Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning Framework for Diabetic Retinopathy Diagnosis

Diabetic Retinopathy (DR) is one of the foremost causes for the presence of blindness in the recent times. Ophthalmologists usually diagnose the presence and severity of DR through visual assessment of the retinal fundus images by manual examination. This process of manual diagnosis of DR is a very laborious and time consuming task. With the increasing rate of diabetic retinopathy patients in the world, the number of color fundus images generated has increased exponentially. Due to this large number, there is a huge delay in recognizing the early symptoms of DR and providing timely treatment. Hence, to address this unmet and increasing need, there is a need for developing an automated framework of Diabetic Retinopathy diagnosis. Hence, in this study, we have proposed a Deep Learning framework for DR diagnosis. The study uses a modified version of one of the standard Convolutional Neural Network (CNN) for solving DR fundus image classification problems. The proposed framework efficiently and quickly report whether the person has DR or not and if present, reports the severity of the disease. The framework implemented helps in giving timely treatment to the patients irrespective of geographical and economic constraints.
Ensemble Model Predictive Control Strategies Can Reduce Exercise Hypoglycemia in Type 1 Diabetes: In Silico Studies

This contribution presents an individualized Ensemble Model Predictive Control (EnMPC) algorithm for blood glucose (BG) stabilization and hypoglycemia prevention in people with type 1 diabetes (T1D) with detectable patterns of exercise. The EnMPC formulation can be regarded as a simplified multi-stage MPC that considers $N_{en}$ future exercise patterns identified from the patient's recent behavior. The control action is determined from a consensus across the ensemble, where each scenario is treated by a specific MPC algorithm, accounting for the underlying disturbance likelihood. The patient's physical activity behavior is characterized by an exercise-specific input signal derived from the convolution of patients exercise records, e.g., obtained from activity monitors, and physiological impact curves from the literature. The proposed EnMPC strategy was tested on the complete in silico adult cohort of the FDA-accepted UVA/Padova metabolic simulator. Results confirm a tangible improvement in time spent below < 70 mg/dL ($\mathbf{p} < 0.001$) without increasing hyperglycemia after 30 min of moderate exercise in comparison to a similarly tuned MPC baseline controller (rMPC). Additionally, there was a significant reduction in the number of hypotreatments ($\mathbf{p} < 0.001$) that the patients received during and after exercise between the two controllers.
Large-Scale extraction of accurate vehicle trajectories for driving behavior learning

Urban environments are still a challenge for Autonomous Vehicles, due to strong interactions with other vehicles and pedestrians. Machine learning methods are increasingly explored to tackle these situations, but their performances are highly conditioned on the availability of vehicle trajectory datasets. Such datasets are usually created with specific infrastructures and equipments, which is costly and not scalable. As a result, only a few datasets of vehicle trajectories are currently available, representing very specific situations such as highway driving, and containing a limited number of trajectories. We argue that to unleash the potential of behavior learning methods for autonomous vehicles, we need large datasets of accurate vehicle trajectories, representing interacting vehicles in very diverse situations. In this paper, we introduce a fully automatic and scalable framework for accurate vehicle trajectory extraction from single fixed monocular traffic cameras. We leverage the fact that traffic cameras represent a very large and cost-effective source of highly diverse vehicle trajectories, as they are generally located at places where traffic is dense and where a lot of interactions occur (e.g intersections). With this work, we aim at developing a framework for effortless and accurate vehicle trajectory dataset creation at large-scale. Furthermore, we provide an open-source implementation of this framework https://github.com/AubreyC/trajectory-extractor
The Influence of Directive Elements of Conformal and Planar Antenna Arrays on the Performances of Azimuth-Elevation DOA-Estimation Methods with Super Resolution

