Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Optimization of Test Socket for Millimeter-Wave Steerable Beam Measurement Technology

In this study, we present a 2×2 broadband dual-polarized antenna array on the 10 (4+2+4) multi-layer organic substrates with size of 13 × 13 × 0.87 mm3 and measured by test socket. The antenna structure is a stacking patch antenna with parasitic elements, the most suitable substrate stack-up is selected for cost-effectiveness and parasitic elements is added for operation bandwidth enhancement. The test socket is consisted of load board and socket to measure the characteristics of the antenna array. By simulation and optimization, test socket can measure performance close to only antenna array. It's very suitable for quick performance check during R&D phase. In the 24.5 to 29.5 GHz range, the realized gain of antenna array in vertical polarization and horizontal polarization is measured from 9.3 to 11.7 dBi. Finally, the 3D beam steering is implemented in four quadrants at 28GHz, and a maximum realized gain of 8.13dBi is achieved in the beam direction of quadrant IV.
Learning Human Motion Intention for pHRI Assistive Control

This work addresses human intention identification during physical Human-Robot Interaction (pHRI) tasks to include this information in an assistive controller. To this purpose, human intention is defined as the desired trajectory that the human wants to follow over a finite rolling prediction horizon so that the robot can assist in pursuing it. This work investigates a Recurrent Neural Network (RNN), specifically, Long-Short Term Memory (LSTM) cascaded with a Fully Connected layer. In particular, we propose an iterative training procedure to adapt the model. Such an iterative procedure is powerful in reducing the prediction error. Still, it has the drawback that it is time-consuming and does not generalize to different users or different co-manipulated objects. To overcome this issue, Transfer Learning (TL) adapts the pre-trained model to new trajectories, users, and co-manipulated objects by freezing the LSTM layer and fine-tuning the last FC layer, which makes the procedure faster. Experiments show that the iterative procedure adapts the model and reduces prediction error. Experiments also show that TL adapts to different users and to the co-manipulation of a large object. Finally, to check the utility of adopting the proposed method, we compare the proposed controller enhanced by the intention prediction with the other two standard controllers of pHRI.
European Society of Hypertension Scientific Newsletter : Update on Hypertension Management 2007 ; 8 : No . 11 revised version ASSESSMENT OF PRECLINICAL TARGET ORGAN DAMAGE IN HYPERTENSION : CAROTID INTIMA-MEDIA THICKNESS AND PLAQUE

Carotid intima-media thickness and plaque High-resolution ultrasound of the carotid arteries may allow the measurement of the intima-media complex in the arterial wall. Population studies, such as the Vobarno [1], the Rotterdam [2] and the Cardiovascular Health Study [3], have clearly demonstrated that systolic blood pressure is a major determinant of an increase in intimamedia thickness (IMT) in the carotid arteries, particularly in hypertensive patients.
UAV-Assisted Search of Emitter with Dynamic Beam: A Reinforcement Learning-Based Method

Search and location of terrestrial emitters, especially those using spectrum illegally/improperly, can facilitate the utilization of spectrum resources and mitigate interference. However, the dynamic directional RF beams make search and geolocation of terrestrial terminals (such as the terminals for low-earth orbit satellite internet) intractable. This paper investigates the issue of searching and geolocating a radio frequency (RF) emitter with an unmanned aerial vehicle (UAV). To handle this issue, a novel UAV autonomous trajectory planning scheme is proposed by employing reinforcement learning (RL) and target probability map (TPM). Based on the detection probability and false alarm probability, a TPM is constructed to assist UAV to perform effective trajectory planning in the absence of knowledge about the uplink beam direction and the potential geographical location of the RF emitter. By employing the TPM information as a part of the state information, a deep-Q-network-based trajectory planing algorithm is proposed for searching. Simulation results confirm the advantages of the proposed algorithm over various baselines in time consumption and geolocation accuracy.
Neurocontroller Training with Using the Neural Network Emulating the Plant

While using the "controller modeling scheme", there are restrictions on the settings of the neural network controller. They are related to the fact that the formation of a training sample is carried out by using a reference controller. It means that the neural network controller obtained after training cannot significantly increase the quality indicators of the system's transients. The application of "emulator and neurocontrol scheme" is demonstrated in the paper. This scheme is considered as an addition to the algorithm for the design of neural network controllers with a deterministic method for choosing the architecture and weight coefficients of a neural network, using controller modeling scheme. An example on improving the qualitative characteristics of the transients of the system by additional configuration the neuro-controller for the nonlinear system "inverse pendulum on a movable base" is given. The purpose of the control was designated as stabilization of the inverse pendulum in a vertical position and moving the mobile base to a set value. To achieve these goals, a neural control scheme, which contains two neural networks: a neurocontroller (performs the function of forming a control effect on the plant) and a neuroemulator (performs the function of simulating a model of the plant and is necessary for calculating the backpropagation error skip and adjusting the weighting coefficients of the neurocontroller) is used. As a result, it is possible to obtain an automatic control system capable of controlling the specified plant.
Geochemical Studies of Black Clays from Leg 43, Deep Sea Drilling Project

