Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning error models for graph SLAM

Following recent developments, this paper investigates the possibility to predict uncertainty models for monocular graph SLAM using topological features of the problem. An architecture to learn relative (i.e. inter-keyframe) uncertainty models using the resistance distance in the covisibility graph is presented. The proposed architecture is applied to simulated UAV coverage path planning trajectories and an analysis of the approaches strengths and shortcomings is provided.
Binary Complementary Sequence Set With Low Correlation Zone

Recently, quasi-complementary sequence sets (QCSSs) including low correlation zone complementary sequence sets (LCZ-CSSs), and low correlation complementary sequence sets (LC-CSSs) have raised lots of concerns. According to the theoretical bounds, the set size of a QCSS is much larger than that of a traditional perfect complementary sequence set (PCSS). As a result, it is believed that QCSSs have potential applications in multi-carrier code-division multiple-access (MC-CDMA) systems to support more users. As binary sequences are desired in practice because of easier realization, we consider the design of binary QCSSs with large set size in this letter. A construction of aperiodic binary LCZ-CSSs is proposed, which leads to aperiodic binary LCZ-CSSs with asymptotically optimal parameters. Additional, aperiodic binary LC-CSSs with asymptotically optimal parameters can also be derived from the proposed construction.
Weather Forecasting Using Artificial Neural Networks

The weather conditions such as Minimum temperature, Precipitation Rate, wind speed, Maximum temperature, pressure etc. have many important roles in the field of cultivation. In a country like India, having precise weather forecasting technology will grant farmers a chance for better productivity of crops. Not only farmers, weather information may by a direct route or not affect industrial sector also. Artificial Neural Network, which is a soft computing technique, is a constructive advance towards building an intuitive system which will be suited for processing the nonlinear conditions of weather and make prognosis. The suggested work focuses on initiating such an intuitive system which can forecast weather with least error rate with a more acceptable architecture.
SPRINT: Subgraph Place Recognition for INtelligent Transportation

Visual place recognition is an important problem in mobile robotics which aims to localize a robot using image information alone. Recent methods have shown promising results for place recognition under varying environmental conditions by exploiting the sequential nature of the image acquision process. We show that by using k nearest neighbours based image retrieval as the backend, and exploiting the structure of the image acquisition process which introduces temporal relations between images in the database, the location of possible matches can be restricted to a subset of all the images seen so far. In effect, the original problem space can thus be restricted to a significantly smaller subspace, reducing the inference time significantly. This is particularly important for scalable place recognition over databases containing millions of images. We present large scale experiments using publicly sourced data that show the computational performance of the proposed method under varying environmental conditions.
MSEF-ImgSeg: An Intelligent Algorithm for Multi Scale Exposure Fusion Using Image Segmentation and GGIF

Multi-scale exposure fusion is a powerful approach to fuse variously low dynamic range images into a high quality image. Fine and attractive information of source images is added in the resultant image. It may yield better fusion result than the images fused by single scale exposure fusion. In multi-scale exposure fusion possibility of halo artifact can be produced and the details in darkest and brightest areas are normally not retained in the final fused image. Based on image segmentation and edge preserving filtration technique a novel algorithm is proposed in this paper for multi-scale exposure images. Taking advantage of super pixel (image segmentation) along with edge preserving technique, details in the darkest and brightest regions are well protected which removes the halo artifacts produced in the fused image. The outcome of experiments proved that proposed algorithm provides better results than other state of the art algorithms used for image fusion.
High Accuracy and Efficiency Grasp Pose Detection Scheme with Dense Predictions

Learning-based grasp pose detection algorithms have boosted the performance of robot grasping, but they usually need manually fine-tuning steps to find the balance between detection accuracy and efficient. In this paper, we discard these intermediate procedures, like sampling grasps and generating grasp proposals, and propose an end-to-end grasp pose detection model. Our model uses the RGB image as the input and predicts the single grasp pose in each small grid of the image. Furthermore, the best grasps are found by non-maximum suppression (NMS) strategy. The clustering and ranking procedures are left for NMS while the network only generates dense grasp predictions, which keeps the network simple and efficient. To achieve dense predictions, the predicted grasps of our detection model are represented by the 6 channels images with each pixel location representing a rated grasp. To the best of our knowledge, our model is the first neural network that attaches a grasp pose in pixel level. The model achieves 96.5% accuracy which costs 14ms for prediction of a 480×360 resolution RGB image in Cornell Grasp Dataset, and 90.4% robot grasping success rate for unknown objects with a parallel plate gripper in the real environment.
On the efficient control of series-parallel compliant articulated robots

