Miracle-dz/UniFAR-FTUs huggingface dataset:
What Drivers Make of Directional Maneuver Information in a Take-Over Scenario

Shared vehicle control is a potential intermediate step in the development of fully automated driving. A specific use-case for shared control is a take-over scenario involving conditional automated driving. Such cooperation essentially requires both partner's awareness of the other's intentions. This paper's authors focus on the communication of intent from the system to the driver. The message has to reliably represent the vehicle's intent, and the driver has to correctly interpret it. To investigate this aspect, we designed a driving-simulator experiment. It features an automated system, which recommends an evasive maneuver's direction during the take-over scenario. Drivers confronted four take-over scenarios, three with correct recommendations and one with an incorrect recommendation. We tested the understanding of and trust in the perceived information as well as resulting gaze behavior. The results reveal a mixed interpretation of the information in the first scenario with a shift to mainly correct understanding in the last scenario. Trust was significantly lower only for the case in which an incorrect recommendation was issued. No significant connection between understanding or trust and gaze behavior was found. We conclude that more information about the recommendation's origin should be provided to promote successful ensuing shared control.
Study on Linearization Performance of Highly Efficient Low-Voltage Biased Driver Including Dual-Stage GaN MMIC

Linear amplification is an essential need for the transmitters of mobile communication systems using complex modulation schemes due to the required high fidelity phase and amplitude response. In this study, a new dual-stage high gain and efficiency driver amplifier and its linearization performance are presented. The GaN MMIC is implemented without an interstage matching network, and in particular, its first stage is biased at a lower voltage than a usual GaN HEMT biasing achieving higher line-up efficiency. The proposed concept does not cause an indeterministic phase response and mitigation on linearity regarding the AM-AM and AM-PM analysis. Therefore it is linearizable with the conventional DPD methods. The final circuit has 28.5 dB linear gain and 76% PAE at p5dB while the output power is around 35 dBm at 2.14 GHz. 39.2% efficiency and −55 dBc ACPR is measured at 29.5 dBm output power with a 7.5 dB PAR LTE5 signal at 4.5 dB back-off.
Optimal Stochastic Teams with Infinitely Many Decision Makers and Mean-Field Teams

We study stochastic static teams with countably infinite number of decision makers, with the goal of obtaining (globally) optimal policies under a decentralized information structure. We present sufficient conditions to connect the concepts of team optimality and person by person optimality for static teams with countably infinite number of decision makers. We show that under uniform integrability and uniform convergence conditions, an optimal policy for static teams with countably infinite number of decision makers can be established as a limit of a sequence of optimal policies for static teams with $N$ decision makers as $N\rightarrow\infty$. Under the presence of a symmetry condition, we relax the conditions and this leads to optimality results for a large class of mean-field optimal team problems where the existing results have been limited to person-by-person-optimality and not global optimality (under strict decentralization). We consider a number of illustrative examples where the theory is applied to setups with either infinitely many decision makers or an infinite-horizon classical stochastic control problem reduced to a static team.
Graphon Mean Field Games and the GMFG Equations

Networks are ubiquitous in modern society and the need to analyse, design and control them is evident. However many technical and social networks apparently grow unboundedly over time. This has the undesirable consequence that, inevitably, any method founded upon techniques whose effectiveness decreases with the size of the network will eventually be overwhelmed. This paper presents a framework called Graphon Mean Field Game (GMFG) theory for the analysis and control of non-cooperative dynamical game systems distributed over networks of unbounded size. This work is based upon the recently developed and profoundly influential graphon theory of large networks and their infinite limits. A theory for the centralized control of asymptotically infinite networks has already been formulated within the framework of dynamical systems on graphons [Gao and Caines, CDC 2017]. The current work greatly extends that analysis to populations of competing dynamical agents for which the game theoretic equilibria are expressed in terms of the newly defined Graphon Mean Field (GMFG) equations, these being a significant generalization of the classical MFG PDEs. Furthermore, in this paper, existence and uniqueness theorems for GMFG equations are given together with a sketch of the corresponding epsilon-Nash theory for GMFG systems.
Estimating vector magnitude from its direction and derivative, with application to bearing-only SLAM filter problem

