Miracle-dz/UniFAR-FTUs huggingface dataset:
Food Appearance Optimizer: Automatic Projection Mapping System for Enhancing Perceived Deliciousness Based on Appearance

This paper proposes a system to enhance the degree of subjective deliciousness of food visually perceived by a person by automatically changing its appearance in a real environment. The system is called the Food Appearance Optimizer. The proposed system analyzes the appearance of food in a camera image and projects an appropriate image onto the food using a projector. The relationship between the degree of subjective deliciousness and four simple appearance features for each food category is modeled using data gathered via a crowdsourcing-based questionnaire. Using this model, the system generates the appropriate projection image to increase the deliciousness of the food.
TuB4.2 - Low-cost and High-speed Nanophotonic Integrated Circuits for Access Networks (Invited)

Ever-increasing data traffic demands higher-speed and lower-cost PICs for the access network. Nanophotonic ICs on a thin membrane is a promising solution providing high density, enhanced speed and reduced assembly cost through electronics co-integration. We present our recent developments on an InP nanophotonic membrane.
System design and resource analysis for persistent robotic presence with multiple refueling stations

Despite the capabilities of unmanned aerial vehicles (UAVs), it is not possible to conduct long-term missions with a just few UAVs due to fuel restrictions. This requires a system that includes multiple UAVs and automated recharging stations for an automatic and persistent service. In order to construct a persistent presence system such as local surveillance and monitoring, it is important to determine the design of the mission and the number of resources required. In this paper, a system consisting of multiple target areas and multiple stations is considered. There are two types of stations: refueling and main stations for maintenance. UAVs can travel further using the refueling stations. A decision-free Petri net model for persistency is developed for cyclic paths including multiple immobile targets and stations. From the Petri net model, we derive a closed-form function for the minimum number of resources in the persistent system. A mathematical model that has the objective function derived from the Petri net is developed. To resolve the computational issue, a genetic algorithm (GA) is used to solve the problem. As the result, the minimum number of resources required and the mission path are derived.
Read Voltage Optimization in MLC NAND Flash Memory via the Density Evolution

The multi-level-cell (MLC) NAND flash memory can typically use multiple reads for obtaining soft information for the LDPC decoder. The multiple reads give the soft information which is quantized to a certain level. The major challenge is that the read voltages must be precisely selected to provide better soft information. In the previous work, the read voltages are selected so that the soft information has the maximum mutual information (MMI). However, the error-correction capability of LDPC decoder is not considered. Therefore, in this work, we analyze the performance of LDPC decoder by density evolution whereby the soft information is quantized. Then the optimal read voltages for given LDPC codes are obtained. As a result, for a regular LDPC code with the read voltages optimized by density evolution can provide the lower BER performance compared with the MMI technique.
The Effects of Physiologically-Adaptive Virtual Environment on User’s Sense of Presence

The effectiveness of Virtual Environments (VEs) may be defined in terms of different aspects. A common one is the degree to which the VE creates in the user the subjective illusion of presence, the sense of being in the VE. Since presence is a subjective condition, it has most commonly been measured by subjective methods, especially by post-questionnaires. However, objective measures have been proposed, mainly based on Electrodermal Activity (EDA), Heart Rate (HR) and Behavior Observation (BO). In this work, it was investigated the possibility of dynamically modulating the sense of presence in a Virtual Reality (VR) experience, based on physiological measures on the fly. Although these measures have already been applied for assessment in VR, whether they can be used to dynamically modulate the presence in VR experiences remain an open question. Subjective, physiological and behavioral reactions of 27 subjects during a fully immersive experience under three different conditions (NS: None Stimulus, PS: Pre-set Time Stimulus and DS: Dynamic Stimulus) were observed, varying the stimulus based on HR and EDA variation. Although it was not found a difference for the SUS presence questionnaire, it was found a significant difference for the physiological measures between the NS and DS condition, showing that it was possible to dynamically modulate the VR experience using real-time HR and EDA to increase the effectiveness of VR experiences.
Secrecy Outage Probability Analysis for Full-Duplex Relaying Networks Based on Relay Selection Schemes

Considering existing eavesdroppers, this paper analyzes the secrecy outage probability (SOP) of the full-duplex (FD) relaying network with imperfect channel state information (CSI), where the decode-and-forward relay protocol is adopted. To enhance the physical-layer security, two relay selection schemes are proposed. The first scheme uses the maximum–minimum principle to select the optimal relay. The other selection scheme is based on partial signal-to-interference-plus-noise ratio. Then, the tight closed-form approximations of the SOP are obtained for three special CSI cases, respectively. Moreover, based on these approximated results, the effects of some system parameters are analyzed for the SOP of the relay system, including the CSI error, the number of relays, the signal-to-noise ratio (SNR) of the eavesdropper, and the residual self-interference caused by the FD model. The numerical results show that at low SNR of the eavesdropper, residual self-interference has a significantly impact on SOP, while at high SNR of the eavesdropper, this effect is negligible. In addition, the SOP of FD model is always superior to the half-duplex (HD) model when the number of relays changes.
Blending Based Multiple-Model Adaptive Control for Multivariable Systems and Application to Lateral Vehicle Dynamics

