Miracle-dz/UniFAR-FTUs huggingface dataset:
MR and CT image fusion using three-stage multiscale decomposition and PCNN

Medical imaging plays the crucial role in the current era of scientific enlargement in every medical therapy and diagnosis applications. In this regard, medical image fusion could be a powerful tool to combine multi modal images by using image processing techniques. But conventional approaches, failed to provide the effective image quality assessments and robustness of fused image. To overcome these drawbacks, in this work three stage multi scale decomposition (TSMSD) using pulse-coupled neural networks with adaptive arguments (PCNN-AA) approach is proposed for multiple modality medical image fusion. Initially, nonsubsampled shearlet transform (NSST) is applied onto both source images to decompose them into high and low frequency bands. Then, nonlinear anisotropic filtering with discrete Karhunen-Loeve transform (NLAF-DKLT) methodology is used to fuse the low frequency bands of the source images. Next, high frequency bands obtained from NSST are fused using PCNN-AA approach. Now, low and high frequency fused bands are reconstructed using NSST reconstruction. Finally, band fusion rule algorithm with pyramid reconstruction is applied to get final fused medical image. Extensive simulation outcome discloses the superiority of proposed TSMSD using PCNN-AA approach as compared to conventional medical image fusion approaches in terms of fusion quality assessment metrics such as correlation coefficient (CC), standard deviation (STD), mean (M), mutual information (MI), and entropy (E).
Application of Signed Quadrature Spatial Modulation in 6G Massive MIMO Systems

Regardless of the number of employing antennas, it has been proved that spatial modulation (SM) uses only a single radio frequency (RF) chain to perform digital signal processing in MIMO wireless communications. Therefore, spatial modulation can be used for realizing the 6G-MIMO systems, which are necessary to equip extremely massive numbers of antennas, since its unique property can completely satisfy energy efficiency, i.e., key performance metric for 6G mobile network. Unfortunately, it is quite difficult to attain “Terabit era” with SM because its spectral efficiency grows logarithmically with the number of transmitted antennas, e.g., just 14 bps/Hz from 1024×1024 16-QAM system. This motivates us to investigate the enhanced version of SM called signed quadrature spatial modulation (SQSM), which theoretically offers greater spectral efficiency. In order to exhibit its potential in 6G wireless system, the performances of such a technique under 6G parameters are primarily investigated in this work. Many research challenges regarding SQSM transmission for 6G systems are also addressed. Moreover, the normalization factor of SQSM is analyzed and generalized to facilitate the research in this field.
Low-Drift RGB-D SLAM with Room Reconstruction Using Scene Understanding

Room reconstruction task is very important for robot motion planning and navigation. Existing indoor dense mapping algorithms are inefficient in cluttered and occlusion environments because the re- constructed building environment consists of unmeaningful plane fragments. In this paper, we present an architecture for online, incremental room reconstruction which combines an accurate RGB-D SLAM and room layout understanding. We proposed an efficient scene understanding method, which detects room’s corners to infer the wireframes and layout planes of room from single RGB-D image, even if the parts of the room are occluded. Moreover, the 3D global features (wireframes and layout planes of the building) can also improve the accuracy of state estimation, especially in geometric indoor environments. These 3D global features are treated as global consistent landmarks, it efficiently bounds the trajectory drift with the travel length increasing. On a public ICL-NUIM dataset, our algorithm achieves higher accuracy than other state- of-arts, and it also builds a geometrically meaningful map.
Handling Missing and Imbalanced Data to Improve Generalization Performance of Machine Learning Classifier

