Miracle-dz/UniFAR-FTUs huggingface dataset:
Spontaneous dimer order, excitation spectrum and quantum-phase transitions in the J1-J2 Heisenberg model

Abstract We overview some recent work and present new results on the ground-state properties and the spectrum of excitations of the two-dimensional frustrated Heisenberg antiferromagnet. Spontaneous dimer order is present in the quantum disordered phase of this model. We study the stability and analyse the structure of the spectrum, including the two-particle singlet excitation branch throughout the disordered phase, as well as in the vicinity of the Neel critical point. The variation in the dimer order parameter is also given, and it is argued that near the critical point it reflects the presence of the low-energy singlet bound state.
Multi-Task Mapping and Resource Allocation Mechanism in Software Defined Sensor Networks

Traditional wireless sensor network (WSN) is deployed based on specific task lacking of flexibility and ability to process and compute multi-task in parallel, which usually leads to deploy redundant WSNs for diversified Internet of things (IoT) requirements. In order to solve the problem, a software defined wireless sensor network (SDWSN) resource scheduling and mapping mechanism is proposed for multi-task scenarios in WSNs. In particular, SDWSN is introduced to support diversified IoT applications on a same WSN by abstracting the physical resources into logical ones. In order to fully explore the resources of WSN, a network virtual layer is set between data layer and control layer of the SDWSN through FlowVisor, which can enable a sensor node in SDWSN to process and compute multi-task in parallel. In addition, a dynamic alliance is establish for each task through the non-linear weight discrete particle swarm (NWDPSO) algorithm to complete resource mapping from logical network to physical network. The results show that the proposed multi-task resource scheduling and mapping mechanism can not only improve resource utilization and load balancing, but also reduces network energy consumption and task completion time.
Data Driven Models for Human Motion Prediction in Human-Robot Collaboration

To improve the safety and effectiveness of human-robot collaboration (HRC), the robot must plan a safe trajectory before the human movement is finished. Therefore, it is necessary to enable proactive robot behavior by making accurate intention prediction decisions early in a human motion. Furthermore, it is desirable to not only provide the long-term trajectory prediction of human motion but also characterize the uncertainty around it. In this paper, we present a human motion prediction framework to predict the motion trajectory of human arm in a reaching task. The proposed framework combines partial trajectory classification and human motion regression. By leveraging on the partial trajectory classification, our framework makes it possible to recognize the human action and to provide a trajectory prediction before the human movement is finished. The human motion regression can compensate the low accuracy of the representative trajectory through the fusion strategy. The proposed framework consists of two phases: online phase and offline phase. The offline phase aims to learn a regression model with optimized hyperparameters and a fusion strategy combining different prediction algorithms. In the online phase, based on the partial motion classification, the future reaching trajectory in a given time step is predicted by using a multi-step Gaussian process regression and representative trajectory. Experimental results show that our proposed framework achieved significant performance.
Accelerator-Aware Fast Spatial Feature Network for Real-Time Semantic Segmentation

Semantic segmentation is performed to understand an image at the pixel level; it is widely used in the field of autonomous driving. In recent years, deep neural networks achieve good accuracy performance; however, there exist few models that have a good trade-off between high accuracy and low inference time. In this paper, we propose a fast spatial feature network (FSFNet), an optimized lightweight semantic segmentation model using an accelerator, offering high performance as well as faster inference speed than current methods. FSFNet employs the FSF and MRA modules. The FSF module has three different types of subset modules to extract spatial features efficiently. They are designed in consideration of the size of the spatial domain. The multi-resolution aggregation module combines features that are extracted at different resolutions to reconstruct the segmentation image accurately. Our approach is able to run at over 203 FPS at full resolution ( $1024\times 2048$ ) in a single NVIDIA 1080Ti GPU, and obtains a result of 69.13% mIoU on the Cityscapes test dataset. Compared with existing models in real-time semantic segmentation, our proposed model retains remarkable accuracy while having high FPS that is over 30% faster than the state-of-the-art model. The experimental results proved that our model is an ideal approach for the Cityscapes dataset. The code is publicly available at: https://github.com/computervision8/FSFNet.
Incident Response System Prototype Using LoRa

