Miracle-dz/UniFAR-FTUs huggingface dataset:
VORTICAL MOTIONS OF BARYONIC GAS IN THE COSMIC WEB: GROWTH HISTORY AND SCALING RELATION

The vortical motions of the baryonic gas residing in large-scale structures are investigated by cosmological hydrodynamic simulations. Proceeding in the formation of the cosmic web, the vortical motions of baryonic matter are pumped up by baroclinity in two stages, i.e., the formation of sheets and filaments. The mean curl velocities are about <1, 1–10, 10–150, and 5–50 km s−1 in voids, sheets, filaments, and knots at z = 0, respectively. The scaling of the vortical velocity of gas can be well described by the She–Leveque hierarchical turbulence model in the range of l < 0.65(1.50) h−1 Mpc in a simulation with a box of size 25(100) h−1 Mpc. The fractal Hausdorff dimension of vortical motions, d, revealed by velocity structure functions, is ∼2.1–2.3(∼1.8–2.1). It is slightly larger than the fractal dimension of mass distribution in filaments, Df ∼ 1.9–2.2, and smaller than the fractal dimension of sheets, Ds ∼ 2.4–2.7. The vortical kinetic energy of baryonic gas is mainly transported by filaments. Both scalings of mass distribution and vortical velocity increments show distinctive transitions at the turning scale of ∼0.65(1.50) h−1 Mpc, which may be closely related to the characteristic radius of density filaments.
Deep Learning-Assisted Video Compression Framework

Deep learning has shown its great potential in video coding. Lots of research has been done regarding deep tools used within traditional schemes or end-to-end deep codec. In this paper, we propose multiple novel deep tools focusing on intra prediction, motion compensation refinement, virtual reference frame, and post-processing and form a deep learning-assisted video compression framework. For intra prediction, a novel data clustering-driven neural network (DCDNN), which could learn deep features of the clustered data, is proposed to assist intra angular modes. For inter prediction, a neural network-based motion compensation refinement (NNMCR) algorithm is applied to enhance the motion compensation. Meanwhile, a V-RF network is employed to generate a more reliable reference frame that can characterize complex motions in natural video signals. For post-processing, a quality adaptive neural network-based in-loop filter (QANNLF) is designed to utilize Quality Parameter (QP) information in the encoder to adjust filter strength for reconstructed videos with various qualities. Our method can obtain on average 14.83% BD-rate saving for the luma component under the all intra configuration and 12.16% under the random access configuration compared with HEVC reference software 16.9.
In-situ infrared spectroscopy for chemical analysis in electronic packaging processes

Epoxy resins are commonly used for the encapsulation of electronic components in electronic packaging processes. The chemical crosslinking process and therefore the curing of these resins during processing have a significant influence on the production and finally on the quality of the components. To follow the chemical changes during the process, a fourier-transform infrared (FT-IR) sensor was integrated into a molding press to measure in-situ infrared spectra during the process. The feasibility and reproducibility of this measurement method is presented. The spectra generated in the process show changes in absorption bands around 916 cm-1and 1230 cm-1equivalent to laboratory measurements, indicating the chemical crosslinking process. Several tests show that the repeatability of this measurement method is very good. Further, the influence of different molding temperatures on the generated infrared spectra was investigated, and no significant change in the width, intensity or shift of the bands could be detected. However, the crosslinking of the material is affected by temperature, with low temperatures leading to a slower crosslinking process. With the use of a model, it is possible to estimate the residual enthalpy of the material based on the spectra, which allows subsequent process optimization.
An FPGA-based HW/SW Co-Verification Environment for Programmable Network Devices

