Miracle-dz/UniFAR-FTUs huggingface dataset:
An Energy Efficient 16T Hybrid-CMOS Full Adder using Novel Full Swing XNOR Logic

In this paper, an energy efficient novel hybrid-CMOS 1-bit Full Adder based on XNOR-XNOR logic is presented. The proposed full adder is designed with 16 transistors, whereas the novel full swing XNOR logic is implemented using 5 transistors. The proposed hybrid full adder is designed in Cadence Virtuoso and the simulation work is carried out in Spectre simulator at $45nm$ technology node and 0.8V of power supply. The proposed work has been compared with recent reported work based on several hybrid design style. The propagation delay, power dissipation and energy consumption are 207.2pS, 45.32nW and 9.39aJ respectively for the proposed hybrid full adder. Addition to these, the effect of process and temperature variation has been analyzed. The novel full swing XNOR helps to improve the nose margin of the proposed HFA. A 4-bit binary adder is also implemented using the proposed 1-bit hybrid full adder for the purpose of test and verification. The propagation delay and power dissipation are 551pS and 168.83nW respectively for the 4-bit binary adder.
Virtual Prototyping of Open Source Heterogeneous Systems with an Open Source Framework Featuring SystemC MDVP Extensions

The increasing need for validating heterogeneous Systems on Chip (SoC) before their costly fabrication gives rise to the development of numerous virtual prototyping tools, allowing not only the modeling of such systems, but also the simulation of the environment in which they will be embedded, including the crucial software. SoC designers indeed require flexible simulation frameworks that follow a component-based approach and that cover a wide field of physical/engineering disciplines… and no questions asked about the simulation intrinsics. Furthermore, for SMEs, laboratories, and enthusiastic makers with limited funding, the cost of these frameworks makes them unaffordable, especially when the target system hardware is supposed to be Open Source too. In this paper, we detail the principles of an Open Source SystemC Multi-Disciplinary Virtual Prototyping (SystemC MDVP) framework, which directly tackles with all these issues. We introduce the key concepts and methodology (relationships between Models of Computation and overall synchronization techniques) and insist on the importance of a “correct by construction” global model elaboration that allows to generate the hierarchy of solvers that make up the final simulator. We then show that a new Model of Computation, Electrical Network, has been added to SystemC MDVP to handle components with non-linear beahviors. The paper concludes with a quick presentation of the echOpen project which aims at designing a low-cost portable echography device modeled and simulated with the nresented nrincinles.
Millimeter-Wave SAR Sparse Imaging With 2-D Spatially Pseudorandom Spiral-Sampling Pattern

For real-time millimeter-wave (MMW) imaging applications, spatially 2-D sparse sampling is one of the most promising techniques to reduce the heavy cost of signal acquisition. Therefore, in this article, we develop a new MMW synthetic aperture radar (SAR) sparse imaging method by establishing a 2-D pseudorandom spiral-sampling pattern. The method consists of two phases: sparse sampling-pattern design and sparse-imaging processing. The sparse sampling-pattern design is modeled as an optimization programming problem, in which the initial sampling pattern is a classical Archimedes spiral, and then, the number of samples per cycle is adjusted by coming up with a modified sigmoid function. This function is employed to increase the angular aperiodicity and randomness over the whole planar aperture. For sparse-imaging processing, a singular value thresholding (SVT) algorithm is applied to reconstruct the complete echo from partial samples observed by sparse sampling, and then, the inversion can be performed via the traditional imaging algorithms. In particular, a performance-guarantee condition of SVT is introduced to control the minimum number of samples, which bridges the relationship between pattern optimization and reconstruction performance. Our motivation is to answer what the undersampling pattern is and to estimate the extent to which the number of samples can be reduced by the current sparse imaging method. Finally, simulations and experiments validate the feasibility and effectiveness of the proposed sampling pattern, and the imaging results further demonstrate that our method with partially observed samples can offer the performance comparable to dense sampling.
Industrial Intelligent Optimization System Based on Industrial Internet Platform

The concept of “Industrial Internet” was first proposed by General Electric in 2012. As a new infrastructure of the fourth industrial revolution, Industrial Internet has become a significant technique to realize digital transformation of industrial economy. The Industrial Internet platform is a service system based on massive data collection, aggregation and analysis, which is oriented to the needs of digitalization, networking and intellectualization of manufacturing industry. In order to solve the problems of current backward industrial data, improve the ability of data acquisition and deal with the industrial interconnection platform, the industrial intelligent optimization system generates the product services required by industrial enterprises and realizes the goal of industrial optimization.
Analysis of a 1kbps Backscatter Receiver with up to -80dBm Tag-to-tag Receive Sensitivity

