Cooperative control involves developing control strategies for individual robots that guarantee synchronized behavior of the states of all the robots in a team in some prescribed sense. This work presents a novel controller that achieves formation control for a group of differential-drive robots. First, we propose a nonlinear feedback control law that guarantees stable tracking of a reference trajectory for a single robot without exceeding the velocity limits of the robot. Using Lyapunov analysis, we obtain the necessary conditions on the control parameters and establish ultimate boundedness on error terms. Next, we formulate the formation control problem as a trajectory tracking problem for the multi-robot system and solve it using the proposed controller. Additionally, we provide constraints on formation size for a planned reference trajectory, ensuring smooth cornering of multi-robot formation without exceeding actuation limits.

We propose an unbounded fully homomorphic encryption scheme, i.e. a scheme that allows one to compute on encrypted data for any desired functions without needing to decrypt the data or knowing the decryption keys. This is a rational solution to an old problem proposed by Rivest, Adleman, and Dertouzos \cite{32} in 1978, and to some new problems appeared in Peikert \cite{28} as open questions 10 and open questions 11 a few years ago. Our scheme is completely different from the breakthrough work \cite{14,15} of Gentry in 2009. Gentry’s bootstrapping technique constructs a fully homomorphic encryption (FHE) scheme from a somewhat homomorphic one that is powerful enough to evaluate its own decryption function. To date, it remains the only known way of obtaining unbounded FHE. Our construction of unbounded FHE scheme is straightforward and noise-free that can handle unbounded homomorphic computation on any refreshed ciphertexts without bootstrapping transformation technique.

This paper evaluates the 60 GHz channel capacity based on wireless data center (WDC) channel models and radio measurements in a productivity server room. The channel capacity is evaluated to study the frequency selectivity effects on three receiver structures, namely a guard interval based OFDM receiver, a decision feedback equalizer (DFE) and a matched filter (MF) based receiver. The capacity is evaluated according to the IEEE 802.11ad channelization scheme with respect to the 10 Gbps Ethernet (10GigE) wired network links. The wireless capacity was found limited in terms of practical coverage distances considering the 10GigE wired throughput constraint

Cybersickness is a drawback of virtual reality (VR), which also affects the cognitive and motor skills of the users. The Simulator Sickness Questionnaire (SSQ), and its variant, the Virtual Reality Sickness Questionnaire (VRSQ) are two tools that measure cybersickness. However, both tools suffer from important limitations, which raises concerns about their suitability. Two versions of the Cybersickness in VR Questionnaire (CSQ-VR), a paper-and-pencil and a 3D –VR version, were developed. Validation and comparison of CSQ-VR against SSQ and VRSQ were performed. Thirty-nine participants were exposed to 3 rides with linear and angular accelerations in VR. Assessments of cognitive and psychomotor skills were performed at baseline and after each ride. The validity of both versions of CSQ_VR was confirmed. Notably, CSQ-VR demonstrated substantially better internal consistency than both SSQ and VRSQ. Also, CSQ-VR scores had significantly better psychometric properties in detecting a temporary decline in performance due to cybersickness. Pupil size was a significant predictor of cybersickness intensity. In conclusion, the CSQ-VR is a valid assessment of cybersickness, with superior psychometric properties to SSQ and VRSQ. The CSQ-VR enables the assessment of cybersickness during VR exposure, and it benefits from examining pupil size, a biomarker of cybersickness.

This paper presents a novel optimization-based full-pose trajectory tracking method to control overactuated multi-rotor aerial vehicles with limited actuation abilities. The proposed method allocates feasible control inputs to track a reference trajectory, while ensuring the tracking of the reference position, and while tracking the closest feasible attitude. The optimization simultaneously searches for a feasible trajectory and corresponding feasible control inputs from the infinite possible solutions, while ensuring smooth control inputs. The proposed real-time algorithm is tested in extensive simulation on multiple platforms with fixed and actuated propellers. The simulation experiments show the ability of the proposed approach to exploit the complex set of feasible forces and moments of overactuated platforms while allocating smooth feasible control inputs.

Interactions of cells via the extracellular vesicles (EVs), manipulate various actions including cancer initiation and progression, inflammation, anti-tumor signaling and cells migration, proliferation and apoptosis in the tumor microenvironment. EVs as the external stimulus can activate or inhibit some receptor pathways in a way that amplify or attenuate a kind of particle release at target cells. This also could be carried out in a biological feedback-loop where the transmitter is affected by the induced release initiated by the target cell due to the EVs received from the donor cell, to create a bilateral process. In this paper, at first we derive the frequency response of internalization function in the framework of a unilateral communication link. This solution is adapted to a closed-loop system to find the frequency response of a bilateral system. The cells overall release as the combination of the natural release and the induced release are reported at the end of this paper and the results are compared in terms of cells distance and reaction rates of EVs on the cell membranes.

