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This article investigates secure consensus of linear multiagent systems under event-triggered control subject to a scaling deception attack. Different from probabilistic models, a sequential scaling attack is considered, in which specific attack properties, such as the attack duration and frequency, are defined. Moreover, to alleviate the utilization of communication resources, distributed static and dynamic event-triggered control protocols are proposed and analyzed, respectively. This article aims at providing a resilient event-triggered framework to defend a kind of sequential scaling attack by exploring the relationship among the attack duration and frequency, and event-triggered parameters. First, the static event-triggered control is studied, and sufficient consensus conditions are derived, which impose constraints on the attack duration and frequency. Second, a state-based auxiliary variable is introduced in the dynamic event-triggered scheme. Under the proposed dynamic event-triggered control, consensus criteria involving triggering parameters, attack constraints, and system matrices are obtained. It proves that the Zeno behavior can be excluded. Moreover, the impacts of the scaling factor, triggering parameters, and attack properties are discussed. CDK inhibitor Finally, the effectiveness of the proposed event-triggered control mechanisms is validated by two examples.This article presents a simple sampling method, which is very easy to be implemented, for classification by introducing the idea of random space division, called ``random space division sampling (RSDS). It can extract the boundary points as the sampled result by efficiently distinguishing the label noise points, inner points, and boundary points. This makes it the first general sampling method for classification that not only can reduce the data size but also enhance the classification accuracy of a classifier, especially in the label-noisy classification. The ``general means that it is not restricted to any specific classifiers or datasets (regardless of whether a dataset is linear or not). Furthermore, the RSDS can online accelerate most classifiers because of its lower time complexity than most classifiers. Moreover, the RSDS can be used as an undersampling method for imbalanced classification. The experimental results on benchmark datasets demonstrate its effectiveness and efficiency. The code of the RSDS and comparison algorithms is available at https//github.com/syxiaa/RSDS.In this article, we present four cases of minimal solutions for two-view relative pose estimation by exploiting the affine transformation between feature points, and we demonstrate efficient solvers for these cases. It is shown that under the planar motion assumption or with knowledge of a vertical direction, a single affine correspondence is sufficient to recover the relative camera pose. The four cases considered are two-view planar relative motion for calibrated cameras as a closed-form and least-squares solutions, a closed-form solution for unknown focal length, and the case of a known vertical direction. These algorithms can be used efficiently for outlier detection within a RANSAC loop and for initial motion estimation. All the methods are evaluated on both synthetic data and real-world datasets. The experimental results demonstrate that our methods outperform comparable state-of-the-art methods in accuracy with the benefit of a reduced number of needed RANSAC iterations. The source code is released at https//github.com/jizhaox/relative_pose_from_affine.In this article, we study the finite-time stabilization and the asymptotic stabilization with probability one of Markovian jump Boolean control networks (MJBCNs) by sampled-data state feedback controls (SDSFCs). Based on the semi-tensor product (STP), we introduce an augmented variable multiplied by the vector form of the switching signal and the state of MJBCN. We find that under SDSFC, the sequence of the states of the augmented variable at sampling instants satisfies the Markov property. Based on the convergences of the switching signal and the augmented variable, we obtain the sufficient and necessary criteria for the finite-time stabilization and the asymptotic stabilization of MJBCNs by SDSFCs, respectively. Moreover, for the two kinds of stabilization, the feedback matrices of SDSFCs are constructed, respectively. Finally, the obtained results are applied to an apoptosis network and a model of the lactose operon in the Escherichia Coli.Gastroesophageal reflux disease (GERD) is a common digestive disorder with troublesome symptoms that has been affected millions of people worldwide. Multichannel Intraluminal ImpedancepH (MIIpH) monitoring is a recently developed technique, which is currently considered as the gold standard for the diagnosis of GERD. In this paper, we address the problem of characterizing gastroesophageal reflux events in MII signals. A GER detection algorithm has been developed based on the sparse representation of local segments. Two dictionaries are trained using the online dictionary learning approach from the distal impedance data of selected patches of GER and no specific patterns intervals. A classifier is then designed based on the l_pnorm of dictionary approximations. Next, a preliminary permutation mask is obtained from the classification results of patches, which is then used in postprocessing procedure to investigate the exact timings of GERs at all impedance sites. Our algorithm was tested on 33 MII episodes, resulting a sensitivity of 96.97% and a positive predictive value of 94.12%.In this article, a novel wearable haptic device, to be worn on the hand and