Miracle-dz/UniFAR-FTUs huggingface dataset:
Extraction of Mobility in 3-D NAND Flash Memory with Poly-Si Based Macaroni Structure

In this paper, mobility model for poly-Si based macaroni structure in 3-D NAND Flash memory is presented. String current was calibrated with technology computer-aided design (TCAD) simulation at room temperature (25°C) with U-shaped trap distribution. Then, unit word line (WL) I-V curve was extracted, and effective mobility was extracted from these data using analytical equation. Mobility model is based on Meyer-Neldel Rule (MNR). We extracted grain boundary barrier height $(\varphi_{\mathrm{b}})$ and fitted with our model. Finally, we fitted effective mobility $(\mu_{\mathrm{eff}})$ with our model and fitted string current with SPICE simulation.
An implementation of the M-tree index structure for PostgreSQL using GiST

Several index structures are competing for efficient operations on various types of data, but there are some forms of information that cannot fit into the existing models, because by forcing them into one of the currently available representations they either lose some of their value or they do not have the necessary properties which would allow them to be used in the first place. In light of this, we implemented the M-tree [1] [2] index structure under the PostgreSQL database management system through a GiST [3] extension to facilitate efficient range-based queries and k-nearest neighbor searches on data that resides only in a metric space. The M-tree structure has parts that require heuristics, of which the implementations are not entirely specified, only suggestions are given. This allows flexibility for adapting to different situations and leaves space for further improvements. We implemented several of the strategies proposed in the original paper alongside some of our own ideas and saw significant speed gains when compared to a sequential scan, our main reference, as the other built-in index structures are not applicable to metric spaces.
Multiple-Relaxation-Time Lattice Boltzmann Model for Squeeze Film Air Damping of Large Knudsen Number in MEMS

The study of squeeze-film damping (SFD) of micro-resonant devices under large Knudsen (Kn) number is of great significance for the design of micro-nano devices. In the case of large Kn number, the Reynolds equation based on the continuum assumption is no longer applicable, so new methods are needed to research SFD. In this paper, a Multiple-Relaxation-Time Lattice Boltzmann Model (MRT-LBM) is established for the SFD of rectangular cantilever in transition regime, and the quality factor results are compared with experimental results together with the results of Monte Carlo (MC) model and FEM simulation. Moreover, the effects of Kn number and the size of the structure are studied. The results show that the MRT-LBM model is consistent with the experimental results under various conditions, and the MC model and FEM simulation show large deviations.
PILDroid: A System for Detecting the Leakage of Privacy Information using the JNI

We live in a period of explosive growth of smart device applications. Specifically, the growth rate of Android applications is amazing. And these Android applications often use JNI (Java native interface). However, research on the leakage of private information using JNI is lacking. In this paper, we propose a system for detecting the leakage of private information using JNI. Our system named PILDroid adopts tainted analysis based on static method. PILDroid can perform the analysis more easily and effectively than the assembly-based analysis systems because it decompiles a JNI into LLVM IR instead of assembly language. And this paper demonstrates an experiment to verify the precision and performance. For the test data, we selected five Android applications: three of which are well known malware, two of which are malware made by us. And experiment results, PILDroid determined that there has a leak of privacy information flow from five malware.
Large Astronomical Time Series Pre-processing and Visualization for Classification using Artificial Neural Networks

Time series analysis is a growing issue in multiple fields of science. One of the most common task is looking for hidden periods in time series data sets. In this work, we have chosen two large astronomical time series collections from BRITE and Kepler K2 projects and analyzed possible approaches for hidden periods search and their classification. Since these data sets are generally large, we were looking for some automated solution based on artificial neural networks that requires some data pre-processing. This work therefore brings a brief overview of possible solutions for looking for hidden periods in astronomical time series with use of pure artificial neural networks or together with more conventional statistical approaches - mainly from a data pre-processing and its visualization point of view.
Feature Extraction and Classification Method of Mine Microseismic Signals Based on CEEMDAN_SE

