Light-fidelity (Li-Fi) is a promising solution to provide high-rate, secure, and green communications to be used in the next generation of wireless networks. Since visible or infrared (IR) light-emitting diodes (LEDs) are used as the optical source and have a nonlinear transfer function, the transmitted modulated signal can be distorted if the signal has a high peak-to-average power ratio (PAPR). Recently, a new modulation scheme called Hadamard-coded modulation (HCM) is proposed which has a low PAPR and produces symbols with discrete levels. In the HCM technique, the symbol levels can be created using multiple LEDs. Therefore, each LED operates in its linear region and just switches on or off. In this paper, a low-complexity transceiver architecture for the HCM-based communication links is proposed and a complete synchronization procedure based on the spread spectrum techniques is presented. Finally, the bit error rate of the system is evaluated by Monte Carlo simulations, and effects of system parameters such as preamble length, fall and rise time of optical devices, and timing jitter on the bit error rate of the link are discussed.

This letter addresses the challenge of determining the maximum sum rate achievable in high mobility scenarios with time and frequency selectivity in 5G V2X communications, particularly in the context of IoT/sensor networks. We derive a closed-form expression for the maximum sum rate of a multicarrier non-orthogonal multiple access (NOMA) system with 𝑛 users, and validate our theoretical results through simulations by comparing them with traditional orthogonal schemes. Our work provides valuable insights into the potential of NOMA for high-speed V2X communications, and offers a novel contribution to the existing literature by presenting a closed-form expression allowing the development of efficient and reliable 5G V2X systems.

In this work, we investigate a practical secure multiple-input single-output (MISO) wireless transmission of information from a source to a destination assisted by cooperative devices. In the first phase, a space-time block code (STBC) is employed; thus, the source does not require any channel state information (CSI). In the second phase, the cooperative devices are divided into two groups, depending on whether they serve for relaying or jamming. Based on the imperfect CSI toward the destination, the devices allocated to the relay group assist the source in forwarding information, whereas those within the jamming group send artificial noise (AN) so as to prevent the eavesdropper from receiving information. Furthermore, the system utilizes two grouping strategies, referred to as random and adaptive. In the former case the role (relaying or jamming) of each device is predetermined randomly, whereas in the latter case it is determined autonomously depending on the decoding results. We analyze the outage probability of our proposed system with and without power allocation optimization under the assumption of imperfect CSI at the cooperative devices, and their accuracy is verified by Monte-Carlo simulations.

Coupled nonlinear dynamical systems are known to display remarkable behavioral similarities to some aspects of neural activity in biology. When considering the computational properties of such a neuromorphic network (or even an electronic biomimetic computation device underpinned by such a network), the question of whether (and how) such a network realizes fundamental logic operations must be examined. In this work, a dynamical system of coupled noisy overdamped nonlinear bistable elements is considered. The background noise floor is used to drive information flow by helping each element switch randomly between its stable states, with the system response quantified via a long-time probability density function. A theoretical representation of such a system is developed and a simple five-element realization is used to demonstrate a continuous version of an XOR gate, through the proper choice of coupling coefficients and controllable external biases. This is a first step towards synthesizing more general functions, a prerequisite for advanced computing and learning applications. Finally, a silicon implementation is proposed and simulated using a verified process model that could be fabricated to generate a working analog computing system.

Ultrasonic measurements at high magnetic field and low temperature necessitate ultra-fast excitations, ultra-high dynamic range of duty-cycle adjustments, as well as real-time, precise and automatic analysis. This paper proposes a dual-mode ultrasonic measurement system, which means the capability of carrying out acoustic measurements in both pulsed and static high magnetic field with low temperature. Based on the “counter-delay-chain” series structure and adaptive jitter compensation in the field-programmable-gate-array(FPGA), the timing control accuracy of 5 nanoseconds and the timing jitter below 160 picoseconds are realized for high-resolution excitation generation and precise sampling window adjustment. The duty cycle adjustment achieves a high dynamic range from 0.23 ppb to 10 % to realize both pulsed field mode and static field mode (denoted as PF-mode and SF-mode) ultrasonic measurements. The independent sampling window adjustment reduces the data size by more than 80%, extends the maximum testing duration and reduces the analysis consuming. Combined with the automatic cross-correlation analysis method, this system can automatically carry out PF-mode tests with a repetition rate above 100 kHz and SF-mode tests for hours with sound velocity accuracy of 2%. Experiments have proved the reliability and feasibility of the system and revealed its application prospect in condensed matter physics experiments.

Post-processing techniques have been shown to improve the quality of the decision stream generated by classifiers used in pattern-recognition-based myoelectric control. However, these techniques have largely been tested individually and on well-behaved, stationary data, failing to fully evaluate their trade-offs between smoothing and latency during dynamic use. Correspondingly, in this work, we survey and compare 8 different post-processing and decision stream improvement schemes in the context of continuous and dynamic class transitions: majority vote, Bayesian fusion, onset locking, outlier detection, confidence-based rejection, confidence scaling, prior adjustment, and adaptive windowing. We then propose two new temporally aware post-processing schemes that use changes in the decision and confidence streams to better reject uncertain decisions. Our decision-change informed rejection (DCIR) approach outperforms existing schemes during both steady-state and transitions based on error rates and decision stream volatility whether using conventional or deep classifiers. These results suggest that added robustness can be gained by appropriately leveraging temporal context in myoelectric control.