Bugs in network devices translate to financial losses for the service providers and degrade the quality of experience for the users. Simulation tools cannot guarantee complete fault coverage as bugs can manifest at any time in live hardware. To mitigate these issues, we propose a novel hardware/software (HW/SW) co-verification tool that targets programmable dataplane network devices. The system integrates cycle-accurate software simulation with a hardware implementation. For the software simulation, open-source tools such as CocoTB and GHDL were used. The Design Under Test (DUT) and our test interfaces are embedded in programmable hardware. Data from the software can be inserted and then extracted in real-time from the input/output (I/O) ports of the DUT. To achieve this functionality the hardware design uses data insertion and extraction blocks which also support assertions. For the hardware implementation, reported experiments have been conducted on a NetFPGA-SUME platform. When a packet flows through the NetFPGA and triggers an assertion, the data present in the DUT at that time can be captured, and sent back to the simulator for further analysis and replay. Each of our design block consumes less than 1% of the available resources on the FPGA.
Noise kernel for the self-similar Tolman-Bondi metric: Fluctuations on the Cauchy horizon

We attempt to calculate the point separated Noise Kernel for self similar Tolman Bondi metric, using a method similar to that developed by Eftekharzadeh et. al for ultra-static spacetimes referring to the work by Page. In case of formation of a naked singularity, the Noise Kernel thus obtained is found to be regular except on the Cauchy horizon, where it diverges. The behavior of the noise in case of the formation of a covered singularity is found to be regular. This result seemingly renders back reaction non-negligible which questions the stability of the results obtained from the semiclassical treatment of the self similar Tolman Bondi metric.
PRODEP: Smart Social Media Procrastination and Depression Tracker

Procrastination refers to the voluntary delay of urgent tasks and can have several negative consequences such as stress, health issues and academic underachievement [47]. It is viewed within physiological research as a self-regulation failure [48]. Similar to procrastination, another severe problem which comes up within lots of people including students and teenagers is “Depression”. Depression is a massively widespread problem among people around the world as well as in Sri Lanka [49]. As a result of procrastination and depression, students has to face academic underachievement. One of the main cause of these widespread problems are Social media over-usage [50]. Therefore this paper presents a new tracker which presented as a mobile application with four main components. This research study is about identifying and tracking users’ facial emotions and eye-aspect ratio to analyze real emotions of the user via device inbuilt webcam to identify user fatigueness and procrastination. This study also analyzes user behavior in two selected social media platforms which are Facebook and Twitter and identifies the negativity and depressiveness of “Sinhala” content using Machine learning based Sentiment analysis approaches. Also as a companion, this paper introduces a chat-bot which communicates with the user in “Singlish” language. Our final products will be a complete mobile application which generates reports to the user based on the analysis done in the four components. As future work we will introduce AutoML approaches instead of traditional machine learning based approaches.
NNASIM: An Efficient Event-Driven Simulator for DNN Accelerators with Accurate Timing and Area Models

In this paper, we propose NNASIM, an efficient timing and area accurate event-driven simulator for custom DNN accelerators. NNASIM is a highly-modular and highly parameterized modeling framework. We build accurate timing and area models for common accelerator modules like GEMM, ALU array, and crossbar using ASIC synthesis flows. These models are fed into the event-driven simulator for fast simulation. NNASIM is integrated with a RISC-V simulator. This approach guarantees the functional correctness of the accelerator simulation at the instruction level. The experimental results show that our model evaluates the performance and area of DNN accelerators with less than 0.76% and 2.83% error, respectively, compared to RTL implementations. NNASIM allows designers to model the performance and area of the accelerator at a high level, and thus enables the systematic microarchitecture design space exploration of the custom accelerators. Index Terms accelerators.
Characterization of Sub-Nyquist TIA with Equalization in Optical Receivers

High-sensitivity optical receivers enable energy-efficient solutions for high-speed optical interconnects. In this article, an analog front-end (AFE), employing a sub-Nyquist CMOS-based trans-impedance amplifier (TIA) with post-TIA equalization, is characterized to give an insight on how to design the AFE for optimizing receiver sensitivity and energy-efficiency. Thus, maximizing SNR is targeted. Sub-Nyquist TIA design tradeoffs were analyzed to study the BW limitation impact on linearity and noise. The characterization utilizes idealized NRZ/PAM-4 samplers with DFE/FFE to analyze the equalization complexity. Optimum SNR is reported for different equalizers, specifying the relaxation achieved in TIA BW requirement with respect to the desired data-rate.
A Mismatch Compensation Scheme for Cyclic-pipelined ADC via Dynamic Element Matching Technique

