Miracle-dz/UniFAR-FTUs huggingface dataset:
IDTVR: A Novel Cloud Framework for an Interactive Digital Twin in Virtual Reality

Digital Twins (DTs) and Virtual Reality (VR) have recently gained significant popularity. The term digital twin refers to a virtual representation of a real-world environment. DTs are known for providing intuitive, accurate, and real-time visualizations of complex physical systems. In this paper, we present a novel, cloud-based framework for creating digital twin environments using VR within a cloud environment. We demonstrate our framework with two real-world case studies inspired by clinical insights obtained from experts in post-stroke rehabilitation. In the case of rehabilitation, sessions within virtual scenarios mimicking patients’ everyday life may significantly improve their rehabilitation quality. Inspired by biofeedback, we used a digital twin in VR to allow patients to enhance hand stability using real-time data streaming. Our second case study demonstrated the utility of our approach for allowing patients to more comfortably navigate within their homes by generating a virtual twin of an apartment. Our work is designed to transcend the boundary between reality and virtuality by letting users interact directly with the real and virtual worlds simultaneously. To achieve real-time synchronization between the real and the virtual environments, we established an infrastructure blueprint that features efficient sensor data collection, streaming, processing, and storage in a secure cloud environment. Data may then be accessed from any site, directly from the cloud, to update the virtual environment. In the end, we show that our infrastructure is generalizable across clinically-relevant tasks. We hope to extend our work to other areas that would benefit from a digital twin-based VR space.
A Quadrature Uncertain-IF IR-UWB Transceiver with Twin-OOK Modulation

Benefiting from good energy efficiency and fine ranging accuracy, impulse-radio ultra-wideband (IR-UWB) has been revived recently for short-distance communications. The I R-UWB transceiver, however, faces two major issues because of using short-duration pulses. Firstly, the short-duration pulse brings synchronization difficulty to the baseband. Even though the standard-compliant preamble [1] or the synchronized-OOK (S-OOK) modulation [2] provides a way for the baseband synchronization, they suffer from degraded energy efficiency or reduced link margin. Secondly, the IR-UWB receiver has a design trade-off between power consumption and demodulation performance. Having a simple architecture and intermittent operation, the noncoherent UWB receiver consumes less power than the coherent UWB receiver, but the demodulation performance is vulnerable to noise and interference. Recently, an uncertain-IF UWB receiver [3], employing a two-step downconversion, not only inherits the advantages of a low-power noncoherent architecture but also improves the sensitivity and the narrowband interference (NBI) tolerance, thus overcoming the disadvantages of the noncoherent demodulation as well. However, in order to achieve a fine ranging accuracy, a high IF is required in the uncertain-IF UWB receiver. In this work, we propose a low-power UWB transceiver architecture with two novel features. One is a twin-OOK (T-OOK) modulation that not only achieves the baseband synchronization efficiently but also improves the spectrum efficiency by applying a frequency-hopping (FH) method. The other is a quadrature uncertain-IF demodulation to improve the ranging accuracy without requiring a high IF. A 6-to-8GHz IR-UWB transceiver based on the proposed methods achieves energy-efficient baseband synchronization, improved spectrum efficiency, good NBI tolerance and 1 cm ranging accuracy.
Tests Based on Skewness and Kurtosis for Multivariate Normality

A measure of skewness and kurtosis is proposed to test multivariate normality. It is based on an empirical standardization using the scaled residuals of the observations. First, we consider the statistics that take the skewness or the kurtosis for each coordinate of the scaled residuals. The null distributions of the statistics converge very slowly to the asymptotic distributions; therefore, we apply a transformation of the skewness or the kurtosis to univariate normality for each coordinate. Size and power are investigated through simulation; consequently, the null distributions of the statistics from the transformed ones are quite well approximated to asymptotic distributions. A simulation study also shows that the combined statistics of skewness and kurtosis have moderate sensitivity of all alternatives under study, and they might be candidates for an omnibus test.
VISTA: A 704mW 4K-UHD CNN Processor for Video and Image Spatial/Temporal Interpolation Acceleration