Since the mine microseismic and blasting signals are non-linear and non-stationary, the traditional linear analysis methods cannot effectively classify the microseismic and blasting signals. In this study, a method for feature extraction and classification of mine microseismic signals based on CEEMDAN_SE (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise_Sample Entropy) is proposed. Firstly, the microseismic of rock mass and blasting vibration signals are decomposed by CEEMDAN. Then five sensitive IMF components of IMF4~IMF8 (microseismic) and IMF1~IMF5 (blasting) are screened out by using correlation coefficient. Next, the sample entropy values of the sensitive IMF components are calculated. Finally, the sample entropy is used as the eigenvector and input into ELM (extreme learning machine) to classify and identify the microseismic and blasting signals. The results show that there is a significant difference between the sample entropy values of microseismic and blasting signals. Combining CEEMDAN_SE with ELM, the accuracy of classification, recognition of microseismic and blasting signals is 91.5%. Compared with EEMD_SE_ELM and EMD_SE_ELM, the classification, recognition accuracy of CEEMDAN_ SE_ELM is improved by 2.5% and 5%, respectively. Therefore, this method provides a new idea for the classification and recognition of mine microseismic signals.
Mixed-Signal Vector-by-Matrix Multiplier Circuits Based on 3D-NAND Memories for Neurocomputing

We propose an extremely dense, energy-efficient mixed-signal vector-by-matrix-multiplication (VMM) circuits based on the existing 3D-NAND flash memory blocks, without any need for their modification. Such compatibility is achieved using time-domain-encoded VMM design. We have performed rigorous simulations of such a circuit, taking into account non-idealities such as drain-induced barrier lowering, capacitive coupling, charge injection, parasitics, process variations, and noise. Our results, for example, show that the 4-bit VMM of 200-element vectors, using the commercially available 64-layer gate-all-around macaroni-type 3D-NAND memory blocks designed in the 55-nm technology node, may provide an unprecedented area efficiency of 0.14 pm2/byte and energy efficiency of ~11 fJ/Op, including the input/output and other peripheral circuitry overheads.
Q-learning Based Backup for Energy Harvesting Powered Embedded Systems

Non-volatile processors (NVPs) are used in energy harvesting powered embedded systems to preserve data across interruptions. In NVP systems, volatile data are backed up to non-volatile memory upon power failures and resumed after power comes back. Traditionally, backup is triggered immediately when energy warning occurs. However, it is also possible to more aggressively utilize the residual energy for program execution to improve forward progress. In this work, we propose a Q-learning based backup strategy to achieve maximal forward progress in energy harvesting powered intermittent embedded systems. The experimental results show an average of 307.4% and 43.4% improved forward progress compared with traditional instant backup and the most related work, respectively.
An Accurate Structure Generation and Simulation of LER affected NWFET

Gate-All-Around (GAA) structures have shown great potential for replacing FinFETs in sub-10 nm technology node by facilitating a better gate control and improved short channel effects. Despite having an excellent gate control, the nanowire FETs (NWFETs) are prone to process-induced structural variability. Out of all, lithography and etching induced line edge roughness (LER) is one of the dominant sources of variability in sub 10 nm technology node GAA devices such as NWFET and nanosheet FET (NSFET). In this paper, we present a methodology to recreate an accurate NWFET surface morphology, where we generate NWFET surface profile using 2D ACF for a given LER variability parameters in MATLAB, and we incorporate the NWFET surface information in the Sentaurus Device Editor software using PERL interfacing to generate the final NWFET device structure. We generate 200 such random structures and simulate it all in a well-calibrated Sentaurus workbench deck. The proposed methodology can also be used to study the LER like variability in NSFET structures.
Electrical Impedance Tomography Data Acquisition Emulation

