Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiple Sharing 7T1R Nonvolatile SRAM With an Improved Read/Write Margin and Reliable Restore Yield

This article presents a new resistive random access memory (RRAM)-based average 7T1R nonvolatile static random access memory (nvSRAM). This multiple sharing (MS) 7T1R uses MS schemes in which some of the transistors play various roles. Therefore, the MS-7T1R can perform a decoupled read, enhance write capability, and improve the restore yield with a small area cost. Furthermore, the MS-7T1R can offer two alternative modes, i.e., a high speed and a stable mode. Compared with existing technologies, such as the previous 6T-based nvSRAMs, the results show that the proposed architecture provides a remarkable restore yield and ~154% improvement in the read static noise margin (at TT corner and stable mode). In addition, the read delay improves by ~23% (at TT corner and high-speed mode). The write “1” problem of the single bitline is effectively resolved with our proposed write strategy. The static write margin of “1” is improved by ~88.6% compared with the conventional 6T ( $\beta =4$ ) at a power of 1.2 V. In addition, dynamic power is effectively reduced by the use of the single bitline and sub-word-line driver technology.
Performance of Polar Coded MIMO Systems with Matched-Filter Detector and Interference Cancellation

Among various MIMO detectors, the matched filter (MF) detector is known as the least complex receiver. It has been shown that with powerful channel coding, MF detector works well under the low SNR regime with diversity-rich environment, such as ergodic fading channels with a large number of antennas. On the other hand, if the diversity order is not sufficient, the performance of MF detector becomes poor. In this paper, we first review the fact that the per-antenna mutual information of MF detector will decrease as the number of both transmit and receive antennas increases over an ergodic Rayleigh fading channel. Since the use of powerful channel codes is essential for the MIMO system with MF detector, we apply polar codes with CRC-assisted successive cancellation list decoder and demonstrate its superiority over the conventional turbo codes. Focusing on such a practical scenario, we further investigate the use of interference cancellation (IC) with MF detector as a low- complexity alternative. After performance comparison of several IC schemes, we also propose an adaptive IC switching approach that can achieve the best performance over a wide range of SNR.
Online Power Allocation for Sum Rate Maximization in TDD Massive MIMO Systems

In this paper, we investigate the power allocation problem with perfect channel state information (CSI) for downlink sum rate maximization in time duplex division massive MIMO systems. We note that the downlink sum rate is generally a non-convex function of the power allocation vector and the corresponding solution space increases exponentially with the number of simultaneous users, which make the optimal power allocation computationally intractable in general. Here, we introduce an online paradigm to achieve a tradeoff between computational complexity and sum rate performance, which utilizes the state- of-art online learning techniques to iteratively update the power allocation according to continuous CSIs. The computational complexity of the proposed algorithm is highly reduced by using the first order optimization technique and the system sum rate is improved by exploiting the time correlation of wireless channels. Simulation results show that the downlink sum rate can be increased by 15\%-100\% by using our proposed algorithm, compared with the conventional average and pathloss-based power allocation schemes.
Safe Grounding Grid Design for HVDC Converter Station

The main goal of this paper is presenting an optimum and safe design for the proposed high voltage direct current (HVDC) grounding grid for converter station at Badr city in Egypt for uniform and non-uniform soil resistivities. Also, the paper discusses the difference between HVDC grounding grids and high voltage alternating current (HVAC) ones. CYMGRD software was used to estimate the step and touch potentials at the soil surfaces at different conditions. The consequences show that the computed results for step and touch potentials are below the safe limits for human in cases of continuous normal and fault conditions for uniform and non-uniform soil resistivities. From results, touch voltage in a non-uniform layer in continues operation is 17.65 V, and equal to 57. 37V in fault operation.
A New Enclosed Method for Transverse Mechanical Testing on CICC Conductors

CFETR, the China Fusion Engineering Test Reactor, is a new tokamak reactor under preliminary design, where the toroidal field coils were designed to generate a magnetic field of 14.3 T. Here the TF conductors are required to operate under a Lorentz force of 1200 kN/m while keeping stable performance. For these requirements, the main goal is to limit the degradation of the conductor. According to the previous study, the conductor can be analyzed with a mechanical method to evaluate the conductor-performance in the electromagnetic field. In this method, the force was distributed equally from the up and downside of the press modules, which has notches in the middle of the conduit. The stress distribution is quite different from the accumulated stress in the Lorentz force direction. Moreover, the strand extension has been found in the notches. Considering these conditions, a new method where the cable surrounded by an enclosed module was designed to simulate this kind of accumulative stress in the cable. The new module is defined as an enclosed jacket press method compared to the former method (jacket with notches) developed at the University of Twente. In this study, the cable's mechanical properties tested by two different modules were compared. The results were analyzed to evaluate the similarity of different press methods for EM simulation. The result shows that the cable pressed with an enclosed module jacket provided more similar deformation trends when comparing to the distribution of electromagnetic stress within the cable.
Cyber Physical and Social Networks in IoV (CPSN-IoV): A Multimodal Architecture in Edge-Based Networks for Optimal Route Selection Using 5G Technologies