Exploratory data analysis (EDA) is an important process for creating a machine learning model. Through data preprocessing, we can see the characteristics of the data and how to handle them. Data preprocessing is part of EDA that handles problems in the data before creating training and testing datasets. As a study case, this research uses the air pollution dataset published by the Ministry of Environment and Forestry of the Republic of Indonesia. The dataset has missing value and imbalanced class problems in which the previous research used complete case analysis for missing value and neglected imbalanced class problems within the dataset. The dataset has more than 5% missing values and has a significant amount of imbalance. The class that has the most amount of data has around 64% of the data, but the least amount of data only has 0.6% of the data. On the other hand, the dataset’s missing values are randomly scattered among the data. This research proposed a preprocessing process to combine Multiple Imputation by Chained Equations (MICE) and Synthetic Minority Oversampling Technique (SMOTE) and tested it with three machine learning methods such as Random Forest, Support Vector Machine, and K-Nearest Neighbour. As a result, the g-mean metrics for those three machine learning approaches improved from 0.2 percent to 3.8 percent.
Paddy seed variety classification using transfer learning based on deep learning

Classification of paddy seed varieties is a critical task that determines the price and quality of rice in the market. Deep learning methods that have been successfully applied in different areas are not even in image classification. In this paper, we proposed a classification of paddy seed varieties by using transfer learning of deep learning with three pre-trained weights: VGG16, InceptionV3, and MobileNetV2. Over 1200 images of paddy seed are collected for custom datasets. The experimental result shows the overall accuracy of VGG16, InceptionV3, and MobileNetV2 are 80.00%, 83.33%, and 83.33% respectively. Test Loss of each model is 52.15%, 28.41%, and 61.95% sequentially. Therefore, InceptionV3 not only gives the best accuracy than others but also the least test loss. This proposed research can be a great advantage for a farmer to identify paddy varieties that aimed to reduce the adulteration of paddy varieties as well.
Design and Control of a Magnetic Driven Worm-like Micro-robot

This paper presents the design and control of a worm-like soft robot, which is driven by a 3-D Helmholtz coils magnetic system. The whole robot adopts soft materials and is manufactured by Stereo Lithography Apparatus technology. NdFeB cylindrical magnets with opposite magnetization directions are glued to the front and rear of the robot, and the different friction forces on the head and tail of the robot are used to realize the worm-like gait movement. Based on the principle of magnetic field superposition and the magnetic dipole model, the robot can be positioned in real time with dual magnetic targets, with an average positioning error of 2.30mm, and the robot's automatic navigation movement is realized through closed-loop feedback.
A Long-focus Camera and Telemetry LiDAR Calibration for Rapid Transit Equipment

The fused use of the long-focus camera and telemetry LiDAR is necessary for advanced long-distance detection requirements in autonomous driving vehicles and unmanned subways, and the extrinsic parameter calibration is a precondition for multi-sensor fusion use. However, the inaccuracy of long-focus camera detection and low accuracy of data association between LiDAR-camera make the calibration a challenging problem. In this paper, we propose a novel calibration method that treat the detection poses as variables in the optimization framework, which significantly reduces the impact of detection errors on calibration accuracy. Furthermore, we construct 3 error terms to obtain better estimation of the variables, and the designed error terms only require plane-to-plane data association, avoiding incorrect data association that leads to failure in extrinsic parameter estimation. Then we made a full comparison with other methods through real-world experiments, which showed that our method achieves the best calibration result.
Force-position perception of soft fingers with variable stiffness based on fiber Bragg grating sensor

In this study, we propose a soft finger driven by shape memory alloy (SMA) and embed with fiber Bragg grating (FBG) sensor, with the purpose of realizing force-position perception of soft fingers when grasping objects. Firstly, the structure and material of the soft finger are introduced, and simulation analysis is carried out by ABAQUS. Then we accomplish the packaging and calibration of FBG pressure sensor to ensure the accuracy of fiber sensing. Finally, the force position prediction experiment of soft finger with variable stiffness is performed, and the experimental, theoretical and simulation results are compared. The results show that the FBG sensor can effectively predict the force-position perception of the soft finger, and at the same time verify the feasibility of improving the stiffness of the finger.
Towards a more practical data-driven biped walking control