Electrical impedance tomography heavily relies on the electrode configuration and employed reconstruction algorithm, apart from precise bioimpedance hardware. In this paper, the debugging framework for algorithm refinement is considered. The lack of ability to test an algorithm before its implementation in a hardware-software complex crucifies the development and the tuning of electrical imaging systems. The study provides a universal electrical impedance tomography validation framework, regarding real-world problems (i.e. noise), independent of any modalities and applications. We tested the accuracy of COMSOL Multiphysics computer-aided design system for electrical studies. The results point out its agreement with analytical estimations of models within a margin of error. Furthermore, we implemented the emulation of real-world bioimpedance data gathering for algorithm tuning and development. Our results showed that despite data being artificial, it is possible to evaluate the elapsed time to digitally process “recorded” signals and to run a reconstruction algorithm. This study's methods may be used in any electrical impedance tomography modalities mainly to test and evaluate the software part of the system.
Osmosensitivity Phenotypes ofAgrobacterium tumefaciens Mutants ThatLackPeriplasmic P-1,2-Glucan

media. Strains withmutations inthechvA orchvBgenedonotaccumulate (3-1,2-glucan intheir periplasm andexhibit pleiotropic phenotypes, including inability toformcrown gall tumors on plants. We examined theeffects ofmediumosmolarity todetermine whether some or allofthese phenotypes result fromsuboptimal periplasmic osmolarity. Themutants grew more slowly thanwild-type cells andexhibited altered periplasmic andcytoplasmic protein content when cultured inlow-osmotic-strength media,butnotwhencultured inhigh-osmotic-strength media.These observations support a roleforperiplasmic glucan inosmoadaptation. However, themutants were avirulent andexhibited reduced motility regardless oftheosmolarity ofthemedium.Therefore, P-1,2-glucan may play roles invirulence andmotility that areunrelated toitsrole inosmoadaptation.
The First Full-size RC Vehicle: Collecting Bangladeshi Road-traffic Data and Doing Research on Autonomous Three Wheeler

The Autonomous machineries are changing our thinking level day by day. Autonomous driving system is a field of likelihood of computer vision research. The flourished countries has already come to the fore and using semi autonomous driving vehicle. They constructed the driving model as their eco-friendly and with their road traffic data. But in Indian sub-continent countries like Bangladesh, there has a little research work and implementation on it. In our research we have made a full size vehicle where we can set up our sensors, cameras and other hardwares to collect the visual training data to train any kind of vision based autonomous vehicle algorithms. We also proposed a CNN model to classify the real-time objects with our dataset. Finally, tested the trained model using the vehicle in real environment and got expected output. Here we used a rickshaw (Bangladeshi traditional three wheeler) as our base vehicle and use some custom made hardware to full-fill our purpose.
Deep Learning for Automatic Colorization of Legacy Grayscale Aerial Photographs

Legacy grayscale aerial photographs represent one of the main available sources for studying the past state of the environment and its relationship to the present. However, these photographs lack spectral information thereby hindering their use in current remote sensing approaches that rely on spectral data for characterizing surfaces. This article proposes a conditional generative adversarial network, a deep learning model, to enrich legacy photographs by predicting color channels for an input grayscale image. The technique was used to colorize two orthophotographs (taken in 1956 and 1978) covering the entire Eurométropole de Strasbourg. To assess the model's performances, two strategies were proposed: first, colorized photographs were evaluated with metrics such as peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM); second, random forest classifications were performed to extract land cover classes from grayscale and colorized photographs, respectively. The results revealed strong performances, with PSNR = 25.56 $\pm$ 2.20 and SSIM = 0.93 $\pm$ 0.06 indicating that the model successfully learned the mapping between grayscale and color photographs over a large territory. Moreover, land cover classifications performed on colorized data showed significant improvements over grayscale photographs, respectively, +6% and +17% for 1956 and 1978. Finally, the plausibility of outputs images was evaluated visually. We conclude that deep learning models are powerful tools for improving radiometric properties of old aerial grayscale photographs and land cover mapping. We also argue that the proposed approach could serve as a basis for further developments aiming to promote the use of aerial photographs archives for landscapes reconstruction.
Classification of Discrete Sequential Protocol Messages Based on LSTM Network and Transfer Learning