Video convolutional neural networks (CNNs) have achieved great success in high-resolution imaging applications, such as video super-resolution (VSR) and demonstrated superior quality and temporal consistency by leveraging time information. In particular, as shown in Fig. 2.6. 1, video CNNs can also support applications like video-frame interpolation (VFI) which is difficult to achieve by single-image CNNs. Therefore, video CNNs have enormous potential for next-generation imaging/display technology. However, there are three design challenges while inferencing high-throughput video CNNs. Firstly, massive external memory access (EMA) and computation complexity are induced since they both grow accordingly as the number of input frames (N) increases. Secondly, tremendous memory usage of feature maps (FMs) is required for supporting cross-frame alignment with in-order frame scheduling. Thirdly, supporting deformable convolution (DC) for alignment costs extra line buffers for irregular samples and computation for bilinear interpolation (BI). In this work, we present a video CNN processor supporting diverse-application video CNN inference at 4K-UHD resolution and address the challenges through three key features: 1) a cuboid-based layer-fusion (CBLF) inference flow to reduce EMA and computation complexity; 2) an alignment-aware memory optimization technique to save the FM memory size; 3) a hardware-model co-design of tile-based offset-confined deformable convolution (TODC) to alleviate the overheads of induced FM line buffers and computation logics for DC.
A Non-Uniform Metasurface-Based Wideband CP Antenna With Additional Filtering Response

A low-profile circular-polarized (CP) metasurface antenna (MSA) with wide axial ratio (AR) bandwidth and additional filtering response is presented. The proposed MSA consists of an array of $4\times 4$ non-uniform corner-truncated patch units, and is excited by a filtering balun through two parallel coupling slots. An additional AR minimum can be produced by the non-uniform units resulting an improved AR bandwidth. The filtering balun with extra resonant stubs can produce three radiation nulls to form a bandpass filtering response. Simulated results indicate that the proposed filtering CP MSA has a fractional bandwidth of 17.7% for $\vert \boldsymbol{S}_{11}\vert < -10\text{dB}$ and AR < 3 dB, which can cover the 5G N79 band (4.4 - 5 GHz). Good bandpass filtering response with a flat in-band gain variation from 7.9 to 8.9 dBic and high out-of-band rejection levels of 31 and 27 dB in the lower and upper stopbands, respectively, are also observed.
Parallel Deployment of VNFs in Service Function Chain: Benefit or Not?

Network Function Virtualization (NFV) technology facilitates flexible and fast service provisioning by deploying network functions in wished positions and chaining order as Service Function Chain (SFC). Empirically, if the processing inside some Virtual Network Functions (VNFs) does not relate to each other, parallel chaining them can further improve the performance, such as reducing the end-to-end hops of SFCs. However, it is often ignored that parallel deploy VNFs will also introduce additional VNFs to replicate, distribute, and merge data. This will add hops and increase resource consumption. Will parallel deployment of VNFs in SFCs necessarily bring benefits? Regarding multiple parallel forms of an SFC, which one is the best? Further, how does the service process of the NFV network affect the parallel decisions of VNFs? In this paper, we design an NFV orchestrator, DP-NFVO, to maximize the revenue of NFV Service Providers and provide quantitative insights into the impact of parallel VNFs deploy-ment. Precisely, we first refine existing VNF parallelism rules and discuss the impact of VNF parallelism on latency and resource consumption, and analyze the optimal form of parallelism for SFCs. Second, we decompose the service request process in NFV networks into multiple stages and provide a joint model of VNF parallelism and deployment. Further, we design a VNF online deployment and parallelism decision policy for DP-NFVO based on Proximal Policy Optimization (PPO) to maximize revenue. Finally, we demonstrate the effectiveness and adaptability of DP-NFVO on parallel processing and the requests service process with extensive experiments.
Skin Cancer Prediction using Deep Learning Techniques