Direction finding is widely spread and highly researching area. There are lots of methods to estimate spatial positions of radio sources. The main task of the paper is to research of direction-of-arrival estimation methods with superresolution such as MUSIC, Capon etc. via the conformal and planar antenna arrays with directive elements for 2D (azimuth and elevation planes) scenarios. The root mean square error (RMSE) rate of estimates by the methods with superresolution such as MUSIC, Capon etc. in azimuth-evaluation cases are calculated via simulation depending on directivity ratio of the elements.
Physiology-Based Run-to-Run Adaptation of Insulin to Carbohydrate Ratio Improves Type 1 Diabetes Therapy: Results from an In Silico Study

The insulin to carbohydrate ratio (CR) is a parameter used by patients with type 1 diabetes (T1D) to calculate the pre-meal insulin bolus and compensate postprandial glucose excursion. However, CR is known to vary over time, within and between days, hence tracking its variations is important for optimizing glucose control. Physicians periodically tune this parameter, by trial and error, based on empirical guidelines and patient's diary, but contemporary diabetes technology has the potential to move to CR adaptation. The aim of this work is to propose an algorithm to adapt patient's CR to physiological and/or behavioral changes based on minimally-invasive everyday-life technology data. We developed a run-to-run (R2R) algorithm for CR adaptation exploiting a physiology-based method for CR optimization. The algorithm retrospectively evaluates the quality of glycemic control and proposes, every 2 days, an adaptation of patient's CR by using patient's minimally-invasive data. The performance of the algorithm was assessed in silico using the single-day University of Virginia/Padova T1D simulator (Visentin et al., J Diabetes Sci Technol 2018) which incorporates a model for intra-day variability of insulin sensitivity and dawn phenomenon. The feasibility and robustness of the algorithm was tested in a 35-day scenario (7 days of run-in), with 3 meals per day, in 100 in silico subjects by including inter-day variability of insulin sensitivity (Toffanin et al., IEEE Trans Biomed Eng 2018) together with suboptimal CR or basal insulin infusion rate. Different values of the R2R gain $(\lambda)$ were tested, ranging from $\pmb{0}$ to 1. In all simulations, CR adaptation improves glycemic control in a significant percentage of virtual subjects, within 5 weeks. Moreover, the method was safe also in case of suboptimal insulin infusion rate. Based on simulation results, a good compromise between safety and efficacy was achieved with $\lambda$ between 0.5 and 1. The proposed R2R algorithm for CR adaptation proved to be effective in silico. These results need to be confirmed clinically. The method can potentially be used in conjunction with algorithms for basal insulin adaptation and/or closed-loop control.
A Novel Optimized Classifier For the Loan Repayment Capability Prediction System

The most suitable predictive modelling technique to predict the loan repayment capability of a customer in a banking industry is classification. Classification is a supervised learning technique in data mining. The loan repayment capability of a customer can be predicted more accurately using random forest algorithm. The accuracy of the prediction depends on various parameters of the random forest algorithm. The main objective of this paper is to prove that optimization of parameters results in a better accuracy for the capability prediction of loan repayment by the customers. This paper illustrates the process of optimization that leads to an improved accuracy in classification. The comparative study explains that optimization can lead to a better accuracy and the experiments were done in weka and R.
A Learning Method of Dual-arm Manipulation for Cloth Folding Using Physics Simulator

This paper describes a learning method of cloth folding manipulation by a dual-arm robot. To reduce the burden on doing manipulation experiments using actual robots, cloth manipulation is virtually learned using physics simulator. The issue on using virtual environments is how to reproduce realistic movements of cloth in the simulator. We adopt methods of determining simulation parameters from three kinds of actual measurement. In addition, we propose a method using Bayesian optimization in order to obtain folding manipulation efficiently using the simulator. The method enables to select appropriate gripping positions and moving trajectories. The effectiveness of these methods were confirmed by means of simulation, and also by experiments using an actual robot.
An Evaluation of Open Source Serverless Computing Frameworks Support at the Edge

