M-ary Aggregate Spread Pulse Modulation (M-ASPM) is the physical layer (PHY) modulation technique that is well suited for use in low-power wide-area networks (LPWANs). Notably, M-ASPM combines high energy-per-bit efficiency, robustness, resistance to interference, and a number of other favorable technical characteristics, with the spread-spectrum ability to maintain the capacity of an uplink-focused network while extending its range. However, when all M-ASPM nodes transmit with the same average power, implementation of such capacity-preserving range extension may become impractical in complicated propagation environments with greatly varying path losses. Favorably, the efficiency of M-ASPM with constant-envelope pulses can be maintained effectively the same as the efficiency of transmitting a continuous constant-envelope waveform. Then the transmit power of different nodes can be adjusted, without sacrificing the transmission efficiency, to compensate for differences in the path attenuation. This enables us to significantly simplify planning and management of the network. In addition, such a variable-power approach generally increases the network capacity and the average energy efficiency of the nodes, as compared with the arrangement of the nodes with a constant transmit power. In this paper, we outline a practical approach to implementing such an energy-efficient M-ASPM power control, that can be used for scaling LPWANs with realistic desired and/or actual areal distributions of the uplink nodes under diverse propagation conditions.

The last decade witnessed the proliferation of automated content analysis in communication research. However, existing computational tools have been taken up unevenly, with powerful deep learning algorithms such as transformers rarely applied as compared to lexicon-based dictionaries. To enable social scientists to adopt modern computational methods for valid and reliable sentiment analysis of English text, we propose an open and free web service named transformer- and lexicon-based sentiment analysis (TLSA). TLSA integrates diverse tools and offers validation metrics, empowering users with limited computational knowledge and resources to reap the benefit of state-of-the-art computational methods. Two cases demonstrate the functionality and usability of TLSA. The performance of different tools varied to a large extent based on the dataset, supporting the importance of validating various sentiment tools in a specific context.

Interference based microwave photonic bandpass filters (MPBF) form an important component of modern RF signal processing. The presence of multiple optical components in an RF photonic system e.g. fiber amplifiers, waveguides, cause a degradation in the MPBF profile and the out-of-band rejection due to the dispersion induced phase shift. Here, we compare the performance, using simulations and experiments, of two interference based MPBF configurations: (i) double-sideband phase modulation (DSB-PM) and (ii) double-sideband intensity modulation (DSB-IM), which exploit stimulated Brillouin scattering (SBS) for sideband processing, under different dispersion conditions. While the profile and out-of-band rejection for the DSB-PM based MPBF degrade drastically under the effect of dispersion, DSB-IM based MPBF achieves an out-of-band rejection of $>$ 40 dB and maintain the 3 dB and 20 dB bandwidths of 16.7 $\pm$ 0.4 MHz and 104.5 $\pm$ 5.4 MHz, respectively, under different dispersion conditions. The DSB-IM based MPBF exploits the bias dependent phase of the modulation sidebands of a z-cut intensity modulator to mitigate the dispersion induced impairment. The use of an intensity modulator for dispersion compensation makes our approach dynamic and compatible with integrated microwave photonic systems.

In this article, I/We report a novel eye-movement assessment method using a digital camera to measure eye conjugacy in healthy individuals while performing a neurological examination. This is clinically significant because this approach overcomes the limitations of complex and expensive setups (e.g., infrared cameras) that often make it impractical to scale up and translate to clinical use. Moreover, this approach removes the need for a calibration procedure which has caused prior studies to exclude participants, potentially introducing selection bias and limiting generalizability. Our study suggests that this technology could be deployed for clinical use in the clinic or pre-hospital setting, including telemedicine or emergency medical services (EMS) encounters to detect neurological injury or diseases that cause neuro-ocular deficits, like stroke.