For some problems, such as monocular visual odometry (VO), vector measurements are given with unknown magnitude. In VO, the magnitude can be found by recognizing features with known position, or with an extra sensor such as an altimeter. This article presents a nonlinear observer that uses the derivative of the vector as an additional measurement for estimating the magnitude of a vector. For the VO example, this means that the velocity can be estimated by fusing the normalized velocity vector with acceleration measurements. The observer exploits the fact that the dynamics of the normalized vector is dependent on the magnitude of the vector. The observer employs methods from nonlinear/adaptive estimation; filters the unit vector on the unit sphere, and retrieves the magnitude of the vector. The observer is shown to be uniformly semi-globally asymptotically (USGAS) stable and uniformly exponentially stable (UES) in a defined region. The observer is applied to the bearing-only SLAM filter problem as an example.
A 1.17-pJ/b, 25-Gb/s/pin Ground-Referenced Single-Ended Serial Link for Off- and On-Package Communication Using a Process- and Temperature-Adaptive Voltage Regulator

This paper describes a short-reach serial link to connect chips mounted on the same package or on neighboring packages on a printed circuit board (PCB). The link employs an energy-efficient, single-ended ground-referenced signaling scheme. Implemented in 16-nm FinFET CMOS technology, the link operates at a data rate of 25 Gb/s/pin with 1.17-pJ/bit energy efficiency and uses a simple but robust matched-delay clock forwarding scheme that cancels most sources of jitter. The modest frequency-dependent attenuation of short-reach links is compensated using an analog equalizer in the transmitter. The receiver includes active-inductor peaking in the input amplifier to improve overall receiver frequency response. The link employs a novel power supply regulation scheme at both ends that uses a PLL ring-oscillator supply voltage as a reference to flatten circuit speed and reduce power consumption variation across PVT. The link can be calibrated once at an arbitrary voltage and temperature, then track VT variation without the need for periodic re-calibration. The link operates over a 10-mm-long on-package channel with −4 dB of attenuation with 0.77-UI eye opening at bit-error rate (BER) of 10<sup>−15</sup>. A package-to-package link with 54 mm of PCB and 26 mm of on-package trace with −8.5 dB of loss at Nyquist operates with 0.42 UI of eye opening at BER of 10<sup>−15</sup>. Overall link die area is 686 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}\,\,\times $ </tex-math></inline-formula> 565 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> with the transceiver circuitry taking up 20% of the area. The transceiver’s on-chip regulator is supplied from an off-chip 950-mV supply, while the support logic operates on a separate 850-mV supply.
Deep Learning With Emerging New Labels for Fault Diagnosis

When recognizing conditions such as mechanical faults, the operating data samples each have a label indicating the sample type, whether normal or a known fault category. However, the class label set of the test data is the same as that in the training set, which means that samples having an unexpected or unknown type are simply incorporated into existing classes rather than being assigned a new label for inclusion in another class. To address this problem, we propose a method called the emerging new labels using a sparse auto-encoder (SAE) method. After extracting features using the traditional SAE, it constructs a distribution from the training sample and identifies those samples that do not fit into known categories. The latter receive new labels and are used for retraining the model, enabling it to identify the new condition. The simulation and gearbox fault diagnosis experiments confirm that our proposed diagnostic model effectively identifies new fault types in samples, improving the diagnostic model’s general application.
Inverse Filtering for Linear Gaussian State-Space Models

This paper considers inverse filtering problems for linear Gaussian state-space systems. We consider three problems of increasing generality in which the aim is to reconstruct the measurements and/or certain unknown sensor parameters, such as the observation likelihood, given posteriors (i.e., the sample path of mean and covariance). The paper is motivated by applications where one wishes to calibrate a Bayesian estimator based on remote observations of the posterior estimates, e.g., determine how accurate an adversary's sensors are. We propose inverse filtering algorithms and evaluate their robustness with respect to noise (e.g., measurement or quantization errors) in numerical simulations.
Learning in a Continuous-Valued Attractor Network

Learning a set of patterns in a content-addressable memory is an important aspect of many neurocomputational systems. Historically, this has often been done using Hebbian learning with attractor neural networks such as the standard discrete-valued Hopfield model. However, such systems are currently severely limited in terms of their memory capacity: as an increasing number of patterns are stored as fixed points, spurious attractors ("false memories") are increasingly created and compromise the network's functionality. Here we adopt a new method for identifying the fixed points (both stored and false memory patterns) learned by attractor networks in general, applying it to the special case of a continuous-valued analogue of the standard Hopfield model. We use computational experiments to show that this continuous-valued model functions as a content-addressable memory, characterizing its ability to learn training examples effectively and to store them at energy minima having substantial basins of attraction. We find that the new fixed point locator method not only identifies learned memories, but also many of the spurious attractors that occur. These results are a step towards systematically characterizing what is learned by attractor networks and may lead to more effective learning by allowing the use of techniques such as selective application of anti-Hebbian unlearning of spurious memories.
A Correlation of Wii Balance Board and Short Physical Performance Battery Test for Balance Assessment in Older Adults

