Simultaneous wireless information and power transfer (SWIPT) enabled wireless cooperative communication system is an emerging technology for future wireless communication applications. Furthermore, the Internet of Things (IoT) serves a diverse range of purposes, some of which are mission-critical and require constantly evolving real-time data. Thus, the information received at the destination must be updated timely manner to ensure its freshness. A performance metric named age of information (AoI) has been introduced to measure the freshness of received information. This study estimates the AoI of a SWIPT assisted decode and forward two-way relay assisted status update system in which two sources attempt to exchange status updates as quickly as possible to the destination. The relay system employs short packet communication to adhere to the latency and reliability requirements of the wireless communication system. We study the average Age of Information (AAoI) at the destination in the proposed relay network and derive approximations for the weighted sum AAoI under two different types of transmission scheduling policies at the relay: transmit without waiting (TWW) and wait until charged (WUC). Furthermore, the effects of transmission power, packet size, the distance between relay and sources and block-length on the weighted sum AAoI of the proposed SWIPT assisted short packet relay network are extensively investigated. The performance differences of the considered transmission policies are compared and insights are provided. Numerical simulations using the Monte Carlo method have been employed to validate derived analytical expressions.

Radars on unmanned vehicles can be used in indoor rescue scenarios to detect missing people based on their motion. In this case, the phase of the radar signals, which provides information about sub-millimeter changes in distance, have to be evaluated. However, in order to detect or to evaluate the motion of a missing person, the motion of the unmanned vehicle itself must be known and compensated for. To estimate the ego-motion of the unmanned vehicle, the radar system measures the phase information for multiple static objects in the radar's environment. A model describing how the motion of the radar affects the phase signals is used to estimate the ego-motion by solving a system of equations. The accuracy of the estimated position, depends on how accurately the phase measurements represent the model. By analytically decomposing the errors in the measurement model, it is shown that the radar's limited angular resolution induces a significant error. To mitigate this source of error, a weighted least squares approach is derived that minimizes the influence of the angular resolution on the position error. By calculating the position error distribution with and without the weighting approach, the benefits of the proposed algorithms are stated. Furthermore, the results are validated using simulations and real world measurements, showing that the proposed algorithm achieves sub-millimeter position accuracy.

The OpenFlexure Microscope is an accessible, 3Dprinted robotic microscope, with sufficient image quality to resolve diagnostic features including parasites and cancerous cells. As access to lab-grade microscopes is a major challenge in global healthcare, the OpenFlexure Microscope has been developed to be manufactured, maintained and used in remote environments, supporting point of care diagnosis. The steps taken in transforming the hardware and software from an academic prototype towards an accepted medical device include addressing technical and social challenges, and are key for any innovation targeting impact in low-resource healthcare.

Although research on self-driving vehicles (SDV) started long ago, developing a robust perception system is still challenging for researchers in academia and industry. Object classification, object detection and tracking, road detection, semantic segmentation, and instance segmentation are all introduced as the perception problems of the autonomous driving platform. Tons of methods have already been proposed to solve these perception problems. Some methods use a single modality (image only or Lidar only), and others use multi-modality. This review paper categorizes all semantic segmentation methods used for self-driving vehicles into three categories depending on their modality. We compare these methods according to their performance and mention the datasets used for evaluation. Some of the most common datasets in this field are also discussed in this paper. We highlight the challenges and scope of future research and discuss the background of this research field so that this paper can be used as a base for a new researcher to start research on semantic segmentation. Impact Statement-Semantic segmentation is a very useful perception tool. It can be used to get per-pixel semantic labels of the scenarios captured by the sensors of autonomous vehicles, which can then be used to identify potential obstacles, based on which planning and control decisions are made. Although many works have discussed semantic segmentation models, there are not many review papers that consolidate these works from a sensory perspective. In this paper, we analyze single and multimodal model architectures and compare their performances to guide researchers in deciding which direction (Lidar or camera, Lidar and camera) may be beneficial for them. The most frequently used datasets in this arena and the main features of the data are all elaborately discussed. Furthermore, the challenges that researchers may encounter, and the scope of future research are also included. This paper could provide a one-stop source for new researchers working in this area.

