Miracle-dz/UniFAR-FTUs huggingface dataset:
Face Recognition with Multi-channel Local Mesh High-order Pattern Descriptor and Convolutional Neural Network

In this paper, we propose a novel Local Mesh High-order Pattern Descriptor (LMHPD) for face recognition. This description is constructed in a high-order derivative space and is integrated with a Convolutional Neural Network (CNN) architecture. Based on the information collected at a local neighborhood of reference pixel with diverse radiuses and mesh angles, a vectorized feature representation of the reference pixel is generated to provide micro-patterns. They are then converted to multi-channels to use in conjunction with the CNN. The CNN adopted in the proposed architecture is generic and very compact with a small number of convolutional layers. However, LMHPD is derived in such a way that it can work with most of the available CNN architectures. For keeping the computational cost and time complexity at the minimum, we propose a lighter approach of high-order texture descriptor with CNN architecture that can effectively extract discriminative face features. Extensive experiments on Extended Yale B and CMU-PIE datasets show that our method consistently outperforms several alternative descriptors for face recognition under various circumstances.
Memory-Restricted Routing with Tiled Map Data

Modern routing algorithms reduce query time by depending heavily on preprocessed data. The recently developed Navigation Data Standard (NDS) enforces a separation between algorithms and map data, rendering preprocessing inapplicable. Furthermore, map data is partitioned into tiles with respect to their geographic coordinates. With the limited memory found in portable devices, the number of tiles loaded becomes the major factor for run time. We study routing under these restrictions and present new algorithms as well as empirical evaluations. Our results show that, on average, the most efficient algorithm presented uses more than 20 times fewer tile loads than a normal A.
Nonlinear Control of Fixed-Wing UAVs with Time-Varying Aerodynamic Uncertainties Via $\mu$-Synthesis

This paper presents a novel application of $\mu$. synthesis design for nonlinear control of fixed-wing UAVs with unstructured, time-varying uncertainties during flight. The nominal/uncertain plant approach is utilized, with additive uncertainty weighting functions incorporating the information for the respective unknown parameters with time-varying structure. The proposed controller design framework is tested and validated through simulation for a circulation control based UAV, which has inherent time-varying aerodynamic characteristics due to lift enhancement. The proposed methodology is not limited to aerodynamic uncertainties only; it can be generalized for navigation and control of UAVs with changing mass, inertia properties and airfoil characteristics with an appropriate modification of the upper/lower bounds used for the derivation of the additive uncertainty weighting functions.
Fault Diagnosis Method Based on Scaling Law for On-line Refrigerant Leak Detection

Early fault detection using instrumented sensor data is one of the promising application areas of machine learning in industrial facilities. However, it is difficult to improve the generalization performance of the trained fault-detection model because of the complex system configuration in the target diagnostic system and insufficient fault data. It is not trivial to apply the trained model to other systems. Here we propose a fault diagnosis method for refrigerant leak detection considering the physical modeling and control mechanism of an air-conditioning system. We derive a useful scaling law related to refrigerant leak. If the control mechanism is the same, the model can be applied to other air-conditioning systems irrespective of the system configuration. Small-scale off-line fault test data obtained in a laboratory are applied to estimate the scaling exponent. We evaluate the proposed scaling law by using real-world data. Based on a statistical hypothesis test of the interaction between two groups, we show that the scaling exponents of different air-conditioning systems are equivalent. In addition, we estimated the time series of the degree of leakage of real process data based on the scaling law and confirmed that the proposed method is promising for early leak detection through comparison with assessment by experts.
Research on the Strategy of Drones Intrusion Detection Based on Game Theory

The drone can be operated covertly and can be used in a cluster. It has a broad application prospect. Because of its tiny size, most of its controllers are low-cost, low-power chips and have limited calculated performance. The traditional intrusion detection system takes up large computational resources and may interfere with the real-time capability of the system. In order to balance the detection performance and the resource costs, this paper establishes a non-cooperative complete information static game model by analyzing the characteristics of drones' sensors, using game theory to regard inertial measurement unit (IMU) and GPS as the strategy object and taking attackers and defenders as the participants. It demonstrates the necessity of implementing intrusion detection programs. By analyzing the mixed Nash equilibrium solution of the model, the optimal defense strategy is to randomly select a sensor for protection according to the ratio of the best strategy. This ratio has a clear fixed relationship with the protection success rate of the intrusion detection system for each sensor. The simulation experiment shows that the game based intrusion detection strategy not only reduces the additional energy consumption, but also effectively improves the security of drone group in attack and defense confrontations.
Adaptive Firefly Algorithm Based on Diversification and Intensification for Superior Solution Set Search

