Communication Signals Research Articles

Adaptive control for uncrewed aerial vehicles based on communication information optimization in complex environments.

The utilization of drone technology thrives in diverse domains, including aviation, military operations, and logistics. The pervasive adoption of this technology aims to enhance efficiency while mitigating hazards and expenditures. In complex contexts, the governing parameters of uncrewed aerial vehicles (UAV) require real-time adjustments for flight safety and efficacy. To improve the attitude estimation accuracy, this article introduces a ATT-Bi-LSTM framework for optimizing UAVs through adaptive parameter control, which integrates the state information gleaned from communication signals. The ATT-Bi-LSTM achieves data feature extraction by means of a two-layer Bidirectional Long Short-Term Memory (BI-LSTM) at its inception to enhance the feature. Subsequently, it harnesses the attention mechanism to amplify the LSTM network's output, thereby enabling the optimal control of UAV positioning. During the empirical phase, we employ optical system data for the comparative validation of the model. The outcomes underscore the commendable performance of the proposed framework in this study, particularly with regard to the three pivotal position indicators: yaw, pitch, and roll. In the comparison of indicators such as RMSR and MAE, the proposed model has the lowest error, which provides algorithm support and important reference for future UAV optimization control research.

Research on an Intelligent Vehicle Trajectory Tracking Method Based on Optimal Control Theory

This study aims to explore an intelligent vehicle trajectory tracking control method based on optimal control theory. Considering the limitations of existing control strategies in dealing with signal delays and communication lags, a control strategy combining an anthropomorphic forward-looking reference path and longitudinal velocity closure is proposed to improve the accuracy and stability of intelligent vehicle trajectory tracking. Firstly, according to the vehicle dynamic error tracking model, a linear quadratic regulator (LQR) transverse controller is designed based on the optimal control principle, and a feedforward control strategy is added to reduce the system steady-state error. Secondly, an anthropomorphic look-ahead prediction model is established to mimic human driving behavior to compensate for the signal lag. The double proportional–integral–derivative (DPID) control algorithm is used to track the longitudinal speed reference value. Finally, a joint simulation is conducted based on MatLab/Simulink2021b and CarSim2019.0 software, and the effectiveness of the control strategy proposed in this paper is verified by constructing a semi-physical experimental platform and carrying out a hardware-in-the-loop test. The simulation and test results show that the control strategy can significantly improve the accuracy and stability of vehicle path tracking, which provides a new idea for future intelligent vehicle control system design.

Drones Detection Using a Fusion of RF and Acoustic Features and Deep Neural Networks.

The use of drones has recently gained popularity in a diverse range of applications, such as aerial photography, agriculture, search and rescue operations, the entertainment industry, and more. However, misuse of drone technology can potentially lead to military threats, terrorist acts, as well as privacy and safety breaches. This emphasizes the need for effective and fast remote detection of potentially threatening drones. In this study, we propose a novel approach for automatic drone detection utilizing the usage of both radio frequency communication signals and acoustic signals derived from UAV rotor sounds. In particular, we propose the use of classical and deep machine-learning techniques and the fusion of RF and acoustic features for efficient and accurate drone classification. Distinct types of ML-based classifiers have been examined, including CNN- and RNN-based networks and the classical SVM method. The proposed approach has been evaluated with both frequency and audio features using common drone datasets, demonstrating better accuracy than existing state-of-the-art methods, especially in low SNR scenarios. The results presented in this paper show a classification accuracy of approximately 91% at an SNR ratio of -10 dB using the LSTM network and fused features.

5G‐based passive radar on a moving platform—Detection and imaging

AbstractIn recent times, the development of radar systems for automotive applications has gathered significant interest due to the increasing necessity of vehicle situational awareness for new active protection systems and the intensive development of higher autonomous level driving solutions. Therefore, the number of active radars in automotive applications is growing, causing spectrum sharing and interference between radars operating in the same band. Alternative solutions without dedicated electromagnetic transmissions, such as joint communication and radar systems, or even no transmission at all, such as passive radars, have emerged in recent years and are promising solutions to help mitigate the interference problem. A passive automotive radar based on 5G communication signals is proposed as an alternative to active radars to provide situational awareness. The data downlink transmissions provided by an operative 5G base station were combined with a dual‐channel passive radar (PR) system deployed on a moving platform to provide moving target detection and radar imaging of the vehicle's surroundings. The outcomes show a possibility of commensal utilisation of the new telecommunication standard for automotive radar applications. The idea of mounting the PR receiver on a moving platform was tested using simulated and real‐life data, which shows great potential for joining the new radio (NR) with sensing capabilities using PR. The theory, numerical experiments, and measurement results are dealt with a cooperative 5G base station and PR demonstrator.