This paper develops a multiple fixed model blending based adaptive parameter identification scheme for multi-input multi-output (MIMO) systems with polytopic parameter uncertainty. The developed identification scheme is proven to be asymptotically stable for uncertain linear time-invariant MIMO systems, and is shown to provide fast adaptation for even uncertain linear time-varying (LTV) systems. Furthermore, utilizing the proposed parameter identification scheme, a linear-quadratic (LQ) optimal multiple model adaptive control (MMAC) scheme is developed for linear MIMO systems with polytopic uncertainties. The proposed MMAC scheme is applied to tracking control of uncertain lateral vehicle dynamics. A set of simulation test results are presented to verify the stability and effectiveness of the proposed MMAC scheme.
An Eigenfunction Approach to Parameter Estimation for 1D Diffusion Problems

The behavior of a distributed parameter system can be represented by an expansion into eigenfunctions. It allows to calculate the output signal in dependence on the parameters of the system. This contribution considers the inverse problem: Estimate the system parameters from noisy measurements of the output. To this end, an eigenfunction expansion serves to establish a state-space description of the distributed parameter system. The corresponding state-space matrices define an extended Kalman filter to perform estimation and tracking of parameter values. This approach is shown here for a diffusion problem in one spatial dimension. The value of the diffusion parameter is estimated from a simulated particle concentration under varying conditions. Problems of this kind arises for example in the emerging field of molecular communications.
Vibration Test of Single Coal Gangue Particle Directly Impacting the Metal Plate and the Study of Coal Gangue Recognition Based on Vibration Signal and Stacking Integration

In order to realize the recognition of coal gangue in the top coal caving process, a scheme of the coal gangue recognition based on the collision vibration signal between coal gangue and the metal plate is proposed in this paper, a systematic and standardized impacting test between coal gangue particles and the metal plate is designed for the first time, the vibration signal standardized processing method by the signal intercepting and the coal gangue impact vibration signal recognition algorithm by stacking integration are innovatively proposed. First, a single particle impact on the metal plate test-bed was designed and constructed. Then 1,000 groups coal and 1,000 groups gangue impact on the metal plate tests were carried out respectively, and the vibration acceleration signals of the metal plate were collected. After that, through the signal intercepting, calculating the time-domain characteristics and HHT processing of the vibration signal, 10 time-frequency characteristics, such as the variance of the intercepted signal and the Hilbert marginal spectrum energy value, are determined to form the feature vector. Finally, based on the two different type of the signal samples, the intercepted signal feature vector, and the original intercepted signal, coal gangue recognition by the seven machine learning algorithms, including the decision tree (DT), random forest (RF), XGBoost, long short-term memory (LSTM), support vector machine (SVM), factorization machine (FM), and stacking integration is carried out respectively, and the basis for selecting recognition schemes is discussed. The results show that the coal gangue recognition rate with the same recognition algorithm by using the intercepted signal samples is higher than that of the feature vector samples, the Staking integration algorithm based on the same sample has the highest recognition rate, and the Staking integration algorithm based on the feature vector has the most significant comprehensive advantage in top coal caving process.
Actuator Fault-Diagnosis and Fault-Tolerant-Control using intelligent-Output-Estimator Applied on Quadrotor UAV

Actuator fault is a prominent system’s issue that attracts the researchers’ attentions. One type of actuator fault is a constant Loss-of-Effectiveness (LoE). In this paper, two systematic algorithms are presented. The first algorithm is designed to detect and diagnosis the actuator fault. This Fault-Detection-and-Diagnosis (FDD) approach relies on an estimator design that is called intelligent Output-Estimator (iOE). The proposed iOE is intended to improve the estimation of the actuator fault based on the Model-Free technique. The second algorithm works online after detecting, isolating and estimating the actuator fault. This Active-Fault-Tolerant-Control (AFTC) algorithm will use the estimated actuator value in a reconfigured control design to compensate for the fault.Real-time flight tests are applied on the Qball-X4 quadrotor. Different LoE actuator-fault scenarios are presented in this paper to validate the proposed algorithms.
Which Channel to Ask My Question?: Personalized Customer Service Request Stream Routing Using Deep Reinforcement Learning