We develop an efficient and near-optimal solution for beamforming in multi-user multiple-input-multiple-output single-hop wireless ad-hoc interference networks. Inspired by the weighted minimum mean squared error (WMMSE) method, a classical approach to solving this problem, and the principle of algorithm unfolding, we present unfolded WMMSE (UWMMSE) for MU-MIMO. This method learns a parameterized functional transformation of key WMMSE parameters using graph neural networks (GNNs), where the channel and interference components of a wireless network constitute the underlying graph. These GNNs are trained through gradient descent on a network utility metric using multiple instances of the beamforming problem. Comprehensive experimental analyses illustrate the superiority of UWMMSE over the classical WMMSE and state-of-the-art learning-based methods in terms of performance, generalizability, and robustness.

When humans and robots work together, ensuring safe cooperation must be a priority. This research aims to develop a novel real-time planning algorithm that can handle unpredictable human movements by both slowing down task execution and modifying the robot's path based on the proximity of the human operator. To achieve this, an efficient method for updating the robot's motion is developed using a two-fold control approach that combines B-Splines and Hidden Markov Models. This allows the algorithm to adapt to a changing environment and avoid collisions. The proposed framework is thus validated using the Franka Emika Panda robot in a simple start-goal task. Our algorithm successfully avoids collision with the moving hand of an operator monitored by a fixed camera.

The impending sixth-generation wireless communication networks are anticipated to guarantee mass connectivity, high integration, and lower power consumption for generating the required beamforming. To achieve these goals, an artificial intelligence (AI) framework is proposed by utilizing holographic MIMO-assisted integrated sensing, localization, and communication. The proposed AI framework ensures lower power consumption to activate the minimum number of grids from the holographic grid array for the generation of holographic beamforming. An optimization problem is formulated to maximize the signal-to-interference-plus-noise ratio received by the users, which in turn maximizes the utility function for sensing considering the user distances, beampattern gains, sensingcommunication loss, and dense locations controlling parameter. A novel AI-based framework is proposed to solve the formulated NP-hard optimization problem by decomposing it into two subproblems: the sensing problem and the communication resource allocation problem. First, a variational autoencoder (VAE) based mechanism is devised to solve the sensing problem mitigating the disputes to obtain the users’ exact location. Second, a sequential neural network-based scheme is utilized to allocate the communication resources to the heterogeneous users for generating the desired beamforming based on the findings of the VAE-based mechanism. Moreover, an extreme case power allocation strategy is presented once a large number of users enter the system. The extreme case power allocation strategy applies when the total power prediction exceeds the total system power for allocating the communication resources to the users. Finally, simulation results validate that the proposed AI-based framework outperforms the long short-term memory method with a cumulative power savings of 34.02% taking the ground truth power into account. Therefore, the proposed AI framework generates effective beamforming to serve the communication users.

In this human activity recognition dataset, there are a total of 53 participants, consisting of 33 adults and 20 youths. In a laboratory experiment, individuals were given a set of synchronized sensors that were attached to their wrist and ankle using customized Velcro bands. These sensors, which were called Triaxial Wocket accelerometers, were lightweight, small, and thin. While wearing these sensors, the participants performed everyday tasks, and the raw acceleration data was collected at a rate of 90 Hz and transmitted wirelessly to a smartphone via Bluetooth.

Wireless communications based on reconfigurable intelligent surfaces (RIS) have upsurged as a key research topic in the communication-theoretic arena. Recently, the RIS concept has evolved into the so-called simultaneously transmitting and reflecting intelligent omni-surfaces (STAR-IOS, or simply IOS), which extend the RIS functionality by incorporating transmission (in addition to reflection) capabilities. To realize STAR-IOS, full and independent reconfiguration capabilities for both reflected and transmitted waves are crucially required. However, such full independent reconfiguration has thus far been hurdled by the intimate coupling between the transmission and reflection behaviour of the IOS elements. To overcome this challenge and realize the full potential of reconfigurable IOS-aided systems, we here advocate for the use of the polarization domain in the design and operation of STAR-IOS. Thanks to the polarization- dependent features of IOS elements, fully independent (reconfigurable) transmission and reflection modes can be delivered, thus bringing key performance improvements and opportunities for new communication scenarios.

A semi-analytical technique to simulate spatially dispersive (SD) cylindrical metasurfaces is proposed and demon- strated. This method is based on the well-known Eigenfunction Expansion (EFE) method and extends the work of [1], [2] by integrating the Extended GSTCs into this pre-existing method specific to cylindrical surfaces. This method is then applied to several cylindrical metasurfaces composed of spatially dispersive wire dipole unit cells, and successfully verified against commercial full-wave simulations. Finally, using a case of a closed metasur- face shell, the usefulness of the proposed EFE-SD method is shown to illustrate the effect of metasurface thickness on the scattered fields inside and outside the shell.