Humans are blessed with the intelligence to create links, develop semantic metaphors and models for reasoning; construct rules for decision making; and to form bounded loops for interaction, socialization and knowledge sharing. But machines are inadequate with these extraordinary abilities rather, numerous algorithms and mathematical models can be used to connect physical resources with cyberspaces to control objects and, develop cognitive learning for optimal decision making. Connected users and devices in closed virtual and physical proximity give direction towards the plethora of real-world applications for physical, social and, cyber computing. Because of the increase in social media networking and 5G communication links offer real-time crowdsourcing and sensing as a complementary base for information. Proceeding this idea, in this study we have proposed Cyber-Physical and Social Networks (CPSN) for two fundamental operations in IoV (Internet of Vehicles) as CPSN-IoV; (1) to define conceptual architecture of CPSN-IoV for data-oriented network for smart infrastructure and, (2) to create the significant virtual space where the instances of smart vehicles, devices, and things will have meaningful links with the real world objects where, CPSN-IoV will evolve, emerge, compete, and collaborate with all connected objects to strengthen the decision making process. To investigate the potential impact of our proposed study, we have simulated the taxicab trajectory data of the urban city of Portugal in OMNeT++ for the in-depth understanding of road topology, connected vehicles and things, and their traffic trends; and users’ social media streams in respective edge for efficient route planning. The results of simulation demonstrate that our proposed framework has the ability to achieve human-machine intellectual association for managing the smart environment.
Route Control for Vehicle Access Point for Pedestrian

This paper describes about a novel route control for vehicle wireless LAN (WLAN) access points (APs) for WLAN stations of pedestrians. To cover more pedestrian mobile users, each vehicle equipped with WLAN AP passes close by the users with changing its route. We investigate tradeoff between extra route cost (by changing the route) and improvement of user quality of services (such as throughput of the WLAN services).
A Force Couple Approach for Cooperative Object Pushing of Rod Object with Multiple Robots

Robots have been developed and used for addressing the intricate tasks present in the society. A single robot is not adequate when problem scenarios become complex. In such situations, a better solution could be achieved by efficiently employing multiple robots. The efficiency is achieved through the appropriate interactions of these robots. Cooperative object transportation with multiple robots is a miniature version of a real-world problem that a multi-robot system (MRS) should accomplish. A number of solutions have been proposed for this particular research problem from the early emergence of the MRS domain. But most of the solutions are focused on the transportation of box type object and hence the results could not be applicable to other object types. In cooperative object transportation problem, the transportation of a narrow rod type object with multiple robots has got very limited attention. In this work, a force-couple approach is proposed for the effective transportation of rod type objects with multiple robots and the effectiveness of the proposed solution is validated with the help of a 2D dynamic multi-robot simulator.
Providing resources in Fog Computing infrastructure: considerations on performance requirements

Fog Computing, as evolving paradigm in modern application scenarios, came to the arena as Cloud Computing developments successor, providing elastic resources and services to end users at the edge of network, while cloud computing are more about providing resources distributed in the core network. Fog computing is able to provide better Quality of Service in terms of delay, power consumption, reduced data traffic over the global network etc. The main feature of Fog computing is its ability to support applications which require low latency, location awareness, and mobility. This paper discusses various aspects of issues may be encountered when designing and implementing fog computing systems. With the purpose of the estimation of the capabilities of Fog Computing, the experimental network implementing the major components has been investigated, with defining the prerequisites for modeling Fog Computing architecture as well as management issues to be considered in the context of fog computing.
Online Estimation and Control for Feed Drive Systems With Unmeasurable Parameter Variations