Wii Balance Board (WBB) is a commercial game device to assess static balance. Previous studies showed the good psychometric properties of WBB for balance assessment and correlation with the clinical tests. But it was lack of evidence to support the correlation between WBB and Short Physical Performance Battery test (SPPB). The purpose of the study was to find the correlation between WBB and SPPB in community older adults. 30 participants participated in this study. They were tested balance performance by WBB with 2 conditions; standing with eyes open and standing with eyes closed. They were further tested their performance with SPPB. Sway velocity was recorded using WBB with MFU static balance test software version 2. Pearson’s correlation coefficient and spearman correlation coefficient were used to analyse the data. The result was found a correlation between standing with eyes closed on WBB and SPPB (r = -0.484, p<0.001). Thus, WBB using with MFU static balance test software version 2 was a valid tool when compare with SPPB. It can be used for balance assessment in clinic.
Single-Shot Person Re-Identification Combining Similarity Metrics and Support Vectors

Person Re-Identification is all about determining a person's entire course as s/he walks around camera-equipped zones. More precisely, person Re-ID is the problem of matching human identities captured from non-overlapping surveillance cameras. In this work, we propose an approach that learns a new low-dimensional metric space in an attempt to cut down multi-camera matching errors. We represent the training and test samples by concatenating handcrafted features. Then, the method performs a two-step ranking using elementary distance metrics and followed by an ensemble of weighted binary classifiers. We validate our approach on CUHK01 and PRID450s datasets, providing only a sample per class for probe and only a sample for gallery (single-shot). According to the experiments, our method achieves CMC Rank-1 results up to 61.1 and 75.4, following leading literature protocols, for CUHK01 and PRID450s, respectively.
Generalized Tree Architecture for Efficient Successive-Cancellation Polar Decoding

This study presents an efficient architecture for the successive-cancellation polar decoder. The proposed architecture is designed based on the conventional tree architecture with the multibit decoding and the overlapped scheduling, but the architecture is generalized considering the high-radix processing, wherein several successive radix-2 kernels are processed within a cycle. The complexity and the throughput are formulated for the generalized tree architecture based on the high-radix processing. A case study is conducted for designing a 1024-bit SC decoder. When the design parameters are set to maximize the figure of merit (FOM), which is defined as the (throughput/complexity), the FOM of the 1024-bit SC decoder designed based on the proposed architecture is estimated to be 2 times higher than that of the SC decoder designed based on the conventional tree architecture.
Neural Network Based Brain Tumor Detection Using Wireless Infrared Imaging Sensor

Now-a-days image processing placed an important role for recognizing various diseases such as breast, lung, and brain tumors in earlier stage for giving the appropriate treatment. Presently, most cancer diagnosis worked according to the visual examination process with effectively. Human visual reviewing of infinitesimal biopsy pictures is exceptionally tedious, subjective, and conflicting due to between and intra-onlooker varieties. In this manner, the malignancy and it’s compose will be distinguished in a beginning time for finish treatment and fix. This brain tumor classification system using machine learning-based back propagation neural networks (MLBPNN) causes pathologists to enhance the exactness and proficiency in location of threat and to limit the entomb onlooker variety. Moreover, the technique may assist doctors with analyzing the picture cell by utilizing order and bunching calculations by recoloring qualities of the phones. The different picture preparing steps required for disease location from biopsy pictures incorporate procurement, upgrade, and division; include extraction, picture portrayal, characterization, and basic leadership. In this paper, MLBPNN is analyzed with the help of infra-red sensor imaging technology. Then, the computational multifaceted nature of neural distinguishing proof incredibly diminished when the entire framework is deteriorated into a few subsystems. The features are extracted using fractal dimension algorithm and then the most significant features are selected using multi fractal detection technique to reduce the complexity. This imaging sensor is integrated via wireless infrared imaging sensor which is produced to transmit the tumor warm data to a specialist clinician to screen the wellbeing condition and for helpful control of ultrasound measurements level, especially if there should arise an occurrence of elderly patients living in remote zones.
Performances Enhancement of Fingerprint Recognition System Using Classifiers