Walking motion is a fundamental skill involved in daily bipedal activities. Generating the walking motion from mocap data requires a controllable and responsive character in both animation and robotics. The physic-based data-driven technologies nowadays have shown great value in the motion imitation of captured walking data. In this paper, we propose a new data-driven bipedal control framework by adjusting the mocap data with physics-based model to achieve a robust and natural performance while keeps the style of the data as complete as possible. In our framework, the desired bipedal action is designed from the balance strategy and a cascaded position-velocity control with gravity compensation is proposed for joint tracking control of the desired action. We demonstrate the effectiveness of this framework through different external perturbation tests. This framework more fully integrates the original data into desired-action design while keeps a robust and stable performance in the simulation.
A Modular Lower Limb Exoskeleton System with RL Based Walking Assistance Control

Lower limb exoskeleton for people suffering from unilateral weakness or even hemiparesis has attracted considerable interest in recent years. For walking assistance in such scenarios, the exoskeleton is expected to help generate symmetrically coordinated motion with the unaffected side. Challenges facing current exoskeletons are related to mechanical design and control strategy. In this paper, we approach the problem by presenting a modular lower limb exoskeleton system that consists of a modular hip orthosis and a modular knee orthosis, each has one electric actuator in the extension/flexion joints. Meanwhile, the exoskeleton system is an under-actuated system with some degree of freedoms remains passive, making it more complex than fully actuated robotic systems where classical PID controller can achieve satisfying performance. We than propose a reinforcement learning based controller to provide walking assistance based on a modeled leaderfollower system. And the walking assistance task can be reformulated as a motion trajectory tracking problem. The proposed control method is validated in simulation platform with data obtained from a healthy subject imitating hemiplegia patients with the designed set of single-side lower limb exoskeleton.
Weather Prediction Analysis by using Hybrid Markov Model and Artificial Neural Network

This proposal depicts a technique of using a combined approach to determine the climatic condition. Individuals in the majority of urban communities around the world, perform various outdoor recreational activities in their spare time, wherein the unfavorable weather may not be appropriate for them. Since the climate in India is inconsistent, a weather analysis must be created to measure climate effectively. By evaluating the atmospheric condition, numerous dangers could be overcome, since that would alert us of the forthcoming accidents that could occur. Thus, so as to do this, the Hidden Markov Model and Artificial Neural Network have been used to apprehend the climatic condition with aggregate cooperation of two state’s climate to predict the third state’s climate. The likelihood of an accident occurring may be reduced by watching this weather report.
Augmentation of Intelligent Agent for Multiple Access Protocols in Wireless Sensor Networks

In any wired or wireless network, the huge data introduced into it are the data generated by the sensors placed in the environment. Wireless Sensor Networks play an important role in capturing the data (including big data), performing the computation and returning the result back to the user. (viz.,a Healthcare Information System). Due to the enormous access of internet by the users distributed geographically, the logical channel must be granted access in an even fashion. Multiple access protocols gain their importance in resolving the access conflict to the shared medium by the sender, receiver and the other intermediate nodes. When two nodes decide to transmit data at the same time, access is granted by dividing the channel to slots based on frequency, time or code. If packets collide in the network, collision avoidance is enhanced by Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocol. An intelligent agent is incorporated in the multiple access protocols for knowledge based collision avoidance. In particular, the learning agent feeds the system by observing the environment, feeds data into the inference engine, processes data, fetches the output. The learning element is responsible for making improvements like efficient bandwidth allocation and energy conservation. It uses feedback from the critic. The performance element is authoritative for selecting external actions. It takes in percepts and decides on actions. Hence the network support system built on Multi Protocol Label Switching (MPLS) identifies the unique traffic being sent from diverse sources. Network Virtualization ensures the data are aggregated and segregated at the respective ingress and egress routers.
Optimizing GPU Deep Learning Operators with Polyhedral Scheduling Constraint Injection