The inevitable capacitor mismatch due to the process variation is one of the major bottlenecks in high-resolution ADC (Analogue to Digital Converter) design. In this work we propose a new compensation strategy which adopts the DEM (Dynamic Element Matching) technique to conventional cyclic ADCs with minimum hardware cost. Theoretical analysis has been done first, and to the best of authors’ knowledge, it is the first time that the relation of the capacitor mismatch, output deviation and THD (Total Harmonic Distortion) has been mathematically established. Consequently, the statistical distribution of expected THD under certain mismatch can be derived. Based on that we further analyse the effect of DEM in frequency domain, which effectively performs a harmonic shaping function to the original output spectrum. Simulation results show that the DEM compensation strategy can effectively boost the worst SINAD (Signal to Noise and Distortion Ratio) Figure from 68.8 dB to 89.5 dB (99.7% yield) under 0.1% capacitor mismatch and 32× oversampling ratio scenario.
Performance Evaluation of Conventional Spectrum-Allocation Policies for C+L Band Elastic Optical Networks

Multiband elastic optical network (MB-EON) technology is a promising solution to support the exponential traffic growth in optical backbone networks. For C-band-only systems, optimal routing and spectrum allocation (RSA) policies have been investigated in the context of EON to enhance its spectrum utilization. However, for a multiband system, the same allocation strategies may not provide the best performance due to the presence of heterogeneous, complex, non-linear, physical-layer impairments (PLI), such as inter-channel stimulated Raman scattering (ISRS). This paper investigates the performance of the state-of-the-art spectrum-allocation policies in different geographies with different channel launch power for C+l band systems. Simulation results show that the Last-Fit policy performs best in terms of traffic admissibility, blocking probability, resource utilization, contiguous aligned available slot ratio (CAASR), and quality of established lightpaths.
Sizing a wind pumped storage hybrid power station for energy sufficiency of Leros’ island

RES penetration increase in power systems is beneficial in several aspects, such as the reduction of greenhouse gas emissions, cost of electricity, and energy dependence on fossil fuels. At the same time, the operation of electrical systems with high RES penetration results in several technical challenges, such as frequency deviations, due to the variability of their primary source of energy. Such issues are more evident in isolated power systems, like the Non-Interconnected Islands (NII) in Greece, where distortions in their power equilibrium lead to relatively large frequency deviations, due to the small inertia and lack of interconnections with larger power systems. In addition, RES development in island systems faces important restrictions, due to the inability to absorb the full potential of RES production during low load time periods. An efficient solution in increasing RES penetration in NIIs, without compromising their power quality, is the utilization of energy storage systems, such as battery energy storage systems or pump-hydro storage plants (PHSPs). This paper proposes fully controllable energy storage systems combined with RES units, which constitute the so-called "hybrid power stations", which can adequately replace the existing conventional units.
An Event-driven Spiking Neural Network Accelerator with On-chip Sparse Weight

Spiking neural networks (SNNs) have widely drew attention of recent research. With brain-spired dynamics and spike-based communication, SNN is supposed to be a more energy-efficient neural network than existing artificial neural network (ANN). To make better use of the temporal sparsity of spikes and spatial sparsity of weights in SNN, this paper presents a sparse SNN accelerator. It adopts a novel self-adaptive spike compressing and decompressing (SASCD) mechanism for different input spike sparsity, as well as on-chip compressed weight storage and processing. We implement the octa-core design on field programmable gate array (FPGA). The results demonstrate a peak performance of 35.84 GSOPs/s, which is equivalent to 358.4 GSOPs/s in dense SNN accelerators for 90% weight sparsity. For the single-layer perceptron model in rate coding implemented on the hardware, SASCD reduces the time step intervals from 2.15 $\mu$ s to 0.55 $\mu$ s.
Equilibrium Propagation and (Memristor-based) Oscillatory Neural Networks