This article presents a comparison of 4 different types of rectifier circuits for piezoelectric energy harvesting. Energy is harvested from multiple piezoelectric elements embedded in an in?sole while simultaneously collecting gait information of the subject wearing it. The study is based on an asymmetric signal generated while walking and the harvested energy is utilized to operate a system under a fixed electrical load. The influence of back-up battery and insole design optimization on the accuracy of collected information is taken into consideration. A comprehensive approach for shoe insole application based on simultaneous piezoelectric sensing and energy harvesting is presented in this work.

Patients with hemiplegia are usually restricted to performing general bilateral activities of daily life (gbADLs). Bilateral training has been verified to contribute to the rehabilitation of physical functions. Although robotic systems are gradually being employed in the field of rehabilitation, few studies have performed simulations with regards to gbADLs for training. Therefore, a novel end-effector bilateral rehabilitation robotic system (EBReCS) for the upper limb is developed in this article for the task rendering of gbADLs, in which the gbADL-corresponding workspace is obtained via modularly designed bilateral parallelogram mechanisms, and interaction rendering of multiple bimanual modes (uncoupled, trans-soft-coupled, trans-semi-coupled, and rotation-coupled) is achieved by implementing the admittance model, the inner force field between robotic end-effectors, and the outer force field distributed around. Experiments of the proposed four rehabilitation training modes were carried out on the healthy subject, with the results showing a feasible method of the EBReCS in the simulation of multiple bimanual coordinated rehabilitation training tasks. In the future, the constructed EBReCS is expected to be clinically tested and exploited for home rehabilitation as a coordinated training device.

Models of seven discrete facial expressions are built on macro-level facial muscle variations for separating distinct affective states. We propose a step-wise Hierarchical Separation and Classification Network (HSCN) that discovers dynamic and continuous macro- and micro-level variations in facial expressions. The HSCN first invokes an unsupervised cosine similarity-based separation method on continuous facial expression data and extracts twenty-one dynamic expression classes from the seven common discrete affective states. Separation between the clusters is then optimised for discovering the macro-level changes in facial muscle activations followed by splitting the upper and lower facial regions for realising and modelling changes pertaining to upper and lower facial muscle activations. A linear discriminant space is developed for clustering the upper and lower facial images on the basis of similar muscular activation patterns. Actual dynamic data and linear discriminant features are mapped for developing a rule-based expert system that would facilitate classification of twenty-one upper and twenty-one lower facial micro-expressions. Using the random forest algorithm, classification accuracies of 76.11\% were observed for dynamic macro-level facial expression classification. A support vector machine provided 73.63\% and 87.68\% accuracies respectively while classifying upper and lower facial micro-expressions. This work provides a novel framework for the dynamic assessment of affective states. Reported methods and results also provide new insight into the dynamic analysis of facial expressions of affective states.

A current limiter (CL), which protects virtual synchronous generators (VSGs) from overcurrents, may significantly alter the operation of the VSGs when activated. In this letter, a transient stability analysis of VSGs equipped with quadrature- (q-) prioritized CL is performed. This study shows that in addition to the instability caused by the positive feedback of the primary controller, a VSG could become unstable after large disturbances due to the failure of the inner voltage controller. In this study, a stability criterion is provided for the voltage control loop.

As smart contracts process digital assets, their security is essential for blockchain applications. Many approaches have been proposed to detect smart contract vulnerabilities. Studies show that few of the reported vulnerabilities are exploited and hypothesize that many of the reported vulnerabilities are false positives. However, no follow-up study is performed to confirm the hypothesis and understand why the reported vulnerabilities are not exploited. In this study, we first collect 136,969 unique real-world smart contracts and analyze them using four vulnerability detectors, namely Oyente, SmartCheck, Slither, and SolDetector. Then, we apply Strauss’ grounded theory approach to manually analyze the source code of the smart contracts reported as vulnerable to recognizing false positives and understand the reasons for false results. In addition, we analyze the transaction logs of the smart contracts reported as vulnerable to identifying and understanding their exploitations. Our results show that 75.37% of the 4,364 smart contracts reported as vulnerable are false positives, and eleven reasons are causing the false positives. After analyzing the 4,106,134 transaction logs of the contracts reported as vulnerable, we find that vulnerabilities of only 67 (0.015%) of the contracts have been exploited in history. We also identify six reasons for demotivating and preventing the attackers from exploiting the vulnerabilities. Our results reveal that state-of-the-art smart contract vulnerability detectors primarily treat the smart contracts as yet another application developed using Object Oriented (OO) languages when analyzing and reporting the smart contract vulnerabilities. Without considering the specific design principles of the Solidity programming language and the characteristics of smart contracts’ application scenarios and execution environments, many of the reported vulnerabilities are not exploitable or not cost-effective to be exploited by adversaries.