Automatic parallel code generation from high-level abstractions such as those manipulated by artificial intelligence and deep learning (AI/DL) frameworks heavily rely on compiler techniques for automatic parallelization and optimization. Many recent advances rely on the polyhedral framework for this task because of its ability to model and to apply a wide range of loop transformations. However, modeling the complexity of the target architecture and of efficient cost models to decide about the best transformation is in general out of reach for a framework based on linear/affine constraints. In this work, we propose to decouple the polyhedral framework into linear and non-linear components. We introduce the constraint tree abstraction which may be generated by a non-linear optimizer and injected to the polyhedral optimization process to build better solutions. We present how to benefit from such a mechanism to generate efficient codes for GPU in the context of AI/DL operators. Our constraint injection allows to drive the polyhedral scheduler towards efficient solutions for load/store vectorization relying both on memory coalescing and vector types. We implemented our scheduler supporting constraint injection and our constraint construction system within a production AI/DL framework. Experiments on well known neural networks show the efficiency of this approach with respect to state-of-the-art polyhedral scheduling for GPU.
Robotic Tactile Recognition System Based on AM-LSTM Model

Nowadays, various kinds of small desktop robotic manipulators appear for sale. Combining with the visual camera, the small manipulator can arrive the target objects and grasp them. However, stable grasp experiments always fail due to the lack of tactile perception. In this paper, we develop a tactile recognition system based on a 4-DOF small desktop robotic arm and two-finger grippers which install 5*5 array tactile sensors. Then, based on this system, a new tactile dataset OMT-13 is built which include the tactile sequences of 13 kinds of objects collected by grasping in different gripper poses and speeds. And employing this dataset, we establish a tactile recognition model —Long Short-Term Memory with attention mechanism (AM-LSTM). Finally, the experiments find that the closing speeds of the gripper affect the tactile data and recognition accuracy of the tactile model. Relative bigger speeds will get higher recognition accuracy. When the speed of the gripper is 20 (46 r/min), the accuracy is the highest, reaching more than 90%.
3D printed Optimization: Bayesian Neural Network Trade-Off between Cost and Load-Bearing

The structural design is an important consideration when designing robots, in special the load-bearing capacity. The optimal balance between material cost and structural strength is crucial to maximize performance while keeping robots light and cheap. This paper introduces a design method for load-bearing 3d printed structures based on Bayesian Neural Network, and uses a bridge as an example to validate the effectiveness of this technique. We prove that our algorithm can accurately predict the performance of a structure based on parameters, and choose the one which combines low cost and high performance. We present an error between actual load-bearing capacity and the predicted value as below 4.585%. In addition, we also propose design patterns based on Bayesian Optimization, and our results show that we can find the best design in 23 iterations. This method helps structural designers simplify the design process, shorten the design time, and saves material costs, which will help roboticists create cheaper, faster and better robots in the future.
Multi-Objective Optimization of a Shading System to Reduce Heating and Cooling Energy Needs of a House in Benguerir, Morocco

This work presents a methodology combining multiobjective optimization with MOBO and building energy simulation with TRNSYS, to optimize shading coefficients to reduce the heating and cooling energy needs of an existing building in Benguerir-Morocco. The optimization was performed using the non-dominated sorting genetic algorithm of (NSGA-II). Optimal values for the shading coefficients of windows located on different orientations were determined. The optimal heating/cooling demand was found to be about 130 kWh/m2.year instead of 207 kWh/m2.year of the energy needs of the building.
A Multi-Fingered Robot Hand with Remote Center of Motion Mechanisms for Covering Joints with Soft Skin