The proliferation of Internet of Things (IoT) and the success of resource-rich cloud services have pushed the data processing horizon towards the edge of the network. This has the potential to address bandwidth costs, and latency, availability and data privacy concerns. Serverless computing, a cloud computing model for stateless and event-driven applications, promises to further improve Quality of Service (QoS) by eliminating the burden of always-on infrastructure through ephemeral containers. Open source serverless frameworks have been introduced to avoid the vendor lock-in and computation restrictions of public cloud platforms and to bring the power of serverless computing to on-premises deployments. In an IoT environment, these frameworks can leverage the computational capabilities of devices in the local network to further improve QoS of applications delivered to the user. However, these frameworks have not been evaluated in a resource-constrained, edge computing environment. In this work we evaluate four open source serverless frameworks, namely, Kubeless, Apache OpenWhisk, OpenFaaS, Knative. Each framework is installed on a bare-metal, single master, Kubernetes cluster. We use the JMeter framework to evaluate the response time, throughput and success rate of functions deployed using these frameworks under different workloads. The evaluation results are presented and open research opportunities are discussed.
A Black-box Method for Accelerating Measurement Algorithms with Accuracy Guarantees

Network Function Virtualization (NFV) enables software implementations of middleboxes such as load balancing, traffic engineering and quality of service. These often rely on network measurement such as per-flow frequency estimation, bandwidth estimation, counting distinct elements and estimating the traffic entropy. Keeping up with the line speed is an active challenge for NFV measurement techniques, and library algorithms are simply too slow. Sampling is a natural technique to increase the measurement throughput, but it requires a certain amount of traffic before accuracy is guaranteed. In this work, we introduce a throughput acceleration method that preserves accuracy from the very first packet. This technique works with a variety of existing measurement algorithms (e.g., the ones mentioned above), and improves their throughput while guaranteeing their correctness throughout the entire measurement. Our work includes a rigors analysis, an extensive evaluation with real network traces, and a real DPDK enabled Open vSwitch implementation.
A Novel Control Architecture for the Detection of False Data Injection Attacks in Networked Control Systems

In this manuscript, we propose a novel control architecture capable of detecting deception attacks affecting networked control systems. In the proposed solution, we borrow and combine the existing concepts of watermarking signal and auxiliary systems in order to detect a broad class of false data injection attacks. We show that by combining the aforementioned concepts, we can detect false data injection attacks without sacrificing control performance. Moreover, we propose a novel nonlinear auxiliary system which is static and does not require any dynamical coupling with the plant dynamics. Finally, the effectiveness of the proposed method is shown by means of a simulation campaign.
A Survey on Deception Techniques for Securing Web Application

Many web applications are developed to handle important and critical tasks, which may attract a large number of attackers. With various types of attacks, there is no finite solution to mitigate it's all. Deception technique is one of the area that can be explore to defend against web attack. Deception can detect, analyzed and defend against advanced web attack that cannot be done using existing anomaly-based detection and prevention techniques. Current deceptive solutions tend to be doubtful to application-layer protocols and lack of study on how deception can be applied at this level. Thus, those solutions can't properly be used to protect against application-layer attacks that are integrally based on elements from the application-layer itself. This research aims to study possible usages of deception techniques that could be incorporated in the context of application-layer traffic of web applications with the purpose of detecting web application attacks. The comparative results from this study will be used to identify which deception techniques are suitable to provide a useful layer of protection for a web application.
Event-based Tactile Image Sensor for Detecting Spatio-Temporal Fast Phenomena in Contacts

We proposed an event-based tactile image sensor that provides tactile information with high temporal and spatial resolution. The sensor consists of an elastomer fingertip in which 361 markers are embedded and an event-based camera which detects temporal changes of intensity in each pixel. When the force is applied to the soft fingertip, it deforms and markers in the fingertip also move. Each pixel in the event-based camera responds to the motion of makers and generates events. Temporal resolution of the proposed tactile sensor is 500 μs and the number of pixels of the event-based camera used here is 128×128. The proposed sensor can detect fast phenomena in contacts and also measure the spatial contact pattern such as contact position and orientation.
Reconfigurable Heterogeneous Energy Harvester with Adaptive Mode Switching