Geochemical studies of Cretaceous-age black clays from DSDP Sites 386 and 387 indicate that the organic matter in the sediments is derived from two different sources. Hydrogen-rich, amorphous organic matter is apparently derived from marine planktonic organisms, while organic matter rich in vitrinite and thermally inert material is terrigenous. The relative amounts of marine and terrestrial organic matter are strongly influenced by the degree to which water conditions were reducing at the time of deposition. The distribution of iron and sulfur within the sediments suggests that marine organic matter was preserved when sulfate-reducing conditions developed at, or near, the sea floor. Under aerobic conditions, only the more stable, terrestrial organic matter escaped oxidative destruction. Variations in the organic composition, sulfur content, and iron geochemistry of the sediments imply that the bottom water conditions changed frequently during deposition of the black clays. The fluctuations between alternately aerobic and anaerobic bottom conditions were probably caused by changes in the organic productivity of surface waters and in the amounts of oxygen supplied by deep water circulation.
A deep learning approach for automation in neurite tracing and cell size estimation from differential contrast images under healthy and hypoxic condition

Chronic hypoxia is known to be a major cause of neurite length retraction followed be degeneration. Specifically, laser scanning confocal microscopy (LSCM) based-contrast imaging is used for monitoring neuronal morphology under hypoxic condition. Although imaging of neurons using LSCM via differential contrast imaging (DIC) is a powerful tool to identify the neuronal states under degenerative condition, fully automated quantification of neurite length and cell shape remains challenging. In this context, we propose an integrated framework that combines panorama imaging of neuronal morphology using LSCM, and deep learning model that allows automated tracing of neurites and cell shape. First, we establish an in vitro hypoxic model using cobalt chloride treatment of N2A cells and perform the large-scale imaging using DIC optics. Next, we tested the performance of U-Net, U-Net++ and FCN architecture using DIC images, where U-Net and U-Net++ demonstrates robustness and accuracy in tracing neurite length and segmentation of cell shape. The result shows that the U-Net++ is able to depict the difference in cell size and neurite length for control and chronic hypoxic condition. The proposed method was also validated and compared with other CNN models including FCN and U-Net. Moreover, the analysis indicates a significant alteration of cell shape and neurite length under hypoxic condition via deep-learning based automated cell segmentation.Clinical Relevance—The proposed framework assumes importance where quantification of neurite length and cell shape from a large dataset remains challenging due to time-consuming manual segmentation by experts. Specially, the framework based on labeling of a small dataset (15-20 images) can be used to identify the neuronal state under neurodegeneration and image-based assessment of neuroprotective drugs.
Improved Multi-Head Self-Attention Classification Network for Multi-View Fetal Echocardiography Recognition

The accurate acquisition of multiview fetal cardiac ultrasound images is very important for the diagnosis of fetal congenital heart disease (FCHD). However, these manual clinical procedures have drawbacks, e.g., varying technical capabilities and inefficiency. Therefore, exploring automatic recognition method for multiview images of fetal heart ultrasound scans is highly desirable to improve prenatal diagnosis efficiency and accuracy. In this work, we propose an improved multi-head self-attention mechanism called IMSA combined with residual networks to stably solve the problem of multiview identification and anatomical structure localization. In details, IMSA can capture short- and long-range dependencies from different subspaces and merge them to extract more precise features, thus making use of the correlation between fetal heart structures to make view recognition more focused on anatomical structures rather than disturbing regions, such as artifacts and speckle noises. We validate our proposed method on fetal cardiac ultrasound imaging datasets from a single center and 38 multicenter studies and the results outperform other state-of-the-art networks by 3%-15% of F1 scores in fetal heart six standard view recognition.Clinical Relevance— This technology has great potential in assisting cardiologists to complete the automatic acquisition of multi-section fetal echocardiography images.
An Interleaved Configuration of Modified KY Converter with High Conversion Ratio for Renewable Energy Applications; Design, Analysis and Implementation

