Miracle-dz/UniFAR-FTUs huggingface dataset:
Static routing and spectrum allocation for mitigating crosstalk in multi-core fiber networks

In this paper, we propose a static routing and spectrum allocation method that mitigates crosstalk, while reducing the number of frequency slots used in multi-core fiber optical networks. When the same frequency slot is simultaneously used in adjacent cores, crosstalk occurs in the multi-core fiber optical networks. The proposed method provides a routing and spectrum allocation strategy based on an integer linear programming. We show that the proposed method mitigates the crosstalk effect efficiently through numerical experiments.
Improved Long Short-Term Memory Network Based Short Term Load Forecasting

The power load sequence is a kind of complex nonlinear time series. As an improved RNN, the long short-term memory (LSTM) network has been applied to short term load forecasting because it can capture the temporal dynamic of nonlinear time series. In order to further improve the precision of load forecasting results, we establish the CEEMD-AE-LSTM load forecasting model. Firstly, complementary ensemble empirical mode decomposition (CEEMD) is used to decompose the load sequence into several subsequences. Secondly, the approximate entropy (AE) of each subsequence is calculated separately, and the subsequences with similar entropy values are merged into new sequences. Finally, we establish the LSTM networks based on the new sequences, and the output result of each LSTM network is superposed as the final load forecasting result. The experimental results show that the CEEMD-AE-LSTM load forecasting model outperform single RNN and LSTM network by comparing the relative error and mean absolute percentage error. Therefore, the CEEMD-AE-LSTM load forecasting model effectively forecast the short term load and upgrade the precision of short term load forecasting.
Improvement of End-to-end Automatic Driving Algorithm Based on Reinforcement Learning

The end-to-end model is an important research direction in the field of automatic driving. Reinforcement learning is a common policy for training end-to-end models. Because the Deep Deterministic Policy Gradient algorithm can solve the problem of continuous motion space, it is a good method to deal with the problem of automatic driving. However, the Deep Deterministic Policy Gradient algorithm uses the random sampling method, which makes the training of neural network slow. We propose a reinforcement learning method combining Prioritized Experience Replay and Deep Deterministic Policy Gradient, making the training convergence faster.
Deployment Schemes of Wireless Sensor Node For In-Situ Measurement of Planetary Surface Mars

To extant the life on Mars, there are investigations assessing the past habitability potential. The evidence of the presence of water on the Mars identified by the detection of some minerals such as hematite and goethite. To study the properties of regolith for planetary surface for in situ measurement, it is necessary to deploy wireless sensor network on MARS surface. There is a challenging task to deploy the sensors on a planet like the MARS and it requires theoretical Perception before applying along any space mission, as it includes large amount of investment. In this paper, we have learned theoretical details involved in the deployment of sensors on the MARS. In this paper, we propose different sensor node deployment strategies for WSN for maximum coverage prediction. Our study also gives sensor fall time, Impact velocity, Number of sensors are used in topology, total pressure, coverage area, etc. Three schemes of deployment topologies have been also compared.
Effects of Congestion Management on Energy Saving Techniques in Interconnection Networks

In post-Dennard scaling energy becomes more and more important. While most components in data-center and supercomputer become increasingly energy-proportional, this trend seems to pass on interconnection networks. Although previous studies have shown huge potential for saving energy in interconnects, the associated performance decrease seems to be obstructive. An increase of execution time can be caused by a decreased bandwidth as well as by transition times which links need to reconfigure and are not able to transmit data. This leads to more contention on the network than usually interconnects have to deal with. Congestion management is used in similar situations to limit the impact of these contentions only to single links and avoiding them to congest the entire network. Therefore, we propose combining energy saving policies and congestion management queueing schemes in order to maintain performance while saving energy. For synthetic hotspot traffic, which we use to stress the network, this combination shows promising results for multiple topologies. In 3D torus, k-ary n-tree, and dragonfly this combination provides a more than 50% lower latency and increases energy efficiency by more than 50% compared to the baseline. Although both techniques aim for fundamental different goals, non of the investigated configurations seems to suffer any disadvantages from their combination.
Nanoscale Manipulation for The Fabrication of Field-emission Air-channel Transistors