Global warming driven rise in sea surface temperatures, climatic changes etc. are accentuating disasters like floods and hurricanes. In addition, during disasters like earthquake, communication systems such as the cellular networks, Internet and telephones invariably get affected including the power supply. Repairing and restoring communication networks and power supply lines can take much longer time periods. Post event, instant restoration of communication network is necessary to provide focused relief and rescue support. Access to wireless communication provides direction to the fire fighters, rescue operators and other emergency service providers the necessary leads in locating the distressed. The paper introduces a technique that help to establish a reliable communication system in the affected area by using a Wi-Fi mesh network and LoRa (Long Range) WAN with inbuilt battery support for long-distance communication. The system would support the first responders in identifying the people with great accuracy using GPS present in the user's devices. The developed prototype found to be working and can be readily used by the relevant authorities.
Intelligent Visual Servoing Using Learning-Based Grasping Configurations and Adaptive Controller Gain

For the traditional image-based visual servoing (IBVS) system, the desired configurations of grasping are predefined for the robot and the gain $\lambda$ in the control law is fixed. While training the robots with new skills, the predefined grasping configurations may increase the workload; meanwhile, the fixed controller gain $\lambda$ may slow down the convergence of the system. To this end, in this paper, an intelligent IBVS system using learning-based grasping configurations and adaptive controller gain is presented. With the Gaussian Mixed Model (GMM), the robot is capable of learning the desired grasping configurations through a certain number of demonstrations. To accelerate the convergence of the IBVS system, a fuzzy logic (FL) unit is applied to adjust the gain $\lambda$. The simulation results demonstrate that the proposed approach enables the IBVS system gain the learning ability on grasping configurations, besides accelerating the convergence.
A Compact Low-Pass Filter Using Dielectric-Filled Capacitor on SISL Platform

This letter proposes a compact low-pass filter (LPF) using dielectric filled capacitor based on the substrate integrated suspended line (SISL) platform. A dielectric filled capacitor with high capacitance density is presented for the first time. Using this type of capacitor, a three-order lumped element LPF with a cutoff frequency of 120 MHz is designed, fabricated, and tested. The measured results agree with the simulated ones. The circuit size of the implemented SISL LPF is only $0.005\,\,\lambda \text{g}\,\,\times0.013\,\,\lambda \text{g}$ , which is very small when compared to the state of art. The measured insertion loss of SISL LPF in the passband is 0.12 dB and the stopband is from 0.35 to 2.27 GHz with a suppression level greater than 20 dB. A seven-order LPF is also designed to further demonstrate the good property of the proposed capacitor.
Evaluation of Deep Neural Networks for Advanced Intrusion Detection Systems

As the information systems are upgrading with the new technologies and becoming technology driven, the chance of getting intrude is also increasing at an unprecedented rate. To curb such activities of malpractices, the intrusion detection system is used to protect the sensitive and confidential data. In this paper, different types of intrusion detection techniques are reviewed and encompass how deep neural networks are deploying better accuracy when compared to classical machine learning techniques. Deep neural networks have been taken into force for intrusion detection with a learning rate of 0.1 and it has been executed for 1000 iterations. The dataset used for training purposes is KDDCup-‘99’ and the results were compared to traditional machine learning algorithms that are trained with the same dataset in DNN layers from one to five. Paper is not associated with research work rather than a comparison of different techniques.
DC-6 GHz GaAs MMIC Compact SPDT Switch