By extending metric space to expression in hash of directed graph, spatiotemporal observation could be described in matrix quantized state. (Working paper, more contents would be added soon)

Small-scale ocean fronts play a significant role in absorbing the excess heat and CO2 generated by climate change, yet their dynamics are not well understood. Existing in-situ and remote sensing measurements of the ocean have inadequate spatial and temporal coverage to map small-scale ocean fronts globally. Additionally, conventional algorithms to generate ocean front maps are computationally intensive and require data with long lead times. We propose machine learning (ML) models to detect temperature and chlorophyll ocean fronts from unprocessed and radiometrically uncorrected satellite im- agery by transfer learning from existing models for edge detection. We use two separate datasets: one based on conventional approaches to ocean front detection, and a second based on human annotated ground truth1. The deep learning front detection approach significantly reduces the resources and overall lead times needed for detecting ocean fronts. The deep learning models are developed with resource-constrained edge compute platforms like CubeSats in mind, as such platforms can address the spatial and temporal coverage challenges. The highest performing models achieve accuracies of 96% and make predictions in milliseconds using unoptimized desktop CPUs and using less than 100 MB of storage; these capabilities are well- suited for CubeSat deployment.

In this paper, we study the outage and ergodic rate performance of a rate-splitting multiple access (RSMA)-based cognitive radio (CR) system, where the secondary transmitter aims to communicate with two RSMA users. Imperfect successive interference cancellation (ipSIC) and channel estimation errors (CEEs) are considered in the proposed analysis. Analytical expressions for the outage probability (OP) and the ergodic rate (ER) are calculated. For a deeper understanding, the asymptotic behavior of OP and ER at high signal-to-noise ratio (SNR) regimes are carried out. Illustrative simulation results are presented and reveal that: i) The OP decreases and the ER gradually increases with the increase of SNR, and eventually approaching a constant; ii) ipSIC and CEEs have a negative impact on the considered system; iii) The outage and ergodic rate performance of RSMA-based CR network outperforms CRNOMA network due to flexible power allocation between common and private messages; iv) The OP first decreases and then increases with the power allocated to the common message, and there exists an optimal power allocation factor to ensure the reliable performance.

Cooperative communications is a core research area in wireless vehicular networks (WVNs), thanks to its capability to mitigate fading and improve spectral efficiency. In a cooperative scenario, the performance of the system is improved by selecting the best relay for data transmission among a group of available relays. However, due to the mobility of WVNs, the best relay is often selected in practice based on outdated channel state information (CSI), which in turn affects the overall system performance. Therefore, there is a need for a robust relay selection scheme (RSS) that improves the overall achievable performance of an outdated CSI. Motivated by this and considering the advantageous features of autoregressive moving average (ARMA), in the present work we model a cooperative vehicular communication scenario with relay selection as a Markov decision process (MDP) and propose two deep Q-networks (DQNs), namely DQN-RSS and DQN-RSS-ARMA. In the proposed framework, two deep reinforcement learning (RL)-based RSS are trained based on the intercept probability, aiming to select the optimal vehicular relay from a set of multiple relays. We then compare the proposed RSS with the conventional methods and evaluate the performance of the network from the security point of view. Simulation results show that DQN-RSS and DQN-RSS-ARMA perform better than conventional approaches, and they reduce intercept probability by approximately 15\% and 30\%, respectively, compared to the ARMA approach.

The short-term sensitivity of a several-kilometers long fiber-optic gyroscope is limited mainly by thermal phase noise and relative intensity noise. Increasing the phase modulation frequency decreases the thermal phase noise but not the relative intensity noise since it behaves as white noise. Here, we propose and experimentally demonstrate that the angular random walk can be effectively decreased by suppressing relative intensity noise at the modulation frequency and its third-order harmonic using direct feedback to the drive current of a superluminescent diode. Our simultaneous suppression of thermal phase noise and relative intensity noise yields an angular random walk of 15 μdeg/√h and a bias instability of 33 μdeg/h using a fiber coil with a length of 5km and an effective area of 280m2 for a measurement time of 40 hours.