A method based on nanoscale manipulation and focused ion beam etching is put forward for the fabrication of nanoscale field emission transistors with air channels. Using this method, the shape and size of the emitter is controllable with nanoscale precision. Material compatibility of the method is demonstrated with SiC, VO2, Ge and Cu. Low voltage field-emission behaviors and air-channel transistor characteristics are also demonstrated with an obvious forward-directional capability.
STAN Based PM2.5 Prediction Model

PM2.5 elements have a great impact on air quality, so it is of great significance to predict PM2.5 concentration for People’s Daily life and health. Aiming at the problem of low prediction accuracy of existing models, we propose a spatial-temporal attention neural network (STAN). Firstly, we introduce a spatial attention module to adaptively extract spatial features between monitoring stations. Then, we use a temporal attention to extract features from encoder hidden states across time series. We evaluate the STAN on PM2.5 prediction with data from Beijing observation stations, and the results show that it is superior to ARIMA LSTM and Seq2seq models in predicting PM2.5 concentration.
On the homogeneity of SnIa absolute magnitude in the Pantheon+ sample

We have analysed the Pantheon+ sample using a new likelihood model that replaces the single SnIa absolute magnitude parameter M used in the standard likelihood model of Brout et. al. (2022) with two absolute magnitude parameters M<, M> and a transition distance dcrit that determines the distance at which M changes from M< to M>. The use of this likelihood dramatically changes the quality of fit to the Pantheon+ sample for a ΛCDM background by Δχ2 = −19.6. The tension between the M< and M> best fit values is at a level more than 3σ with a best fit dcrit very close to 20 Mpc. The origin of this improvement of fit and M< − M> tension is that the new likelihood model, successfully models two signals hidden in the data: 1. The volumetric redshift scatter bias systematic and 2. A mild signal for a change of intrinsic SnIa luminosity at about 20 Mpc. This interpretation of the results is confirmed by truncating the z < 0.01 Hubble diagram data from the Pantheon+ data where the above systematic is dominant and showing that the M< − M> tension decreases from above 3σ to a little less than 2σ. It is also confirmed by performing a Monte Carlo simulation which shows that the maximum significance of the SnIa luminosity transition ($\Sigma \equiv \frac{|M_>-M_<|}{\sqrt{\sigma _{M_>}^2+\sigma _{M_<}^2}}$) in the real data, is larger than the corresponding maximum significance of $94{{\%}}$ of the corresponding homogeneous simulated samples.
A Distributed Approach to the Multi-Robot Task Allocation Problem Using the Consensus-Based Bundle Algorithm and Ant Colony System

We propose a distributed approach to solve the multi-robot task allocation problem. This problem consists of two distinct sets: robots and tasks. The objective is to assign tasks to robots while optimizing a given criterion. This problem is known to be $\mathcal {NP}$ -hard even with small numbers of robots and tasks. The field of survivors’ search and rescue is adopted: i.e. some Unmanned Aerial Vehicles are used to rescue a number of survivors. We choose this problem, given its importance in everyday life: (a) survivors are the tasks; (b) Unmanned Aerial Vehicles are the robots; and (c) the objective is to rescue the maximum number of survivors while minimizing the makespan (time elapsed between rescuing the first and last survivors) and traveled distances. The approach is composed of two phases: inclusion and consensus. During the inclusion phase, each Unmanned Aerial Vehicle builds a bundle of survivors using the Ant Colony System. During the consensus phase, Unmanned Aerial Vehicles resolve conflicts in their bundles of survivors (i.e. a survivor is being chosen by more than two Unmanned Aerial Vehicles), using an adequate coordination mechanism. The approach is implemented using Java programming language and JADE multi-agent Framework. The performance of our approach is compared to five state-of-the-art multi-robot task allocation solutions. Simulation results show that the proposed approach outperforms these solutions, in terms of: (i) makespans; (ii) traveled distances; and (iii) exchanged messages.
Fuzzy Comprehensive Evaluation of Wind Turbine Operation Condition Based on Weights Calculation via Factor Analysis

