Search Results (17)

This paper provides an overview of the three-year effort to design and implement a prototypical sense-and-avoid (SAA) system based on a multisensory architecture leveraging data fusion between optical and radar sensors. The work was carried out within the context of the Italian research project named TERSA (electrical and radar technologies for remotely piloted aircraft systems) undertaken by the University of Pisa in collaboration with its industrial partners, aimed at the design and development of a series of innovative technologies for remotely piloted aircraft systems of small scale (MTOW < 25 Kgf). The system leverages advanced computer vision algorithms and an extended Kalman filter to enhance obstacle detection and tracking capabilities. The “Sense” module processes environmental data through a radar and an electro-optical sensor, while the “Avoid” module utilizes efficient geometric algorithms for collision prediction and evasive maneuver computation. A novel hardware-in-the-loop (HIL) simulation environment was developed and used for validation, enabling the evaluation of closed-loop real-time interaction between the “Sense” and “Avoid” subsystems. Extensive numerical simulations and a flight test campaign demonstrate the system’s effectiveness in real-time detection and the avoidance of non-cooperative obstacles, ensuring compliance with UAV aero mechanical and safety constraints in terms of minimum separation requirements. The novelty of this research lies in (1) the design of an innovative and efficient visual processing pipeline tailored for SWaP-constrained mini-UAVs, (2) the formulation an EKF-based data fusion strategy integrating optical data with a custom-built Doppler radar, and (3) the development of a unique HIL simulation environment with realistic scenery generation for comprehensive system evaluation. The findings underscore the potential for deploying such advanced SAA systems in tactical UAV operations, significantly contributing to the safety of flight in non-segregated airspaces Full article

This paper provides an analytical framework to address the definition of sensing requirements in non-cooperative UAS sense and avoid. The generality of the approach makes it useful for the exploration of sensor design and selection trade-offs, for the definition of tailored and adaptive sensing strategies, and for the evaluation of the potential of given sensing architectures, also concerning their interface to airspace rules and traffic characteristics. The framework comprises a set of analytical relations covering the following technical aspects: field of view and surveillance rate requirements in azimuth and elevation; the link between sensing accuracy and closest point of approach estimates, expressed though approximated derivatives valid in near-collision conditions; the diverse (but interconnected) effects of sensing accuracy and detection range on the probabilities of missed and false conflict detections. A key idea consists of focusing on a specific target time to closest point of approach at obstacle declaration as the key driver for sensing system design and tuning, which allows accounting for the variability of conflict conditions within the aircraft field of regard. Numerical analyses complement the analytical developments to demonstrate their statistical consistency and to show quantitative examples of the variation of sensing performance as a function of the conflict geometry, as well as highlighting potential implications of the derived concepts. The developed framework can potentially be used to support holistic approaches and evaluations in different scenarios, including the very low-altitude urban airspace. Full article

Ensuring the energy transition in order to decrease CO₂ and volatile organic compounds emissions and improve the efficiency of energy processes requires the development of advanced materials and technologies for the electrical energy sector. The article reviews superconducting materials, functional nanomaterials used in the power industry mainly due to their magnetic, electrical, optical, and dielectric properties and the thin layers of amorphous carbon nitride, which properties make them an important material from the point of view of environmental protection, optoelectronic, photovoltaic and energy storage. The superconductivity-based technologies, material processing, and thermal and nonthermal plasma generation have been reviewed as technologies that can be a solution to chosen problems in the electrical energy sector and environment. The study explains directly both—the basics and application potential of low and high-temperature superconductors as well as peculiarities of the related manufacturing technologies for Roebel cables, 1G and 2G HTS tapes, and superconductor coil systems. Among the superconducting materials, particular attention was paid to the magnesium di-boride MgB₂ and its potential applications in the power industry. The benefits of the use of carbon films with amorphous structures in electronics, sensing technologies, solar cells, FETs, and memory devices were discussed. The article provides the information about most interesting, from the R&D point of view, groups of materials for PV applications. It summarises the advantages and disadvantages of their use regarding commercial requirements such as efficiency, lifetime, light absorption, impact on the environment, costs of production, and weather dependency. Silicon processing, inkjet printing, vacuum deposition, and evaporation technologies that allow obtaining improved and strengthened materials for solar cell manufacturing are also described. In the case of the widely developed plasma generation field, waste-to-hydrogen technology including both thermal and non-thermal plasma techniques has been discussed. The review aims to draw attention to the problems faced by the modern power industry and to encourage research in this area because many of these problems can only be solved within the framework of interdisciplinary and international cooperation. Full article