Weakly Connected Oscillatory Networks (WCONs) are bio-inspired models which exhibit associative memory properties and can be exploited for information processing. It has been shown that the nonlinear dynamics of WCONs can be reduced to equations for the phase variable if oscillators admit stable limit cycles with nearly identical periods. Moreover, if connections are symmetric, the phase deviation equation admits a gradient formulation establishing a one-to-one correspondence between phase equilibria, limit cycle of the WCON and minima of the system’s potential function. The overall objective of this work is to provide a simulated WCON based on memristive connections and Van der Pol oscillators that exploits the device mem-conductance programmability to implement a novel local supervised learning algorithm for gradient models: Equilibrium Propagation (EP). Simulations of the phase dynamics of the WCON system trained with EP show that the retrieval accuracy of the proposed novel design outperforms the current state-of-the-art performance obtained with the Hebbian learning.
A Compact Low-Noise Digital Pixel with 15-bit Two-Step PFM-based ADC for IRFPAs

A compact low-noise digital pixel with 15-bit pixel-level two-step analog-to-digital converter (ADC) is presented in this paper. The coarse quantization adopts a 10-bit pulse frequency modulation conversion with a proposed threephase charge-reset circuit. The charge-reset circuit reduces the reset noise charge which is the major noise of this pixel. The fine quantization is implemented by a 5-bit single-slope ADC. It reuses most of the coarse quantization circuit to realize high precision quantization in compact pixel. A 640×512 readout integrated circuit for infrared focal plane array with the 15μm pitch digital pixel is designed in the 0.18 μm1P5M CMOS process for verification. Post-layout simulation results of the digital pixel show that the RMS noise at maximum signal is 0.51LSB, and the integral nonlinearity is +2.3LSB/-3.0LSB. The power consumption per pixel is only 57. 2nW at 120fps.
Spatial incoherence of solar granulation: a global analysis using BiSON 2B data

A poor understanding of the impact of convective turbulence in the outer layers of the Sun and Sun-like stars challenges the advance towards an improved understanding of their internal structure and dynamics. Assessing and calibrating these effects is therefore of great importance. Here we study the spatial coherence of granulation noise and oscillation modes in the Sun, with the aim of exploiting any incoherence to beat-down observed granulation noise, hence improving the detection of low-frequency p-modes. Using data from the BiSON 2B instrument, we assess the coherence between different atmospheric heights and between different surface regions. We find that granulation noise from the different atmospheric heights probed is largely incoherent; frequency regions dominated by oscillations are almost fully coherent. We find a randomised phase difference for the granulation noise, and a near zero difference for the evanescent oscillations. A reduction of the incoherent granulation noise is shown by application of the cross-spectrum.
A Low Power DNN-based Speech Recognition Processor with Precision Recoverable Approximate Computing

This paper proposes a low power speech recognition processor based on an optimized DNN with precision recoverable approximate computing. In order to accelerate and improve energy utilization of DNN, an approximate multiplier based on cartesian genetic programming with weight pre-classification and mismatch compensation is proposed. A partial retraining scheme based on approximate noise is proposed to recover the accuracy loss caused by approximate computing. Experimental results show that the proposed approximate multiplier reduces power consumption by 42.9%, and the partial retraining scheme can recover accuracy of 3.03%~4.34%. Implemented under 22nm, the proposed processor can support the recognition of 10 keywords under different noise types and signal-to-noise ratios (5dB~clean), while the recognition accuracy is 83.36% ~89.82% and power consumption is 8. 6μ W.
A High-Speed ADC for a Multi-Band 5G V2X Wireless Receiver

