Miracle-dz/UniFAR-FTUs huggingface dataset:
A Skin Lesion Segmentation Method for Dermoscopic Images Based on Adaptive Thresholding with Normalization of Color Models

In medical image processing, the skin lesion segmentation problem plays a vital role, because it is necessary to improve quality of extracting skin lesion features to classify the skin lesion. Hence, imaging diagnosis systems can detect skin cancer early. It is necessary to treat the skin cancer, especially, melanoma – one of the most dangerous form of skin cancer. In this paper, we proposed two adaptive methods to estimate the global threshold used for skin lesion segmentation based on normalization of the color models: RGB and XYZ. The skin lesion segmentation based on our proposed methods gives better result than the Otsu segmentation method regarding the grayscale model. This comparison is assessed on popular metrics for image segmentation, such as Dice and Jaccard scores. Experiments are tested on the famous ISIC dataset.
The role teams and organizational factors play in disruptive innovations in ambidextrous organizations

One of the biggest challenges that large companies challenge at commercializing innovations is how to successfully transfer the project to the business unit at the moment of scaling up the innovation. This research has deployed multiple case methodology to study the process of transference of twelve disruptive innovations in ambidextrous organizations to improve the understanding of how corporate ventures face the transition from a disruptive innovation to a receiving business unit. The study identifies different crucial team activities within the process of transferring the projects to the receiving business units as well as key organizational factors between the R&D team, the business unit and the corporation, as it follows: 1) the achievement of first sales and running of pilot projects, 2) early-relations with the potential receiving business unit, 3) misfit between the innovation project and the business unit, 4) transfer of the R & D team to the receiving team, 5) presence of champions and project support network. The research also provides insights about the most appropriate time for the realization of the transfer.
Ultrawideband Metamaterial-Based Array of Cold-Electron Bolometers

In this report we present the concept of a planar array of cold-electron bolometers, which may potentially absorb 50 to 80 percent of the incident terahertz power with linear polarization in the frequency range 50-800 GHz. The array consists of a periodically arranged unit cells each containing a ring with the four bolometers. The performance of the array unit cell is simulated numerically. The experimentally measured frequency response of the bolometric array with 10×10 unit cells in the frequency range 230-370 GHz is presented. (Abstract)
Real-time Multisensory Affordance-based Control for Adaptive Object Manipulation

We address the challenge of how a robot can adapt its actions to successfully manipulate objects it has not previously encountered. We introduce Real-time Multisensory Affordance-based Control (RMAC), which enables a robot to adapt existing affordance models using multisensory inputs. We show that using the combination of haptic, audio, and visual information with RMAC allows the robot to learn afforance models and adaptively manipulate two very different objects (drawer, lamp), in multiple novel configurations. Offline evaluations and real-time online evaluations show that RMAC allows the robot to accurately open different drawer configurations and turn-on novel lamps with an average accuracy of 75%.
Model-Based On-line Estimation of Time-Varying Nonlinear Joint Stiffness on an e-Series Universal Robots Manipulator

Flexibility commonly exists in the joints of many industrial robots due to the elasticity of the lightweight strain-wave type transmissions being used. This leads to a dynamic time-varying displacement between the position of the drive actuator and that of the driven link. Furthermore, the joint flexibility changes with time due to the material slowly being worn off at the gear meshing. Knowing the stiffness of the robot joints is of great value, e.g. in the design of new model-based feedforward and feedback controllers, and for predictive maintenance in the case of gearing unit failure. In this paper, we address on-line estimation of robot joint stiffness using a recursive least squares strategy based on a parametric model taking into account the elastic torques’ nonlinear dependency on transmission deformation. Robustness is achieved in the presence of measurement noise and in poor excitation conditions. The method can be easily extended to general classes of serial-link multi-degree-of-freedom robots. The estimation technique uses only feedback signals that are readily available on Universal Robots’ e-Series manipulators. Experiments on the new UR5e manipulator demonstrate the effectiveness of the proposed method.
SLAMBench 3.0: Systematic Automated Reproducible Evaluation of SLAM Systems for Robot Vision Challenges and Scene Understanding

As the SLAM research area matures and the number of SLAM systems available increases, the need for frameworks that can objectively evaluate them against prior work grows. This new version of SLAMBench moves beyond traditional visual SLAM, and provides new support for scene understanding and non-rigid environments (dynamic SLAM). More concretely for dynamic SLAM, SLAMBench 3.0 includes the first publicly available implementation of DynamicFusion, along with an evaluation infrastructure. In addition, we include two SLAM systems (one dense, one sparse) augmented with convolutional neural networks for scene understanding, together with datasets and appropriate metrics. Through a series of use-cases, we demonstrate the newly incorporated algorithms, visulation aids and metrics (6 new metrics, 4 new datasets and 5 new algorithms).
Environmental Factors Influencing the Adoption of Digitalization Technologies in Automotive Supply Chains