There is a growing need for early diagnosis of skin cancer because of the rapid growth rate of melanoma skin cancer, its high treatment costs and high mortality rate. The detection of skin cancer cells was usually done manually, and most cases require a lengthy cure. Currently the main problem in skin cancer detection is high misclassification rate and low accuracy. This paper provides a technique based on deep learning techniques to detect the cancer from skin images. Convolutional neural network-based model consisting of six layers with hidden layers is used in this work. The problem of low accuracy is addressed with the help of regularization technique and features are selected with the help of convolution method. To improve the accuracy of the model hyper parameter tuning along with model parameter tuning are performed. Publicly available dataset is used in the research which contains images with cancer and normal instances. The major steps in this work includes data collection, preprocessing, data cleaning, visualization, and model development. At the end a comparative analysis is performed with state-of-the-art techniques. The proposed model achieved good accuracy of 88% on HAM dataset as compared to state of the art techniques.
A Multipath Pairing Method Based on Matrix Pencil

In this paper, an improved pairing method is proposed to solve the multi-pole problem in the parameter estimation of matrix pencil (MP) algorithm and its derivatives in a multipath environment. This method distinguishes eigenvalues obtained by decomposing the spatial dimension matrix, further constructs eigenvalues of the time dimension, and finally updates eigenvalues. Simulation results show that the MP algorithm based on this method avoids the failed pairing caused by the multiplicity of eigenvalues and achieves more accurate parameter estimation.
Alongshore bed load transport on the shoreface and inner shelf

Bed load transport rates on the shoreface and shelf are determined by tidal currents, wave-current interaction and grain size. There is, however, a strong lack of field data and validated models because bed load transport under waves cannot be measured in the field, while bed load transport by currents without waves commonly is barely measurable in spring tidal conditions. Herein, bed load transports were carefully measured with a calibrated sampler in spring tidal conditions without waves at a water depth of 13-18 m in fine and medium sands at 2 to 8.5 km offshore the Dutch coast. Near-bed flow velocity was recorded at 2 Hz. The measurements are used to derive an empirical bed load model, in which transports are normalized by grain size and density. The model produces bed load transports that are at least a factor 5 smaller than predicted by existing models. However, they agree with a large laboratory data set of sand and gravel transport in currents near incipient motion. Cohesion of sediment due to mud in-mixing or biological activity was excluded. Including turbulence probabilistically in bed load models strongly improves predictions near incipient motion, and predict 20% more alongshore transport annually for currents only. The effect of wave-current interaction is predicted to be twice as large, and the combined effect results in 100% larger transports. The effect of wave stirring is gives much larger flood and ebb transports but the net transport is the same as for the combined wave-current interaction and turbulence case. An overestimation of the current velocity leads to much larger transports than any of the model combinations. Concluding, the effects of turbulence, wave-current interaction and wave stirring are of secondary importance compared to the choice of empirical or existing bedload predictor and the representation of the current climate
Baryon: Efficient Hybrid Memory Management with Compression and Sub-Blocking

Hybrid memory systems are able to achieve both high performance and large capacity when combining fast commodity DDR memories with larger but slower non-volatile memories in a heterogeneous way. However, it is critical to best utilize the limited fast memory capacity and slow memory bandwidth in such systems to gain the maximum efficiency. In this paper, we propose a novel hybrid memory design, Baryon, that leverages both memory compression and data sub-blocking techniques to improve the utilization of fast memory capacity and slow memory bandwidth, with only moderate metadata overheads and management complexity. Baryon reserves a small fast memory area to efficiently manage and stabilize the irregular and frequently varying data layouts resulted from compression and sub-blocking, and selectively commits only stable blocks to the rest fast memory space. It also adopts a novel dual-format metadata scheme to support flexible address remapping under such complex data layouts with low storage cost. Baryon is completely transparent to software, and works with both cache and flat schemes of hybrid memories. Our evaluation shows Baryon achieves up to 1.68× and on average 1.27× performance improvements over state-of-the-art designs.
Bioimpedance MR Scanning For Hypertension and Functional Experience