In this paper, a new high efficiency, high step-up, non-isolated, interleaved DC-DC converter for renewable energy applications is presented. In the suggested topology, two modified step-up KY converters are interleaved to obtain a high conversion ratio without the use of coupled inductors. In comparison with the conventional interleaved DC-DC converters such as boost, buck-boost, SEPIC, ZETA and CUK, the presented converter has higher voltage gain that is obtained with a suitable duty cycle. Despite the high voltage gain of the proposed converter, the voltage stress of the power switches and diodes is low. Therefore, switches with low conduction losses can be applied to improve the converter efficiency. Moreover, due to utilization of interleaving techniques, the input current ripple is low which makes the suggested converter a good candidate for renewable energy applications such as PV power system. Operation principle and steady-state analysis of the proposed converter in continuous conduction mode (CCM) and discontinuous conduction mode (DCM) are discussed in detail. Also, theoretical efficiency of the proposed converter is calculated. Finally, in order to evaluate the proposed converter operation by a renewable energy source such as a PV, the simulation results are presented. Moreover, a 220W prototype of the presented DC-DC converter is designed and implemented in the laboratory to verify its performance. Keyword: DC-DC converter, KY converter, Interleaved, High voltage gain, Low voltage stress.
Latent Markovian Modelling and Clustering for Continuous Data Sequences

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Pulsation artifact removal from intra-operatively recorded local field potentials using sparse signal processing and data-specific dictionary*

Neural recordings frequently get contaminated by ECG or pulsation artifacts. These large amplitude components can mask the neural patterns of interest and make the visual inspection process difficult. The current study describes a sparse signal representation strategy that targets to denoise pulsation artifacts in local field potentials (LFPs) recorded intraoperatively. To estimate the morphology of the artifact, we first detect the QRS-peaks from the simultaneously recorded ECG trace as an anchor point. After the LFP data has been epoched with respect to each beat, a pool of raw data segments of a specific length is generated. Using the K-singular value decomposition (K-SVD) algorithm, we constructed a data-specific dictionary to represent each contaminated LFP epoch in a sparse fashion. Since LFP is aligned to each QRS complex and the background neural activity is uncorrelated to the anchor points, we assumed that constructed dictionary will be formed to mainly represent the pulsation artifact. In this scheme, we performed an orthogonal matching pursuit to represent each LFP epoch as a linear combination of the dictionary atoms. The denoised LFP data is thus obtained by calculating the residual between the raw LFP and its approximation. We discuss and demonstrate the improvements in denoised data and compare the results with respect to principal component analysis (PCA). We noted that there is a comparable change in the signal for visual inspection to observe various oscillating patterns in the alpha and beta bands. We also see a noticeable compression of signal strength in the lower frequency band (<13 Hz), which was masked by the pulsation artifact, and a strong increase in the signal-to-noise ratio (SNR) in the denoised data.Clinical Relevance— Pulsation artifact can mask relevant neural activity patterns and make their visual inspection difficult. Using sparse signal representation, we established a new approach to reconstruct the quasiperiodic pulsation template and computed the residue signal to achieve noise-free neural activity.
Improved Expectation Propagation Assisted Grouped Generalized Composition Spatial Modulation for Massive MIMO Systems

In this paper, a novel index and composition modulation (ICM) transmission scheme, termed as grouped generalized composition and spatial modulation (G-GCSM), is proposed for massive multiple-input multiple-output (MIMO) systems. Specifically, it amalgamates the concepts of composition modulation (CM), generalized spatial modulation (GSM) and spatial multiplexing to attain high spectral efficiency (SE) and low implementation complexity. In the G-GCSM scheme, transmit antennas are divided into several groups and the GCSM transmission structure is employed independently in each group, facilitating the bit-to-index mapping issue in massive MIMO scenarios. Additionally, at the receiver side, an improved expectation propagation (EP) detector is designed for the proposed G-GCSM scheme, which exploits the inner sparsity of the transmitted vector in G-GCSM. Simulation results demonstrate the superiority of the proposed scheme over the existing GSM schemes in terms of bit error rate (BER) performance under the same SE conditions. Moreover, the proposed improved EP detector is able to provide a significant performance gain over the conventional minimum-mean-squared error (MMSE) detector in both determined and under-determined massive MIMO systems.
A Deep Neural Framework to Estimate Spectral Phase and Magnitude In Application to Single-Channel Speech Enhancement

Many speech enhancement (SE) methods that are already in use only estimate the magnitude spectrum and leave the phase spectrum alone. In this paper, we suggest a way for single-channel SE (SCSE) to estimate the spectrum's phase and magnitude. A phase compensation process is applied to noise-contaminated speech and estimates the phase spectrum. Adaptive power law transformation moves the power from the energy-rich voiced segments of the contaminated signal to the weak-energy unvoiced segments, while the entire signal's energy stays same. The energy-redistributed noisy speech signals are used to train the attention-gated long short-term memory (LSTM) recurrent neural network in order to estimate the magnitude of the ideal ratio mask (IRM). Attention-gated LSTM reduces the computational load of conventional LSTM by replacing a forgetting gate without performance loss. During speech synthesis, the speech signals are reconstructed by putting together the estimated phase and magnitude spectra. The test results show that the proposed method did better than the other methods and made speech clearer and better in noisy environments.
Mutation Testing for Temporal Alloy Models