This paper develops an online parameter estimation and control method for both rigid and flexible feed drive systems with unmeasurable parameter variations. The perturbations of the state-space model caused by the parameter variations are formulated, thereby making it possible to obtain the parameter variations in real-time by estimating the perturbations. To estimate the perturbations, they are regarded as the extended states and estimated through the extended-state-observer. With the estimation method, a novel state feedback control structure with double integrators is further proposed, which can eliminate the steady-state tracking error at constant velocities. The H $_\infty $ optimization technique is used to design the proposed state feedback controller. A simulation is conducted that integrates the estimator and the controller for a ball screw setup with mass-dependent resonant modes, where several proposed state feedback controllers are linearly interpolated into a gain-scheduling controller and scheduled by the estimated mass. The results demonstrate that the designed gain-scheduling state feedback controller outperforms a linear-time-invariant state feedback controller and an adaptive backstepping sliding mode controller. The proposed method is experimentally validated on a rigid linear-motor-driven motion stage, of which the results indicate the proposed estimation method can accurately estimate the parameter variations.
A Robust Spectrum Sensing Scheme for Large Antenna Array Based on Spatial Higher-Order Statistics

A novel robust spectrum sensing method is proposed based on the higher-order statistics of signals received over a large antenna array. The proposed method formulates the test statistics by estimating multiple cumulants of the spatial samples, and in turn differentiates the primary signals from the Gaussian noise within a short sensing period. This work is an extension of [1] and enhances significantly its detection performance by utilizing the statistical information of more than one cumulant. In addition, it remains robust in the presence of unknown noise correlation that often exists in large antenna array.
New Approach for Lightning Protection of Substations

Lightning protection of substations is a crucial element of the overall insulation coordination approach used in power systems. IEEE Std. 998 (2012) is the world’s leading standard for providing guidance to engineers designing lightning protection for substations. It includes various methods for achieving protection corresponding to the equipment BIL. In this paper, the principles behind one of these methods, the "leader inception theory", are presented. A description is provided of how the method is implemented with a computer program via an example of a real substation. Effective interception of lightning relies not only on the method but also the hardware used. Hence, the paper also discusses the importance of corona minimization from air terminals during the period preceding the descent of the downward leader. The methodology and results of a new series of corona performance testing carried out on a variety of air terminals in a high-voltage laboratory are presented and discussed.
Polarization and Depolarization Processes of Polyimide Based on a Non-Invasive Surface Potential Measurement

Conventional surface potential measurement settings with electrostatic probes are not applicable when the test sample is polarized under a high voltage. This paper proposed a non-invasive surface potential measurement setup and analyzed the device feasibility through a finite element simulation and experiment. Surface potential of polyimide films was measured by this non-invasive setup with applied voltage ranging from -9 kV to 9 kV. Results showed that non-invasive surface potential measurement can obtain a complete observation of surface charge dynamics in polarization and depolarization processes. Parameters such as surface potential peak values, fast decay amplitude of the double exponential decay model, and the steady-state surface potential at both polarization and depolarization processes increase approximately linearly with the applied voltage. The decay time constant of the negative polarized sample was larger than that of the positive polarized sample and the steady-state surface potential of the negative was higher than the positive one, which indicated the surface charge accumulation and dissipation of the negative polarized polyimide were slower than the positive polarized polyimide.
Subspace Learning and Feature Selection via Orthogonal Mapping

A plethora of dimensionality reduction (DR) techniques, stemming from statistics, machine learning, or graph theory, has been developed. Their ultimate objective is to eliminate data redundancy without a significant information loss. A general formulation, known as graph embedding, offers a unified framework for describing several well-known DR algorithms. In this paper, the inclusion of several DR algorithms within this unified framework is demonstrated. The enforcement of orthogonality to the projection matrix within this framework is proven to be of vital importance, since Orthogonal Neighborhood Preserving Projections, Orthogonal Locality Preserving Projections, Orthogonal Isometric Projection, Orthogonal Linear Discriminant Analysis, and Orthogonal Local Tangent Space Alignment algorithms outperform their non-orthogonal counterparts, e.g., Neighborhood Preserving Embedding, Locality Preserving Projections, Isometric Mapping, Linear Discriminant Analysis, and Local Tangent Space Alignment. One may simultaneously also impose the $\ell _{2,1}$ norm regularization on the projection matrix, seeking for row-sparsity. This leads to the known Joint Feature Selection and Subspace Learning (JFSSL) framework. All DR algorithms are employed within JFSSL. It is proven that the use of orthogonal mapping algorithms within JFSSL against their non-orthogonal counterparts does not improve the recognition rate of NN classifier.
120-GHz 8-Stage Broadband Amplifier With Quantitative Stagger Tuning Technique