Applications of Exosomal miRNAs from Mesenchymal Stem Cells as Skin Boosters.

The skin is the outer layer of the human body, and it is crucial in defending against injuries and damage. The regenerative capacity of aging and damaged skin caused by exposure to external stimuli is significantly impaired. Currently, the rise in average life expectancy and the modern population's aesthetic standards have sparked a desire for stem-cell-based therapies that can address skin health conditions. In recent years, mesenchymal stem cells (MSCs) as therapeutic agents have provided a promising and effective alternative for managing skin regeneration and rejuvenation, attributing to their healing capacities that can be applied to damaged and aged skin. However, it has been established that the therapeutic effects of MSC may be primarily mediated by paracrine mechanisms, particularly the release of exosomes (Exos). Exosomes are nanoscale extracellular vesicles (EVs) that have lipid bilayer and membrane structures and can be naturally released by different types of cells. They influence the physiological and pathological processes of recipient cells by transferring a variety of bioactive molecules, including lipids, proteins, and nucleic acids such as messenger RNAs (mRNAs) and microRNAs (miRNAs) between cells, thus playing an important role in intercellular communication and activating signaling pathways in target cells. Among them, miRNAs, a type of endogenous regulatory non-coding RNA, are often incorporated into exosomes as important signaling molecules regulating protein biosynthesis. Emerging evidence suggests that exosomal miRNAs from MSC play a key role in skin regeneration and rejuvenation by targeting multiple genes and regulating various biological processes, such as participating in inflammatory responses, cell migration, proliferation, and apoptosis. In this review, we summarize the recent studies and observations on how MSC-derived exosomal miRNAs contribute to the regeneration and rejuvenation of skin tissue, with particular attention to the applications of bioengineering methods for manipulating the miRNA content of exosome cargo to improve their therapeutic potential. This review can provide new clues for the diagnosis and treatment of skin damage and aging, as well as assist investigators in exploring innovative therapeutic strategies for treating a multitude of skin problems with the aim of delaying skin aging, promoting skin regeneration, and maintaining healthy skin.

From Visions to Reality: Investigating the Interplay of Vehicle Kinematics and Light-band eHMI in a Real Vehicle Study

Highly automated vehicles (HAVs) will interact with pedestrians in urban environments. This requires efficient communication tools to ensure mutual understanding. Past research showed that pedestrians mostly used vehicle kinematics to communicate with vehicles, e.g., the vehicle’s speed and distance. However, pedestrians required further explicit communication signals when the traffic situation was ambiguous. Light-band external human–machine interfaces (eHMIs) transmit additional explicit communication signals to pedestrians, e.g., the vehicle’s yielding intent. To this point, the precise interplay of vehicle kinematics and eHMIs for HAVs has not yet been determined. Nevertheless, previous research showed that combining both means of communication has great potential to increase pedestrian perceived safety and to ensure a safe interaction. Only a little research used real vehicle studies to investigate the interaction between pedestrians and HAVs in a close-to-reality experimental setting. However, this would ensure the transferability of experimental results to future urban traffic. Therefore, this study aimed to address this research gap by investigating the effects of vehicle kinematics, eHMIs, and their interplay in a real-world pedestrian crossing on pedestrians’ behaviors and subjective evaluations. In this field experiment, we applied a light-band eHMI on a Wizard-of-Oz test vehicle, an actual vehicle instructed as an HAV. We investigated the effects of vehicle kinematics (early yielding vs. late yielding) and the eHMI status (no eHMI, static eHMI, dynamic eHMI) on pedestrians’ crossing behavior and subjective evaluation in a low-speed real-world setting. The static eHMI displayed the vehicle automation status by a static illuminated eHMI. The dynamic eHMI conveyed the automation status and the vehicle’s yielding intent. This study focused particularly on the interplay of vehicle kinematics and eHMI status. We assumed that the crossing initiation was shorter when a dynamic eHMI was combined with an early yielding compared to a late yielding in this real-world setting. Moreover, we hypothesized that pedestrians’ subjective evaluations are more positive for a well-coordinated interplay of eHMI and vehicle kinematics. The results showed that pedestrians initiated their crossing earlier with dynamic eHMI vs. no eHMI or static eHMI. Furthermore, they perceived a dynamic eHMI as safer and more trustworthy compared to no eHMI or a static eHMI. Combining an early yielding and dynamic eHMI increased participants’ perceived safety of the vehicle behavior and trust and improved pedestrians’ affective evaluations compared to a late yielding with dynamic eHMI. Overall, this real vehicle study highlighted the importance of implicit and explicit communication signals and their well-coordinated interplay for pedestrians’ future interactions with HAVs in a real-world setting.