Present anatomy detection solutions generally use data mining techniques to use large annotated datasets to learn how anatomy is detected. These solutions are subject to various constraints, including the use of suboptimal techniques in function development and the influence of technologically inappropriate anatomy detection search schemes. To deal with those problems, a process pursuing a new model in which the identification problem is reformulated as an artificial agent learning task is suggested. The anatomy model and object study with deep strengthening learning with the capabilities of multi-scale image processing in a single behavioral context are integrated. This approach tested over 1 million image slices, demonstrating that, although it increased detection precision by 20–30 percent, it dramatically outperformed state-of-the-art solutions to identify different anatomical constructions without loss from a clinical acceptance perspective. By 2–100 magnitude orders for the detection speed in comparison methods, guaranteeing that large three-dimensional scans produce unparalleled real-time performance is strengthened.
Decomposing bit planes to reduce compression requirement for lossless channels

Segmentation of images to bit planes is one of the techniques for scalable image compression. Assuming our source image to be from a uniform quantizer, we categorize its bit planes on the basis of their significance, into two groups called MSB-planes and LSB-planes. The MSB planes contain low-entropy structural information, whereas LSB planes contain high entropy texture information. Due to the different nature of information and entropy of the two groups, they can be coded and reconstructed by different algorithms. The structural nature of MSB planes, make them more compressible at entropy coding stage, whereas the low significance of LSB-planes can be exploited against their high entropy. We realize the later by subsampling of LSB-planes, which in turn requires attention to the close coupling of MSB and LSB planes at the reconstruction stage. We introduce an estimation algorithm for the LSB planes. The reconstructed images are found to be perceptually comparable to the original images. Quantitative comparison also shows significant coding gain in terms of SNR vs. bit rate.
Physics-Informed Neural Networks for prediction of transformer’s temperature distribution

Physics-Informed Neural Networks (PINNs) are a novel approach to the integration of physical models into Neural Networks when solving supervised learning problems. PINNs have shown potential in mapping spatio-temporal input and the solution of a partial differential equation (PDE). However, despite their advantages for many applications, they often fail to train when target PDEs contain high frequencies or multi-scale features. Thermal modelling of power transformers is fundamental for improving their efficiency and extending their lifetime. In this work, we investigate the performance of different PINN architectures applied to a 1D heat diffusion equation with a specific heat source representing the heat distribution inside a transformer. Measurements, which include the top-oil temperature, the ambient temperature and the load factor are taken from a transformer in service. We demonstrate the limitations of PINNs, propose possible remedies, and provide an overall assessment of the potential of using PINNs for transformer thermal modelling.
Exploring Normalization Techniques in Neural Networks for Bitcoin Candlestick Price Prediction

Bitcoin is a high-risk asset with a potentially high return. Predicting Bitcoin candlestick, i.e., open, high, low, and close (OHLC) prices, can help investors make trading decisions. This paper aims to explore various data normalization techniques in neural networks to enhance candlestick price prediction. In this study, the sliding window normalization techniques were compared with the whole set normalization techniques for forecasting the daily Bitcoin OHLC prices using two neural network algorithms: Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). The investigated normalization techniques for both the whole set and the sliding window set included z-score normalization, min-max normalization, and relative change normalization. Historical OHLC prices over several days were used to predict the next day's OHLC prices. The results show that the sliding window normalization techniques outperformed the whole set normalization techniques in terms of RMSE and MAPE with the best technique being the GRU algorithm using the sliding window relative change normalization achieving MAPE of 2.25% and RMSE of 870.52.
Establishing a Generalizable Framework for Generating Cost-Aware Training Data and Building Unique Context-Aware Walltime Prediction Regression Models