In order to evaluate the performance of wind turbines to ensure their safe and stable operation, a performance evaluation method using fuzzy comprehensive evaluation where the weights are calculated by the factor analysis is proposed. Adopting the factor analysis method, we calculate the corresponding weight of operation index according to intrinsic correlation of them based on wind turbine operation data, and combined with the fuzzy comprehensive evaluation algorithm, a performance evaluation model for wind turbines based on weights calculation via factor analysis is established. The model is verified using the data from the Supervisory Control and Data Acquisition (SCADA) system of a wind farm and applied to a 1.5 MW wind turbine for comparison and analysis. The results show that the proposed evaluation method is effective to determine the operational condition of a wind turbine.
Compact Dual-Band and Tri-Band Tunable Bandpass Filters Based on Semicircular Substrate Integrated Waveguide

In this paper, compact dual- and tri-band bandpass filters with tunable center frequencies based on semicircular substrate integrated waveguide (SCSIW) are proposed. The dual-band bandpass filter is achieved by the fundamental mode and the first higher order mode of the SCSIW cavity. Two passbands are independently adjustable through the perturbation of posts and a pair of complementary split-ring resonators (CSRRs), respectively. The metal posts are loaded at the SCSIW cavities to adjust the center frequency of the first passband while the CSRRs are etched on the surface of the SCSIW in order to control the second passband center frequency. And similarly, the tri-band bandpass filter is achieved by the fundamental mode, the first higher order mode and second higher order mode of the S CSIW cavity, and two pairs of CSRRs can be employed to disturb the second and third passbands of the tri-band filter. The simulation results indicate that each passband has good independent adjustability without mutual interference. And the out-of-band rejection is also good overall.
Fast and Reliable Group Attendance Marking System Using Face Recognition In Classrooms

Face recognition is a widely used authentication technique for various uses like Attendance marking, Human computer interaction, Security access, E-learning[23], online proctoring [22] [25] etc. Here we use face recognition for marking attendance for large groups of people together in a classroom[14] [24]. Compared to other traditional attendance marking methods face recognition will provide better accuracy and prevents proxy attendance, but it is a complex and time consuming process when it applied to a group of people simultaneously. In this proposed system we are focusing on reducing the complexity of the group face recognition process by finding a suitable scenario which will improve the accuracy and reduce the time complexity. We have used the advanced deep learning convolutional neural network models for face recognition. Neural networks will convert the facial features to vector embeddings and uses for face recognition. System has a final classification layer which will recognize the person and mark their presence in the attendance marking system. As per the analysis we have noticed that by marking attendance a group of 10 to 12 faces one can achieve maximum accuracy in less processing time.
Synchrophasor Assisted Real Time Angular Stability Awareness Program for Emerging Smart Power Grids

Real time monitoring of power system angular stability condition is essential to prevent contingencies like blackouts. In this context, this paper proposes a power grid stability awareness system powered with wide area synchrophasor system. A stability impact index based on center of inertia referenced energy of individual bus voltage angle is developed for assessing real time stability of the system. Along with that, an algorithm for identifying coherent group of generators based on a hybrid index which represents both bus angle and topological information of the system is also presented. Coherent group identification helps the operator to take necessary course of action based on system status. The proposed approach for stability assessment and coherent generator identification were validated using case studies conducted on IEEE 39 bus test system.
A Compact Octa-Band Planar Antenna for LTE Mobile Phone

