Miracle-dz/UniFAR-FTUs huggingface dataset:
V-band Sub-Harmonic Gate-Pumped Resistive Mixer With a 180o Hybrid Using an In-Phase Power Divider Merging With an Out-Of-Phase Marchand Balun

A high isolation gate-pumped resistive sub-harmonic mixer with a miniature 180° hybrid is demonstrated using 90 nm CMOS process. An in-phase power divider and an out-of-phase Marchand balun form a reduced-size 180° hybrid with shared coupled lines. For a sub-harmonic mixer, the input matches at LO/RF ports of the 180° hybrid are achieved using high/low coupling coefficients of the shared equal-length coupled lines. Moreover, isolations between RF and LO ports are achieved at all frequencies because a Marchand balun maintains balanced signals and a power divider provides in-phase equal magnitude signals at all frequencies. The sub-harmonic mixer achieves a conversion gain around 0-3 dB from 58-67 GHz with 0 dBm LO pumping power. The LO-to-RF isolation is better than 36 dB within the operating bandwidth.
SyRG-PV-Battery Based Standalone Microgrid with D-EPLL Based VSC Control Algorithm

This paper proposes a solar photovoltaic (PV)-battery-pico-hydro turbine driven synchronous reluctance generator (SyRG) based microgrid. The SyRG driven by pico-hydro turbine is the only AC generator, so a VSC (Voltage Source Converter) with damped E-PLL (Enhanced Phase Locked Loop) based control algorithm provides voltage regulation, harmonics mitigation and load balancing while feeding the nonlinear loads. To provide power balancing, a storage battery is connected across the DC link of VSC through a DC-DC bidirectional converter (BDC). The BDC with a storage battery, provides the optimal power flow to maintain the power equilibrium between load and sources through DC bus voltage regulation. Moreover, BDC mitigates the second harmonic current from the battery charging and discharging current. A solar PV array is connected to the DC bus of VSC. For extracting the maximum power from the solar PV array, the MPPT (Maximum Power Point Tracking) is achieved by BDC. In the laboratory, a prototype of the system is developed and tested under different situations to confirm the effectiveness and viabilty of control strategy.
A Multidimensional Trust Inference Model for the Mobile Ad-Hoc Networks

Due to the absence of the central administrator and the dynamic network topology of the Mobile Ad-hoc Networks (MANETS), trust management is a major issue for designing the routing protocols of the network. In this paper, to identify the malicious nodes in the ad-hoc network, a trust inference model based on recommendations is proposed. This multidimensional model is aware of the energy, the channel and history of the interactions between the nodes. The proposed trust model evaluates trust by using two main components: the direct interaction trust (Primary trust) calculated using the Bayesian statistical method and recommendations from the neighbors (secondary trust). The metrics used for calculating the primary trust are: the received signal strength, time of the each interaction, the intimacy of nodes and the frequency of interactions. This trust inference model is implemented on top of the conventional Dynamic Source Routing (DSR) protocol. The results of the simulations affirms that the proposed model enhance effectiveness of routing in existence of malicious peers in the MANETs.
Real-Time Image Matching and Tracking for Autonomous Quadrotor Helicopters

An algorithm for real-time matching and tracking over image sequences is presented. The goal is that a quadrotor helicopter autonomously approaches a selected target. Therefore, it is necessary to locate the target in live images. In this paper, the target is a point in a window which is selected by an operator in a reference image. This reference point is projected into the live image and tracked over consecutive images. In order to reliably project the reference point into the live image, a robust matching algorithm is proposed. Projective transformations are applied to the reference image to extract additional keypoints and to gain invariance to out-of-plane perspective transformations. A rating based on a color descriptor and on the cross correlation values of regions around inlier keypoints is used to evaluate the quality of the estimated homography matrix. Since the matching algorithm does not exploit the beneficial characteristics of image sequences and because its processing time is not short enough for high frame rates, a tracking algorithm is presented. In order to avoid divergence, tracking is re-initialized based on the rating of the matching result. High detection rates even in image sequences with large viewpoint changes are achieved. Moreover, through parallelization the algorithm offers very low processing times. It is shown that images can be processed at high frame rates in real-time.
Facial Expression Recognition by Inter-Class Relational Learning