This paper demonstrates the benefits that high-frequency surface wave radars (HFSWR) are bringing to maritime safety and security in off-shore activities at over the horizon distances. As a primary means for remote sensing of marine and maritime environment, a network of HFSWRs is deployed in the western part of the Gulf of Guinea and covers an area of over 100 km². Alongside HFSWRs, usual maritime sensors are utilized for vessel tracking as well, however, only satellite automatic identification systems (SAIS) and land automatic identification systems (LAIS) are capable of covering over the horizon distances. Unfortunately, both LAIS and SAIS require vessel cooperation in order to provide any data, which is often abused by vessels conducting illegal activities. Here, analysis is done in which AIS and HFSWR data are compared in order to identify a pattern of behavior of non–cooperative vessels (vessels with onboard AIS devices turned off) so a proper risk assessment may be achieved. It is shown that typical patterns can be easily recognized for two illegal activities which plague the waters where this study is conducted. Those illegal activities are oil bunkering and piracy, both conducted off-shore and out of the reach of the usual coastal sensors such as X or S band radars. Furthermore, tracks created whilst conducting illegal activities are easily distinguishable from others in the overall operational picture. Additionally, it should be pointed out that numerous vessels are switching off their AIS devices when they leave the coastal regions in order to avoid detection by pirate vessels. This behavior can also be easily recognized and must not be mixed with the illegal activities mentioned above. Full article

In the future vehicular networks with an increased number of transceiver antennas and higher vehicle speeds, more frequent beam switching is required to ensure the quality of communication, which poses challenges to beam tracking speed and resource efficiency. Integrated sensing and communication (ISAC) provide a new solution to cope with this problem since radar echo can help to predict the vehicle’s future location and beam direction. Therefore, we present a radar-assisted beam tracking algorithm based on Extended Kalman filtering (EKF) and multi-road side unit (RSU) cooperation in this article. Each RSU uses EKF and radar echo to predict and track the vehicle position and upload the prediction information to the edge server (ES). By deploying multiple RSUs, the ES uses the uploaded distributed sensing information for joint estimation and thus improves the accuracy of vehicle location prediction, which is used for the beam tracking task at the next moment. Considering the real complex road conditions, we investigate two scenarios where vehicles move linearly or curvilinearly. Simulation results show that the proposed method with multiple base station cooperation improves the spectral efficiency by 34% and 20% over non-cooperative beam tracking in linear and curvilinear mobility, respectively. In addition, compared with traditional beam tracking based on beam scanning and signaling feedback, radar-assisted beam tracking significantly reduces the communication overhead. Full article

Sixth generation (6G) wireless networks require very low latency and an ultra-high data rate, which have become the main challenges for future wireless communications. To effectively balance the requirements of 6G and the extreme shortage of capacity within the existing wireless networks, sensing-assisted communications in the terahertz (THz) band with unmanned aerial vehicles (UAVs) is proposed. In this scenario, the THz-UAV acts as an aerial base station to provide information on users and sensing signals and detect the THz channel to assist UAV communication. However, communication and sensing signals that use the same resources can cause interference with each other. Therefore, we research a cooperative method of co-existence between sensing and communication signals in the same frequency and time allocation to reduce the interference. We then formulate an optimization problem to minimize the total delay by jointly optimizing the UAV trajectory, frequency association, and transmission power of each user. The resulting problem is a non-convex and mixed integer optimization problem, which is challenging to solve. By resorting to the Lagrange multiplier and proximal policy optimization (PPO) method, we propose an overall alternating optimization algorithm to solve this problem in an iterative way. Specifically, given the UAV location and frequency, the sub-problem of the sensing and communication transmission powers is transformed into a convex problem, which is solved by the Lagrange multiplier method. Second, in each iteration, for given sensing and communication transmission powers, we relax the discrete variable to a continuous variable and use the PPO algorithm to tackle the sub-problem of joint optimization of the UAV location and frequency. The results show that the proposed algorithm reduces the delay and improves the transmission rate when compared with the conventional greedy algorithm. Full article

The 4G network is not sufficient for achieving the high data requirements of smart city users. The 5G network intends to meet these requirements and overcome other application issues, such as fast data transmission, video buffering, and coverage issues, providing excellent mobile data services to smart city users. To allocate a channel or spectrum to a smart city user for error-free transmission with low latency, the accurate information of the spectrum should be detected. In this study, we determined the range of non-cooperative detection techniques, such as matched filter detection with inverse covariance approach (MFDI), cyclostationary feature detection with inverse covariance approach (CFDI), and hybrid filter detection with inverse covariance approach (HFDI); based on the results of these methods, we provided highly accurate spectrum information for smart city users, enabling sustainable development. To evaluate the performance of the proposed detection techniques, the following parameters are used: probability of detection (P_D), probability of false alarms (P_fa), probability of miss detection (P_md), sensing time, and throughput. The simulation results revealed that the HFDI detection method provided sustainable results at low signal-to-noise ratio ranges and improved channel detection and throughput of approximately 17% and 10%, respectively. Full article