forearm, is introduced. Using the modalities of vibration, pressure, and heat application, the device attempts to replicate four core components of communication. The four components - co-presence, phatic communication, back-channeling, and direction giving - are simulated through haptic profiles individually unique to a section or combined as an encompassing system of the device. This paper evaluates the performance of the device through three testing phases with sighted-hearing and DeafBlind individuals. Results indicate that a strong majority of the tested haptic profiles show statistical significance in replication between individuals. This article is unique in its collaboration with the protactile DeafBlind community, individuals who communicate solely through touch, by furthering understanding on how to generate intuitive tactile profiles in wearable haptic devices.Tactile sensations are based on stimulation elicited on the skin through mechanical interactions between the skin and an object. Hence, it is important to consider skin properties as well as objects. In this article, we aim to develop wearable artificial fingers for quantitative evaluations reflecting individual differences in human fingers. In a previous study, a wearable skin vibration sensor was attached to artificial fingers and it was demonstrated that the skin vibrations differed according to the dimension of surface ridge and the artificial finger is useful for roughness evaluation. This article improved the artificial finger to measure the contact force and friction in addition to the skin vibration. A small three-axis force sensor was embedded into the base of the finger, and normal and friction forces were estimated via a multi-regression method. Furthermore, artificial fingers with different hardness were prepared and six different textures were used to investigate tactile evaluation. Experimental results showed that the artificial fingers could measure normal and friction forces along with the skin vibration and were useful to evaluate textures. Resulting distributions of the vibration intensity and friction coefficient were different for the soft and hard artificial fingers, indicating the complex influence of skin properties on tactile sensations.The contact between the fingertip and an object is formed by a collection of micro-scale junctions, which collectively constitute the real contact area. This real area of contact is only a fraction of the apparent area of contact and is directly linked to the frictional strength of the contact (i.e., the lateral force at which the finger starts sliding). As a consequence, a measure of this area of real contact can help probe into the mechanism behind the friction of skin on glass. In this article, we present two methods to measure the variations of contact area; one that improves upon a tried-and-true fingertip imaging technique to provide ground truth, and the other that relies on the absorption and reflection of acoustic energy. To achieve precise measurements, the ultrasonic method exploits a recently developed model of the interaction that incorporates the non-linearity of squeeze film levitation. The two methods are in good agreement ($\rho =0.94$) over a large range of normal forces and vibration amplitudes. Since the real area of contact fundamentally underlies fingertip friction, the methods described in the article have importance for studying human grasping, understanding friction perception, and controlling surface-haptic devices.Implantable brain machine interfaces for treatment of neurological disorders require on-chip, real-time signal processing of action potentials (spikes). In this work, we present the first spike sorting SoC with integrated neural recording front-end and analog unsupervised classifier. The event-driven, low power spike sorter features a novel hardware-optimized, K-means based algorithm that effectively eliminates duplicate clusters and is implemented using a novel clockless and ADC-less analog architecture. The 1.4 mm2 chip is fabricated in a 180-nm CMOS SOI process. The analog front-end achieves a 3.3 μVrms noise floor over the spike bandwidth (400 - 5000 Hz) and consumes 6.42 μW from a 1.5 V supply. The analog spike sorter consumes 4.35 μW and achieves 93.2% classification accuracy on a widely used synthetic test dataset. In addition, higher than 93% agreement between the chip classification result and that of a standard spike sorting software is observed using pre-recorded real neural signals. Simulations of the implemented spike sorter show robust performance under process-voltage-temperature variations.The classification of clinical samples based on gene expression data is an important part of precision medicine. In this manuscript, we show how transforming gene expression data into a set of personalized (sample-specific) networks can allow us to harness existing graph-based methods to improve classifier performance. Existing approaches to personalized gene networks have the limitation that they depend on other samples in the data and must get re-computed whenever a new sample is introduced. Here, we propose a novel method, called Personalized Annotation-based Networks (PAN), that avoids this limitation by using curated annotation databases to transform gene expression data into a graph. Unlike competing methods, PANs are calculated for each sample independent of the population, making it a more efficient way to obtain single-sample networks. Using three breast cancer datasets as a case study, we show that PAN classifiers not only predict cancer relapse better than gene features alone, but also outperform PPI (protein-protein interactions) and population-level graph-based classifiers.