The protein composition of exosomes released by prostate cancer cells is distinctly regulated by androgen receptor-antagonists and -agonist to stimulate growth of target cells.

Prostate cancer (PCa) is a prevalent malignancy in men worldwide, ranking as the second leading cause of cancer-related death in Western countries. Various PCa hormone therapies, such as androgen receptor (AR)-antagonists or supraphysiological androgen level (SAL) reduce cancer cell proliferation. However, treated cells may influence the growth of neighboring cells through secreted exosomes in the tumor microenvironment (TME). Here, the change of protein content of exosomes secreted from PCa cells through treatment with different AR-antagonists or SAL has been analyzed. Isolation of exosomes via ultracentrifugation of treated human PCa LNCaP cells with AR-agonist and various AR-antagonists; analysis of cellular senescence by detection of senescence associated beta galactosidase activity (SA β-Gal); Western blotting and immunofluorescence staining; Mass spectrometry (MS-spec) of exosomes and bioinformatic analyses to identify ligand-specific exosomal proteins. Growth assays to analyze influence of exosomes on non-treated cells. MS-spec analysis identified ligand-specific proteins in exosomes. One thousand seventy proteins were up- and 52 proteins downregulated by SAL whereas enzalutamide upregulated 151 proteins and downregulated 42 exosomal proteins. The bioinformatic prediction indicates an up-regulation of pro-proliferative pathways. AR ligands augment hub factors in exosomes that include AKT1, CALM1, PAK2 and CTNND1. Accordingly, functional assays confirmed that the isolated exosomes from AR-ligand treated cells promote growth of untreated PCa cells. The data suggest that the cargo of exosomes is controlled by AR-agonist and -antagonists and distinct among the AR-antagonists. Further, exosomes promote growth that might influence the TME. This finding sheds light into the complex interplay between AR signaling and exosome-mediated communication between PCa cells.

Gangliosides as Therapeutic Targets for Neurodegenerative Diseases.

Gangliosides, sialic acid-containing glycosphingolipids, are abundant in cell membranes and primarily involved in controlling cell signaling and cell communication. The altered ganglioside pattern has been demonstrated in several neurodegenerative diseases, characterized during early-onset or infancy, emphasizing the significance of gangliosides in the brain. Enzymes required for the biosynthesis of gangliosides are linked to several devastating neurological disorders, including Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), amyotrophic lateral sclerosis (ALS), hereditary spastic paraplegia (HSP). In this review, we summarized not only the critical roles of biosynthetic enzymes and their inhibitors in ganglioside metabolism but also the efficacy of treatment strategies of ganglioside to address their significance in those diseases.

Role of the gut microbiota in complications after ischemic stroke.

Ischemic stroke (IS) is a serious central nervous system disease. Post-IS complications, such as post-stroke cognitive impairment (PSCI), post-stroke depression (PSD), hemorrhagic transformation (HT), gastrointestinal dysfunction, cardiovascular events, and post-stroke infection (PSI), result in neurological deficits. The microbiota-gut-brain axis (MGBA) facilitates bidirectional signal transduction and communication between the intestines and the brain. Recent studies have reported alterations in gut microbiota diversity post-IS, suggesting the involvement of gut microbiota in post-IS complications through various mechanisms such as bacterial translocation, immune regulation, and production of gut bacterial metabolites, thereby affecting disease prognosis. In this review, to provide insights into the prevention and treatment of post-IS complications and improvement of the long-term prognosis of IS, we summarize the interaction between the gut microbiota and IS, along with the effects of the gut microbiota on post-IS complications.