Merging developments in RFID and wireless sensor networks have increased the interest in using backscatter radio for tag-to-tag communication. Low power consumption is a major concern, often limiting the choice to incoherent receivers such as diode envelope detectors. Link budget calculations require receiver sensitivity specifications, however accurate characterizations are scarce. In this work, we show that the receiver sensitivity is strongly related to the exciter incident power. Full-range analysis of a zero-bias diode detector shows a decreasing noise factor at higher incident powers, which leads to improved sensitivity. On the other side, at strong incident powers, the phase noise of the exciter interferes with the received signal, setting an lower bound to the sensitivity of the backscatter receiver. This work proposes a semi-passive RFID tag capable of 1kbps to 10kbps data rates in the 434MHz band using off the shelf components. The tag has a low power consumption of 85μW during receive, 45μW during transmit and less than 4μW in sleep. The receiver sensitivity at 1kbps shows a 25dB variation as a function of exciter incident power between -50 to 10dBm, while a peak sensitivity of around -80dBm is measured at an incident power of -25dBm.
A Novel Highly Sensitive, Highly Birefringent and Low Loss Suspended Core PCF Sensor for Alcohol Detection in THz Regime

The use of photonic crystal fiber (PCF) in a wide variety of applications involving THz communications, sensing useful and harmful gas and liquids, imaging and spectroscopy is getting well established with the researches of last two decades. We are demonstrating a novel design of a suspended core PCF for sensing alcohol in this paper. The shape of the designed core of the fiber is elliptical with circular air pores incorporated into it, while the cladding of the fiber has been designed to be hexagonal in shape filled completely with air that results in high relative sensitivity along with good birefringence. Extensive simulations have been carried out using COMSOL design environment to evaluate the proposed architecture. Proposed architecture provides state-of-the-art relative sensitivity of 80.31% with birefringence of 0.016 at 0.65 THz, which outperforms all other published architectures.
Compact Modeling of Multi-Layered MoS2 FETs Including Negative Capacitance Effect

In this article, we present a channel thickness dependent analytical model for MoS<sub>2</sub> symmetric double-gate FETs including negative capacitance (NC) effect. In the model development, first thickness dependent model of the baseline 2D FET is developed, and later NC effect is included in the model using the Landau-Khalatnikov (L-K) relation. To validate baseline model behavior, density functional theory (DFT) calculations are taken into account to obtain numerical data for the <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\Lambda $ </tex-math></inline-formula> valley dependent effective masses and differences in the energy levels of N-layer (N = 1, 2, 3, 4, and 5) MoS<sub>2</sub>. The calculated layer dependent parameters using DFT theory are further used in a drift-diffusion simulator to obtain electric characteristics of the baseline 2D FET for model validation. The model shows excellent match for drain current and total gate capacitance of baseline FET and NCFET against the numerical simulation.
Effect of Atomic Interface on Tunnel Barrier in Ferroelectric HfO2 Tunnel Junctions

We have demonstrated the dependence of the atomic terminations on ferroelectric tunnel junctions (FTJs) based on ferroelectric HfO2 using density functional theory calculation. The atomistic structures of HfO2 FTJs with various interfaces are constructed and their device performances are calculated. We have found that the potential barrier is significantly tailored by atomic species of the terminating atom of HfO2. In particular, the atomistic effect contributes to the electric field across the tunnel barrier, which leads to asymmetric behavior. We demonstrate that the ON/OFF current ratio of FTJs can be improved by adjusting the atomic terminations, albeit without the external asymmetric structure such as dissimilar metal electrodes and additional composite layers.
Cross Stockwell transform aided Random Forest based surface condition identification of Metal Oxide Surge Arrester employning Leakage current signal

This paper proposes a methodology to monitor the surface condition of Metal Oxide Surge Arrester (MOSA) in power system. Generally, the surface of polymeric housed MOSAs are affected due to various pollutants. These pollutants may result overheating of the arrester which can leads to failure of arrester. This can affect power system reliability. Therefore, a method has been proposed to estimate surface contamination of MOSA in this article based on time frequency analysis of leakage current. According to the result it can be said that proposed methodology returned very good accuracy regarding surface contamination level estimation of MOSA which in process enhances the system reliability. Therefore, the proposed methodology is useful for condition monitoring of MOSA at polluted environment. This methodology can also be used to estimate surface contamination level of insulators in service.
The Optimized Triloop Control Strategy of Integrated Motor-Drive and Battery-Charging System Based on the Split-Field-Winding Doubly Salient Electromagnetic Machine in Driving Mode