Certain correlation exists between various expressions of the human face, yet few works have explored it. In this paper, we propose to recognize facial expressions by extracting independent expressive representation through a novel learning procedure, called Inter-class Relational Learning (IcRL). First, an elaborately designed CNN architecture is utilized for simultaneously extracting features from two different expression images. Then, two extracted features are integrated with a random ratio so that a hybrid feature can be attained. An attention module is proposed to assign a weight for each pixel of the hybrid feature. At last, we feed the weighted hybrid feature to the subsequent classification module. The goal of the whole network training is to output the correct ratio of each expression. Different from previous work, our novel method is capable of learning the mutual relations between different classes of expression and enlarging Fisher’s criterion between any two classes, i.e., the ratio of inter-class distance to intra-class distance, by accurately discriminating the hybrid feature. In this way, we will be able to enhance the discriminative power of learned features of each expression and, therefore, can improve the classification performance. The IcRL method has been evaluated on five public expression databases: CK+, JAFFE, TFEID, BAUM-2i, and FER2013. The experimental results demonstrate the superiority of the proposed method over many state-of-the-art approaches.
Sub-Word Parallel Precision-Scalable MAC Engines for Efficient Embedded DNN Inference

To enable energy-efficient embedded execution of Deep Neural Networks (DNNs), the critical sections of these workloads, their multiply-accumulate (MAC) operations, need to be carefully optimized. The SotA pursues this through run-time precision-scalable MAC operators, which can support the varying precision needs of DNNs in an energy-efficient way. Yet, to implement the adaptable precision MAC operation, most SotA solutions rely on separately optimized low precision multipliers and a precision-variable accumulation scheme, with the possible disadvantages of a high control complexity and degraded throughput. This paper, first optimizes one of the most effective SotA techniques to support fully-connected DNN layers. This mode, exploiting the transformation of a high precision multiplier into independent parallel low-precision multipliers, will be called the Sum Separate (SS) mode. In addition, this work suggests an alternative low-precision scheme, i.e. the implicit accumulation of multiple low precision products within the multiplier itself, called the Sum Together (ST) mode. Based on the two types of MAC arrangements explored, corresponding architectures have been proposed to implement DNN processing. The two architectures, yielding the same throughput, are compared in different working precisions (2/4/8/16-bit), based on Post-Synthesis simulation. The result shows that the proposed ST-Mode based architecture outperforms the earlier SS-Mode by up to ×1.6 on Energy Efficiency (TOPS/W) and ×1.5 on Area Efficiency (GOPS/mm2).
Automated Pain Severity Detection Using Convolutional Neural Network

Pain is a sensation of physical discomfort which is caused by any kind of physical injury or illness and it is one of the most crucial factor for the patient’s recovery. Although pain assessment can be done by simple observation and self-report, objectively it is a difficult task to accomplish. In recent past, several approaches have been utilized for pain recognition by researchers, However, existing state of art techniques have several drawbacks, such as using conventional handcraft feature engineering methods, which requires domain expertise and very deep convolutional neural network which are computationally expensive in terms of training, therefore requires high computational power. In this paper, we suggest that shallowing the convolutional network can also achieve competitive performance and reduce the computational burden at the same time. In view of this, we present a convolutional neural network architecture, which utilizes only three convolutional layers. Thus, having lesser parameters and computationally efficient network. Furthermore, the proposed CNN architecture has been evaluated on UNBC McMaster shoulder pain dataset. The experiment exhibits that the result of proposed CNN based approach has achieved 93.34 % overall accuracy for multiclass pain recognition. The performance signifies, that the proposed method outperformed the existing handcraft feature-based methods and gives competitive results with other deep convolutional neural network-based methods, which are computationally expensive.
Neural Network-Based Long-Term Place Recognition from Omni-Images

In robotics perception tasks, visual place recognition has drawn attention as a significant research topic on the grounds of its agile applications without using the global positioning system such as mobile robot navigation, augmented reality, and self-driving vehicles. Owing to the great performance improvement in most computer vision challenges based on deep learning, visual place recognition follows this trend. In this paper, we handle long-term visual place recognition. The long-term visual place recognition can be simplified by substituting it for a conventional supervised classification problem using a convolutional neural network. The proposed network is learned through only a single fisheye-formed illumination-invariant image, captured on Google Street View, for each class. Afterward, sequences of omnidirectional photographs measure how well the network performs. Even though a four-year gap exists between the two datasets, it seems that the proposed network discriminates well against challenges stemming from extreme visual changes.
Psychophysiological Stress Control via Heart Rate Entrainment

The aim of this work is to develop a real-time system able to classify and passively reduce stress. Heart Rate (HR) and Skin Conductance Response (SCR) are used for stress classification. Entrainment phenomenon is exploited as a possible way to reduce stress via an auditory stimulus. A Support Vector Machine (SVM) produces 84% of stress classification accuracy and a slight effect is obtained for stress reduction.
Sensitivity Comparison of Macro- and Micro-Electrochemical Biosensors for Human Chorionic Gonadotropin (hCG) Biomarker Detection