In this paper, a novel octa-band antenna with a compact area of $72\text{mm}\times 8\text{mm}$ is proposed for mobile phone applications. The proposed antenna is mainly composed of a driven strip (including three branches) and a parasitic ground strip (including two branches). By properly merging the 0.25- and 0.75-wavelength modes of these strips, the lower band (704–960 MHz) and the higher band (1710–2690 MHz) are covered. The design process and the working mechanism are analyzed. A prototype of this antenna is fabricated to verify the design. The measured results depict that octa bands are covered with a low profile of 8 mm.
A Secure and Privacy-Preserving Technique Based on Contrast-Enhancement Reversible Data Hiding and Plaintext Encryption for Medical Images

Protection of medical data has become a prerequisite in medical imaging clouds due to the semi-trusted cloud. Aiming at preserving patients’ privacy and increasing the security of medical images in the cloud, this letter proposes a secure and privacy-preserving technique which provides a new security mechanism for medical data. In this technique, a novel reversible data hiding (RDH) based on adaptive texture classification is proposed to embed privacy data into medical images for preserving patients’ privacy and improving image quality, and plaintext encryption is proposed to encrypt the marked medical image into the similar image of target image for increasing image security. Extensive experiments have shown that the proposed RDH is better than other RDH methods. Plaintext encryption can reduce the attacker's attention and increase the security of medical images well.
Visual Odometry Using Non-Overlapping RGB-D Cameras

In this paper, a method to achieve visual odometry by two RGB-D cameras is proposed. The two cameras are placed to face in opposite directions. We adopt a novel method to obtain the position and attitude relationship between the front and rear camera coordinate systems, which uses a motion capture system and 2-dimensional bar code. Additionally, for the data observed by different cameras, a tightly coupled scheme is adopted for data fusion, and the feasibility of the algorithm is verified. At last, we achieve visual odometry by two non-overlapping RGB-D cameras. The average error is near 2.5 mm for the entire recorded image dataset while the average displacement is 40.61 mm. The result shows that the designed visual odometry is more robust and accurate than the visual odometry of a single camera.
Rectenna Design with High Efficiency at Lower Input Power Density for Sensor Network Applications

A high gain antenna array with flexible polarization configuration for wireless power transmission is proposed in this paper. An air substrate is utilized to improve the radiating efficiency of the array, and the feeding network is placed on the other side of the ground plane to realize a compact integrated rectenna design. By simply moving the feed location with respect to the symmetrical line of each patch element, the dual-polarized configuration of the array can be realized. Two array prototypes had been designed, optimized and fabricated. With $2^{\star}2$ systems, the prototypes achieve high gain up to 13 and 10 dBi and total efficiency exceeding 95%. The array has been integrated with a rectifier to form a rectenna illustrating its applicability for wireless power transmission in powering the sensor nodes for IoT applications.
Comparative Analysis of MPPTT Algorithms for PV Grid Tied Systems: A Review

The PV systems associated with the grid will be a very significant renewable energy source. Numerous researches believe that in approaching years greater than 40% of the energy on the planet will bring about by Photo-Voltaic Grid Tied (PVGT) systems. For this reason, it is crucial to enhance the performance of PVGT systems and diminish the system costs. In order to headway the performance, we should note that PV system characteristics at the output is nonlinear and depends on solar irradiance and temperatures. Beholding to this aspect, a Maximum Power Point Tracking-Technique (MPPTT) is engaged to improve the energy yields to a greater extent from the PVGT system. This paper comparatively analyses about the three eminent maximum power point algorithms-Perturbation and observation (P&O), incremental conductance (In.C), and Fractional voltage at Open Circuit (Fvoc). Simulation verification with P&O and In.C algorithms is done to find a suitable algorithm for the specific PVGT systems.
Simulation Analysis of Ansys HFSS and CST Microwave Studio for Frequency Selective Surface

