampling resources, as well as improved spatiotemporal resolution. We present a method for adaptive and data-driven sampling in the water-column with AUVs that follows the sense-plan-act control paradigm. The method uses a hexagonal grid graph to discretize the survey area into a honeycomb-pattern, which gives both equilateral survey paths and a small branching factor. Using this graph, we present results from a typical environmental sampling application where the goal is to search for and explore temperature gradients. The main use case for this algorithm is to prioritize the strongest gradients within the AUV spatiotemporal envelope. We present results from field trials in Trondheimsfjorden, Norway, where the AUV successfully explored a river front. We find that the algorithm performs as expected, exploring the area and revisiting sections containing gradients.
Sea Surface Temperature Nowcasting with 3-channel Convolutional LSTM

Accurate prediction of sea surface temperature (SST) is of great importance for ocean-related industries such as fisheries. In this paper, we propose a generative adversarial model for SST nowcasting. Our model combines a modified Convolutional Long Short-Term Memory model (convL-STM) as a generator, and a multi-layer Convolutional Neural Network (CNN)as a discriminator. The model utilizes spatial correlation as well as temporal correlation within the previous sea states, including not only sea surface temperature but also ocean current. Experiments show that our model is capable of generating consistent and accurate regional SST predictions.
Evaluation of the Effect of ASMR on Reduction of Mental Stress: EEG Study

In this study, we demonstrate the effect of ASMR on the EEG activation patterns during the performing of mental tasks in normal subjects. The EEG measured during the performing mental tasks with applying ASMR sound is analyzed, and the power spectrum was calculated in alpha and high-beta (gamma) frequency band. Relatively large size of maze is used as a mental task. Our analysis showed that a significant decrease in alpha-band activity and a significant increase in beta-band activity is marked between resting condition and performing mental tasks. When applying ASMR, both alpha and beta activity during the performing mental task is backed to the level for the resting condition. In conclusion, these results imply that ASMR has the capacity to reduce mental stress.
Deep Reinforcement Learning Based Energy Efficient Underwater Acoustic Communications

Due to the unique channel characteristics and the difficulty of battery charging, energy efficient transmission is a critical topic in underwater acoustic communications (UACs). This paper considers the transmit frequency and power selection of a single link in the UAC, aiming to achieve best tradeoffs between packet delivery ratio and energy consumption, i.e., maximize the energy efficiency. Unlike traditional optimization approaches which rely on system statistics, we employ the deep reinforcement learning (DRL) technique to learn the optimal transmission strategy on the fly, without requiring any prior environmental information. Since two different actions need to be selected, traditional DRL algorithms are no longer applicable to our problem. Motivated by this, we put forth a new DRL algorithm that decides and evaluates two types of actions separately, referred to as two-action selection–based deep Q-network (TAS–DQN). The numerical results demonstrate that TAS–DQN outperforms Q–learning and original DQN, and achieves near optimal energy efficiency of the network.
Effects of transcranial direct current stimulation in brain-computer interface

The potential of brain-computer interfaces (BCIs) to replace lost neuronal function in the form of neuroprostheses is attracting much attention as a new technology. However, BCI performance varies among subjects, and the factors affecting BCI performance are poorly understood. Transcranial direct current stimulation (tDCS) is a non-invasive technique that moderates cognitive and motor function. Therefore, we investigated whether tDCS on the motor area enhances functional connectivity related to controlling an BCI and improves control performance. We found that functional connectivity between the motor area and both the caudate and cerebellum after stimulation were higher than that before stimulation, which suggests that stimulation may strengthen connectivity between basal ganglia and motor area. However, BCI performance after the intervention was significantly worse than that before, which conflicted with our hypothesis that stimulation would improve BCI performance. Participants were instructed to modulate their own brain relative to baseline using motor imagery. We suspect that the effect may have disturbed their brain self-regulation, because the stimulation affected signals not only during BCI control, but also during baseline. In the future, we will assess the mechanism underlying this decline in BCI performance to enhance BCI performance through finding stimulation which specifically affect BCI control.
Parasitic Capacitance Analysis of Three-Independent-Gate Field-Effect Transistors