Previous literature shows that there are different environmental factors with different impacts on the adoption of technologies in a supply chain context. Thus, the adoption of technologies in supply chains may vary according to different environmental factors. Despite the existence of several studies about adoption of technologies in supply chain contexts that include environmental factors, there is a gap in identifying which environmental factors influence the adoption of digitalization technologies in supply chains. The purpose of this study is therefore to identify and analyze the environmental factors that influence the adoption of digitalization technologies in the supply chain. An exploratory qualitative research was conducted using semi-structured interviews with Portuguese managers of companies at several tiers of the automotive supply chain. Environmental factors were pointed as particularly critical drivers to promote the adoption of digitalization technologies in the automotive supply chain. Such adoption is mainly driven by the Original Equipment Manufacturer (OEM), through coercive and normative pressures over the other tiers of the supply chain. Relevant factors identified are: compliance with standards and legislation, market and industry pressures, and benchmark the evolution of supply chain partners. This study contributes to the literature with new knowledge concerning new specificities of the environmental factors that showed an important influence on the adoption decision.
Morphology-Specific Convolutional Neural Networks for Tactile Object Recognition with a Multi-Fingered Hand

Distributed tactile sensors on multi-fingered hands can provide high-dimensional information for grasping objects, but it is not clear how to optimally process such abundant tactile information. The current paper explores the possibility of using a morphology-specific convolutional neural network (MS-CNN). uSkin tactile sensors are mounted on an Allegro Hand, which provides 720 force measurements (15 patches of uSkin modules with 16 triaxial force sensors each) in addition to 16 joint angle measurements. Consecutive layers in the CNN get input from parts of one finger segment, one finger, and the whole hand. Since the sensors give 3D (x, y, z) vector tactile information, inputs with 3 channels (x, y and z) are used in the first layer, based on the idea of such inputs for RGB images from cameras. Overall, the layers are combined, resulting in the building of a tactile map based on the relative position of the tactile sensors on the hand. Seven different combination variations were evaluated, and an over-95% object recognition rate with 20 objects was achieved, even though only one random time instance from a repeated squeezing motion of an object in an unknown pose within the hand was used as input.
Towards an effective management of corporate entrepreneurship activities

Corporate entrepreneurship (CE) is experiencing continuously increasing interest from scholars and practitioners. One reason for this seems to be rooted in the organizational structures of established companies, which are cumbersome for implementing organizational agility and for developing radical innovations. In view of the advancing digitalization, however, exactly this is required in order to be successful in the long-term. CE is a promising managerial tool that offers a wide range of options to pursue the creation of new businesses and to support the companies’ transformation in order to adapt to changes in the environment. Even though CE offers a broad range of opportunities, the effective management is a challenge. One reason for this is the ambiguity when it comes to the differences between the various CE forms and the objectives that can be achieved by those. This study, which is based on 13 in-depth interviews from eight high-tech companies, contributes to a better understanding of CE by offering a first harmonized set of CE objectives that is suitable to compare and differentiate across the different forms. In addition to that, three CE types, offering a new perspective on how to differentiate CE forms, are identified and give implications for a more effective management.
Design of a TTC Antenna Using Simulation and Multiobjective Evolutionary Algorithms

The design of a Compact Dual-band Equatorial helix antenna using Computational Electromagnetic Methods together with multiobjective optimization algorithms is presented. These antennas are used for Telemetry, Tracking, and Control of satellites from the terrain base station. In order to optimize the parameters an antenna, a simulation–optimization process is shown along a real case study. The parameters of the antenna that fulfills the radiation patterns needed for the communication are obtained using a simulation tool called MONURBS together with two well-known multiobjective algorithms: NSGA-II and SPEA-2. In this paper, a comparison with previous designs and the antenna prototype is presented, showing that this approach can obtain multiple valid solutions and accelerate the design process.
Energy Efficiency of Massive MIMO Systems With Low-Resolution ADCs and Successive Interference Cancellation

This paper studies the influence of signal detection schemes on the energy efficiency (EE) of uplink multiple-input-multiple-output (MIMO) systems with low-resolution analog-to-digital converters (ADCs). Assuming equal transmission rates for all users, we derive the optimal power allocation and their analytical approximations for zero-forcing (ZF) and ZF successive interference cancellation (ZF-SIC) receivers. Both the cases with perfect channel state information (CSI) and with imperfect CSI are considered. The EE with different receivers is compared. The results indicate that for uplink massive MIMO systems with low-resolution ADCs, the radio-frequency circuit power consumption can be significant because a large number of antennas are required to compensate for the loss due to quantization errors while the number of base station antennas needed with the ZF-SIC receiver is significantly smaller than that with the ZF receiver. Meanwhile, the increase of power consumption of signal processing with ZF-SIC can be moderate, due to the fact that the receiver weights are reused in a coherent block and the dimensionality is reduced. Consequently, the ZF-SIC receiver is able to improve the overall EE for massive MIMO systems with practical ADCs. We also conduct an approximation analysis for a multi-cell scenario with the pilot contamination and inter-cell-interference considered.
Open Innovation in Customer Experience of Mass-Catering Service