This paper introduces a GaAs MMIC Single Pole Double Throw (SPDT) switch up to C-band using pHEMT device. This switch operates from DC to 6GHz. Insertion loss obtained is less than 1dB. , input and output returnloss are better than 15dB at 6GHz. Isolation of the switch obtained is better than 35dB. 0.25um process is used for designing SPDT switch. Reflective switch topology is adopted for this design. Die dimension is 1.36mm x 1.4mm x 0.1mm. Typical Switching speed is 20nS.
Thirteen Level Multilevel Inverter Structure having Single DC Source and Reduced Device Count

This paper proposes a three-phase thirteen level multilevel inverter using a single dc source and reduced device count. The proposed hybrid topology has a 3-level T-type inverter cascaded with a half-bridge module followed by a 3-level floating capacitor (FC) unit having two capacitors in each phase. The ratio of dc-link and floating capacitors FC1 and FC2 are maintained at 8:4:1 respectively to generate higher levels in the output pole voltages. The primary objective of the proposed inverter is to improve the output voltage quality using only ten switches per phase which makes it suitable as a grid interfacing power converter. Both the floating capacitors and dc bus capacitors are appropriately balanced at the required voltage levels using existing redundant switching states. A PWM technique is proposed to balance the FCs with common-mode voltage injection to extend the linear modulation range so that dc bus utilization reaches up to 0.63 times the dc-link voltage. The performance of the inverter is analysed during different transient and steady-state conditions.
Advanced Transitive-Closure-Graph-Based Placement Representation for Analog Layout Design

Due to complexity and susceptibility of analog layouts, maturity state of analog physical design automation has largely lagged behind that of the digital counterpart. It demands effective placement representations to handle nontrivial analog placement topologies especially in the advanced nanometer technologies. In this paper, we propose an advanced transitive-closure-graph-based placement representation (ATCG) to effectively and efficiently deal with analog placement. The versatility and flexibility of ATCG can ensure it to accurately control spacing and merging constraints uniquely required by analog layout design. Moreover, we also present a redundancy control scheme among the representation states in order to generate high-performance analog placement with high computation efficiency. Our experimental results demonstrate high efficacy of our proposed representation.
A Comparative Study of Activation Functions and Training Algorithm of NAR Neural Network for Crop Prediction

The proposed study in this paper provides long-term crop prediction for Tamilnadu, India. Nonlinear Autoregressive (NAR) Neural Network (NN) with different parameter settings has been used to facilitate the correct quality and quantity of crop production. At the core of this study is to compare the effect of training algorithms (such as trainlm, trainbr, trainscg, traincgf, trainbfg, traincgf) and activation functions (such as tansig, elliotsig, logsig and purelin) in the performance of the crop yield forecasting model. This study showed that activation functions elliotsig and tansig with the training algorithm trainbr of NARNN delivered the most promising results based on the smallest error between actual and predicted value compared to the other activation and training functions of NARNN.
FASTER R-CNN for cell counting in low contrast microscopic images

Cell imaging is a difficult problem in medical imaging and the acquisition of unstained frames to reduce the side effects makes this even more challenging. As the field of automation is moving forward at ever-faster rates, cell counting and classification is an omnipresent yet repetitive task that would benefit greatly from this field. The counting of contiguous cells in a specific area could provide crucial contribution to work done in clinical trials. Cell counting, sadly, is most often conducted manually by humans and is time and resource consuming. Due to cells touching each other, non-uniform background, variations in the shape and size of cells, and different techniques of image acquisition, the task becomes even more difficult. In this paper we describe and test a convolutional neural network approach, employing CNN with region proposal called Faster-RCNN for cell counting in low contrast microscopic images. Several hyperparameters of the approach are tested and preprocessing procedures are employed to achieve an average precision of 79%
Metamaterial Loaded Nine High Gain Vivaldi Antennas Array for Microwave Breast Imaging Application