Transcranial focused ultrasound (tFUS) has emerged as a new non-invasive brain stimulation (NIBS) modality, with its exquisite ability to reach deep brain areas at a high spatial resolution. Accurate placement of an acoustic focus to a target region of the brain is crucial during tFUS treatment; however, distortion of acoustic wave propagation through the intact skull casts challenges. High-resolution numerical simulation allows for monitoring of the acoustic pressure field in the cranium but also demands extensive computational loads. In this study, we adopt a super-resolution convolutional neural network (SRCNN) technique to enhance the prediction quality of the FUS acoustic pressure field in the targeted brain regions. The training dataset was acquired by numerical simulations performed at low-(1.0 mm) and high-resolutions (0.5 mm) on three ex vivo human calvaria. Five different 3D SRCNN models were trained by using a multivariable dataset, which incorporated information on the acoustic pressure field, wave velocity, and skull computed tomography (CT) images. We achieved an accuracy of 80.87±4.50% in predicting the focal volume with a substantial improvement of 86.91% in computational cost compared to the conventional high-resolution numerical simulation. The results suggest that the method can greatly reduce the simulation time without sacrificing accuracy.

Background: Thyroid Nodules are more and more widespread, with the frequency of thyroid cancers increasing accordingly. In routine practice, the practician’s goal is to determine if a nodule is malignant or suspicious, and thus requires surgery. However, nearly one-third of thyroid nodules fall into the ‘indeterminate’ classification segment, often leading to the use of surgery as a diagnostic tool, when it could have been avoided with a better preoperative diagnosis. Molecular tests exist to address this issue, but they are markedly onerous. Results: In this paper, we present an accurate, cost-less method to predict the malignancy of thyroid nodules using a categorical thyroid nodules dataset. The prediction is achieved through the use of a new supervised autoencoder neural network. Results show that the Supervised Autoencoder Neural Network outperforms classical classification methods on all evaluation metrics. A proof-of-concept Graphical User Interface was developed to demonstrate the medical usefulness of the latent space of the trained network. Conclusions: Aside from the prediction itself, the Supervised Autoencoder Neural Network provides three medically interesting characteristics: a prediction confidence score, consistent feature ranking, and an interpretable latent space. All of those characteristics are valuable from the practician’s point of view, as they allow for an efficient, cost-less and well-informed prognosis.

Fix $k = \mathbb Z_2$ to be a field of characteristic 2, let $A$ denote the Steenrod algebra over $k.$ A problem of immense difficulty in algebraic topology is the determination of a minimal set of $A$-generators for the polynomial ring $P_q = k[x_1, \ldots, x_q] = H^{*}(k^{q}, k)$ on $q$ generators $x_1, \ldots, x_q$ with $|x_1|= |x_2| = \cdots = |x_q| =  1.$ By way of equivalence, one may choose to write an explicit basis for the cohit space $\pmb{Q}^{q} := k\otimes_{A} P_q$ in each non-negative degree $n.$ This subject, which has now a long history, is the content of the classical “hit problem” proposed in [Abstracts Papers Presented Am. Math. Soc.  \textbf{833} (1987), 55-89]. Furthermore, it is closely related to the $q$-th transfer homomorphism $Tr_q^{A}$ constructed by William Singer in [Math. Z. \textbf{202} (1989), 493-523]. This map $Tr_q^{A}$ passes from the space of $G(q)$-coinvariant $k\otimes _{G(q)} P_A((P_q)_n^{*})$ of $\pmb{Q}^{q}$ to the $k$-cohomology group of the Steenrod algebra, ${\rm Ext}_{A}^{q, q+n}(k, k),$ wherein $G(q)$ stands for the general linear group of degree $q$ over the field $k,$ and $P_A((P_q)_n^{*})$ is the primitive part of $(P_q)^{*}_n$ under the action of $A.$ Particularly, the assertion that $Tr_q^{A}$ is always an injective map has been conjectured by Singer himself, but as of now, this remains an open problem for all $q\geq 4.$ Accordingly, the aim of the present study is to deal with the Singer conjecture for rank 4 in certain internal degrees. Specifically, by the usage of the techniques of the hit problem in four variables, we explicitly determine the structure of the coinvariant $k\otimes _{G(4)} P_A((P_4)_{n}^{*})$ in some generic degrees $n.$ Then, applying these results and a representation of $Tr_4^{A}$ via the lambda algebra, we state that Singer’s conjecture is true for rank $q = 4$ in respective degrees $n.$ This has significantly contributed towards the ultimate proof of Singer’s conjecture within the rank 4 case.