Literature suggests that it is of value for organisations to receive customer feedback to stay competitive in the marketplace. Feedback may be used to improve existing and to develop new products and services. Customer experience is considered as one of the main focal areas of the innovation process. This paper presents a study on the customer experience of a mass-catering service company. An inductive research approach was adopted to explore the attitudes of the customers towards the service and the customers’ improvement ideas regarding the service. The primary qualitative data of roughly 200 interviews were collected with semi-structured interviews by approximately 50 interviewers throughout 2018. The generic research questions were operationalised by six interview questions. The respondents were found to have complex and sometimes contradicting views regarding the service. Gender and occupation were found to have an impact on the responses. Four general categories were identified according to which attitudes and improvement ideas could be captured and analysed: food, service, place and price. These categories were found to have relationships with each other.
Process-Integrated State Estimation of Optical Systems With Macro–Micro Manipulators Based on Wavefront Filtering

To date, the assembly of optical systems is still not fully automated. Automated assembly can be facilitated by having an optical simulation at hand, which, in turn, requires knowledge about the current state of the optical system. Due to the strict demands on the positioning tolerances, the uncertainties of the positioning system play an important role and lead to non-negligible deviations from the nominal poses. Therefore, the actual poses of the optical components need to be estimated in order to correct misaligned components. Furthermore, it is beneficial to develop methods that utilize the dedicated primary sensor and to avoid additional external sensors. In this letter, we employ a macro–micro manipulator for moving optical components and utilize filtering methods for realizing an in-process state estimation. For this, the uncertainty of the positioning system, as well as sensor noise, need to be identified, which lay the groundwork for methods such as the extended Kalman filter, iterated extended Kalman filter, unscented Kalman filter, or particle filter. In this letter, we compare these methods in simulation with current nonlinear approaches from the literature with respect to the estimation error. Experimental verification is carried out by a macro–micro manipulator comprised of a Cartesian piezo-driven 3-DOF positioning system attached to a 6-DOF industrial robot. With the proposed filtering approach and macro–micro manipulator, the pose of a bi-convex lens is estimated via a wavefront sensor.
Visual Localization at Intersections with Digital Maps

This paper deals with the task of ego-vehicle localization at intersections, a significant task in autonomous road driving. We propose an online vision-based method that can hence be applied if the intersection is visible. It relies on stereo images and on a coarse street-level pose estimate, used to retrieve intersection data from a digital map service. Pixel-level semantic segmentation, and 3D reconstruction from state-of-the art Deep Neural Networks are coupled with an intersection model; this allows good positioning accuracy compared to the state-of-the-art in this task. To demonstrate the effectiveness of the method and make it possible to compare it with other methods, an extensive activity has been conducted in order to set up a dataset of approaches to an intersection, which has then been used to benchmark the proposed method. The dataset is made available to the community, and it currently includes more than forty intersection approaches, from KITTI. Another important contribution of the paper is the definition of criteria for the comparison of different methods, on recorded datasets. The proposed method achieves nearly sub-meter accuracy in difficult real conditions.
A Lightweight Force-Controllable Wearable Arm Based on Magnetorheological-Hydrostatic Actuators

Supernumerary Robotic Limbs (SRLs) are wearable robots augmenting human capabilities by acting as a co-worker, reaching objects, support human arms, etc. However, existing SRLs lack the mechanical backdrivability and bandwidth required for tasks where the interaction forces must be controlled such as painting, drilling, manipulating fragile objects, etc. Being highly backdrivable with a high bandwidth while minimizing weight presents a major technological challenge imposed by the limited performances of conventional electromagnetic actuators. This paper studies the feasibility of using magnetorheological (MR) clutches coupled-to a low-friction hydrostatic transmission to provide a highly capable, but yet lightweight, force-controllable SRL. A 2.7 kg 2-DOFs wearable robotic arm is designed and built. Shoulder and elbow joints are designed to deliver 39 and 25 Nm, with 115 and 180° of range of motion. Experimental studies conducted on a one-DOF test bench and validated analytically demonstrate a high force bandwidth (>25 Hz) and a good ability to control interaction forces even when interacting with an external impedance. Furthermore, three force-control approaches are studied and demonstrated experimentally: open-loop, closed-loop on force, and closed-loop on pressure. All three methods are shown to be effective. Overall, the proposed MR-Hydrostatic actuation system is well-suited for a lightweight SRL interacting with both human and environment that add unpredictable disturbances.
Garbage Box (G-Box) Designing and Monitoring