<italic>Three-Independent-Gate Field-Effect Transistors</italic> (TIGFETs) are a promising alternative technology that aims to replace or complement CMOS at advanced technology nodes. In this paper, we extracted the parasitic and intrinsic capacitances of a silicon-nanowire TIGFET device using three-dimensional numerical simulations in an attempt to accurately compare its capacitances and, consequently, circuit-level performances to CMOS at comparable nodes. Analytical models of the parasitic capacitances of a TIGFET transistor were derived using techniques such as the equivalent Schwarz-Christoffel transformation and standard cylindrical capacitors and show close agreement with numerical simulations. The maximum capacitance of a TIGFET transistor is <inline-formula> <tex-math notation="LaTeX">$2\times $ </tex-math></inline-formula> larger than for a 15 nm CMOS <italic>High Performance</italic> (HP) device due to the TIGFET’s two additional gated contacts, but this is countered by its ability for multiple modes of operation which reduces the effective switching capacitance per device. A TIGFET transistor sees, on average, only a 30% increase in total capacitance compared to a CMOS HP device. Additionally, the TIGFET’s increased device functionality can be used to modify the circuit-level architecture of a TIGFET-based design to mitigate the performance impact of its larger device-level capacitance. This combination of a TIGFET’s <xref rid="deqn1" ref-type="disp-formula"><italic>(1)</italic></xref> multiple modes of operation, and <xref rid="deqn2-deqn3" ref-type="disp-formula"><italic>(2)</italic></xref> circuit-level architecture lead to enhanced system performance. In particular, we show that at the 15 nm technology node TIGFET technology has 18% lower energy-delay product for a fan-out of 4 and higher when using 1-bit full-adder logic circuit than for the equivalent node CMOS HP.
A Wideband and High Efficiency Ka-band GaN Doherty Power Amplifier for 5G Communications

This paper presents a wideband and high efficiency Ka-band gallium nitride (GaN) doherty power amplifier (DPA) fabricated by 0. 15µm GaN-HEMT on SiC technology. The Tee-line network impedance inverter which is formed utilizing output capacitance of transistors was designed to increase operation bandwidth. Moreover, it was possible to realize the impedance inverter at millimeterwave band by using Metal-Insulator-Metal capacitor on via-holes (MIM -on-Via) structure which can decrease parasitic inductances. As a result, the fabricated 2-stage DPA has achieved a measured saturation output power of 36.1–36.5dBm, a 6dB back-off PAE of 25-27%, and a peak PAE of 26.7–31.8% in the frequency band of 26–30GHz. This fractional band is 14.6%. To the best of authors' knowledge, this performance is the widest bandwidth with high efficiency in Ka-band GaN DPA. Additionally, the measured ACPR is −31.1dBc and EVM is 3.2% at the average output power of 28dBm under 45MHz 64QAM modulated signal.
Diver Communication and Localization System using Underwater Acoustics

Diving operations are still predominant today in many underwater applications despite the advances in robot-assisted technologies. Apart from being invaluable in carrying out many complex tasks underwater, diving is also a popular form of recreational activity. To improve diver safety and enable new scenarios under which divers can operate, a robust and reliable communication and localization system is extremely useful. Communicating from the surface to divers, and between divers themselves, is a challenge. Currently, most commercial diving operations rely on rudimentary communication using hand signals and floats among the divers. In order to communicate with the topside application (e.g. topside manager) of the dive operation from the surface, tethered systems are used. Dive logs including the depth, pressure, etc., are measured using dive computers but to monitor these parameters and track the divers in real-time on the surface, is a challenge. This can be resolved using the advances made in underwater acoustic communications. There is a strong need for a system capable of logging basic diver statistics and providing real-time feedback of the location and condition of the divers, to enhance the safety and effectiveness of dive operations. A diver communication and localization solution that allows two-way communication between the topside application and the divers with simultaneous localization of the divers is designed, implemented, and demonstrated. The proposed system allows the topside manager to monitor real-time dive parameters including diver's depth, location, and tank pressure.
Joint Synchronization and Attocell Identification With Received Power Estimation in VLC Networks

Optical attocell networks building upon visible light communication (VLC) offer several advanced features including load balancing, mobility management etc. as in radio-frequency networks. Attocell identification is crucial to perform the features’ functionalities. Attocells are allocated with identities and their own synchronization signals (SSs) are generated accordingly. The signal is periodically broadcasted and used for cell identification and synchronization in receiver side. In this letter, we revise SS design for VLC domain and propose a joint synchronization and cell identification including SS received power estimation mechanism. The performance of proposed mechanisms is then evaluated though Monte-Carlo simulations.
Iris Location and Recognition by Deep-Learning Networks Based Design for Biometric Authorization

In this work, the effective deep-learning network based design is studied for iris biometric authentication. Firstly, the system locates and detects the position of the iris by the YOLO based deep-learning detector, and then it strengthens the iris features by histogram equalization. Finally, the iris image is classified by the VGG-16 based model. By the self-made near infrared (NIR) database, the proposed deep-learning based design achieves the recognition accuracies up to 98% at the mode with intruders. Besides the performances of false acceptance rate (FAR) and false rejection rate (FRR) are also evaluated. Compared with the previous studies, the proposed design provides the effective performance of accuracy and does not need the iris segmentation process.
TxTrack v0.9: A Lagrangian ocean analysis web-framework for trajectory modeling