Selectivity and sensitivity are important figures of merit in the design and optimization of electrochemical biosensors. The efficiency of a fabricated immunosensing surface can easily be influenced by several factors, such as the detection limit, non-specific binding, and type of sensing platform. Here, we demonstrate the effects of macro- and micro-sized planner working electrodes (4 mm and $400~\mu \text{m}$ diameter, respectively) on electrochemical behavior and the ability of the developed biosensor to detect human chorionic gonadotropin (hCG), which is a biomarker of several tumors. The fabricated screen-printed sensor was constructed by modifying the carbon macro- and micro-electrodes with a linker, 1-pyrenebutyric acid-N-hydroxy-succinimide ester (PANHS), and immobilization of anti-hCG antibodies to specifically detect the hCG protein. The characterization of the developed electrodes was performed by cyclic voltammetry (CV) and square wave voltammetry (SWV). Each immunosensing system showed unique electrochemical behavior, which might be attributed to the arrangement of particles on the surface. However, a smaller micro-electrode surface area was found to show higher sensitivity (1 pg/mL) compared to the macro-electrode sensor with a lower detection limit of 100 pg/mL. Further, Raman spectroscopy analysis confirmed that the micro-electrode had a relatively low density of defects and disorder compared to the macro-electrode. The proposed assay represents a promising approach that is highly effective for specific detection of an analyte and can be exploited to target biomarkers for a variety of point-of-care diagnostic applications.
Facial Recognition Based on Machine Vision

This paper proposes a recognition and face tracking based on computer vision techniques using OpenCV libraries by applying multiple phases in cascade. The algorithm allows for a more robust tracking because it combines face and eye detection. Besides, it detects edges and cuts the region of interest (ROI) where the face is located. After that, the algorithm verifies if there is any face in the ROI passing again the face and eye detector. The identification is done through a comparison of the detected face and a stored database of images.
Differential Evolution Based Regional Coverage-Enhancing Algorithm for Directional 3D Wireless Sensor Networks

Wireless sensor networks (WSNs) are adopted in a variety of fields where coverage enhancing is a critical challenge because of the requirements of service quality, cost, and energy consumption. Coverage-enhancing approaches have currently attracted a lot of interest owing to their superior abilities in the deployment of the WSNs, e.g., maximum coverage, minimum sensors, and minimum energy. In this paper, a differential evolution-based regional coverage-enhancing algorithm is proposed for directional 3D WSNs, which is able to maximizing coverage while minimize the number of sensors. First, a directional cone perception model is designed to better display the actual sensing performance of sensor nodes. Subsequently, the coverage region is established to describe the perceptual range of nodes. Thereafter, a three-stage coverage-enhancing method is derived, which includes the pitch angle optimization, the deflection angle optimization and the redundant node sleeping. These strategies are designed to maximize the perception range of a single sensor node, maximize the coverage rate, and minimize the number of nodes, respectively. Finally, simulation results show that our method is able to ensure better performance compared to the state-of-the-art frameworks.
WGAN-Based Robust Occluded Facial Expression Recognition

Research on facial expression recognition (FER) technology can promote the development of theoretical and practical applications for our daily life. Currently, most of the related works on this technology are focused on un-occluded FER. However, in real life, facial expression images often have partial occlusion; therefore, the accurate recognition of occluded facial expression images is a topic that should be explored. In this paper, we proposed a novel Wasserstein generative adversarial network-based method to perform occluded FER. After complementing the face occlusion image with complex facial expression information, the recognition is achieved by learning the facial expression features of the images. This method consists of a generator <inline-formula> <tex-math notation="LaTeX">$G$ </tex-math></inline-formula> and two discriminators <inline-formula> <tex-math notation="LaTeX">$D_{1}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$D_{2}$ </tex-math></inline-formula>. The generator naturally complements occlusion in the expression image under the triple constraints of weighted reconstruction loss <inline-formula> <tex-math notation="LaTeX">$l_{wr} $ </tex-math></inline-formula>, triplet loss <inline-formula> <tex-math notation="LaTeX">$l_{t} $ </tex-math></inline-formula>, and adversarial loss <inline-formula> <tex-math notation="LaTeX">$l_{a} $ </tex-math></inline-formula>. We optimize the discriminator <inline-formula> <tex-math notation="LaTeX">$D_{1}$ </tex-math></inline-formula> to distinguish between real and fake by constructing an adversarial loss <inline-formula> <tex-math notation="LaTeX">$l_{a} $ </tex-math></inline-formula> between the generated complementing images, original un-occluded images, and small-scale-occluded images based on the Wasserstein distance. Finally, the FER is completed by introducing classification loss <inline-formula> <tex-math notation="LaTeX">$l_{c} $ </tex-math></inline-formula> into <inline-formula> <tex-math notation="LaTeX">$D_{2}$ </tex-math></inline-formula>. To verify the effectiveness of the proposed method, an experimental analysis was performed on the AffectNet and RAF-DB datasets. The visual occlusion complementing results, comparison of recognition rates of facial expression images with and without de-occlusion processing, and T-distributed stochastic neighbor embedding visual analysis of facial expression features all prove the effectiveness of the proposed method. The experimental results show that the proposed method is better than the existing state-of-the-art methods.
Formal Analysis of the Signal Protocol Using the Scyther Tool