This paper presents a chance-constrained rollover-free manipulation planning method for robotic arms under payload mass uncertainty. The corresponding motion planning problem is stated as a chance-constrained nonlinear optimal control problem (NOCP) subject to kinematics and rollover stability constraints. The latter takes the form of a chance constraint that ensures a certain probability of the robot maintaining dynamic rollover stability in the presence of payload mass uncertainty. To achieve efficient solutions to the NOCP, a novel geometric bound for the stability region is derived. The novel bound is then utilized to modify the rollover-stability constraint. To showcase its benefit, comparisons between the proposed bound of probabilistic rollover stability measure and the naive noise model are provided through statistical analysis. The formulation’s practicality is demonstrated through experiments with a Kinova Jaco 2 arm mounted on a free-to-roll platform. Results demonstrate greater robustness of the robot’s motion plan to mass uncertainty and computational efficiency of the trajectory generation.Â

Weber-Maxwell electrodynamics combines classical Weber electrodynamics and Maxwell's equations, including all four field equations and the Lorentz force, into a single de facto equivalent three-dimensional wave equation. From classical Weber electrodynamics, Weber-Maxwell electrodynamics inherits properties in which the concept of the magnetic field is unnecessary, and Newton's third law is satisfied under all circumstances. From Maxwell's electrodynamics, Weber-Maxwell electrodynamics inherits the ability to be compatible with electromagnetic waves. This article shows that in Weber-Maxwell electrodynamics, all conservation laws are satisfied, and that electromagnetic waves in an isolated system do not possess energy and momentum, but only mediate them between particles of matter. Furthermore, the article shows that the modern formulation of Weber electrodynamics is clearly superior to standard electrodynamics in electrical engineering, because it not only eliminates the internal contradictions, but also represents considerable simplification and compression.

Monitoring arterial blood pressure (ABP) in anesthetized patients is crucial for preventing hypotension, which can lead to adverse clinical outcomes. Thus, several efforts have been made to develop an artificial intelligence-based hypotension prediction index. Nevertheless, the use of these indices is limited because they may not provide a convincing interpretation of the association between predictors and hypotension. Herein, we developed an interpretable deep learning model that forecasts hypotension occurrences 10 min before a given 90 s ABP record. Internal and external validations of model performance reported the area under the receiver operating characteristic curve (AUC) as 0.9145 and 0.9035, respectively. Furthermore, the hypotension prediction mechanism can be physiologically interpreted by using predictors representing ABP trends that are automatically generated in the proposed model. Ultimately, we demonstrate high-applicability of a deep learning model that has a high accuracy performance and provides an interpretation of the association between ABP trends and hypotension in clinical practices.

We consider the problem of detecting small and manoeuvring objects with staring array radars. Coherent processing and long-time integration are key to addressing the undesirably low signal-to-noise/background conditions in this scenario and are complicated by the object manoeuvres. We propose a Bayesian solution that builds upon a Bernoulli state space model equipped with the likelihood of the radar data cubes through the radar ambiguity function. Likelihood evaluation in this model corresponds to coherent long-time integration. The proposed processing scheme consists of Bernoulli filtering within expectation maximisation iterations that aims at approximately finding complex reflection coefficients. We demonstrate the efficacy of our approach in a simulation example.

Recent study suggests dynamical topological phase could be derived into additional dimension of time. By adding interpretation of this phenomenon with a topological interaction within simplified model, possibility of approaching many-timeline in real world could be suggested.