The trajectory model-TxTrack is an open-source project for oil spill topics. The central design concept of the version v0.9 written in this article is to provide a concise user interface as a protocol; composable abstractions to encapsulate various dynamic processes and an extensible Lagrangian trajectory model between particles and gridded data from Texas coastal high-frequency radar (TxHFR) and a web-based platform for data visualization. TxTrack follows the modern scientific software engineering concept. , i.e., a modular structure. Thus, TxTrack's feature is not only that it can directly operate the simulation of particle trajectories through web pages, but also can use Python programming language to perform marine scientific research. The model has three components. The first component is the external data access component. This component allows data to be pulled directly from the THREDDS server and also from the local directories where they exist. The second component is the computing kernel. The computing kernel computes oil tracking based on the Lagrangian approach with parallel computing. The third component is the visualization component that provides a user-friendly interface page that is readable from any browser. The model validation has been calibrated from available surface drifters, shows a good correlation with the trajectories and reveals its abilities as a management tool. The prediction of spills from this model will be very useful in establishing an effective response to oil spilled in the coastal environment.
Vertical Accuracies of Mass-Market GNSS Receivers and Antennas in Ellipsoid Reference Survey Strategy for Marine Applications

This paper describes the vertical accuracies achievable using Low-power, Mass-market, Multifrequency, Multi-constellation GNSS (LM3GNSS) receivers, and antennas, and evaluates the results as a precursor to providing alternatives to high-end GNSS hardware on emerging remotely operated and unmanned systems, such as autonomous surface vehicles (ASV), small unmanned aircraft systems (sUAS), and offshore GNSS buoys. The LM3GNSS receivers are relatively new in the market, and they are affordable ($243 - $2,670). Given their low-power characteristics, they readily fulfill the power budget requirement aboard marine systems. In the first round of our studies, we conducted five experiments in a minivan, which traveled at approximately 80 km per hour (50 mph) with matching pairs of receivers and antennas. We assume that the results should be comparable to solutions aboard a marine vessel at typical hydrographic survey speeds (10 - 20 km per hour {5 - 10 knots}). The matching pairs of receivers include four LM3GNSS hardware, Trimble NetR9 receiver, and Zephyr3 antenna. The results show that LM3GNSS receivers are capable of achieving vertical positioning uncertainties that are within 10 cm of the geodetic results (95% confidence level).
F-LEMMA: Fast Learning-Based Energy Management for Multi-/Many-Core Processors

Over the last two decades, as microprocessors have evolved to achieve higher computational performance, their power density has also increased at an accelerated rate. Improving energy efficiency and reducing power consumption are therefore critically important to modern computing systems. One effective technique for improving energy efficiency is dynamic voltage and frequency scaling (DVFS). With the emergence of integrated voltage regulators (IVRs), the speed of DVFS can reach microsecond ( $\mu \text{s}$ ) timescales. However, a practical and effective strategy to guide fast DVFS remains a challenge. In this article, we propose F-LEMMA: a fast, learning-based, hierarchical DVFS framework consisting of a global power allocator in the kernel space, a reinforcement learning-based power management scheme at the architecture level, and a swift controller at the digital circuit level. This hierarchical approach leverages computation at the system and architecture levels with the short response time of the swift controller to achieve effective and rapid $\mu \text{s}$ -level power management supported by the IVR. Our experimental results demonstrate that F-LEMMA can achieve significant energy savings (35.2%) across a broad range of workloads. Conservatively compared with existing state-of-the-art DVFS-based power management schemes that can only operate at millisecond timescales, F-LEMMA can provide notable (up to 11%) energy-delay product (EDP) improvements across benchmarks. Compared with state-of-the-art nonlearning-based power management, our method has a universally positive effect on evaluated benchmarks, proving its adaptability.
ProEGAN-MS: A Progressive Growing Generative Adversarial Networks for Electrocardiogram Generation