In this article, a metamaterial loaded high gain nine antennas based microwave breast imaging system has presented to detect an unwanted tumour in the breast phantom. The proposed antenna is designed on a modified version of a basic Vivaldi antenna. Single negative metamaterial structure has been loaded to the modified version Vivaldi to enhance far-field antenna properties like gain and radiation pattern, which is essential for the breast imaging system. The proposed antennas have achieved −10dB impedance bandwidth of 8.45 GHz (2.0- 10.45 GHz) and 7.9 dBi maximum gain in the operating band. To verify the time-domain performance, the fidelity factor is also calculated for the face-to-face orientation, which is near about 92.89%. After optimisation in high load phantom, near field directivity (NFD) is also analysed with the workload to ensure that the near field radiation properties. Finally, the nine metamaterial (MTM) antennas arrays are placed around the breast phantom in a hemispherical order to take the data of scattering parameters for tumour object analysis in a CST simulation environment. The extracted data has been processed through an imaging algorithm to reconstruct the image for identifying the unwanted tumour object inside the breast phantom.
Efficient Convolutional Neural Network for Pest Recognition - ExquisiteNet

Nowadays, due to the rapid population expansion, food shortage has become a critical issue. To stabilize food production, preventing crops from being attacked by pests is very important. In general, farmers use pesticides, however, the improper use also kills insects such as bees that are beneficial to crops. If the number of bees is too few, the supplement of food in the world will be short. Besides, excessive pesticides seriously pollute the environment. Accordingly, farmers need a machine that automatically recognizes the pests. Recently, deep learning is popular because of its effectiveness in the field of image classification. In this paper, we propose an efficient model called ExquisiteNet to recognize the pests. ExquisiteNet mainly consists of two blocks. One is double fusion with squeeze-and-excitation-bottleneck block (DFSEB block), and the other is max feature expansion block (ME block). ExquisiteNet only has 0.98 M parameters and its computing speed is fast almost the same as SqueezeNet. To evaluate our model’s performance, the model is testedd on a benchmark pest dataset called IP102. The model achieves a higher accuracy of 52.32% on the test set of IP102 without any data augmentation than that of many state-of-the-art models such as ResNet101, ShuffleNetV2, MobileNetV3-large, EfficientNet, and so on.
FastSCCNet: Fast Mode Decision in VVC Screen Content Coding via Fully Convolutional Network

Screen content coding have been supported recently in Versatile Video Coding (VVC) to improve the coding efficiency of screen content videos by adopting new coding modes which are dedicated to screen content video compression. Two new coding modes called Intra Block Copy (IBC) and Palette (PLT) are introduced. However, the flexible quad-tree plus multi-type tree (QTMT) coding structure for coding unit (CU) partitioning in VVC makes the fast algorithm of the SCC particularly challenging. To efficiently reduce the computational complexity of SCC in VVC, we propose a deep learning based fast prediction network, namely FastSCCNet, where a fully convolutional network (FCN) is designed. CUs are classified into natural content block (NCB) and screen content block (SCB). With the use of FCN, only one shot inference is needed to classify the block types of the current CU and all corresponding sub-CUs. After block classification, different subsets of coding modes are assigned according to the block type, to accelerate the encoding process. Compared with the conventional SCC in VVC, our proposed FastSCCNet reduced the encoding time by 29.88% on average, with negligible bitrate increase under all-intra configuration. To the best of our knowledge, it is the first approach to tackle the computational complexity reduction for SCC in VVC.
RFID-Based WIMEC-LANDMARC Indoor Location Algorithm

In view of the low positioning accuracy caused by the multi-path effect of Location Identification Based on Dynamic Active RFID Calibration(LANDMARC) system and the existing factors that affects the stability of the system, an RFID-Based LANDMARC indoor positioning optimization algorithm, namely Weigth Improverment and Mean Error Correction(WIMEC-LANDMARC), is proposed. The accuracy of the positioning system is improved by means of the average error correction, that is, the LANDMARC algorithm is adopted to locate the neighbor reference tags, and the average error is obtained to correct the initial position of the target to be measured and reduce the error of the positioning result. By improving the formula of reference tags' weight, the system outliers are eliminated and the stability of the positioning system is improved when Euclidean distance is zero. The experimental results show that the WIMEC-LAND MARC algorithm can effectively reduce the error of the target to be measured and eliminate the outliers, thus achieving the effect of improving the accuracy and stability of LANDMARC indoor positioning algorithm.
A Short-term Wind Forecasting Framework using Ensemble Learning for Indian Weather Stations