In this paper, we propose a conjecture that endogenous security without any prior knowledge is similar to perfect secrecy without any prior knowledge. To prove the conjecture, we first establish a cryptography model of instinct function security to transform the security problem in the network domain into an encryption problem in the cryptographic domain. Then, we inherit and apply the established ideas and means of the Perfect Secrecy, and propose the concept, definition and corollaries of the perfect instinct function security (PIFS) corresponding to the Perfect Secrecy. Furthermore, we take the DHR system as a concrete implementation of PIFS and propose the DHR Perfect Security Theorem corresponding to Shannon’s Perfect Secrecy Theorem. Finally, we prove that the DHR satisfying the “One-Time Reconstruction” constraint is the sufficient and necessary condition to achieve perfect security. This means that the existence of PIFS is also proven. The analysis shows that any reconfigurable system can be encrypted by its construct and that the PIFS converts the one-way transparent superiority of the attacker into a double-blind problem for both the attacker and the defender, which leads to that the attacker is impossible to obtain useful construction information from the attacks and unable to find a better way than blind trial-and-error or brute-force attacks. Since the attackers are required to have the new powerful ability to crack the structure cryptogram, the threshold of cyber security is raised to at least the same level as cryptogram deciphering, thereafter the ubiquitous cyber threats are destined to be significantly reduced.

Spin-Vertical Cavity Surface Emitting Lasers (spin-VCSELs) are undergoing increasing research effort for new paradigms in high-speed optical communications and photon-enabled computing. To date research in spin-VCSELs has mostly focused on Quantum-Well (QW) devices. However, novel Quantum-Dot (QD) spin-VCSELs, offer enhanced parameter controls permitting the effective, dynamical and ultrafast manipulation of their light emission’s polarization. In the present contribution we investigate in detail the operation of QD spin-VCSELs subject to polarization modulation for their use as ultrafast light sources in optical communication systems. We reveal that QD spin-VCSELs outperform their QW counterparts in terms of modulation efficiency, yielding a nearly two- fold improvement. We also analyse the impact of key device parameters in QD spin-VCSELs (e.g. photon decay rate and intra-dot relaxation rate) on the large signal modulation performance with regard to optical modulation amplitude and eye-diagram opening penalty. We show that in addition to exhibiting enhanced polarization modulation performance for data rates up to $250Gb/s$, QD spin-VCSELs enable operation in dual (ground and excited state) emission thus allowing future exciting routes for multiplexing of information in optical communication links.

We experimentally demonstrate an O-band single-lane 200 Gb/s intensity modulation direct detection (IM/DD) transmission system using a low-chirp, broadband, and high-power directly modulated laser (DML). The employed laser is an isolator-free packaged module with over 65-GHz modulation bandwidth enabled by a distributed feedback plus passive waveguide reflection (DFB+R) design. We transmit high baud rate signals over 20-km standard single-mode fiber (SSMF) without using any optical amplifiers and demodulate them with reasonably low-complexity digital equalizers. We generate and detect up to 170 Gbaud non-return-to-zero on-off-keying (NRZ-OOK), 112 Gbaud 4-level pulse amplitude modulation (PAM4), and 100 Gbaud PAM6 in the optical back-to-back configuration. After transmission over the 20-km optical-amplifier-free SSMF link, up to 150 Gbaud NRZ-OOK, 106 Gbaud PAM4, and 80 Gbaud PAM6 signals are successfully received and demodulated, achieving bit error rate (BER) performance below the 6.25%-overhead hard-decision (HD) forward-error-correction code (FEC) limit. The demonstrated results show the possibility of meeting the strict requirements towards the development of 200Gb/s/lane IM/DD technologies, targeting 800Gb/s and 1.6Tb/s LR applications.

In many natural and engineered systems, the circle and the spiral are the appropriate primitives. As example, rotating motion of fluid around a common centerline constitutes a vortex. In many of these situations, the associated fractal dimension is of analytical interest. The box counting approach to determining this dimension is not appropriate for such cases, because unlike squares and boxes that span 2- and 3-dimensional spaces, circles and spheres don’t. Since self-similarity or scale invariance is not an exact property, we propose the computation of the dimension is such cases based on the number of circles or spheres in the mapping process. This is illustrated by a few examples. How this may apply to spiral patterns that are repeated across scale is indicated.