Customer services are critical to all companies, as they may directly connect to the brand reputation. Due to a great number of customers, e-commerce companies often employ multiple communication channels to answer customers’ questions, for example, Chatbot and Hotline. On one hand, each channel has limited capacity to respond to customers’ requests; on the other hand, customers have different preferences over these channels. The current production systems are mainly built based on business rules that merely consider the tradeoffs between the resources and customers’ satisfaction. To achieve the optimal tradeoff between the resources and customers’ satisfaction, we propose a new framework based on deep reinforcement learning that directly takes both resources and user model into account. In addition to the framework, we also propose a new deep-reinforcement-learning-based routing method—double dueling deep Q-learning with prioritized experience replay ( $\mathsf{PER-DoDDQN}$ ). We evaluate our proposed framework and method using both synthetic and a real customer service log data from a large financial technology company. We show that our proposed deep-reinforcement-learning-based framework is superior to the existing production system. Moreover, we also show that our proposed $\mathsf{PER-DoDDQN}$ is better than all other deep Q-learning variants in practice, which provides a more optimal routing plan. These observations suggest that our proposed method can seek the tradeoff, where both channel resources and customers’ satisfaction are optimal.
Improved microwave cavity sensor for sheet resistance evaluation of conductive nano-films

This work presents an improved sheet resistance evaluation sensor for conductive nano-films. The proposed sensor is based on a microwave cylindrical cavity with an aperture on one of its endplates. Conductive nano-film is placed close to the aperture and thus perturbs the electromagnetic field inside the cavity. Measurement results show that the quality factor of the cavity can be related to the sheet resistance of nano-film. The advantage of the proposed sensor is that it is able to evaluate smaller samples. Namely, it has higher spatial resolution compared with conventional cavity sensor.
A Tutorial Introduction to Security and Privacy for Cyber-Physical Systems

This tutorial provides a high-level introduction to novel control-theoretic approaches for the security and privacy of cyber-physical systems (CPS). It takes a risk-based approach to the problem and develops a model framework that allows us to introduce and relate many of the recent contributions to the area. In particular, we explore the concept of risk in the context of CPS under cyber-attacks, paying special attention to the characterization of attack scenarios and to the interpretation of impact and likelihood for CPS. The risk management framework is then used to give an overview of and map different contributions in the area to three core parts of the framework: attack scenario description, quantification of impact and likelihood, and mitigation strategies. The overview is by no means complete, but it illustrates the breadth of the problems considered and the control-theoretic solutions proposed so far.
Self-Adaptive Healing for Containerized Cluster Architectures with Hidden Markov Models

Edge cloud environments are often build as virtualized coordinated clusters of possibly heterogeneous devices. In the emerging of cluster platforms like Kubernetes or Docker Swarm, scalability is based on resource utilization. Resource utilization has been used for capacity planning and for forecasting resource demand. However, due to the large scale and complex structure of these architectures, analyzing large amount of monitoring data may cause a huge resource overhead that affects the performance of anomaly detection and the accuracy of anomaly location. To address such challenges, we propose a self-adaptive healing approach that detects, identifies, predicts and recovers anomalies in clustered architectures. The approach will be evaluated to assess the accuracy of the mechanism.
A Privacy-Preserving RLWE-Based Remote Biometric Authentication Scheme for Single and Multi-Server Environments

Lwamo <italic>et al.</italic> recently proposed a robust and efficient remote single and multi-server biometric authentication scheme using smart card and <italic>RSA</italic>. The scheme is vulnerable to the smart card lost attacks; therefore, the scheme cannot resist offline guessing attacks and user impersonation attacks, and cannot provide forward security and user anonymity. To address these issues, we propose a new privacy-preserving ring learning with errors (<italic>RLWE</italic>)-based remote biometric authentication scheme (<italic>RRBAS</italic>) for single and multi-server environments. <italic>RRBAS</italic> is the first lattice-based remote biometric authentication scheme for multi-server environments. Security analysis show that <italic>RRBAS</italic> can satisfy the authenticated key exchange (<italic>AKE</italic>) security in the random oracle model, resist known security attacks, and provide post-quantum security. The experimental evaluation and comparative analysis show that <italic>RRBAS</italic>’s computational efficiency is better than that of Lwamo <italic>et al.</italic>, while the communication efficiency is slightly lower than traditional schemes because of the large-size ciphertext of the lattice-based cryptosystem, but it is fully capable of session key agreement in single and multi-server environments.
Adaptive Control for a Tilted-Motors Hexacopter UAS Flying on a Perturbed Environment