A Network-Group Target State and Network Topology Estimation Method Based on Signals Containing Delay, Doppler and Address

Network-group targets are a set of objectives that adhere to a shared communication protocol, perform common tasks, and exhibit relatively coordinated movements. Typically, network-group targets emit radar and communication signals. However, they often employ a silent mode to evade our perception. Despite this, they continue to exchange data through their communication networks. By intercepting the communication signals of these targets, this paper proposes a method for estimating the state and network topology of network-group targets based on random finite set (RFS) theory. This method is based on the modeling of network-group targets using a labeled multi-Bernoulli (LMB) RFS. In state estimation, the usual method involves extracting the localization parameters from the signals in the first step and use these parameters for target tracking in the second step. This study focused on estimating the kinematic states of network-group targets using communication signals containing delay and Doppler information received by multiple mobile sensor platforms. The proposed method considers the coherency between frequency-hopping pulses in the signals, resulting in an improved estimation performance. In addition, considering that network-group targets require network access for information exchange, graph theory concepts were utilized to estimate the network topology of network-group targets by address measurement. The simulation results validated the effectiveness of the proposed method and demonstrated its superior performance.

ISAC towards 6G Satellite–Terrestrial Communications: Principles, Status, and Prospects

With the evolution of fifth-generation (5G) to sixth-generation (6G) communication systems, the utilization of spectrum resources faces incremental challenges. Integrated sensing and communication (ISAC) technology, as a crucial element in 6G technology, is expected to enhance energy efficiency and spectrum utilization efficiency by integrating radar and communication signals, achieving environmental awareness, and enabling scene interconnection. Simultaneously, to realize the vision of seamless coverage in 6G, research on integrated satellite-terrestrial communication has been prioritized. To integrate the advantages, ISAC for integrated satellite–terrestrial networks (ISTNs) in 6G has emerged as a potential research direction. This paper offers an extensive overview of the present state of key technologies for ISAC and the development of ISTNs. Meanwhile, with a focus on the ISTN-oriented 6G ISAC system, several hotspot topics, including future application scenarios and key technological developments, are outlined and demonstrated.

The Tumor Microenvironment: Signal Transduction.

In the challenging tumor microenvironment (TME), tumors coexist with diverse stromal cell types. During tumor progression and metastasis, a reciprocal interaction occurs between cancer cells and their environment. These interactions involve ongoing and evolving paracrine and proximal signaling. Intrinsic signal transduction in tumors drives processes such as malignant transformation, epithelial-mesenchymal transition, immune evasion, and tumor cell metastasis. In addition, cancer cells embedded in the tumor microenvironment undergo metabolic reprogramming. Their metabolites, serving as signaling molecules, engage in metabolic communication with diverse matrix components. These metabolites act as direct regulators of carcinogenic pathways, thereby activating signaling cascades that contribute to cancer progression. Hence, gaining insights into the intrinsic signal transduction of tumors and the signaling communication between tumor cells and various matrix components within the tumor microenvironment may reveal novel therapeutic targets. In this review, we initially examine the development of the tumor microenvironment. Subsequently, we delineate the oncogenic signaling pathways within tumor cells and elucidate the reciprocal communication between these pathways and the tumor microenvironment. Finally, we give an overview of the effect of signal transduction within the tumor microenvironment on tumor metabolism and tumor immunity.