Wind speed forecasting has proven to be crucial for many domains like power generation. However, it is quite convoluted and demanding due to the unpredictability of the wind. Numerous physical and statistical methods have been employed in the past by the researchers in India and over the years, these methods have been improvised. While the physical methods prove to be computationally expensive and the statistical methods represent weaker generalizations of non-linear features, there arises a need to develop much more efficient and robust approaches that can augment with the traditional methodologies. Artificial Intelligence has shown tremendous potential in providing solutions to time-series forecasting tasks, akin to the precise task that this work aims to address. Various machine learning and deep learning algorithms have been found to be a prominent choice for forecasting of various weather parameters. This work aims at implementing two machine learning algorithms, namely, Light Gradient Boosting Machine (LGBM) and Long Short-Term Memory (LSTM) network to obtain short-term forecasts of wind speed of Indian weather stations. Robust feature engineering approaches like Miss Forest to fill in the missing values, a novel feature representation for giving importance to recent data, and Fast Fourier Transform (FFT) with Digital Filters for removal of outliers and noises in the data have been used. Finally, stacking, an ensemble learning has been implemented using LGBM and LSTM as base learners and Random Forest as the metalearner. The average Root Mean Square Error (RMSE) (in m/s to tenths) obtained by individual models is high. 24-hour wind forecast errors of LGBM and LSTM are 8.19 and 21.17, whereas the forecast error using the proposed ensemble framework is 5.74.
Stiffness analisys of the Tripteron parallel manipulator

This article was devoted to the stiffness modeling of parallel robot Tripteron, which contains 3 separate legs directed along axes X, Y and Z. Two different techniques, Virtual joint modeling(VJM) and Matrix structural analysis(MSA) were applied in order to obtain the desired elastostatic model of the robot. Cartesian stiffness matrices were determined in a semi-analytical manner, corresponding deflections of the end-effector under external loads were represented in the form of deflection maps. Both approaches were analyzed and compared according to the computational cost and difference between results.
HFAM: Soft Hydraulic Filament Artificial Muscles for Flexible Robotic Applications

The use of soft artificial muscles (SAMs) is rapidly increasing in various domains such as haptics, robotics, and medicine. There is a huge need for a SAM that is highly compliant and facile to fabricate with performance characteristics similar to human muscles. This paper introduces bio-inspired soft hydraulic filament artificial muscles (HFAMs) that can be extended and contracted under fluid pressures. The HFAMs, which have a high aspect ratio of at least 5000, use a simple and low-cost fabrication method of insertion, enabling scalability and mass-production while increasing its generated force via a stiff constrained helical layer and an adjustable stretch ratio of their inner silicone microtube. The developed muscles can produce a high elongation of 246.8% and a high energy efficiency of 62.7%. In addition, the HFAMs can generate a higher contraction force compared to existing state-of-the-art devices via their constrained hollow layer and the adjustable stretch ratio of the inner microtube, enabling a tunable force capability. Experiments are carried out to validate the HFAM performance including durability, lifting, frequency response and energy efficiency tests. The HFAM capabilities are demonstrated via various experiments, offering a potential substitute for the conventional tendon-driven mechanisms with less friction loss and stable energy efficiency while working against long and tortuous paths. A HFAMs-driven soft exoskeleton glove that could assist in grasping multiple objects is developed and evaluated. The new muscles open great opportunities for research and commercial sectors including emerging applications such as soft wearable devices and flexible surgical robots.