This manuscript presents the modeling and control for a tilted-motors hexacopter unmanned aircraft system, which is subject to external perturbations. A tilt angle for each rotor on the rotorcraft is considered allowing a fully-actuated system, capable to exert horizontal forces. Given the robustness properties and the management of control effort, the controller design is based on adaptive sliding mode technique providing stabilization and tracking of desired trajectories under a perturbed environment. Finally, numerical simulations show the feasibility and advantages of the proposed scheme.
Enhanced Data Rates for Energy Harvesting Systems

In this article, we suggest to optimize packet length for energy harvesting systems. The network contains three nodes A, B, and C. Node B harvests energy from electromagnetic wave transmitted by A to communicate with node C. Packet length optimization allows to increase data rates. Two adaptation protocols are studied: In the first one, the adaptation of packet and Modulation and Coding Scheme (MCS) is made with respect to Average Signal to Noise Ratio (ASNR). In the second protocol, packet length and MCS are adapted to Instantaneous SNR (ISNR). Results show that packet length optimization allow 1–2 dB gain with respected to fixed packet length.
A distributed proximal-point algorithm for Nash equilibrium seeking in generalized potential games with linearly coupled cost functions

We address the generalized Nash equilibrium seeking problem for a population of noncooperative agents playing potential games with linear coupling constraints over a communication network. We consider a class of generalized potential games where the coupling in the cost functions of the agents is linear, i.e., $J_{i}(x_{i}, x_{-i}): =f_{i}(x_{i})+\ell_{i}(x_{-i})^{\top}x_{i}$ where $\ell_{i}$ is linear. By exploiting this special structure, we design a distributed algorithm with convergence guarantee under mild assumptions, i.e., (non-strict) monotonicity of the pseudo-subdifferential mapping. The potential of the proposed algorithm is shown via numerical simulations on a networked Nash Cournot game, where we observe faster convergence with respect to standard projected pseudo-gradient algorithms.
Quantification of loading effects in interconnections of stochastic reaction networks

Modular design of networks in synthetic biology is highly desirable but difficult to achieve due to loading effects that change the properties of upstream modules upon connection with downstream networks. Precise quantification of these loading effects would allow us to predict the behavior of large interconnected networks more accurately, and enable us to systematically identify insulator circuits that can help in achieving modularity. Most of the existing results on this topic apply only in the deterministic setting and hence they do not account for the stochastic nature of biomolecular interactions. In this work we propose a novel sensitivity-based metric for quantifying loading effects in the stochastic setting. We discuss how this metric can be efficiently computed for stochastic reaction dynamics and demonstrate its usefulness in rational design of insulator circuits.
SmartEye: An Accurate Infrared Eye Tracking System for Smartphones

The capability to estimate where a user is looking on a screen is known as gaze estimation or eye tracking. It has been used in medical applications including assessment of mood and learning disorders, and brain injury diagnosis. If accurate eye tracking could be integrated into commodity smartphones these diagnostics could be broadly deployed at very low cost. The highest accuracy and most robust eye tracking methods employ infrared cameras and illumination which are not yet available on all standard smartphones. In this paper, we present an accurate infrared eye tracking system on a smartphone, named SmartEye, on an industrial prototype phone equipped with an infrared camera and illumination. The system is accurate in the presence of head pose variation and device movements in the user’s hands, and requires only a one-time calibration routine to measure specific parameters of the user’s eye. Our system achieves a gaze estimation bias of 0.57° at a 20cm distance from the user, 5 times better than state-of-the art mobile device eye-tracking systems that do not use infrared illumination. Our system also allows for free head movements at distances between 20-40cm with a moderate increase in average gaze bias (to ~1°), and can operate at 12fps. This enhanced accuracy and increased mobility can expand significantly the range of eye-tracking applications that can be supported by smartphones.
Material Surface Reproduction and Perceptual Deformation with Projection Mapping for Car Interior Design

Car interior design, such as dashboard, broadly consists of two parts. One is shape design, where the processes of 2D drawing, 3D modeling and evaluation with full-scale mockups are iterated, which takes a massive amount of time and cost. The other is material design such as surface property tuning, where designers compare material samples. This way, however, has a limitation on the number of material samples to compare and does not allow applying of the samples of interest to the whole mockups in early phases. In this paper, we apply projection mapping technique to boost the design process by altering the appearance of the surface of projected objects and enabling various shape and material evaluations in early phases. Our proposed system uses multiple projectors, one of which is 4K projector to reproduce fine leather surface. Utilizing physiological and psychological depth cues, the system allows the user to perceive the projected mockup as deformed. Psychological experiments confirm that users perceive deformation and have controlled impression on leather reproduced with certain parameters. In addition, we discuss the usability of the proposed system as a support system of car interior design.