Transcriptional and ultrastructural changes of macrophages after african swine fever virus infection

African swine fever (ASF) is a highly impactful infectious disease in the swine industry, leading to substantial economic losses globally. The causative agent, African swine fever virus (ASFV), possesses intricate pathogenesis, warranting further exploration. In this study, we investigated the impact of ASFV infection on host gene transcription and organelle changes through macrophage transcriptome sequencing and ultrastructural transmission electron microscopy observation. According to the results of the transcriptome sequencing, ASFV infection led to significant alterations in the gene expression pattern of porcine bone marrow derived macrophages (BMDMs), with 2404 genes showing upregulation and 1579 genes downregulation. Cytokines, and chemokines were significant changes in the expression of BMDMs; there was significant activation of pattern recognition receptors such as Toll-like receptors and Nod-like receptors. According to the observation of the ultrastructure, mitochondrial damage and mitochondrial autophagy were widely present in ASFV-infected cells. The reduced number of macrophage pseudopodia suggested that virus-induced structural changes may compromise pathogen recognition, phagocytosis, and signal communication in macrophages. Additionally, the decreased size and inhibited acidification of secondary lysosomes in macrophages implied suppressed phagocytosis. Overall, ASFV infection resulted in significant changes in the expression of cytokines and chemokines, accompanied by the activation of NLR and TLR signaling pathways. We reported for the first time that ASFV infection led to a reduction in pseudopodia numbers and a decrease in the size and acidification of secondary lysosomes.

Codebook-Based Solutions for Reconfigurable Intelligent Surfaces and Their Open Challenges

Reconfigurable intelligent surfaces (RIS) is a revolutionary technology to cost-effectively improve the performance of wireless networks. We first review the existing framework of channel estimation and passive beamforming (CE & PBF) in RISassisted communication systems. To reduce the excessive pilot signaling overhead and implementation complexity of the CE & PBF framework, we conceive a codebook-based framework to strike flexible tradeoffs between communication performance and signaling overhead. Moreover, we provide useful insights into the codebook design and learning mechanisms of the RIS reflection pattern. Finally, we analyze the scalability of the proposed framework by flexibly adapting the training overhead to the specified quality-of-service requirements and then elaborate on its appealing advantages over the existing CE & PBF approaches. It is shown that our novel codebook-based framework can be beneficially applied to all RIS-assisted scenarios and avoids the curse of model dependency faced by its existing counterparts, thus constituting a competitive solution for practical RIS-assisted communication systems.

INTEGRATED CIRCUITS AND THEIR APPLICATIONS IN ELECTRONICS

The article deals with the integrated circuits and their applications in electronics. Integrated circuits (ICs) have revolutionized the field of electronics by enabling the integration of multiple components onto a single chip. This miniaturization has led to significant advancements in various electronic devices and systems. ICs offer numerous benefits, including compact size, energy efficiency, reliability, high speed, cost-effectiveness, flexibility, and scalability. The ability to combine different functions on a single chip has made ICs indispensable in a wide range of applications, from consumer electronics to industrial automation. The integration of digital signal processing, memory management, communication interfaces, and other functions on a single IC has enabled the development of sophisticated and high-performance devices.

Infants’ brain responses to social interaction predict future language growth

In face-to-face interactions with infants, human adults exhibit a species-specific communicative signal. Adults present a distinctive "social ensemble": they use infant-directed speech (parentese), respond contingently to infants' actions and vocalizations, and react positively through mutual eye-gaze and smiling. Studies suggest that this social ensemble is essential for initial language learning. Our hypothesis is that the social ensemble attracts attentional systems to speech and that sensorimotor systems prepare infants to respond vocally, both of which advance language learning. Using infant magnetoencephalography (MEG), we measure 5-month-old infants' neural responses during live verbal face-to-face (F2F) interaction with an adult (social condition) and during a control (nonsocial condition) in which the adult turns away from the infant to speak to another person. Using a longitudinal design, we tested whether infants' brain responses to these conditions at 5months of age predicted their language growth at five future time points. Brain areas involved in attention (right hemisphere inferior frontal, right hemisphere superior temporal, and right hemisphere inferior parietal) show significantly higher theta activity in the social versus nonsocial condition. Critical to theory, we found that infants' neural activity in response to F2F interaction in attentional and sensorimotor regions significantly predicted future language development into the third year of life, more than 2 years after the initial measurements. We develop a view of early language acquisition that underscores the centrality of the social ensemble, and we offer new insight into the neurobiological components that link infants' language learning to their early brain functioning during social interaction.