This paper describes a generalizable framework for creating context-aware wall-time prediction models for HPC applications. This framework: (a) cost-effectively generates comprehensive application-specific training data, (b) provides an application-independent machine learning pipeline that trains different regression models over the training datasets, and (c) establishes context-aware selection criteria for model selection. We explain how most of the training data can be generated on commodity or contention-free cyberinfrastructure and how the predictive models can be scaled to the production environment with the help of a limited number of resource-intensive generated runs (we show almost seven-fold cost reductions along with better performance). Our machine learning pipeline does feature transformation, and dimensionality reduction, then reduces sam-pling bias induced by data imbalance. Our context-aware model selection algorithm chooses the most appropriate regression model for a given target application that reduces the number of underpredictions while minimizing overestimation errors.
Entropy Model of Nanoregions in Relaxor Ferroelectrics

Based on characteristics of polar nanoregions in relaxor ferroelectrics and cooperatively rearranging regions in super-cooled liquid, the identical physics process is considered that the added foreign molecules will restrain the phase transition accompanied with an intermediated state of congregated polar nanoregions. The normal molecules possess tendency to aggregate together below transition temperature. The analyzed result supposes that the polar nanoregions have higher local Curie temperature. The derived results indicate that the temperature dependent change in magnitude of the polar nanoregions causes dielectric dispersion, and applied dc electric field will restrict the change to diminish the dielectric dispersion.
A Frequency Hopping Phase-Based Indoor Tracking System using Particle Filter for Passive RFID Tags

In this paper, a particle filter (PF) based RFID tag tracking method is designed. Based on the tag phase information, the multiple signal classification algorithm (MUSIC) and the Chinese remainder theorem (CRT) algorithm are designed to obtain the angle and distance information respectively, and the PF algorithm is designed through the angle and distance information model. Simulation results show that the system can achieve a median tracking error of 1.94cm.
Terahertz Dual-band Coding Metasurface for Vortex beam Manipulation

In this paper, a dual-band 3-bit coding metasurface for vortex beam manipulation is proposed. The unit cell of single-layer coding metasurface consists of two orthogonal split ring resonators (SSRs) for two operating frequencies. The dual-band two polarization reflected waves can be separately manipulated by the structure of SRRs. Based on Jones matrix theory, a dual-band 3-bit reflective metasurface is designed to generate two $l=1$ vortex beams at 0.9 THz for the x-polarized incident wave, as well as, a vortex beam and four plane waves at 0.6 THz for the y-polarized incident wave.
Demonstration of an 1200V/20A 4H-SiC Multi-Step Trenched Junction Barrier Schottky Diode

In this study, we have realized a novel 1200 V 4H-SiC Multi-step trenched junction barrier Schottky (MTJBS) diodes with ultra-low leakage current. The MTJBS diode substantially alleviates the tradeoff dilemma between forward voltage drop and blocking voltage, thus enabling a larger design window that effective reverse blocking and excellent forward conduction can be achieved at the same time. Design considerations and device performances of the Multi-step trenched JBS devices are compared with those of the conventional planar JBS diodes via numerical simulation and analyses. It is demonstrated that the shielding effect of the conventional structure under high voltage remain effective only when the junction spacing is narrow (n<2μm), while the surface electric field of 4H-SiC Multi-step trenched JBS diode is decreased significantly by the deep barrier shielding layer. The surface electric field is only 0.18–0.95 MV/cm2 for 4H-SiC Multi-step trenched JBS diode with a varied junction spacing from 1 to 3 μm. When the surface electric field reach 1MV/cm2, the junction spacing of Multi-step trenched JBS diode is wider to 3 μm in comparision with 0.7 μm junction spacing of the conventional JBS diodes. Moreover, first demonstration of the MTJBS prototype fabrication and device characterization are also detailed in this study. The reverse leakage current with the MTJBS structure has been reduced by 2 orders of magnitude compared to the conventional planar JBS structure.