In conventional single objective optimization, presenting multiple alternatives is difficult because the aim is to search for only one global optimal or suboptimal solution. In this study, we propose a superior solution set search problem as an optimization problem to simultaneously find multiple excellent solutions in multimodal functions. In addition, we analyze the characteristics of the Firefly Algorithm (FA), which divides the search process into multiple clusters, and using this property, we efficiently search for the superior solution set. We interpret the metaheuristics strategy (i.e., diversification and intensification) of the superior solution set search problem and clarify the effects of parameters on the cluster search dynamics of FA in terms of the metaheuristics strategy. After the above analysis, we propose an Adaptive FA that preliminarily performs parameter adjustment according to set target value schedule, evaluate indicators of diversification and intensification for the superior solution set search problem, and verify their usefulness by conducting a numerical experiment using three typical benchmark functions.
Local Motion Planning for Ground Mobile Robots via Deep Imitation Learning

Imitation learning have been recently applied to a number of robotic-related tasks. In this work, a novel approach based on imitation learning will be applied to the local motion planning problem for mobile robots. Based solely on a monocular RGB camera and set of expert demonstrations, our learned model can predict an accurate steering angle actions to be performed by the local motion planner of a mobile robot. In order to train and validate our model, we collected large amount of labelled RGB images from a monocular camera mounted on a ground mobile robot platform while driving the robot manually by human demonstrator. The proposed approach has achieved resilient results in capturing the behavior of a human demonstrator and provided a smooth and safe steering trajectories. Quantitatively, the model has also achieved lower mean absolute error of on a step-by-step basis of steering angular velocity with only 0.1 radians/s error over all the testing dataset.
Predicting Personality Traits Using Smartphone Sensor Data and App Usage Data

Human behavior is complex - often defying explanation using traditional mathematical models. Intermediate forms, such as those gleaned from personality inventories can be employed to predict stylized aspects of behavior. Novel sensing systems have made tracking behavior possible with unprecedented fidelity, leading to the hypothesis that intermediate psychological models might be derived directly from records of behavior. Being able to extract a personal profile or personality type from data directly obtainable from a mobile phone without participant interaction could have applications for marketing, or for initiating social or health interventions. In this paper, we model a well-established personality inventory, the Big Five framework. Daily routines of participants were measured from parameters readily available from smartphones, and supervised machine learning was used to create a model from that data.
Automatic Mapping of the Sum-Product Network Inference Problem to FPGA-Based Accelerators

In recent years, FPGAs have been successfully employed for the implementation of efficient, application-specific accelerators for a wide range of machine learning tasks. In this work, we consider probabilistic models, namely, (Mixed) Sum-Product Networks (SPN), a deep architecture that can provide tractable inference for multivariate distributions over mixed data-sources. We develop a fully pipelined FPGA accelerator architecture, including a pipelined interface to external memory, for the inference in (mixed) SPNs. To meet the precision constraints of SPNs, all computations are conducted using double-precision floating point arithmetic. Starting from an input description, the custom FPGA-accelerator is synthesized fully automatically by our tool flow. To the best of our knowledge, this work is the first approach to offload the SPN inference problem to FPGA-based accelerators. Our evaluation shows that the SPN inference problem benefits from offloading to our pipelined FPGA accelerator architecture.
Unsupervised Anomaly Based Botnet Detection in IoT Networks

Anomaly-based detection of the IoT botnets with emphasis on feature selection is elaborated in this paper. Due to the rapid growth of the Internet of Things technology, the number of vulnerable devices that become a part of a botnet has grown significantly. The detection of such malicious traffic is essential for taking timely countermeasures. While the idea of anomaly-based attack detection is not new and has been extensively studied, much less attention has been paid to dimensionality reduction in learning models induced for IoT networks. In this paper, we showed that it is possible to induce high accurate unsupervised learning models with reduced feature set sizes, which enables to decrease the required computational resources. Training one common model for all IoT devices, instead of dedicated model for each device, is another design option that is evaluated for resource optimization.
Convolutional Neural Networks for Automatic Threat Detection in Security X-Ray Images

In this paper we apply Convolutional Neural Networks (CNNs) to the task of automatic threat detection, specifically conventional explosives, in security X-ray scans of passenger baggage. We present the first results of utilizing CNNs for explosives detection, and introduce a dataset, the Passenger Baggage Object Database (PBOD), which can be used by researchers to develop new threat detection algorithms. Using state-of-the-art CNN models and taking advantage of the properties of the Xray scanner, we achieve reliable detection of threats, with the best model achieving an AUC of the ROC of 0.95. We also explore heatmaps as a visualization of the location of the threat.
A Modified Map-Drift Algorithm for SAR Autofocusing