Robot hands are crucial for dexterous interaction with objects. The joints of existing robot hands typically cannot be completely covered with skin. This paper demonstrates a robot hand for which the palmar side of the joints, which is the side that is typically in contact with objects, can be nearly completely covered with a continuous, thick skin layer. This is possible with remote center of motion (RCM) mechanisms. Covering the palmar side of the joints with a thick skin layer has potential benefits for grasp stability, safety and allows the inclusion of tactile sensors in the joints in future work. A coupling mechanism is used in combination with the RCM mechanisms for the distal interphalangeal (DIP) and proximal interphalangeal (PIP) joints. Our evaluation shows that the hand can grasp various objects with different postures. The abduction/adduction mechanism, which is implemented in the metacarpophalangeal (MCP) joint in addition to the flexion/extension movement with an RCM joint, is demonstrated. It is further demonstrated that the RCM joints ensure that even thin objects that are grasped at the joints will make contact with soft skin.
A Distributed Persistent Coverage Algorithm of Multiple Unmanned Aerial Vehicles in Complex Mission Areas

In a dynamic and complex environment, persistent coverage can effectively reduce the uncertainty of the area. This paper designs a distributed area persistent coverage algorithm for the multi-UAV system based on the area coverage information maps. UAVs rely on the information obtained from their detection and local communication to make online coverage decisions. The algorithm considers the detection probability of the sensors and the importance of different areas to achieve a controllable coverage. Moreover, according to collision avoidance and decentralization rules in the distributed anti-flocking method, UAVs can disperse in the area as far as possible and avoid collisions. Simulation results show that the algorithm can achieve continuous and stable coverage of the task area with good scalability, adaptability, and robustness.
$8\times 8$ Microring Optical Switch on a Dual-layer Si3N4-on-SOI Platform

We demonstrate an $8\times 8$ microring switch based on a dual-layer Si3N4-on-SOI platform. Experimental results show the chip has a fiber-to- fiber insertion loss < 9.23 dB, crosstalk < -22.5 dB and optical bandwidth of 70 GHz.
Edison: measuring scaffolding accuracy with edit distance

Motivation The quality of reference genomes critically affects analyses of next generation sequencing experiments. During the construction of the reference genome, contigs are organized into their underlying chromosomes in the scaffolding step. Historically, the quality of scaffolding software has been difficult to evaluate in a systematic and quantitative fashion. To this end, we identified genomic edit distance as a compelling method for evaluating the quality of a scaffold. Results We present Edison, a Python implementation of the Double Cut and Join (DCJ) edit distance algorithm. Edison calculates the overall accuracy of a given scaffold using a reference genome and also provides scores for characterizing different aspects of the scaffolding accuracy, including grouping, ordering, and orientation. All metrics are calculated on a length-weighted basis, which rewards the correct placement of longer contigs over shorter ones. By creating 1000 random assemblies of the S. cerevisiae genome, we show that our scaffolding accuracy provides a more reliable metric than the commonly used metric, N50. Edison can be used to benchmark new scaffolding algorithms, providing insights into the strengths and weaknesses of each approach. Availability and Implementation Edison is available under an MIT license at https://github.com/Noble-Lab/edison.
Exploration of Sub-Optimal Constellation Maps for Coherent Fiber-Optic Communication links by Auto-Encoder Type Machine Learning

Autoencoder (AE) was applied to the exploration of sub-optimal constellation maps and decision boundaries in a coherent optical fiber communication link. Single-wavelength point-to-point single-mode fiber coherent optical communication link that considers wavelength dispersion and self-phase modulation was incorporated in the middle of AE and sub-optimal constellation maps / decision boundaries were automatically acquired by learning AE under various conditions. The transmitting side of AE automatically configured an encoder, so called AE modulation (AEM), from one-hot-vectors to IQ constellations considering noise, wavelength dispersion, and self-phase modulation (SPM) given during transmission, and the receiving side automatically configured a decoder having linear decision boundaries from the received IQ constellations to one-hot vectors. Setting an appropriate signal-to-noise power ratio (SNR) during AE learning resulted in constellations and decision boundaries stable for wide range of operating SNR. The obtained BER characteristic for 16AEM was approaching to the one of the compensated 16 quadrature amplitude modulation (16QAM).