The new era of IoT brings the necessity of smart synergy for diverse communication and computation entities. The two extremes are, on the one hand, the 5G Ultra-Reliable Low-Latency Communications (URLLC) required for Industrial IoT (IIoT) and Vehicle Communications (V2V, V2I, V2X). While on the other hand, the Ultra-Low Power, Wide-Range, Low Bit-rate Communications, such as Sigfox, LoRa/LoRaWAN, NB-IoT, Cat-M1, etc.; used for smart metering, smart logistics, monitoring, alarms, tracking applications. This extreme variety and diversity must work in synergy, all inter-operating/inter-working with the Internet. The communication solutions must mutually cooperate, but there must be a synergy in a broader sense that includes the various communication solutions and all the processing and storage capabilities from the edge cloud to the deep-cloud. In this paper, we consider a non-orthogonal multiple access (NOMA)-based device to device (D2D) communication system coexisting with a cellular network and utilize Greedy Asynchronous Distributed Interference Avoidance Algorithm (GADIA) for dynamic frequency allocation strategy. We analyze a max–min fairness optimization problem with energy budget constraints to provide a reasonable boundary rate for the downlink to all devices and cellular users in the network for a given total transmit power. A comprehensive simulation and numerical evaluation is performed. Further, we compare the performance of maximum achievable rate and energy efficiency (EE) at a given spectral efficiency (SE) while employing NOMA and orthogonal frequency-division multiple access (OFDMA). Full article

Forward error correction coding is the most common way of channel coding and the key point of error correction coding. Therefore, the recognition of which coding type is an important issue in non-cooperative communication. At present, the recognition of FEC codes is mainly concentrated in the field of semi-blind identification with known types of codes. However, the receiver cannot know the types of channel coding previously in non-cooperative systems such as cognitive radio and remote sensing of communication. Therefore, it is important to recognize the error-correcting encoding type with no prior information. In the paper, we come up with a neoteric method to identify the types of FEC codes based on Recurrent Neural Network (RNN) under the condition of non-cooperative communication. The algorithm classifies the input data into Bose-Chaudhuri-Hocquenghem (BCH) codes, Low-density Parity-check (LDPC) codes, Turbo codes and convolutional codes. So as to train the RNN model with better performance, the weight initialization method is optimized and the network performance is improved. The experimental result indicates that the average recognition rate of this model is 99% when the signal-to-noise ratio (SNR) ranges from 0 dB to 10 dB, which is in line with the requirements of engineering practice under the condition of non-cooperative communication. Moreover, the comparison of different parameters and models show the effectiveness and practicability of the algorithm proposed. Full article

Non-contact detection of the breathing patterns in a remote and unobtrusive manner has significant value to healthcare applications and disease diagnosis, such as in COVID-19 infection prediction. During the epidemic prevention and control period of COVID-19, non-contact approaches have great significance because they minimize the physical burden on the patient and have the least requirement of active cooperation of the infected individual. During the pandemic, these non-contact approaches also reduce environmental constraints and remove the need for extra preparations. According to the latest medical research, the breathing pattern of a person infected with COVID-19 is unlike the breathing associated with flu and the common cold. One noteworthy symptom that occurs in COVID-19 is an abnormal breathing rate; individuals infected with COVID-19 have more rapid breathing. This requires continuous real-time detection of breathing patterns, which can be helpful in the prediction, diagnosis, and screening for people infected with COVID-19. In this research work, software-defined radio (SDR)-based radio frequency (RF) sensing techniques and machine learning (ML) algorithms are exploited to develop a platform for the detection and classification of different abnormal breathing patterns. ML algorithms are used for classification purposes, and their performance is evaluated on the basis of accuracy, prediction speed, and training time. The results show that this platform can detect and classify breathing patterns with a maximum accuracy of 99.4% through a complex tree algorithm. This research has a significant clinical impact because this platform can also be deployed for practical use in pandemic and non-pandemic situations. Full article