Fingerprint recognition is best known and generally used as a biometric technology because of their high acceptability, immutability, and uniqueness. A fingerprint consists of ridges and valleys pattern also known as furrows. These patterns fully develop in the mother’s womb and remain constant throughout the whole lifetime of the individual. The ridge bifurcation and ridge termination are the main minutiae features that are extracted for identification of individuals in fingerprint recognition system. The aim of this paper is to enhance the performance of the fingerprint recognition systems using classifiers. To achieve the aim, fingerprints from the FV2002 database are used, before these fingerprints are evaluated, image enhancement and binarization is applied as a pre-processing on fingerprints, by combining many methods to build a database of fingerprint features having minutia marking and minutia feature extraction. The fingerprint recognition is presented by image classification using MATLAB classifiers, i.e., Decision Tree, Linear Discriminant Analysis, medium Gaussian support vector machine (MG-SVM), fine K-nearest neighbor, and bagged tree ensemble. The aim of this paper is to make a comparison between classifiers for performance enhancement of the fingerprint recognition system. The MG-SVM classifiers significantly give the highest verification rate of 98.90% among all classifies used.
Interactive Voice Response Server Voice Network Administration Using Hidden Markov Model Speech Recognition System

This paper describes our experience to create a secure Telephony Amazigh Spoken System over the network. The designed system was created by combining HMM automatic speech recognition and IVR technologies. It allows the user to control from distance the security network and backup system using his voice to network tasks administration managing and his biological voiceprint for security identification. This research project is carried out using Amazigh language for the interaction system. The designed system was implemented on the open source platform. Our experimental results show that the administrator recognition rates were all above 80 % whereas the non-admin recognition rate is less than 6% which demonstrates the security aspect of our system.
Symmetric Multilevel Imperfect Secret Sharing

We generalize secret sharing to a symmetric multilevel imperfect secret sharing (SMISS) problem. To measure the secrecy of the system, we introduce a security level for each set of encoded messages, which is measured by the equivocation of the source message given the encoded messages in this set. The security levels of all the subsets with the same cardinality are assumed to be identical. This problem in its general case is complicated. In this paper, we explicitly characterize the rate regions of the general two-level and three-level SMISS problems.
Impact of User Awareness, Trust, and Privacy Concerns on Sharing Personal Information on Social Media: Facebook, Twitter, and Instagram

Facebook, Twitter, and Instagram are the most often and most widely used social-media sites, especially in Indonesia. The number of social-media users is increasing every year, accompanied by increased risks. One of those risks is to the security and privacy of users, as shown by the user-data leak experienced by Facebook in 2018. These issues led the authors to conduct research relating to the influence of privacy concerns, trust, and user awareness on the readiness to share personal information via social-media sites. A research model was developed based on several hypotheses. The research model was confirmed, and experiment was conducted from 340 social-media users through a questionnaire-based online survey over a period of 2 months. To confirm the structural model used in this research, reliability and validity analyses were performed. Structural equation model (SEM) analysis was used to detect relationships among constructs in the research model. The results of this research show that user awareness, trust, and privacy concerns have a positive and significant effect on sharing personal data on social media. Then we discussed the implications of our results for policy-making, practice, and future research.
Scattered Fields of a 2-D Rectangular Room Composed of Cinder Block Walls Using Floquet–Fourier Series Expansion

In this paper, the 2-D scattered fields from a rectangular room composed of cinder block walls, illuminated by a source outside are derived. First, we assume that the structure is repeated periodically with a periodicity of <inline-formula> <tex-math notation="LaTeX">$P$ </tex-math></inline-formula>. For analysis of a periodic structure, the Floquet theorem is used. Second, the dielectric profile is expressed by a Fourier series and tangential electric and magnetic fields are given by the Floquet series. In fact, the structure is a multilayer periodic one. By applying the boundary conditions, periodic fields in all layers are obtained. Third, by increasing the periodicity <inline-formula> <tex-math notation="LaTeX">$P$ </tex-math></inline-formula>, estimated fields due to the structure alone are obtained. The numerical results due to excitation by a plane wave, a Gaussian wave, and a line source radiation are all in excellent agreement with those of a finite-element method.
Channel-based Rate Selection for Commodity RFID Networks

Backscatter communication has been an emerging research field in power-constrained and short-range wireless sensor networks. In RFID networks, sensors need to transfer masses of sensing data via backscatter communication. In order to meet this need, we propose a novel Channel-based Rate Selection framework (CRS). Specifically, we design a novel channel selection algorithm that covers each backscatter node at first and benefits as many nodes as possible meanwhile. Then, considering the difference among the rate requirements of different backscatter nodes, we choose different data rates for different nodes on a selected channel. We implement all experiments via traces captured by a commercial reader. Experiment results show that CRS achieves up to 21.4% throughput gain compared with state-of-the-art rate adaptation algorithm.