Signal is a popular cryptographic protocol designed by the Open Whisper System that delivers end-to-end encryption for instant messaging (IM) conversations. Furthermore, Signal is considered the core security protocol for numerous applications such as Facebook Messenger and the Google Allo application. The increased use of the signal protocol in different IM applications has made it imperative, which also makes it highly attractive for the intruders. Therefore, security analysis for the signal protocol is deemed necessary to ensure its capability in the security domain. This paper conducts a formal analysis of the security protocol using Scyther, which is a model checking tool that has been used in analysis of different protocols. In this work, the signal protocol is analyzed in three phases: registration phase, sending and receiving message phase, and sending a reply phase. The obtained results are addressed and compared with those from mathematical and other model checking approaches.
Transfer of learning in skilled sequence production : the motor-independence of temporal representations in piano performance /

Two forms of temporal structure (meter and rhythm) and motor movements are manipulated in music performance to identify the components of performers' sequence representations. A transfer of learning paradigm, in which one variable changes or remains the same from training to test, was used in three experiments to identify whether meter (periodic accent structure) and rhythm (duration pattern of events) are motoric or abstract (non-motoric) components of mental representations for piano performance. Sixteen skilled adult pianists performed short musical sequences ten times, and then performed test sequences that contained the same or different temporal structure and motor movements four times as quickly as possible. The difference between the sequence duration of the last training trial and the mean sequence duration of the four test trials was taken as a measure of transfer of learning. In all experiments, the serial ordering of pitches was held constant from training to test.
Digital retina: revolutionizing camera systems for the smart city

The primary viewpoints presented in this article are as follows: (1) The method to real-time gather and aggregate all kinds of urban big data, especially image and video data from video surveillance networks, and subsequently analyze and mine the value of these big data in the city brain to effectively support the urban operation and management is a key problem in the development of smart cities. (2) Recently, some city brains are established to mine the large visual data source to obtain valuable insights about the activities in the city (e.g., the urban traffic status). However, it is recognized that compression will inevitably affect visual feature extraction, and consequently degrading the subsequent analysis and retrieval performance. More importantly, it is impractical to aggregate all video streams from hundreds of thousands of cameras distributed across the city into a city brain for big data analysis and retrieval. These issues and challenges are rooted in the camera framework currently in use. (3) To address these challenges, a new camera framework should be developed from the fact that retina can encode both pixels and features. Such a retina-like camera, or directly referred to as digital retina, is typically equipped with a globally unified timer and an accurate positioner, and can output two streams simultaneously, including a compressed video stream for online/offline viewing and data storage, and a compact feature stream extracted from the original image/video signals for visual analysis and search. By real-time feeding only the feature streams into the city brain, these digital cameras form a compound-eye camera system for the smart city. (4) To promote the wide application of digital retinas in the smart city, the relevant works should be addressed in the near future, including standardization, hardware implementation, open-source software development, and the deployment of large-scale testbeds.
A New Color-to-Gray Conversion Method Based on Edge Detection

In recent years, the line projection approach is popular and simple for the color-to-gray conversion. This paper presents a new line projection global mapping method based on edge detection. Firstly, we calculate the ratio of the number of each candidate gray image boundary points to the number of the color image’s. All the gray images are determined by exhaustive search method. Secondly, we choose the maximum ratio as the optimal line projection direction. The method proposed in this paper focuses on the edge information of the image. Experimental results show that the proposed method is superior to the typical other methods in terms of the images with obvious regional features.
Phase Method for Measuring the output Signal of a Single-Mass MEMS Gyroscope as a Possibility of Improving its Performance

A method is proposed for determining the angular rate in a micromechanical gyroscope based on separation of the signal phase rather than extraction of the envelope of secondary oscillations of the inertial mass. The theoretical and experimental tests have shown the possibility of using this method to measure the angular rate in principle. The proposed phase method has some advantages over the commonly used amplitude method.
Efficient Error Estimator for Model Order Reduction of Linear Parametric Systems

We propose an error estimator for reduced-order modeling of linear parametric dynamical systems. The error estimator can be easily extended to output error estimation of reduced-order models for steady linear parametric systems. It is sharp and cheap to compute. Using the error estimator, the reduced-order model can be adaptively obtained with high reliability. Numerical results show that the error estimator can accurately estimate the true error even for transfer functions with many resonances. Compared with an existing error bound, the proposed error estimator can be orders of magnitudes sharper and needs much less computational time.