Torque distribution in redundant robots that combine the potential of asymmetric series-parallel actuated branches and multi-articulation pose a non-trivial challenge. To address the problem, this work proposes a novel optimization based controller that can accommodate various quadratic criteria to perform the torque distribution among dissimilar series and parallel actuators in order to maximize the motion efficiency. Three candidate criteria are composed and their performances are compared during periodic squat motions with a 3 degree of freedom series-parallel compliant articulated leg prototype. It is first shown that by minimizing a criterion that takes into account the actuator hardware specifications such as torque constant and transmission ratio, the gravity-driven phases can be lengthened. Thereby, this particular criterion results in slightly better performance than when adopting a strategy that maximizes the torque allocation to the higher efficiency actuators. Furthermore, valuable insights such as that the efficacy of maximum utilization of the highly-efficient parallel actuation branches decreases progressively at high frequencies were observed.
Backscatter-assisted Wireless Powered Communication Networks with Multiple Antennas

Backscatter communication (BC) and wireless powered communication (WPC) are the two revolutionized technologies conceived as a promising technological solution to realize future Internet-of-Things (IoT) networks. By integrating BC into the WPC system enables more efficient utilization of the network resources, and also it improves the system performance significantly. In this paper, we developed a BC assisted WPC network, so-called BC-WPCN, which consists of multiple energy-constrained sensor nodes (SNs), powered by a dedicated power source (PS) and the data access point (DAP). In this system, PS performs channel estimation and energy beam-forming that helps the SNs to achieve energy harvesting and information backscattering. Each SNs are accessed sequentially at the DAP to mitigate multi-user interference-effect. Closed-form expressions are provided to investigate the system performance through outage probability and achievable throughput. Numerical results showed that, compared to conventional WPCN systems, the proposed BC-WPCN model achieves better throughput performance with an advantage of reduced system hardware complexity.
Emerging Techno-Socio spaces of Juvenile Correctional Facility

One of the highly crucial, yet uninvestigated problems architects face today is the disciplinary character of architecture. Disciplinary— “the way that architecture defines, creates, distributes, and applies the knowledge within its domain of influence”—is increasingly central to the discussions about the present and future direction of the field. Architectural knowledge is diverse and complex, drawing from a range of fields that influence how building or environment is imagined, designed, described, constructed, and sold and how it performs, once built. The study of the prison built environment is a subject in its own right meeting ground for a number of design guidelines. There is sense in which the prison built environment can arise naturally from the activity that the architects uniquely design. The knowledge of prison architecture is strictly an outcome of particular methods, their epistemological assumptions, and institutional mechanisms that facilitate knowledge production. Architectures of control have been considered to direct and/or prohibit certain types of behaviors within a given space. Generating the built environment, the environment which deliver the situation for human interaction, lures on numerous disciplines, together with architecture and law. The paper will investigate the role of architecture in the design of prisons in order to understand how we could develop a project for a prison that is a place for both punishment and rehabilitation with the possible disciplinary design approaches aimed at designing a new jail typology considering safety, security & surveillance. The effectiveness of a prison is not given only by the efficiency of the justice system in force, but also by the articulation of spaces and by the quality of the architecture. The contradiction of the prison is revealed by its need to respond to two opposing demands: punishment and reintegration. The aim of this paper is to sketch a view of how architecture regulates behavior into the nature of prison architecture. To investigate how architectures of control physically prevent or direct a certain type of behavior along with regulating user behavior through the psychological effects of the surrounding built environments.
Real-Time Monitoring of Thermal Response and Life-Time Varying Parameters in Power Modules

This article introduces new technologies for monitoring thermal response and life-time varying parameters of power electronic modules in real time without interrupting normal converter operation. The technologies are embedded in an adaptive thermal observer, which integrates temperature measurements and electrothermal models to estimate device losses and temperatures at reliability-critical locations of the power module. The observer structure includes a model reference adaptive system for adaptively calibrating device switching-loss models. This improves thermal monitoring accuracy, avoids time-consuming offline calibrations, and creates the opportunity for efficiency estimations during power module operation. The adaptive observer together with small-signal loss-excitation and system-identification technologies extract the thermal impedance of the power module in magnitude and phase over multiple frequency decades. This in situ thermal impedance spectroscopy has the unique feature of continuously providing information on the thermal interface of the power devices without interrupting normal inverter operation. The extracted information, i.e., life-time varying electrical and thermal parameters, enables diagnosing power module state-of-health with improved accuracy compared to prior technologies. It even allows separating different degradation effects, such as bond wire lift-off, device and base-plate solder delamination as well as deterioration of the convection process. With these new features, next generation monitoring systems can extract thermal response and degradation and thus protect power modules from severe thermal stress and overload. Furthermore, the obtained state-of-health information can be selectively used to reduce thermal stress and to schedule predictive maintenance leading to a safer and more reliable operation of future converter systems.
A Conceptual Framework for AI-based Operational Digital Twin in Chemical Process Engineering