Trajectory deviations in airborne synthetic aperture radar (SAR) will induce phase errors and degrade the quality of SAR images. For obtaining high resolution SAR images, motion compensation (MOCO) is performed by using the GPS or INS, furthermore, autofocusing algorithms should also be used for residual phase errors (RPE) correction. Map-Drift (MD), a commonly used SAR autofocusing algorithm, has the ability of correcting quadric phase errors (QPE) for improving the SAR imaging quality. In this paper, an improved MD algorithm is proposed. In the proposed method, principal component analysis (PCA) is adopted for fitting the linear equation between Doppler rate and range. Real-scenario experiment shows that the proposed method can provide more precise SAR imaging results compared with the conventional MD.
Decision Fusion in Distributed Cooperative Wireless Sensor Networks

In this paper, the distributed decision fusion (DF) problem of cooperative wireless sensor networks is considered, where a group of sensors send their local decisions to a fusion center via a set of relays using decode-and-forward relaying. The likelihood ratio test under Neyman–Pearson sense is used to derive the optimal DF rule (DFR). Moreover, three suboptimal fusion rules are derived to enable a broad range of complexity-performance tradeoffs. The performance of the optimal and suboptimal fusion schemes is evaluated in terms of probability of detection, false alarm, and fusion decision error where analytical expressions are derived for the suboptimal schemes, while Monte Carlo simulation is used for the optimal DFR. The obtained analytical and simulation results show that the decision error performance of the network is mainly determined by the number and accuracy of the local sensors, while the relaying process could be effective in mitigating the fading channel effects. Furthermore, no significant improvement is achieved by increasing the number of relays beyond three for each sensor.
Automatic User Authentication for Privacy-Aware Human Activity Tracking Using Bluetooth Beacons

In this paper, we introduce a novel mechanism to facilitate automatic user authentication for privacy-aware vision based human activity tracking. The mechanism relies on the Bluetooth beacon technology, which localizes a user who is present in the field of view of a camera. Unlike our previous mechanism designed for the same purpose, which requires a user to push a button on the smartwatch he or she is wearing and make a predefined gesture to register with the activity tracking system, this new mechanism does not require the user to alter his or her work routine when entering the view of the tracking system. Hence, the proposed mechanism significantly increases the usability of the tracking system. While the importance of automatic user authentication might not be obvious to researchers, it is essential for the acceptance of the activity tracking technology in its intended venues where workers will be monitored on their jobs as demonstrated by our previous field study and by our interviews with business owners. We report the experimental result based on two types of Bluetooth beacon devices, one from Estimote, and the other from Gimbal. Through the experiments, we identify several challenges in using these commercial-off-the shelf beacon devices for automatic user authentication, including the lack of synchronized beacon signal transmission by different beacon devices, nonuniform beacon transmission with occasional large gaps, and delay in beacon signal detection or reporting, and propose solutions to these issues.
Clipping-and-Bank-Filtering Technique in Joint Crest Factor Reduction and Digital Predistortion for Power Amplifiers

Crest factor reduction (CFR) and digital predistortion (DPD) are often used to enhance power amplifiers (PA) power efficiency while compensating for its nonlinearity. Among the joint CFR/DPDs in the literature, the one based on a parametric model has advantages on running complexity because the CFR and the DPD can be merged into a single model in the implementation. The model coefficients estimation is based on a reference correction signal computed by clipping the predistorted signal. In this paper, we propose a clipping-and-bank-filtering (CABF) method for generating the reference correction signal. The performance obtained by the proposed CABF approach outperforms the conventional clipping methods in terms of adjacent channel power ratio at PA output. Its effectiveness is validated by simulation results on a Wiener model Power Amplifier with a Long Term Evolution-Advanced signal.
OCMAS: Online Page Clustering for Multibank Scratchpad Memory

Scratchpad memory (SPM), a software-controlled on-chip memory, is being increasingly used in embedded systems to reduce on-chip memory energy consumption. To further reduce energy consumption, multibank SPM architecture is proposed. In multibank SPM, each bank can be accessed independently, and unused banks can enter the low power mode, thus reducing leakage energy. However, if both frequently and infrequently used data exist in the same bank, the bank will not be able to enter the low power mode, resulting in less energy reduction. To address this issue, we propose online page clustering for multibank SPM (OCMAS) to reduce the leakage energy in multibank SPM. OCMAS groups SPM pages with similar access frequencies into the same bank, allowing banks containing infrequently used data to stay in low power mode longer. We also propose a method to dynamically adjust the thresholds for determining <italic>cold pages</italic> (pages that contain infrequently used data), so banks that contain cold pages can enter the low power mode with a shorter idle timeout. Compared to conventional timeout-based, periodic drowsy, and bank-based methods, OCMAS can reduce the energy delay product by up to 37.67% (18.14% on average), 39.53% (22.38% on average), and 132.32% (23.34% on average) in 32 KB 4-bank SPM, by up to 25.33% (15.71% on average), 29.87% (15.92% on average), and 72.25% (22.64% on average) in 32 KB 8-bank SPM, by up to 28.99% (13.45% on average), 30.71% (14.74% on average), and 96.67% (19.94% on average) in 16 KB 4-bank SPM, and by up to 30.18% (10.05% on average), 32.13% (11.62% on average), and 65.56% (16.2% on average) in 16 KB 8-bank SPM. The area overhead is approximately 0.72%, which is insignificant.
Visual Augmentation of Printed Materials with Intelligent See-Through Glass Displays: A Prototype Based on Smartphone and Pepper's Ghost