Electrocardiogram (ECG) is a physiological signal widely used in monitoring heart health, which is of great significance to the detection and diagnosis of heart diseases. Because abnormal heart rhythms are very rare, most ECG datasets have data imbalance problems. At present, many algorithms for ECG anomaly automatic recognition are affected by data imbalance. Conventional data augmentation methods are not suitable for the augmentation of the ECG signal, because the ECG signal is one-dimensional and their morphology has physiological significances. In this paper, we propose a ProGAN based ECG sample generation model, called ProEGAN-MS, to solve the problem of data imbalance. The model can stably generate realistic ECG samples. We evaluate the fidelity and diversity of the data generated by the model and compare the data distribution of the original and generated data. In addition, in order to show the diversity of the generated ECG data more intuitively, we manually checked the diversity and calculate the statistics of the data. The results show that compared with other ECG augmentation methods based on GANs, the ECG data generated by our model has higher fidelity and diversity, and the distribution of generated samples is closer to the distribution of original data. Finally, we established neural network models for arrhythmia classification, and used them to evaluate the improvement of the classification model performance by ProEGAN-MS. The results show that augmented data by ProEGAN-MS can effectively improve the insufficient sensitivity and precision of the classification model.
Evolutionary Algorithm-Based and Network Architecture Search-Enabled Multiobjective Traffic Classification

Network traffic classification technology plays an important role in network security management. However, the inherent limitations of traditional methods have become increasingly obvious, and they cannot address existing traffic classification tasks. Very recently, neural architecture search (NAS) has aroused widespread interest as a tool to automate the manual architecture construction process. To this end, this paper proposes NAS based on multiobjective evolutionary algorithms (MOEAs) to classify malicious network traffic. The main purpose is to simplify the search space by reducing the spatial ratio and number of channels of the model. In addition, the search strategy is changed in the effective search space, and the utilized strategies include EAs with the nondominated sorting genetic algorithm with the elite retention strategy (NSGA-II), strength Pareto evolutionary algorithm (SPEA-II) and multiobjective particle swarm optimization (MOPSO) to solve the formulated multiobjective NAS. Through comprehensive comparison of the population convergence times, model accuracies, Pareto optimality sets, model complexities and running speeds of the strategies, it is concluded that the model based on NSGA-II search has the best performance. The experimental results of the current machine learning algorithms and artificial learning methods based on the network are compared, showing that our method achieved better classification performance on two public datasets with a lower computational complexity, as mainly measured by FLOPs. Our approach is able to achieve 99.806% and 99.369% F1-score with 11.501 MB and 4.718 MB FLOPs on both IDS2012 and ISCX VPN dataset respectively.
Modeling and Balance Control of Supernumerary Robotic Limb for Overhead Tasks

Overhead manipulation tasks often require collaborations between two operators, which becomes challenging in confined spaces such as in a compartment. Supernumerary Robotic Limb (SuperLimb), as a promising wearable robotic solution, can provide assistance in terms of broader workspace, wider manipulation functionalities and safer working conditions. However, the safety concerns of human-centered SuperLimb interaction mechanisms are rarely studied to date, particularly regarding human standing balance. This study proposes a balance controller by which one individual operator can accomplish overhead tasks with the assistance of SuperLimb via tunable interaction force and supporting force regulation. The SuperLimb-human interaction is modeled and a dynamics control method based on QR decomposition (also known as QR factorization, in which a matrix is factorized into an orthogonal matrix and an upper triangular matrix) is adopted to decouple joint torques of the SuperLimb and the interaction forces. Therefore, the supporting forces can be regulated independently to guarantee the operator-SuperLimb interaction forces in a safe region. Force plate is used for measuring the CoP position as an evaluation method of the standing balance. The critical horizontal push force is learned through experiment to guide the balance controller. This method is implemented on a SuperLimb prototype worn on the operator's back, to provide necessary supporting forces on overhead object while allowing the operator to move freely underneath.
Dynamic ADC Resolution-Mapping for Millimeter-Wave Massive MIMO Systems

The radio-frequency (RF) chains, phase shifters (PSs), and analog-to-digital converters (ADCs) play the dominant role of power consumption in the uplink hybrid millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) networks. To mitigate power consumption, an energy-efficient switch and inverter (SI) based partially-connected (PC) architecture with the Gram-Schmidt (GS) antenna selection strategy is addressed. The design of a variable-resolution ADC configuration is addressed under an independent upper bound of power consumption for each ADC in this paper. However, the resulting ADC resolution mapping becomes more complicated due to variant ADC power bounds. A simple ADC bit-allocation algorithm, namely, the sum-resolution (SR) ADC, is proposed. By replacing the total power constraint on ADCs to improve both the achievable sum-rate (ASR) and energy efficiency (EE) performance of the hybrid mmWave massive MIMO system. The SR-ADC solutions in closed form reveal that the optimal ADC resolution is proportional to the square power of the signal-to-noise ratio (SNR) in RF chains. Simulation results demonstrate that the proposed SR-ADC approach offers enhanced improvements on the ASR and EE, and exhibits prominent advantages on the number of activated RF chains compared with the fixed total power system.