An integrated motor-drive and battery- (IMB) charging system based on the split-field-winding doubly salient electromagnetic machine (DSEM) is proposed in this article, where field winding of DSEM is integrated as the filter inductance of the front-end dc–dc converter. In the driving mode, the coupling relationship between the front-end dc–dc converter and rear-end inverter for DSEM is deduced, and the corresponding basic control method is given to achieve independent control of the front-end dc–dc converter and rear-end inverter based on the power balance of the cascade converter. Due to the constraints analysis of the IMB system in the dynamic procedure, an optimized triloop control strategy of speed, power, and dual-threshold voltage is proposed to improve output performance. Finally, the simulation model and experimental platform are established to verify the effectiveness and the steady and dynamic performance of the IMB system with the proposed driving control strategy.
Multi-camera Marker-based Real-time Head Pose Estimation System

Estimating human head pose is a common task in many applications such as human attention modeling, fitting model to video, and visual reality… In this work, we implement a complete fiducial marker based human head pose estimation system. Our system uses fiducial markers as the key points to do Perspective n-Point, solves the 2D to 3D correspondence problem, and 3D points to 3D points registration. We also apply the depth data from RGB-D cameras as extra information to find the associated rotation and translation of the object head in the camera coordinate. Besides that, to overcome occlusion situation and also increase the tracking accuracy, we surround the user by multiple cameras. Our system will then fuse all pose results and introduce the final output as the most reliable and stable pose of the current object's head. At the end, our system could quickly detect the movement of object's head, and achieve high output frame rate that usually required in the real-time controlling applications.
Superpixel Segmentation by Forgetting Geodesic Distance

Superpixel segmentation could be of benefit to computer vision tasks due to its perceptually meaningful results with similar appearance and location. To obtain the accurate superpixel segmentation, existing methods introduce geodesic distance to fit the object boundaries. However, conventional geodesic distance easily suffers from error accumulation and excessive time consumption. This paper proposes a fast superpixel segmentation method based on a new geodesic distance, called forgetting geodesic distance. In contrast to the conventional geodesic distance, the forgetting geodesic distance utilizes a forgetting factor to gradually reduce the influence of previous path cost and focuses on the latest pixels’ difference. Intuitively, a pixel with lower difference with respect to the latest path contextual distance will be more similar with the corresponding region. In the new path, the path cost devotes much greater attention to the latest pixels’ difference and could significantly relieve error accumulation. The pixels are also aggregated with less dependence on seeds as the path extends, which avoids the seed updating. The experimental results validate that the proposed method obtains 2 percent and 1 percent gain on average compared with most of the state-of-the-art methods in terms of BSD500 and VOC2012 datasets, respectively.
ANTI HYPERGLYCEMIC EVALUATION OF TERMINALIA CHEBULA LEAVES

Objective: The antihyperglycaemic potentiality of Terminalia chebula leaves has not yet been investigated thoroughly compared to its fruit counterpart. Therefore, the purpose of this study was to assess the hypoglycaemic potentiality of Terminalia chebula Retz leaves both in vitro and in vivo.Methods: Fresh leaves of T. chebula were collected, authenticated and grounded to a fine powder. The powdered material was extracted in methanol. The hypoglycaemic potentiality of the extract was accessed in vitro using enzyme alpha-amylase and alpha-glucosidase. The antihyperglycaemic activity of the methanol extract active fraction was accessed in vitro and in vivo. The active fraction thus obtained was partially characterized using Fourier transform infrared spectroscopy (FTIR) and High-performance liquid chromatography (HPLC) analysis.Results: The crude leave methanol extract of Terminalia chebula demonstrated 100% α glucosidase inhibition with IC50–0.956±0.342 mg/ml compared to standard drug acarbose. Oral administration of the active fraction to diabetic rats loaded with maltose significantly (P<0.05) retarded the postprandial spike of blood glucose level compared to standard drug acarbose. Partial characterization of the fraction reveals the presence of hydrosoluble tannin gallic acid.Conclusion: The study provides an in vitro and in vivo rationale evidence of Terminalia chebula leaves to retard postprandial hyperglycemia.
Compact Wideband Four Element Optically Transparent MIMO Antenna for mm-Wave 5G Applications

A four-element compact wide-band optically transparent MIMO antenna with a full ground plane is proposed. The four elements transparent MIMO system has a compact size of $24\times 20$ mm2 with the undivided ground plane as most of the real-time systems demand a common reference. The complete antenna system achieves around 85% transparency due to a combination of AgHT-8 and Plexiglas which forms the transparent conductive patch/ground and substrate, respectively. The antenna geometry leads dual-band operation ranging from 24.10 - 27.18 GHz (Impedance bandwidth = 12%) and 33 - 44.13 GHz (Impedance bandwidth $=28.86$ %) targeting the mm-wave 5G applications. The 4-element antenna system achieves isolation between inter-elements >16 dB and maximum gain value of greater than 3 dBi with more than 75% efficiency. The proposed transparent MIMO antenna is evaluated in terms of diversity gain (DG), envelope correlation coefficient (ECC), total active reflection coefficient (TARC), and mean effective gain (MEG) where decent MIMO performance with isolation more than >16 dB between the adjacent and other elements is achieved. Transparent MIMO antenna achieves directional patterns for the operating band with the value of DG >9, ECC < 0.1, TARC value less than −15dB, and the ratio of MEG within the agreed limit of ±3 dB confirming acceptable MIMO/diversity performance.
An Initialization Method of Deep Q-network for Learning Acceleration of Robotic Grasp