As digitalization is becoming more and more an integral part of business in all sectors, the digital twin paradigm starts to play a more crucial role. This paper primarily aims at describing a generic framework for digital twin development in chemical process industry from an operational perspective. The main building blocks of the operational digital twin are presented, namely data management, process modeling, process optimization, production scheduling, and process control, as well as the deployment. Strong emphasis is put on the advanced process control hierarchy. Additionally, the role of artificial intelligence in the development and deployment of operational digital twin in process industry is presented, particularly regarding surrogate modeling, predictive modeling and AI supported optimization and control. Consequently the potential for new business models induced by digitalization is discussed, and an outlook for prospective research topics is provided.
Successive Stiffness Increment and Time Domain Passivity Approach for Stable and High Bandwidth Control of Series Elastic Actuator

For safe human-robot interaction, various type of flexible manipulators have been developed. Especially series elastic actuator (SEA) based manipulators have been getting huge attention since the elastic element of SEA prevents people from injury when undesirable collision happens. Moreover, it improves system durability by absorbing impact force, which could damage actuators. However, the elastic element inside SEA manipulator causes low system bandwidth which limits the speed performance of conventional impedance control approaches. To alleviate the low bandwidth issue of impedance controlled SEA while guaranteeing system stability, we implement Time Domain Passivity Approach (TDPA) and Successive Stiffness Increment (SSI) approach, which was invented in haptic and teleoperation domain. Impedance controlled SEA is reformulated as a two-port electrical circuit network for implementing TDPA. In addition, a pair of input and output power conjugate variable, dominating the system passivity is identified for implementing SSI approach. Experimental results showed that TDPA and SSI approach can render the stiffness of the impedance controller, which decides the bandwidth, upto 350 kN/m without any stability issue, while normal impedance controller only render upto 120 kN/m. Although both of the approaches significantly increased the bandwidth of the impedance controlled SEA, TDPA slightly outperformed in stability, and SSI outperformed in tracking.
Enhancing Notation-Based Code Cloning Method With an External-Based Identifier Model

Code clone detection is known for solving code paradigm problem in software development. Malware analysts also apply this technique to detect whether a set of malware applications originated from the same malware family based on the similarity in their source code. Until now, existing notation-based approaches are based on standard identifier notations and generating signatures from the notations output. Specifically, for a set of words (or lexemes), the analysts apply rules to determine the type of a lexeme and label each lexeme with a token type. So far, none of the existing code clone detection models considers collecting identifiers from an external source. In this paper, we propose a novel External-based Identifier Model for code clone detection. The proposed model assumes the existence of an external source code that can be used as a supervisor for identifying and labeling a specific set of lexemes. By introducing the external-identifiers into source code detection, our model could distinguish between multiple fragments of code that share the same sequence of standard tokens. One of the study cases for our model is Android analysis where the Android Open Source Project can be used as an external source. The experiment on one millions line of Android source code has shown that our proposed solution could reduce the number of multiple code mapping to single signature situations in comparison with the traditional method. Furthermore, the experiment on code suggestion has proved that our model could reduce the suggestion step for providing faster output than a notation-based approach.
A Parameter Extraction Method for the Li-Ion Batteries With Wide-Range Temperature Compensation

An accurate battery model is essential for precise estimation of battery performance indicators, such as state of charge (SoC), state of health (SoH), etc. It helps in determining the optimum energy management and therefore, optimum utilization of the battery capacity. Some of the battery model parameters may change with the variation of environmental conditions, such as operating temperature, and therefore, accurate temperature compensated model estimation is necessary to minimize the error in battery model and SoC estimation. This article presents a novel parameters extraction method for the Thevenin equivalent circuit model of Li-ion batteries considering their resiliency on temperature effect. The proposed approach represents each of the RC parallel circuits of the Thevenin-based equivalent circuit battery model as a first-order linear time-invariant (LTI) system. The resistance and capacitance values for each of the RC circuits in the model have been identified as the parameters of a standard LTI system using the system identification theory and represented them as the temperature-compensated model parameters. The proposed model can be applied to various applications, such as SoC, SoH estimation, and battery energy management. In order to demonstrate the suitability of the proposed modeling approach, the model is augmented with the conventional extended Kalman filter to enable accurate SoC estimation under varying operating temperatures. The accuracy and effectiveness of the proposed modeling approach and its suitability to enhance SoC estimation under a wide operating temperature range ($-$5 to 45 $^\circ$C) have been validated through laboratory experiments in the LabVIEW platform.
Procedural Generation of Multistory Buildings With Interior