This paper presents a novel methodology for characterizing and creating a digital twin of turbid media without the need for reference beams, employing a unique architecture for the physics-informed neural network. Unlike previous approaches utilizing various deep neural network architectures that often function as black boxes, our method prioritizes interpretability, offering a clearer understanding of the underlying processes of light propagation through turbid media. Additionally, unlike classical solutions relying on reference beams, our approach eliminates the dependence on beam quality and associated issues prevalent in both internal and external reference-based methods. The possibility and use case of gradient calculation through this presented digital twin are showcased by solving the reverse problem to retrieve the initial wavefront shape of the light that passed through this medium, known as image transmission. Surprisingly, the results surpassed the accuracy of models directly optimized for this task, underscoring the precision of the proposed digital twin. This capability represents a pivotal advancement for future developments in neuromorphic and deep learning computation and training using such a photonic and optical systems.

Line waves (LWs) are exceptional electromagnetic (EM) modes found at the junctions of impedance surfaces, satisfying either electromagnetic duality or the parity-time symmetry. To date, the implementation of LWs has been limited primarily to employing electric impedance surfaces. In our study, we unveil that a planar junction between an electric impedance surfaces, and a magnetic media sustains the LW exhibiting backscattering-immune transportation. The entirely magnetic media (ϵ = 1, and μ ≠ 1) supports TE-polarized surface magnon polaritons while we tailor the electric surface impedance to support TM-polarized surface waves. Remarkably, the envisioned line-wave waveguide emerges as a pseudospin-filtered channel, facilitating a uni-dimensional wave generated through the synergistic interplay of two surface waves possessing perpendicular polarizations. Our theoretical findings, corroborated by simulations, unveil a new type of polaritonic wave characterized by remarkably confined properties. This discovery holds promise for manipulating light on the nanoscale, opening avenues for the implementation of magneto-electric pseudospin-polarized waveguide.

In this paper, we study a new round of novel coronavirus disease 2019 (COVID-19) infection scenarios due to a mutated strain in the middle of the epidemic. By considering the characteristics of the presence or absence of symptoms, the severity of symptoms, and vaccination, we develop an extended compartmental model containing 11 compartments and describe its compartmental transformation process from both discrete and continuous perspectives by probabilistic cellular automata and a system of ordinary differential equations. Further, we analyze the relevance of the endemic equilibrium points as well as the trajectory types corresponding to the ordinary differential equations. Based on the model, we simulate the reoccurrence of an outbreak in the United States in the winter of 2021, and the results fit well with the actual data and show the consistency between the two forms of Probabilistic Cellular automata and the system of Ordinary Differential Equations. The effects of contact network and vaccination are investigated by applying the model to different scenarios. The results show that premature relaxation of social isolation campaigns may lead to subsequent waves of infection and that widespread vaccination is effective in reducing the number of severe illnesses and deaths. The significance of this paper is to provide a model of infectious diseases for mid-epidemic and to assist in epidemic prevention policy-making through Probabilistic Cellular Automata.

In order to correct different numbers of erasures in distributed storage systems, the design of locally repairable codes with hierarchical locality (H-LRCs) is crucial. In this paper, we study h-level H-LRCs where h is not limited to 2. We present four constructions of cyclic h-level H-LRCs with length ln1 such that gcd(l, q) = 1 and n1|(q−1). These four classes of cyclic h-level H-LRCs are optimal with respect to the generalized Singleton-like bound. The minimum Hamming distances of them are d = δ1, δ1+1, δ1+2, δ1+δh respectively. Furthermore, these four classes of cyclic h-level H-LRCs have new and flexible parameters which are not covered in the literature. By setting the parameter l appropriately for the third construction, one can construct optimal cyclic h-level H-LRCs with length n|(q−1) and distance d = δ1+σ where σ is not limited to 0, 1, 2, δh.