Transcranial magnetic stimulation (TMS) is utilized as a treatment method for a variety of neurological and psychiatric disorders. The standard dose parameter for administration of TMS is known as the resting motor threshold (RMT). Between individuals, the RMT has traditionally been thought to reflect differences in neuroanatomy; however, the functional state of the brain has also been shown to have an influence on this value. In this study, 19 mild traumatic brain injury participants (mTBI) resting state electroencephalography (EEG) was obtained before undergoing TMS treatment. Various time frequency and power spectra values were derived from this EEG data to examine the relationship between RMT and neurophysiology. These EEG connectivity metrics were then used to establish relationships with RMT alone and accounting for differences in individual neuroanatomy. We found that the incorporation of EEG functional connectivity improved multiple regression model predictions of inter-participant RMT variability than neuroanatomy alone. Future investigations into the evaluation of EEG in predicting RMT variability should include a larger participant population with a healthy control.

Mobile Edge Computing (MEC) is a key technology towards delay-sensitive and computation-intensive applications in future cellular networks. In this paper, we consider a multi-user, multi-server system where the cellular base station is assisted by a UAV, both of which provide additional MEC services to the terrestrial users. Via dual connectivity (DC), each user can simultaneously offload tasks to the macro base station and the UAV-mounted MEC server for parallel computing, while also processing some tasks locally. We aim to propose an online resource management framework that minimizes the average power consumption of the whole system, considering long-term constraints on queue stability and computational delay of the queueing system. Due to the coexistence of two servers, the problem is highly complex and formulated as a multi-stage mixed integer non-linear programming (MINLP) problem. To solve the MINLP with reduced computational complexity, we first adopt Lyapunov optimization to transform the original multi-stage problem into deterministic problems that are manageable in each time slot. Afterward, the transformed problem is solved using an integrated learning-optimization approach, where model-free Deep Reinforcement Learning (DRL) is combined with modelbased optimization. Via extensive simulation and theoretical analyses, we show that the proposed framework is guaranteed to converge and can produce nearly the same performance as the optimal solution obtained via an exhaustive search.

Personality classification from text is a very popular domain of research among the domain of natural language processing. Personality of an individual has been found to be a very important characteristic when analyzing an individual for a particular purpose. Especially in fields such as e-recruitment, personality is a determining factor of if an individual has a placement at a particular workplace. The author aims to explore various personality classifications such as ‘The Big Five’ and the “Myer Briggs Type Indicator’ and various approaches in which text classification when it comes to detecting personality. Both machine learning and deep learning approaches are examined and their inner workings, benefits and limitations are detailed as well. We expect that this article will provide a thorough insight to personality classification of text by a numerous number of approaches.

We present a deep neural network-based framework for designing multi-band microstrip antennas given a desired impedance matching spectrum. The approach enables a design methodology that generates the desired antenna structures rapidly (under a second) through an effective deep learning- enabled search of a large design space and eliminates the need for extensive domain knowledge of antenna design. The framework is built on our innovations in tandem neural networks consisting of two cascaded neural networks. Our structures are parameterized in an exponentially large design space of discrete variables (pixels), leading to the realization of nonintuitive structures. This end-to-end synthesis in terms of discrete variables is enabled by introducing a new type of “smooth thresholding” activation function, which, along with crucial regularization terms in the network loss function, aids in designing our structures. We perform extensive neural network optimizations and study various trade-offs in the design process. We demonstrate the efficacy of our methods by generating single and dual-band resonant structures, which can be up to 50% more compact in terms of area, and up to 18 % thinner in terms of substrate height than conventional structures, while retaining competitive performance parameters in terms of gain, polarization properties, radiation efficiency, and fractional bandwidth.

With deep neural networks based function approximators, reinforcement learning holds the promise of learning complex end-to-end robotic controllers that can map high-dimensional sensory information directly to control policies. However, a common challenge, especially for robotics, is sample-efficient learning from sparse rewards, in which an agent is required to find a long sequence of “correct” actions to achieve a desired outcome. Unfortunately, inevitable perturbations on observations may make this task trickier to solve. Here, this paper advances a novel robust goal-conditioned reinforcement learning approach for end-to-end robotic control in adversarial and sparse reward environments. Specifically, a mixed adversarial attack scheme is presented to generate diverse adversarial perturbations on observations by combining white-box and black-box attacks. Meanwhile, a hindsight experience replay technique considering observation perturbations is developed to turn a failed experience into a successful one and generate the policy trajectories perturbed by the mixed adversarial attacks. Additionally, a robust goal-conditioned actor-critic method is proposed to learn goal-conditioned policies and keep the variations of the perturbed policy trajectories within bounds. Finally, the proposed method is evaluated on three tasks with adversarial attacks and sparse reward settings. The results indicate that our scheme can ensure robotic control performance and policy robustness on the adversarial and sparse reward tasks.

This paper developed an ultrasound concave 2D ring array transducer for retinal visual stimulation of the brain to restore vision in blind people. Theory, simulation, and experiment were presented.Â