This article is about the procedural generation of accessible multistory buildings including stairwells. We present an algorithm featuring a natural bottom-up approach, where each building is defined by a set of rooms that can be connected by doors or stairways and are equipped with windows. Our approach is robust so that neither the rooms, nor the connections, nor the objects can be placed in invalid positions. In the second part, we enhance the process by a procedural growth algorithm with automatic corridor detection and stairwell placement. Finally, we propose an automatic mesh generation for either procedurally or manually designed buildings. We evaluate our approach by creating a full implementation of the algorithm, and judge it based on the resulting realism, accessibility, and diversity of a generated test set of buildings.
Stiffness Effect on Periodic Locomotion of Legged Robot Based on Dimensional Analysis and Fixed Points Search Method

The high complexity of dynamic equations of legged robot with spring legs often leads to difficulty in analyzing the influence of spring stiffness on periodic locomotion of legged robots. To deal with this problem, this paper firstly applies dimensional analysis method to determine the relationship among physical quantities. And through the dimensionless processing for dynamic equations, the relatively independent expression of the spring stiffness, i.e. relative spring stiffness, is obtained, which can lower down the coupling degree among the physical quantities in dynamic equations. Then, the fixed point distribution of the dynamic model for legged robot, with respect to different stiffness, is presented based on the fixed point theory. Finally, the regularities of influence of spring stiffness on the fixed point are summarized by analyzing the simulation results.
Analysis of Character Dimensions in Vehicle Number Plates

The dimensions of characters in a vehicle number plate are clearly specified by the Motor Vehicles Act of India. Any deviation from the specifications attracts penalty. Presently the inspecting authorities are not taking this aspect seriously due to the difficulties in measuring the dimensions manually. No work related to estimating the actual dimensions of the characters in the number plates have been observed. This work addresses this problem of automatically detecting the real dimension of the alphanumeric characters in the number plate to check the conformity with the relevant laws. Number plate images have been acquired manually and the characters are segmented through image processing. The segmented objects are classified between the characters based on their pixel distribution using machine learning techniques. The observed pixel distribution has been used to train a machine learning based classifier for character identification. The algorithm was developed and simulated using MATLAB. The trained classifier when cross-validated against a test dataset, an overall improvement of accuracy in character recognition was observed over the MATLAB inbuilt Optical Character Recognition library performance, both using the same dataset. The dimensions of the segmented objects are estimated using pixel march technique in MATLAB and the results were validated against the manually measured character dimensions and a maximum error of 13% was observed with respect to the measured dimensions.
LAUN Improved StarGAN for Facial Emotion Recognition

In the field of facial expression recognition, deep learning is extensively used. However, insufficient and unbalanced facial training data in available public databases is a major challenge for improving the expression recognition rate. Generative Adversarial Networks (GANs) can produce more one-to-one faces with different expressions, which can be used to enhance databases. StarGAN can perform one-to-many translations for multiple expressions. Compared with original GANs, StarGAN can increase the efficiency of sample generation. Nevertheless, there are some defects in essential areas of the generated face, such as the mouth and the fuzzy side face image generation. To address these limitations, we improved StarGAN to alleviate the defects of images generation by modifying the reconstruction loss and adding the Contextual loss. Meanwhile, we added the Attention U-Net to StarGAN’s generator, replacing StarGAN’s original generator. Therefore, we proposed the Contextual loss and Attention U-Net (LAUN) improved StarGAN. The U-shape structure and skip connection in Attention U-Net can effectively integrate the details and semantic features of images. The network’s attention structure can pay attention to the essential areas of the human face. The experimental results demonstrate that the improved model can alleviate some flaws in the face generated by the original StarGAN. Therefore, it can generate person images with better quality with different poses and expressions. The experiments were conducted on the Karolinska Directed Emotional Faces database, and the accuracy of facial expression recognition is 95.97%, 2.19% higher than that by using StarGAN. Meanwhile, the experiments were carried out on the MMI Facial Expression Database, and the accuracy of expression is 98.30%, 1.21% higher than that by using StarGAN. Moreover, experiment results have better performance based on the LAUN improved StarGAN enhanced databases than those without enhancement.