We consider significantly enhancing the angular resolution of a single-chip multiple-input multiple-output (MIMO) radar sensor equipped with only 3 transmit and 4 receive antennas for radar imaging. We adopt the slow-time code-division multiplexing (ST-CDM) transmission scheme, which is a low-cost viable option to attain a large virtual array without reducing the unambiguous Doppler range. Moreover, to significantly improve the angular resolution without increasing antenna numbers, we introduce a practical 3 × 4 sparse linear array (SLA) design. We extend the iterative adaptive approach (IAA) to the slow-time IAA (ST-IAA) algorithm, aiming to mitigate the impact of waveform separation residuals and the high angular sidelobes on ST-CDM MIMO radar imaging equipped with SLAs. Furthermore, we construct a single-chip ST-CDM MIMO radar prototype with the 3 × 4 SLA design for experimental validations. Numerical and experimental results are provided to demonstrate the enhanced imaging performance of the radar prototype when used with the ST-IAA algorithm.

To solve real-world expensive constrained multi-objective optimization problems (ECMOPs), surrogate/approximation models are commonly incorporated in evolutionary algorithms to pre-select promising candidate solutions for evaluation. However, the performance of existing approaches are highly dependent on the relative position of unconstrained and constrained Pareto fronts (UPF and CPF, respectively). In addition, the uncertainty information of surrogate models is often ignored, which can misguide the search. To mitigate these key issues (among others), an efficient probabilistic selection based constrained multi-objective EA is proposed, referred to as PSCMOEA. It comprises novel elements such as (a) an adaptive search bound identification scheme based on the feasibility and convergence status of evaluated solutions (b) a probabilistic selection method backed by theoretical formulations of model mean and uncertainties to conduct search in the predicted space to identify promising solutions (c) an efficient single infill sampling approach to balance feasibility, convergence and diversity across different stages of the search and (d) an adaptive switch to unconstrained search based on certain search conditions. Numerical experiments are conducted on an extensive range of challenging constrained problems using low evaluation budgets to simulate ECMOPs. The performance of PSCMOEA is benchmarked against five competitive state-of-the-art algorithms, to demonstrate its competitive and consistent performance.

Deep Neural Networks (DNNs) have gained considerable traction in recent years due to the unparalleled results they gathered. However, the cost behind training such sophisticated models is resource intensive, resulting in many to consider DNNs to be intellectual property (IP) to model owners. In this era of cloud computing, high-performance DNNs are often deployed all over the internet so that people can access them publicly. As such, DNN watermarking schemes, especially backdoor-based watermarks, have been actively developed in recent years to preserve proprietary rights. (Add that no backdoor watermark guarantee). Nonetheless, there lies much uncertainty on the robustness of existing backdoor watermark schemes, towards both adversarial attacks and unintended means such as fine-tuning neural network models. In this paper, we extensively evaluate the persistence of recent backdoor-based watermarks within neural networks in the scenario of fine-tuning, we propose/develop a novel data-driven idea to restore watermark after fine-tuning without exposing the trigger set. Our empirical results show that by solely introducing training data after fine-tuning, the watermark can be restored if model parameters do not shift dramatically during fine-tuning. Depending on the types of trigger samples used, trigger accuracy can be reinstated to up to 100%. Our study further explores how the restoration process works using loss landscape visualization, as well as the idea of introducing training data in fine-tuning stage to alleviate watermark vanishing.

The noisy shuffling channel models the conditions encountered in DNA storage systems, where transmitted data segments experience random permutation and substitution errors. Reliable communication over this channel requires effective indexing and channel coding strategies for segment order restoration and error correction. This paper introduces a concatenated coding approach for communication over the noisy shuffling channel, using Reed-Solomon (RS) codes as outer codes and polar codes as inner codes. A coset-based indexing method, derived from polar codes, is proposed. A joint decoder is designed to detect the permutation pattern and perform polar decoding simultaneously. An approximate analysis of the frame error rate (FER) using random coding is conducted. Additionally, a mapping between the cosets of the polar code and subsets of its frozen bits is established to design cosets achieving lower FERs compared to a commonly used explicit indexing method. Furthermore, a list decoding approach is devised, providing a trade-off between the computational complexity of the joint decoder and its performance.