Writing declarative models has numerous benefits, ranging from automated reasoning and correction of design-level properties before systems are built, to automated testing and debugging of their implementations after they are built. Alloy is a declarative modeling language that is well-suited for verifying system designs. A key strength of Alloy is its scenario-finding toolset, the Analyzer, which allows users to explore all valid scenarios that adhere to the model’s constraints up to a user-provided scope. Despite the Analyzer, writing correct Alloy models remains a difficult task, partly due to Alloy’s expressive operators, which allow for succinct formulations of complex properties but can be difficult to reason over manually. To further add to the complexity, Alloy’s grammar was recently expanded to support linear temporal logic, increasing both the expressibility of Alloy as well as the burden for accurately expressing properties. To address this, this paper presents μAlloyτ, an extension to Alloy’s mutation testing framework that accounts for the newly introduced temporal logic, including updating μAlloyτ’s test generation capability to produce temporal test cases. Experimental results reveal μAlloyτ is efficient at generating and checking mutations and μAlloyτ’s automatically generated tests are effective at detecting faulty temporal models.
Real-Time Whole-Body Collision Avoidance and Path Following of a Snake Robot Through MPC-based Optimization Strategies

The work in this paper delves into the challenge of whole elongated body's obstacle avoidance during path following for a class of bionic snake robots. Currently, most studies focus solely on preventing the robot's head from colliding with obstacles through designed controllers. However, due to the unique elongated structure and biomimetic locomotion modes of snake robots, it is unavoidable that the rest of the robot's body could still collide with obstacles. To resolve this problem, we propose a novel real-time optimization obstacle avoidance strategy for a class of terrestrial snake robots with multi-link elongated body using model predictive control (MPC). Moreover, by leveraging the elongated body characteristics of the robot, an improved path guidance strategy is also developed. The effectiveness of the proposed strategies is verified and validated through extensive simulations and experiments on a custom-built nine-link elongated snake robot. The results demonstrate that all links of the robot can well avoid obstacles while continuing to track the given path.
Robot Learning to Mop Like Humans Using Video Demonstrations

Though mopping the floor is a mundane and tedious daily task, enabling robots to perform it comparably to humans remains a challenge. Hand-coding desired mopping behaviors for variable surfaces and situations is particularly difficult. In this paper, we develop a robotic system for mopping the floor by mimicking the human behavior demonstrated in videos. Our baseline robotic system uses traditional computer vision techniques for tracking and inverse kinematics. Our proposed robot mop learning system comprises advanced computer vision techniques, Time Contrastive Network (TCN), and reinforcement learning. Using these, we devise a reward function for the mopping task. We use a Universal 10e robotic arm attached to a mop to perform the mopping task and a first-person camera attached on top of the robotic arm to provide feedback for robotic learning. We evaluate our proposed robot mop learning system's imitative similarity using optical flow, distance in mop location, and force applied to the floor, as well as cleaning efficiency using a white glove method.
Water Quality Monitoring Using Unmanned Aerial Systems Imagery and a Novel Autonomous Surface Vessel

Although the science and technology for retrieval of key water quality parameters from satellite data has matured significantly, the resolution and quality of remote sensing data can be limited by technical constraints, such as the sensor's spatial and spectral resolution and the unavailability of usable satellite data for extended time periods due to cloud coverage. In comparison, unmanned aerial systems (UASs) based multispectral remote sensing applications have the potential to provide critical information needed for water quality monitoring of water bodies at a local scale. However, there are a few challenges to the feasibility of using UAS-based remote sensing for water quality monitoring. Some of those challenges were addressed in this study, a set of algorithms were developed for a suite of water quality parameters, and a time-series analyses was performed. Several field campaigns were undertaken to collect field data from the water over the Merill Shell oyster reef in the Western Mississippi Sound. A multispectral sensor on a drone to collect low altitude aerial imagery and simultaneously, an autonomous surface vessel (ASV) with a plethora of sensors were deployed over the same sites. A series of automated procedures were developed and implemented for the geometric and radiometric corrections of the UAS imagery and algorithms were developed for a suite of water quality parameters using empirical and machine learning approaches. The algorithms were applied to the image mosaics and a time-series analysis of the water quality parameters with discharge was performed to determine the relative influence of the major rivers on the water quality of the study area. All the developed algorithms are promising with reasonably high R2 and low RMSE. Use of ASV and UAS data together helped develop robust algorithms, using which the relative influence of several rivers on the water quality on the oyster reef could be evaluated.