Life-cycle profiling of cuticular hydrocarbons in the cotton bollworm Helicoverpa armigera (Lepidoptera: Noctuidae)

Cuticular hydrocarbons (CHCs) are the main components of the wax layer present on insects’ surface, serving as protective agents against environmental stress and/or as communication signals. It has been documented that the chemical composition of CHCs varies with species and even with developmental stage or sex of the same species. However, studies involving in a life-cycle identification of CHCs are rare. In this work, we attempted to profile the components and relative abundance of CHCs in the cotton bollworm Helicoverpa armigera at all the four developmental stages and in both sexes of pupae and adults. A total of 29 CHCs were putatively identified by gas chromatography-mass spectrometer analysis, including 14 n-alkanes, 4 methyl-branched alkenes and 11 methyl-branched alkanes, with 15 of them being detected at all the four developmental stages. Of the 29 CHCs, the straight-chain alkanes with 27, 29 and 31 carbon atoms were the major CHCs. All three classes of CHCs (n-alkanes, methyl-branched alkanes and methyl-branched alkenes) were found in both larvae and pupae, while no unsaturated hydrocarbons were observed in eggs and adults. Methyl-branched alkanes were the main component found in eggs and adults, while the composition of n-alkanes and methyl-branched alkanes in larvae and pupae varied with ages. Principal component analysis (PCA) revealed that the CHC profiles in larvae and adults changed significantly with ages, while the CHC profiles in early pupae (1-day-old) showed distinct difference from those of late pupae. Furthermore, sexual dimorphism in the CHC profile was observed in mature adults (3-day-old and 5-day-old), with male adults possessing more n-C31 and n-C33, but less 3-MeC29 than females.

Solving Edges Deletion Problem of Generalized Petersen Graphs

The dependability and effectiveness of a network can be investigated using a variety of graph-theoretic techniques, and the network's connectivity determines how reliable it is. Diameter in a network is often used to measure the efficiency of a network in the event that a network experiences issues such as a decline in the communication signal or a breakdown in the communication between its components. This paper examines the increase in diameter of the generalized Petersen graph after removing a certain number of edges. Finding the exact values of f(n,t) that represent the maximum diameter of an altered generalized Petersen graph GP(n,k,t) obtained after removing edges from for k=1 and t≥2.

Multi-antenna dual-blind deconvolution for joint radar-communications via SoMAN minimization

In joint radar-communications (JRC) applications such as secure military receivers, often the radar and communications signals are overlaid in the received signal. In these passive listening outposts, the signals and channels of both radar and communications are unknown to the receiver. The ill-posed problem of recovering all signal and channel parameters from the overlaid signal is termed as dual-blind deconvolution (DBD). In this work, we investigate DBD for a multi-antenna receiver. We model the radar and communications channels with a few (sparse) continuous-valued parameters such as time delays, Doppler velocities, and directions-of-arrival (DoAs). To solve this highly ill-posed DBD, we propose to minimize the sum of multivariate atomic norms (SoMAN) that depend on unknown parameters. To this end, we devise an exact semidefinite program using theories of positive hyperoctant trigonometric polynomials (PhTP). Our theoretical analyses show that the minimum number of samples and antennas required for perfect recovery is logarithmically dependent on the maximum of the number of radar targets and communications paths rather than their sum. We show that our approach is easily generalized to include several practical issues such as gain/phase errors and additive noise. Numerical experiments show the exact parameter recovery for different JRC scenarios.

Level Process Station Control Using ALE111 Communication Module

A noteworthy example of the technological evolution is the ALE111, a cutting-edge system equipped with advanced functionalities designed to monitor the status of field wireless devices. One of its key features is the implementation of bumpless output, a capability that ensures seamless transitions and adjustments in the control process. Remarkably, the ALE111 enables wireless process control, a capability traditionally associated with wired control systems utilizing 4-20 mA signal communication. This wireless paradigm not only introduces a new level of flexibility but also contributes to the overall enhancement of process control reliability. The ALE111 goes a step further by incorporating redundant configurations, a strategic approach that adds an extra layer of reliability to the system. By doing so, potential points of failure are mitigated, and the overall robustness of the process control system is substantially improved. Key Word: ALE111 Communication Module, Ethernet, Level Process Station, Vnet, Field Control Station ,HIS, Data Acquisition Card