A novel quantitative design method is proposed herein for a 120-GHz 8-stage broadband amplifier using the stagger tuning technique. The pole frequencies of the unit amplifiers are determined by the parasitic capacitors and the size of the matching inductors, and resonance peaks are formed at the pole frequency. We proposed the stagger tuning technique at two different frequencies, where an inductance of four stages is chosen to match the pole frequency at a lower frequency (fL) and an inductance of the other four stages is chosen for the upper frequency (fH). With this technique, the maximum bandwidth can be achieved with the given amplifier gain. Moreover, it was shown that the amplifier designed with stagger tuning at two different frequencies yielded the lowest noise figure in the target band because the gain was concentrated in the target band. A 120-GHz 8-stage amplifier is designed with the proposed technique and fabricated using 40-nm CMOS technology. The measured peak gain of the amplifier is 20.6 dB, and the 3-dB bandwidth is 40.6 GHz while consuming 45-mW of dc power.
Limited-Feedback Distributed Relay Selection for Random Spatial Wireless Networks

This paper considers a location-based optimal relay selection scheme for a relay-assisted wireless network where available decode-and- forward relays are distributed as a homogeneous Poisson point process. To solve an optimum relay selection problem, a central entity or the source requires information pertaining to all relay locations. Since the task of feeding this information back is impractical, we investigate a threshold-based limited feedback distributed relay selection policy. We show that the total number of relays feeding back is a Poisson distributed random variable. For a given threshold-based limited feedback distributed relay selection policy, we obtain analytical expressions for the average rate and the outage probability over the fading and no-fading communication scenarios. The derived analytical expressions are verified and the performance achieved by the proposed relay selection policy is illustrated through extensive simulations. It is observed that the limited feedback distributed relay selection policy can achieve almost the same performance with the optimum relay selection policy by only utilizing location information from a few number of relays.
A light-weighted ANN architecture for the classification of cyber-threats in modern communication networks

In modern communication networks, the integrity of the security is of great importance, since the existence of cyber attacks may lead to disastrous financial and social consequences. The anomaly detection constitutes an essential part of network security. This paper proposes a two-stage procedure to provide a solution regarding the anomaly detection and threat identification. The proposed method is suitable for modern communication networks and upcoming smart networks. The first stage of the method concerns the detection of abnormal incidents and the second stage involves the identification of the type of cyber threats, in case of an attack. The method based on the development of artificial neural network models and the UNSW-NB15 dataset is used to validate the proposed methodology. The experimental results confirm that the proposed method identifies all type of threats in comparison to the already known methods that identify only the threats that appear frequently.
Machine Learning based Prediction of Anatomical Therapeutic Chemical (ATC) Class of Drug Like Molecule

Drug discovery is costly and complex process with low success rate. Machine learning based computational method has been applied in different fields including drug discovery. Prediction of Anatomical Therapeutic Chemical (ATC) class of drug like molecule can provide therapeutic area or organ or system on which drug can be used. Computational prediction of ATC class of molecules only from structure (2D of 3D) can expedite the drug discovery process because no lab resources are required and even it can be done before the synthesis of any target molecule. In present research, Machine learning based method was developed using information of all small approved drug molecules. High accuracy of prediction in case of all 14 ATC class in independent test sets was achieved which warrants high reliability and usage of developed method.
Medium and Long-Term Electricity Trading Considering Renewable Energy Participation

The medium and long-term electricity trading approach considering uncertain renewable energy participation is established based on the bi-level model in this paper. The upper-level is designed to maximize the profitability of power generation companies, while the lower-level aims to maximize the social welfare. Afterwards, a hybrid algorithm combining the discrete and continuous particle swarm optimization is proposed to solve the upper-level model, and the nonlinear programming method is used to address the lower-level model. Finally, verification is performed on the improved IEEE 39-bus system, and simulation results demonstrate that the proposed approach could indeed improve the profit of renewable energy as well as increase the social welfare of power generation enterprises.
Digital Assisted Current Sensing Scheme for on-chip Power Management

In this paper, we introduce a current sensing scheme for on-chip power management. The proposed scheme uses the voltage drop across the MOSFET switch in a DC-DC converter for sensing the current and a time-domain circuitry for digitization. Minimal use of analog circuitry in the architecture not only reduces the power consumption but also makes the design simple to realize. The scheme offers tolerance against variations in process and temperature by comparing the sensed voltage against a reference voltage that is reflective of the amount of variation present. Proposed circuit schematic was designed and simulated using 55nm CMOS technology alongside a switching regulator. The simulated circuit could measure currents up to 600mA with a maximum measurement error of only +/−2.5%. The proposed scheme could measure current within a measurement time of 2uS while consuming only 200uA from a 1.8V supply.