We propose a new heterogeneous reconfigurable energy harvester with an algorithm to control reconfiguration of energy harvester and receive mode, i.e., information decoding (ID) or energy harvesting (EH), so as to enhance EH efficiency and achievable rate for simultaneous wireless information and power transfer (SWIPT). Especially, we design the heterogeneous reconfigurable energy harvester which consists of low-power and high-power energy harvester (LP/HP-EH) blocks, each comprised of multiple EH circuits in parallel. Practical energy harvesters have their own favorable input range due to turn-on sensitivity and saturation effects. Considering this, we propose a heterogeneous reconfiguration algorithm which optimizes the number of EH circuits being activated and the power-splitting ratio. Further, an adaptive mode switching (MS) problem is formulated which maximizes the achievable rate under the energy-causality condition. Our proposed algorithm can be applied to low-power wide area network (LP-WAN) with wireless EH capability for self-sustainability, thereby realizing battery-free IoT network.
Backstepping Boundary Control of a 1-D 2 × 2 Unstable Diffusion-Reaction PDE System with Distinct Input Delays

We consider the stabilization problem for a 1-D 2 × 2 unstable diffusion-reaction partial differential equation system with distinct input delays. The input delays are represented with a system of first-order hyperbolic PDEs, leading to a 2 + 2 × 2 mixed-type system of hyperbolic-parabolic type. We establish two integral transformations, which admit a target system with triangular trace terms. Exponential stability is shown for the target system in the H1 × H1 norm. The transformations admit a companion mixed-type gain kernel PDE system. We employ method of characteristics and Galerkin methods to show existence of solutions to the kernel PDE, guaranteeing the invertibility of the transformation and thus confirming the exponential stability results.
A Coupling Simulation of Converter Field Circuit for Active Radial Electromagnetic Bearing Based on Simplorer and Maxwell

Under the background of intelligent new manufacturing, rotating machinery is developing towards the direction of high speed, high precision, intelligence and automation. Bearing, as the key component of high-speed precision rotor, has higher and higher requirements. As a high performance electromechanical integrated bearing, active magnetic bearings(AMBS) have a wide application prospect. In this paper, based on Simplorer and Ansoft Maxwell, the converter field-circuit coupling joint simulation of active radial magnetic bearing is carried out, including the structural design of active radial magnetic bearing and the establishment of the finite element model of active radial magnetic bearing in Maxwell. The mathematical model of single-degree-of-freedom of active radial magnetic bearing is established and the PID controller is designed. Power amplifier circuit is established in Simplorer, active radial magnetic bearing control program C module is created, and active radial electromagnetic bearing finite element model created in Maxwell is imported into Simplorer for joint simulation. Finally, the simulation results of magnetic force line, magnetic density, current stiffness and displacement stiffness of active radial magnetic bearing based on PID controller are given. Research shows that applying control strategy based on Simplorer field-circuit coupling joint simulation method combining software and hardware features, the simulation results more close to reality, the whole design of the active magnetic bearing system is verified.
Invariant Sliding Window Filtering for Attitude and Bias Estimation

This paper considers sliding window filtering in an invariant framework for estimation of attitude and rate gyro bias in a matrix Lie group formulation. The multiplicative extended Kalman filter (MEKF) and invariant extended Kalman filter (IEKF), variants of the extended Kalman filter well suited to estimation on matrix Lie groups, are discussed. The sliding window formulation of both the MEKF and IEKF is presented, leading to the sliding window filter (SWF), the invariant SWF (ISWF), and the imperfect ISWF for systems that are not group affine. Simulation results for an attitude and heading reference system with bias are presented, comparing the ISWF to the traditional $\mathbf{SWF,}$ MEKF, and IEKF.
Multi-Feature Clustering Approach for Firearm Wound Identification on CT Images