Discrete Sequential Protocol Messages (DSM) is the main data component in some communication networks such as Internet of Things, which is usually short, length-variable and unlabeled. This paper proposes a method based on long short-term memory (LSTM) for supervised classification of encrypted DSM data. And for unsupervised classification, the LSTM-TL method is utilized by transferring the pre-trained LSTM model on source domain to the target domain. Experiments were carried out on Text, ACARS, HTTP & SSH and AIS datasets. For supervised classification, F-Score and accuracy both reached 0.96. For unsupervised classification, FScore and accuracy were both greater than 0.96 on the first three datasets, while only 50% and 67% respectively on AIS dataset. The reason was analyzed by using dynamic time warping. The results also show that the data length can be processed is shorten to 10 bytes by processing DSM as one-dimensional time series while the traditional methods often require much more bytes.
Starch and sugar conversion in Dioscorea esculenta tubers and Curcuma longa rhizomes during storage

An investigation was carried out to reveal the changes in the concentration of soluble carbohydrates under tuber dormancy in food yam (Dioscorea esculenta) tubers and in an economically important spice cum medicinal plant turmeric (Curcuma longa) rhizomes under storage. Harvested, fully matured tubers of yam and rhizomes of turmeric were stored in wooden boxes under the conditions of 28 ± 2°C temperature and 65 to 75% relative humidity in dark. The moisture content, dry weight, starch, sugars, organic acids and respiration were studied in the tubers during 1 to 7 weeks after storage individually. This investigation revealed that the moisture content, dry weight and starch levels decreased gradually with a concomitant increase in sugar content under different stages of dormancy.
Versatile rotary actuators for small-scale robotic systems

Modern robotics has been greatly facilitated by the standardized tool sets surrounding actuation and sensing for human-scale systems, with the design and control techniques for electromechanical motors serving as a prime example. No such tool sets have become standard at the millimeter- and micrometer-scale however, often leaving actuation and sensing as bottlenecks when developing small-scale robotic systems. In this paper, we develop manufacturing and algorithmic tools for a promising class of small-scale rotary actuator – rotary pouch motors. We present a design approach that allows for integration of both actuation and sensing into low-profile, laminate linkages of almost arbitrary length and complexity and demonstrate this approach with the design of a three degree-of-freedom (DOF) spatial manipulator. We also present dynamic models of rotary pouch motors and integrate these models into estimation and control laws that we use for real-time control of a 1-DOF joint. The presented state-estimation algorithm exhibits a root-mean-square (RMS) error of approximately 0.87° and the presented control law demonstrates RMS tracking error of approximately 6.70° when tracking sinusoids up to 3 Hz. By developing rotary pouch motors as controllable dynamic systems, they can used in myriad small-scale robotic systems, including insect-scale legged robots and millimeter-scale manipulators.
On the Performance of Non-Profiled Differential Deep Learning Attacks against an AES Encryption Algorithm Protected using a Correlated Noise Generation based Hiding Countermeasure

Recent works in the field of cryptography focus on Deep Learning based Side Channel Analysis (DLSCA) as one of the most powerful attacks against common encryption algorithms such as AES. As a common case, profiling DLSCA have shown great capabilities in revealing secret cryptographic keys against the majority of AES implementations. In a very recent study, it has been shown that Deep Learning can be applied in a non-profiling way (non-profiling DLSCA), making this method considerably more practical, and able to break powerful countermeasures for encryption algorithms such as AES including masking countermeasures, requiring considerably less power traces than a first order CPA attack. In this work, our main goal is to apply the non-profiling DLSCA against a hiding-based AES countermeasure which utilizes correlated noise generation so as to hide the secret encryption key. We show that this AES, with correlated noise generation as a lightweight countermeasure, can provide equivalent protection under CPA and under non-profiling DLSCA attacks, in terms of the required power traces to obtain the secret key.
An Approach for Solving the Graph Coloring Problem using Adjacency Matrix