This paper presents a radio architecture for 5G Vehicle to Everything (V2X) applications. A frequency planning suitable for the aforementioned radio architecture is proposed. A programmable Time-Interleaved (TI) Successive Approximation Register (SAR) Analog to Digital Converter (ADC) required for this radio architecture is presented. The effect of process and temperature (PT) variations on the performance of Monotonic SAR ADC is investigated. A calibration technique is proposed to account for PT variations in the Monotonic SAR ADC. Additionally, simulation results for TI SAR ADC designed in 22nm FDSOI which works at two different sampling frequencies for V2X applications are presented.
The Missing Satellite Problem Outside of the Local Group. I. Pilot Observation

We present results from a pilot observation of nearby (∼20 Mpc) galaxies with masses similar to that of the Milky Way (MW) to address the missing satellite problem. This is the first paper from an ongoing project to address the problem with a statistical sample of galaxies outside of the Local Group (LG) without employing an assumption that the LG is a typical halo in the universe. Thanks to the close distances of our targets, dwarf galaxies around them can be identified as extended, diffuse galaxies. By applying a surface brightness cut together with a careful visual screening to remove artifacts and background contamination, we construct a sample of dwarf galaxies. The luminosity function (LF) of one of the targets is broadly consistent with that of the MW, but the other has a more abundant dwarf population. Numerical simulations by Okamoto seem to overpredict the number of dwarfs on average, while more recent predictions from Copernicus Complexio are in better agreement. In both observations and simulations, there is a large diversity in the LFs, demonstrating the importance of addressing the missing satellite problem with a statistically representative sample. We also characterize the projected spatial distributions of the satellites and do not observe strong evidence for alignments around the central galaxies. Based on this successful pilot observation, we are carrying out further observations to increase the sample of nearby galaxies, which we plan to report in our future paper.
Alternative Methods of Translational Gains by Viewpoint Manipulation in the Pitch Direction

This paper proposes a new method of translational manipulation for redirected walking through pitch-oriented viewpoint manipulation, which is known to change the walking speed of a user. Pitch viewpoint manipulation adds a slope to the virtual reality environment. In reality, walking on a slope increases or decreases the walking speed, and this study conducts an investigation based on the assumption that similar fluctuations would occur in a virtual reality environment. Through an experiment, the variation in walking speed by dynamically manipulating the viewpoint in the pitch direction for a 5 m walking section, and the perception threshold, which is the range in which the viewpoint can be manipulated without being noticed by the user, were investigated. The results showed that the walking speed significantly decreased with pitch gains of -0.5° in the downward direction, and 3° and 5° in the upward direction. It was discovered that perceptual thresholds of 0.17° and -1.12° in the upward and downward directions, respectively, were imperceptible, indicating that the viewpoint could be manipulated without being perceived.
Frequency Compensation Scheme for a Full GaN OpAmp driving 1-nF load

This paper presents a frequency compensation scheme of a full GaN operational amplifier for smart power applications. The amplifier is based on a previous topology originally developed for nMOS technology and here adapted for a modern GaN process. The solution is able to drive a capacitive load as high as 1 nF and a suitable design strategy has been developed. The operational amplifier exhibits a very high nominal DC gain of about 135 dB, a unity-gain bandwidth of about 560 kHz with 60° phase margin, a slew rate of about 0.83 V/$\mu$s and a nominal quiescent current consumption of 200 $\mu$A from a 6-V supply.
ConfAx: Exploiting Approximate Computing for Configurable FPGA CNN Acceleration at the Edge

The number of CNN-based applications executing at the Edge has been considerably increasing. Considering that CNNs are recognized error-resilient and the varied Edge conditions, we exploit hardware-level Approximate Computing to optimize FPGA-based CNN accelerators without any model retraining or other modifications. Given that, we propose ConfAx, a fully configurable multi-target Framework that navigates the accuracy-performance-resource trade-off to deploy different versions of CNN FPGA approximate accelerators. With an Edge case study (video surveillance), we show that ConfAx reduces power (up to $1.65\times)$ and energy (up to $ 1.44\times$) over a state-of-the-art accelerator at minor accuracy penalties (0.88% on average).