Damage evaluation and trajectory analysis are critical to the emergency treatment firearm wound which is disturbed by complex wound shape and heterogeneous filling materials. Consequently, accurate identification firearm wound is essential to evaluate the firearm wound. In this study, a firearm wound identification algorithm based on multi-feature clustering was presented. This identification algorithm was divided into three stages: feature extraction, K-means clustering and Gaussian Mixture Model clustering. Six features were extracted from porcine CT volume data, and clustering results from k-means clustering method were used as the input Gaussian Mixture Model. The average accuracy, sensitivity, specificity and Dice similarity coefficient were 0.92, 0.95, 0.63 and 0.53, respectively. Our results showed that the hybrid method with six features was a potential method to identify complex firearm wound.
Comparison of the Impact of Different Household Occupancies on Load Matching Algorithms

Necessary balancing of supply and demand due to an increasing amount of renewable energy sources in the grid can be achieved with demand side management (DSM) resp. scheduling loads like a freezer, a water heater or space heating. Engaging in DSM with such controlled thermal storages can be lucrative for customers to reduce their electricity costs with small impact on their comfort. This paper proves that the height of those savings depends not just on the control algorithms but also on the occupancy of the dwelling. Simulations with object models of the scheduled appliances show the performance of different price based set point calculation algorithms. Estimation of possible savings for the different selected occupancies is achieved in this work by comparison of the simulation results of different load matching algorithms to each other and between the different occupancies. This enables recommendations on the selection of the algorithm depending on the occupancy of an existing dwelling with respect to maximum possible electricity cost savings. This can come in handy for example for dwelling and apartment owners who are renting out dwellings.
Generation of Scenes in Intersections for the Validation of Highly Automated Driving Functions

The simulation of traffic scenes in the environment of an automated vehicle promises to make significant contribution to the validation of automated driving. The construction of models which describe traffic scenes in a generic manner is complicated, since the parameter space of the scenes is infinite. This paper introduces a statistical approach to generate traffic scenes in intersections. A generic model which allows us to represent the scenes is proposed. The concept of Bayesian networks is used to fit the model onto a publicly accessible dataset and to infer traffic scenes from the model. A quantitative evaluation of the results is achieved by the calculation of the total variation distance (TVD) between the distributions of several physical properties.
Face and Gender Recognition System Based on Convolutional Neural networks

In the existing research, face features and gender attributes are separated, resulting in face recognition errors and gender recognition errors in complex backgrounds. In this work, we propose the Face and Gender Recognition System that uses convolutional neural networks (CNN). The system consists of two components: one is face recognition module and two is gender recognition module. Both face recognition module and gender recognition module use pre-trained CNN to extract face and gender features in the image. Specifically, in the face recognition module, we use the public datasets Labeled Faces in the Wild (LFW), YouTube Face (YTF) and VGGFace2 to train CNN, which improves the precision. In the gender recognition module, we use the public dataset Adience to train CNN and improve the best recognition accuracy from 91.80% to 93.22%
PID-Type Control of Robot Manipulators by Using Gravity and Inertial Effects Compensation

The paper describes an approach to the robot manipulator control based on a compensation of non-linear gravity terms and inertial terms in the robot motion equations and applying the PD or PID-type control law. While benefits of the compensation of the gravity force effects are well known, the inertial effects compensation, which seems to be more difficult to implement, brings additional enhancement of the control quality. An approximate compensation can be carried out in a relatively simple and efficient way, if the robot dynamics is represented by a mass-point model. The remaining influences can be treated as disturbances. Unlike the inverse dynamics approach, complete and precise mathematical model of the robot is not needed, which is an important advantage, since models of robots containing more than three links are usually very complex and difficult to obtain.