This paper describes a G-Box, a garbage box that can be monitored using mobile phone. The G-Box is designed to attract, to stimulate, to educate, and to train the kids to throw the garbage in the correct place, so that the clean environment can be achieved. The system of the garbage box in this research is designed in two parts, i.e. G-Box (Garbage Box) and P-Box (Prize Box). The G-Box and P-Box can communicate using Bluetooth. When the kids have thrown the garbage in the G-Box, the G-Box will give the notification trough MP3 player by saying “Thank you very much, please get your candy in the P-Box”. The G-Box also sends the signal to the P-Box to throw away the candy from the P-Box as the prize for the kids that have thrown away the garbage in the correct place (in this case, the G-Box).
Autonomous Laparoscopic Robotic Suturing with a Novel Actuated Suturing Tool and 3D Endoscope

Compared to open surgical techniques, laparoscopic surgical methods aim to reduce the collateral tissue damage and hence decrease the patient recovery time. However, constraints imposed by the laparoscopic surgery, i.e. the operation of surgical tools in limited spaces, turn simple surgical tasks such as suturing into time-consuming and inconsistent tasks for surgeons. In this paper, we develop an autonomous laparoscopic robotic suturing system. More specific, we expand our smart tissue anastomosis robot (STAR) by developing i) a new 3D imaging endoscope, ii) a novel actuated laparoscopic suturing tool, and iii) a suture planning strategy for the autonomous suturing. We experimentally test the accuracy and consistency of our developed system and compare it to sutures performed manually by surgeons. Our test results on suture pads indicate that STAR can reach 2.9 times better consistency in suture spacing compared to manual method and also eliminate suture repositioning and adjustments. Moreover, the consistency of suture bite sizes obtained by STAR matches with those obtained by manual suturing.
Detect in RGB, Optimize in Edge: Accurate 6D Pose Estimation for Texture-less Industrial Parts

In order to solve robotic bin-picking problem in many industrial applications, accurate 6D object pose estimation is one of fundamental technologies. This paper presents a method for accurate 6D pose estimation from a single RGB image for texture-less industrial parts. These objects are common but still challenging to deal with, due to the fact that poor surface texture and brightness makes difficult to compute discriminative local appearance descriptors. The proposed method mainly consists of two stages, which ranges from the detection stage to the optimization stage. Firstly, all known objects in the RGB image are detected with 2D bounding box via a tiny convolutional neural network. Then, the second stage will optimize the 6D pose in the Edge image given several coarse initializations. These coarse initializations are generated from the Edge image via a hypothesis-evaluation scheme. Furthermore, the proposed method is validated by achieving state-of-the-art results of texture-less industrial parts for RGB input. According to practical experiments, the proposed method is accurate and robust enough to be applied on the robotic manipulation platform to complete a simple assembly task.
Active Multi-Contact Continuous Tactile Exploration with Gaussian Process Differential Entropy

In the present work, we propose an active tactile exploration framework to obtain a surface model of an unknown object utilizing multiple contacts simultaneously. To incorporate these multiple contacts, the exploration strategy is based on the differential entropy of the underlying Gaussian process implicit surface model, which formalizes the exploration with multiple contacts within an information theoretic context and additionally allows for nonmyopic multi-step planning. In contrast to many previous approaches, the robot continuously (a slides along the surface with its end-effectors to gather the tactile stimuli, instead of touching it at discrete locations. This is realized by closely integrating the surface model into the compliant controller framework. Furthermore, we extend our recently proposed sliding based tactile exploration approach to handle non-convex objects. In the experiments, it is shown that multiple contacts simultaneously leads to a more efficient exploration of complex, non-convex objects, not only in terms of time, but also with respect to the total moved distance of all end-effectors. Finally, we demonstrate our methodology with a real PR2 robot that explores an object with both of its arms.
Data-Driven Contact Clustering for Robot Simulation

We propose a novel data-driven learning-based contact clustering (i.e., of contact points and contact normals) framework for rigid-body robot simulation, with its accuracy established/verified by real experimental data. We first construct an experimental robotic setup with force/torque (F/T) sensors to collect real contact motion/force data. We then design a multilayer perceptron (MLP) network for the contact clustering based on the full motion and force/torque information of the contacts. We also adopt the constraint-based optimization contact solver to facilitate the learning of our MLP network during the training. Our proposed data-driven/learning-based contact clustering framework is then verified against the experimental setup, compared with other techniques/simulators and shown to significantly (or meaningfully) enhance the accuracy of contact simulation as compared to them.