Augmented reality technologies have been applied in educational contexts to enhance traditional textbooks with so-called mixed reality books where static printed context is augmented with dynamic content. These techniques sometimes exploit mobile devices held in mid-air or displays placed behind the textbook. This paper presents a design case outlining a concept for augmenting printed material which operated in the 2D plane by superimposing images on top of the printed material. The contents of a smartphone display are reflected via the printed surface. Several use cases are discussed. The method holds potential for both education and accessibility.
Robust Stability of Spreading Blocks in Aggregate Computing

Self-stabilizing information spreading algorithms are an important building block of many distributed systems featuring in aggregate computing. The convergence dynamics of self-stabilizing information spreading, however, have not previously been characterized, except in the special case of a distance finding variant known as the Adaptive Bellman-Ford (ABF) Algorithm. As a step towards understanding the behavior of these algorithms, particularly when interconnected with other building blocks, it is important to develop a framework to demonstrate their robust stability. Thus, we analyze an extremely general information spreading algorithm of which ABF is a special case. We provide a proof of global uniform asymptotic stability, upper bound on the time to converge, and ultimate bounds on state error in face of persistent perturbations.
Evaluation of SiC-Based Three-Phase PFC Rectifier Design

High performance three–phase PWM rectifier topologies are mandatory in first stage of telecommunication power supplies due requirements regarding standards of EMC and power grid as well compactness and high efficiency provided. In this context two and three level topologies have been addressed in this application successfully, considering different control and modulation strategies. The paper proposes an analytical comparative analysis of current efforts at system level performance of SiC-based 15 kW three–phase PWM rectifier topologies.
An ADMM Algorithm for MPC-based Energy Management in Hybrid Electric Vehicles with Nonlinear Losses

In this paper we present a convex formulation of the Model Predictive Control (MPC) optimisation for energy management in hybrid electric vehicles, and an Alternating Direction Method of Multipliers (ADMM) algorithm for its solution. We develop a new proof of convexity for the problem that allows the nonlinear dynamics to be modelled as a linear system, then demonstrate the performance of ADMM in comparison with Dynamic Programming (DP) through simulation. The results demonstrate up to two orders of magnitude improvement in solution time for comparable accuracy against DP.
GaN HEMT Darlington Power Amplifier with Independent Biasing for High-Efficiency Low-Distortion Wide-Dynamic-Range Adjustment

Wide-dynamic-range operation for high-efficiency and low-distortion characteristics has been realized with a novel independently biased GaN HEMT Darlington power amplifier. The nonlinearity of each GaN HEMT has been successfully cancelled to reduce third-order inter-modulation distortion (IMD3) for the wide dynamic range by means of independently adjusting each gate bias voltage. Without affecting the IMD3, power-added efficiency (PAE) can be optimized with independent drain voltage supplies. Thus, high-efficiency and low-distortion characteristics can be simultaneously realized with the proposed Darlington power amplifier. The fabricated GaN HEMT Darlington power amplifier exhibited a PAE of 36% and an IMD3 of −35 dBc with an output power of 30 dBm at 1.45 GHz. A high quality of 64-QAM constellations has been retained for an output power range from 20 to 32 dBm.
Encrypted Average Consensus with Quantized Control Law

This paper proposes an encrypted control algorithm to the average consensus problem for agents whose communication topology is represented by a strongly connected digraph. Unlike many applications that use encryptions, encrypted control systems require encryptions and decryptions repeatedly at every sampling time. Thus, it is beneficial if the control algorithm allows us to balance the cipher strength and processing time. This property is acquired by constructing a finite-level uniform quantizer whose sensitivity changes with the evolution of the system, designing its associated control law, and then combining it with the Paillier cryptosystem. Numerical analysis of the quantized control law is also presented.
Development of dense collagenous construct mimicking native corneal stroma based on collagen compression process

This study describes the development of a dense collagen construct having complex biochemical components mimicking a native cornea as a scaffold for constructing an in vitro corneal stroma. The dense collagenous construct with the biochemical components was fabricated from the collagen compression process with addition of a corneal-derived decellularized extracellular matrix (dECM). The fabricated construct was verified that the biochemical components of the dECM reliably promoted to maintain the normal phenotype of the corneal keratocyte, with mechanical stability enough for manual handling during in vitro cell culture. The results indicated that the biomimetic collagenous construct would be a promising tool for construction of in vitro corneal tissue relevant to the native tissue as a basis material.