In this paper, the simulations of Ansys HFSS and CST Microwave Studio for frequency selective surface (FSS) are discussed. The definitions of the Floquet modes, mesh density, and the distance between Floquet port and FSS surface affects the accuracy of the corresponding transmission coefficients. According to the example of a typical biplanar symmetric hybrid radome, the efficient simulation conditions of the two softwares for FSS are summarized.
Metal-Assisted Solid-Phase Crystallization Process for Vertical Monocrystalline Si Channel in 3D Flash Memory

In order to improve the channel conductance of 3D flash memory cell, metal-assisted solid-phase single crystallization process has been demonstrated for the first time. Metal induced lateral crystallization (MILC) process is well-known for the thin film transistors (TFTs). We tried to apply this technology to channel Si in a vertical memory holes of 3D flash memory. Monocrystalline growth was confirmed by in- situ TEM and nano-beam diffraction (NBD) analysis. Moreover, it shows superior device characteristics (Icell, Vth, sub-threshold slope, transconductance) and those uniformities with maintaining memory performance and reliability.
Co Active Electrode Enhances CBRAM Performance and Scaling Potential

In this abstract, we report for the first time the low-current performance enhancement combined with the improvement of the scaling potential in CBRAM devices by adopting an etch-friendly alternative material, Co, as active electrode, based on theoretical considerations and experimental results. Co is proven to yield, with respect to Cu, faster/lower voltage switching and more stable conductive filaments, irrespective of the switching layer, thanks to its higher cohesive energy. By further optimizing the switching layer, we show that the introduction of Co as active electrode is a breakthrough for boosting CBRAM performances, enabling fast and low-power switching, long endurance lifetime and optimal data retention.
Advances in the Automated Test and Measurement Infrastructure of Narrowband Wireless WAN

Wireless communication technologies play a major role to enable megatrends like Internet of Things (IoT) and Industry 4.0. The Narrowband Wireless WAN (NBWWAN) introduced to meet the long range and low power requirements of spatially distributed wireless communication use cases. These networks introduce additional challenges in testing because the network topology and RF characteristics become particularly complex and thus a multitude of different scenarios must be tested. This paper describes the infrastructure for automated testing of radio communication and for systematic measurements of the network performance of NBWWAN.
Input-observer Event-based Secure Consensus Control of A Linear Multi-agent System under Deception Attacks

An input-observer event-based secure consensus control strategy is designed for a linear multi-agent system (MAS) under deception attacks in this paper. A novel input-observer is proposed to estimate the sum of errors among agents, and then, based on the estimation signal from this input-observer, the consensus control strategy with the event-triggered mechanism is introduced for reduction of unnecessary communications. It is proven that the MAS achieves secure consensus control in mean square sense, and the Zeno behavior is ruled out. Finally, the simulation results show the effectiveness of the proposed secure strategy.
One Robot for Many Tasks: Versatile Co-Design Through Stochastic Programming

Versatility is one of the main factors driving the adoption of robots on the assembly line and in other applications. Compared to fixed-automation solutions, a single industrial robot can perform a wide range of tasks (e.g., welding, lifting). In other platforms, such as legged robots, versatility is a necessity to negotiate varied terrains. The ability to balance performance across these anticipated scenarios is one of the main challenges to the process of design. To address this challenge, this letter proposes a new framework for the computational design of versatile robots by considering the interplay between mechanical design and control across multiple tasks and environments. The proposed method optimizes morphology parameters while simultaneously adjusting control parameters using trajectory optimization (TO) so that a single design can fulfill multiple tasks. As its main contribution, the letter details an approach to combine methods from stochastic programming (SP) with TO to address the scalability of these multi-task co-design problems. To assess the effects of this contribution, this letter considers the problems of designing a planar manipulator to transport a range of loads and a hopping monopod robot that must jump across a variety of terrains. The proposed formulation achieves faster solution times and improved scalability in comparison to state of the art co-design solutions. The resulting designs are also shown to be more versatile in terms of providing improved energy cost and task completion times across multiple scenarios.