This paper proposes a novel approach for enhancing physical layer security (PLS) in wireless networks by utilizing a combination of reconfigurable intelligent surfaces (RIS) and artificial noise (AN). The proposed aerial RIS (A-RIS) concept utilizes a RIS-attached unmanned aerial vehicle (UAV) that hovers over the network area to improve the signal quality for legitimate users and jam that of illegitimate ones. We propose a method of virtually partitioning the RIS, such that the partition phase shifts are configured to improve the intended signal at a legitimate user while simultaneously increasing the impact of AN on illegitimate users. Additionally, thanks to the availability of closed-form optimal RIS portions, the proposed deployment approaches converge in less than a second, making it suitable for dynamic A-RIS deployment.

If A and B are sets such that A is a subset of B, generalisation may be understood as the inference from A of a hypothesis sufficient to construct B. One might infer any number of hypotheses from A, yet only some of those may generalise to B. How can one know which are likely to generalise? One strategy is to choose the shortest, equating the ability to compress information with the ability to generalise (a “proxy for intelligence”). We examine this in the context of a mathematical formalism of enactive cognition. We show that compression is neither necessary nor sufficient to maximise performance (measured in terms of the probability of a hypothesis generalising). We formulate a proxy unrelated to length or simplicity, called weakness. We show that if tasks are uniformly distributed, then there is no choice of proxy that performs at least as well as weakness maximisation in all tasks while performing strictly better in at least one. In other words, weakness is the pareto optimal choice of proxy. In experiments comparing maximum weakness and minimum description length in the context of binary arithmetic, the former generalised at between 1.1 and 5 times the rate of the latter. We argue this demonstrates that weakness is a far better proxy, and explains why Deepmind’s Apperception Engine is able to generalise effectively.

According to the World Health Organization, approximately 6 in 100 people suffer from Post-traumatic stress disorder (PTSD) at some point in their lives. PTSD is a mental disorder with a set of symptoms observed in a person following exposure to a life-threatening event or witness a death. The primary characteristic of PTSD is the persistence over time of certain symptoms: flashbacks, traumatic nightmares, acute stress and symptoms of depression. In a stressful situation, the whole body goes into tension. This has an energy cost and affects the voice, breathing, daily gestures and the face and which are referred to in this survey paper as external symptoms. The diagnosis of PTSD is based on the Diagnostic and Statistical Manual of Mental Disorders (also known by the acronym DSM) classification using standard questionnaires where the patient self-reports their condition based on their symptoms. Indeed, these questionnaires have several limitations and give an imprecise diagnosis. Sensors and wearable based technologies can play a key role in improving the diagnosis, the prognosis and the assistance of PTSD. Recently, Computer-Aided Diagnosis (CAD) systems have been proposed for PTSD detection based on external symptoms and brain activity using video and EEG sensors. To the best of our knowledge, this is the first survey paper that gives a literature overview of machine learning based approaches for PTSD diagnosis using video and EEG sensors. In addition, a comparison between existing approaches and a discussion about the potential avenues for future PTSD research is provided.

We design a multiple-model state estimator where each filter runs a propagation model with a different source term associated to the hypothesis of the source being positioned in a specific element of the discretized domain. A null hyphotesis is added to account for the absence of the source in the domain. The decision on the propagation model (i.e., source position) that is more likely given the available acoustic measurements is taken based on the mode probabilities associated to each hypothesis. To handle the high dimension of the large-scale field estimation problem and reduce the computational complexity, the multiple-model filter is implemented by using the ensemble Kalman filter. Finally, the effectiveness of the proposed multiple-model spectral-element ensemble Kalman filter is demonstrated through simulation experiments in underwater acoustic environments with regular and irregular seabed geometry and via comparison with the standard matched-field processing method.