In conventional energy systems, the synchronization of power generation and consumption is largely handled by thermal power plants. The increasing share of volatile renewable energy generation which does not follow the demand, makes it necessary to shift the synchronization tasks to the system and demand level. The intended demand side consumption management is usually incentivized via financial mechanisms. However, the optimum design of related incentives is still unclear. It is usually discussed under the term "system friendliness" which in turn still lacks a broadly accepted definition. In this article we introduce a new technical definition of "system friendliness" and develop comprehensive and holistic related indicators. Our findings demonstrate that today's regulatory environments that usually support residential self-consumption maximization are counterproductive. Instead, we show that dynamic end-user prices coupled with dynamic feed in tariffs are able to incentivize an almost 100% system friendly behavior of distributed prosumers.

Non-Orthogonal Multiple Access (NOMA) received significant attention in the field of wireless communications during the last ten years, particularly focusing on Power-Domain NOMA (PD-NOMA) that uses a successive interference cancellation (SIC) receiver. This review paper reexamines PD-NOMA from a pragmatic angle and sheds light on its virtues and limitations in uplink and downlink scenarios. We observe that the concept of PD-NOMA in the downlink can be seen as a pure signal constellation design and that a simple threshold detector is sufficient at the receiver end. For the uplink, we first address the fairness concerns arising from the mismatch between the users' data rates that can be achieved by the SIC receiver and the fair rates suggested by the power distribution among users. Furthermore, we discuss findings from a recent analysis comparing PD-NOMA to MultiUser MIMO under a common system framework. This study reveals that assigning the same power levels to users, thereby aligning PD-NOMA with MultiUser MIMO, optimizes performance in terms of average bit error rate. These insights raise questions about the fundamental utility of PD-NOMA and suggest that its best application is hierarchical multiple access, where devices with different power capabilities are paired together.

The Einstein's theory of special relativity is based on his two postulates. The first is that the laws of physics are the same in all inertial reference frames. The second is that the velocity of light in the vacuum is the same in all inertial frames. The theory of special relativity is considered to be supported by a large number of experiments. This paper revisits the two postulates according to the new interpretations to the exact solutions of moving sources in the laboratory frame. The exact solutions are obtained using the classic Maxwell's theory, which clearly show that the propagation velocity of the electromagnetic waves of moving sources in the vacuum is not isotropic; the propagation velocity of the electromagnetic waves and the moving velocity of the sources cannot be added like vectors; the transverse Doppler effect is intrinsically included in the fields of the moving sources. The electromagnetic sources are subject to the Newtonian mechanics, while the electromagnetic fields are subject to the Maxwell's theory. We argue that since their behaviors are quite different, it is not a best choice to try to bind them together and force them to undergo the same coordinate transformations as a whole, like that in the Lorentz transformations. Furthermore, the Maxwell's theory does not impose any limitations on the velocity of the electromagnetic waves. To assume that all objects cannot move faster than the light in the vacuum need more examinations. We have carefully checked the main experiment results that were considered as supporting the special relativity. Unfortunately, we found that the experimental results may have been misinterpreted. We here propose a Galilean-Newtonian-Maxwellian relativity, which can give the same or even better explanations to those experimental results.

A document by Shadi Attarha. Click on the document to view its contents.

This paper proposes a new approach for convergence analysis of continuous action iterative dilemma (CAID) to reach a stable consensus outcome within the prescribed time. Unlike usual game theory, where players can only choose between two options, i.e., cooperation or defection, CAID lets players pick from varying options and make more nuanced decisions. In the proposed strategy, a prescribed function, which is tunable by the user, adapts the learning rate of the player's strategy. This new method guarantees that players will eventually agree on a single strategy, regardless of where they started initially, and it achieves this agreement in a predefined time set by the user. The Lyapunov analysis guarantees the convergence of strategy to a consensus within a prescribed time. The simulation results of the proposed scheme with two evolutionary game examples demonstrate faster convergence and fewer iterations compared to the state-of-the-art method.

In this article, we review some of the recent efforts and advancements in the implementation of reconfigurable metasurfaces and metamaterials tailored for antenna applications. Originally limited by fixed properties, these structures are increasingly integrated with different technologies to achieve real-time reconfigurability and versatility in response to evolving operational conditions and constraints. This work investigates the motivations and technical approaches driving this development, with a specific focus on reconfigurable metasurfaces designed for antenna systems operating within the microwave range (L-band to K-band). By analyzing limitations and experimentally validated approaches, the article offers a comprehensive exploration of the performance and limitations of common reconfigurability schemes in microwave metadevices.