Coloring a graph is a known and a classical problem in graph theory. It is also a known NP problem. In a graph G, the solution of coloring a graph is about coloring all the vertices of the graph G in such a manner so that any two adjacent vertices do not get the similar color. This problem also requires that the number of colors that are used for coloring the graph are also minimum. There are several ways in which a graph can be presented. For example an adjacency matrix. In our paper we have used adjacency matrix to showcase the graph coloring solution. An adjacency matrix is a 2 dimensional array. The rows and columns of this array are therefore can also be called as the vertices of the graph. The value 0 indicates that the vertex on × row and y column is not connected. However if the value is 1 it indicates that the vertex on × row and y column are connected to each other. We have proposed an algorithm using which we calculate the diagonal values that is for which × row and y column value is same. These will represent the final colors allocated to the vertices.
Opportunities for Cross-Layer Design in High-Performance Computing Systems with Integrated Silicon Photonic Networks

With the ever growing complexity of high-performance computing (HPC) systems to satisfy emerging application requirements (e.g., high memory bandwidth requirement for machine learning applications), the performance bottleneck in such systems has moved from being computation-centric to be more communication-centric. Silicon photonic interconnection networks have been proposed to address the aggressive communication requirements in HPC systems, to realize higher bandwidth, lower latency, and better energy efficiency. There have been many successful efforts on developing silicon photonic devices, integrated circuits, and architectures for HPC systems. Moreover, many efforts have been made to address and mitigate the impact of different challenges (e.g., fabrication process and thermal variations) in silicon photonic interconnects. However, most of these efforts have focused only on a single design layer in the system design space (e.g., device, circuit or architecture level). Therefore, there is often a gap between what a design technique can improve in one layer, and what it might impair in another one. In this paper, we discuss the promise of cross-layer design methodologies for HPC systems integrating silicon photonic interconnects. In particular, we discuss how such cross-layer design solutions based on cooperatively designing and exchanging design objectives among different system design layers can help achieve the best possible performance when integrating silicon photonics into HPC systems.
Path Planning and Control of Mobile Soft Manipulators with Obstacle Avoidance

Many soft robotic systems for manipulation have been developed for their compliance and their smooth obstacles avoidance capabilities contrary to rigid systems. In order to increase human task aid, a new class of robots known as mobile soft manipulators has thus emerged, where the path planning of the whole system, both soft manipulator and its mobile platform, remains a real scientific challenge. For that purpose, in this paper, we suggest a curvature control algorithm for that class of hyper-redundant mobile soft manipulator approaching and handling objects with simultaneous obstacle avoidance based on potential field of velocity. The algorithm is based on principle of sliding mode, which converges in a finite time. The soft manipulator kinematics is modeled in 2D by the mean of Pythagorean Hodograph (PH) quintic curve theory with length constraints. The result of this modeling and control is applied on Festo Robotino-XT, mobile and soft manipulator robot.
GRAMARCH: A GPU-ReRAM based Heterogeneous Architecture for Neural Image Segmentation

Deep Neural Networks (DNNs) employed for image segmentation are computationally more expensive and complex compared to the ones used for classification. However, manycore architectures to accelerate the training of these DNNs are relatively unexplored. Resistive random-access memory (ReRAM)-based architectures offer a promising alternative to commonly used GPU-based platforms for training DNNs. However, due to their low-precision storage capability, these architectures cannot support all DNN layers and suffer from accuracy loss of the learned models. To address these challenges, we propose GRAMARCH, a heterogeneous architecture that combines the benefits of ReRAM and GPUs simultaneously by using a high-throughput 3D Network-on-Chip. Experimental results indicate that by suitably mapping DNN layers to processing elements, it is possible to achieve up to 53X better performance compared to conventional GPUs for image segmentation.