Generally, self-supervised learning of robotic grasp utilizes a model-free Reinforcement Learning method, e.g., a Deep Q-network (DQN). A DQN makes use of a high-dimensional Q-network to infer dense pixel-wise probability maps of affordances for grasping actions. Unfortunately, it usually leads to a time-consuming training process. Inspired by the initialization thought of optimization algorithms, we propose a method of initialization for accelerating self-supervised learning of robotic grasp. It pre-trains the Q-network by the supervised learning of affordance maps before the robotic grasp training. When applying the pre-trained Q-network a robot can be trained through self-supervised trial-and-error in a purposeful style to avoid meaningless grasping in empty regions. The Q-network is pre-trained by supervised learning on a small dataset with coarse-grained labels. We test the proposed method with Mean Square Error, Smooth L1, and Kullback-Leibler Divergence (KLD) as loss functions in the pre-training phase. The results indicate that the KLD loss function can predict accurately affordances with less noise in the empty regions. Also, our method is able to accelerate the self-supervised learning significantly in the early stage and shows little relevance to the sparsity of objects in the workspace.
Physics-augmented Neural Compact Model for Emerging Device Technologies

This paper proposes a novel compact modeling framework based on artificial neural networks and physics informed machine learning techniques. This physics- augmented neural compact model shows highly accurate fitting abilities and physically consistent inferences even at the unseen data. It is also scalable and technology independent, and consequently, is suitable for electrical modeling of new emerging devices. In addition, this neural compact model is able to cover both digital and analog circuit analysis due to the weight decay regularization as well as high order derivative losses. Finally, it is applied to promising DRAM and Logic technologies to be evaluated in terms of its scalability and fitting accuracy. The CMC’s (Compact Model Coalition) standard model API (Application Programming Interface) supports the custom model implementation for SPICE. Therefore, this framework enables the circuit simulators to assess technology-independent PPA (Power, Performance, Area) and early-stage DTCO (Design Technology Cooptimization) for new emerging devices.
Multimodal System Framework for Feature Level Fusion based on CCA with SVM Classifier

In the biometric recognition, feature fusion is a critical step, especially in case of heterogeneous and incompatible feature representation. Most of the existing methods cannot extract features that are discriminative and at the same time affine invariant, which limits the classification performance. To address this issue, we propose an efficient feature level fusion approach using canonical correlation analysis (CCA) with SVM classification. The feature level fusion approach integrates different features extracted from different modalities into a more discriminative and compact feature representation, which can be further used for classification. We also propose an efficient algorithm that optimizes the whole framework of the multimodal system. The CCA + PCA subspace learning approach is presented to couple dimensionality reduction and feature fusion collectively, which can be used for both unsupervised and supervised learning. The experimental results on the SDUMLA multimodal database show that the proposed approach significantly outperforms than the prevalent feature fusion approaches. We demonstrate that an efficient multimodal classification can be achieved with a significant reduction in the number of feature dimensions by exploiting canonical correlation analysis on the extracted feature sets from iris and fingerprint modalities. We also demonstrate that subspace learning maximizes the correlation between Iris and Fingerprint modalities and excels the recognition performance of the individual modality.
An artificial intelligence approach to real-time automatic smoke detection by unmanned aerial vehicles and forest observation systems

Natural disaster events, like volcanic eruptions and earthquakes, usually cause serious havoc and result in property losses for millions of Euro and unfortunately sometimes in loss of human life. These events are very hard to predict and are out of human control. Much like them, wildfires can be devastating and can lead to significant destructions, but fortunately they can be detected, controlled and prevented from spreading. The evolution of the wildfire detection and prevention technologies is long. The traditional methods rely on human involvement and include the use of manned fire watchtowers and piloted fire observation aircrafts. These solutions are very effective, but they are also very costly and sometimes can be extremely dangerous. With the technological advancements in the different engineering areas, safer and cheaper solutions for early forest fire detection were slowly developed - fire detection satellites, fire and lightning detectors, wireless sensor networks, as well as static optical or thermal cameras. Nevertheless, all of these solutions have disadvantages, which calls for their constant improvement, as well as for the development of more advanced and reliable systems. In this paper we present our experience in the area of the systems for early forest fire and smoke detection. Following the introduction section, we analyse in details an artificial intelligence (AI) approach to real-time automatic smoke detection, which is suitable for use in camera-based static forest observation systems and unmanned aerial vehicles. The paper concludes with discussions on the capability of the presented approach and with directions for further work.