Mobile CrowdSensing (MCS) is an emerging paradigm in the distributed acquisition of smart city and Internet of Things (IoT) data. MCS requires large number of users to enable access to the built-in sensors in their mobile devices and share sensed data to ensure high value and high veracity of big sensed data. Improving user participation in MCS campaigns requires to boost users effectively, which is a key concern for the success of MCS platforms. As MCS builds on non-dedicated sensors, data trustworthiness cannot be guaranteed as every user attains an individual strategy to benefit from participation. At the same time, MCS platforms endeavor to acquire highly dependable crowd-sensed data at lower cost. This phenomenon introduces a game between users that form the participant pool, as well as between the participant pool and the MCS platform. Research on various game theoretic approaches aims to provide a stable solution to this problem. This article presents a comprehensive review of different game theoretic solutions that address the following issues in MCS such as sensing cost, quality of data, optimal price determination between data requesters and providers, and incentives. We propose a taxonomy of game theory-based solutions for MCS platforms in which problems are mainly formulated based on Stackelberg, Bayesian and Evolutionary games. We present the methods used by each game to reach an equilibrium where the solution for the problem ensures that every participant of the game is satisfied with their utility with no requirement of change in their strategies. The initial criterion to categorize the game theoretic solutions for MCS is based on co-operation and information available among participants whereas a participant could be either a requester or provider. Following a thorough qualitative comparison of the surveyed approaches, we provide insights concerning open areas and possible directions in this active field of research. Full article

The cognitive wireless sensor network (CWSN) is an important development direction of wireless sensor networks (WSNs), and spectrum sensing technology is an essential prerequisite for CWSN to achieve spectrum sharing. However, the existing non-cooperative narrowband spectrum sensing technology has difficulty meeting the application requirements of CWSN at present. In this paper, we present a non-cooperative spectrum sensing algorithm for CWSN, which combines the multi-resolution technique, phase space reconstruction method, and singular spectrum entropy method to sense the spectrum of narrowband wireless signals. Simulation results validate that this algorithm can greatly improve the detection probability at a low signal-to-noise ratio (SNR) (from −19dB to −12dB), and the detector can quickly achieve the best detection performance as the SNR increases. This algorithm could promote the development of CWSN and the application of WSNs. Full article

Spectrum sensing is an important task in cognitive radio. However, currently available Analog-to-Digital Converters (ADC) can hardly satisfy the sampling rate requirement for wideband signals. Even with such an ADC, the cost is extremely high in terms of price and power consumption. In this paper, we propose a spectrum-sensing method based on single-channel sub-Nyquist sampling. Firstly, a serial Multi-Coset Sampling (MCS) structure is designed to avoid mismatches among sub-ADCs in the traditional parallel MCS. Clocks of the sample/hold and ADC are provided by two non-uniform sampling clocks. The cooperation between these two non-uniform sampling clocks shifts the high sampling rate burden from the ADC to the sample/hold. Secondly, a power spectrum estimation method using sub-Nyquist samples is introduced, and an efficient spectrum-sensing algorithm is proposed. By exploiting the frequency-smoothing property, the proposed efficient spectrum-sensing algorithm only needs to estimate power spectrum at partial frequency bins to conduct spectrum sensing, which will save a large amount of computational cost. Finally, the sampling pattern design of the proposed serial MCS is given, and it is proved to be a minimal circular sparse ruler with an additional constraint. Simulations show that mismatches in traditional parallel MCS have a serious impact on spectrum-sensing performance, while the proposed serial MCS combined with the efficient spectrum-sensing algorithm exhibits outstanding spectrum-sensing performance at much lower computational cost. Full article

The Internet of Things (IoT) concept is aiming at being an integral part of the next generation networking services by introducing pervasiveness and ubiquitous interconnectivity of uniquely-identifiable objects. The massive availability of personalized smart devices such as smartphones and wearables enable their penetration into the IoT ecosystem with their built-in sensors, particularly in Mobile Crowd-Sensing (MCS) campaigns. The MCS systems achieve the objectives of the large-scale non-dedicated sensing concept in the IoT if a sufficient number of participants are engaged to the collaborative data acquisition process. Therefore, user recruitment is a key challenge in MCS, which requires effective incentivization of cooperative, truthful and trustworthy users. A grand concern for the participants is the battery drain on the mobile devices. It is a known fact that battery drain in a smartphone is a function of the user activity, which can be modeled under various contexts. With this in mind, we propose a new social activity-aware recruitment policy, namely Sociability-Oriented and Battery-Efficient Recruitment for Mobile Crowd-Sensing (SOBER-MCS). SOBER-MCS uses sociability and the residual power of the participant smartphones as two primary criteria in the selection of participating devices. The former is an indicator of the participant willingness toward sensing campaigns, whereas the latter is used to prioritize personal use over crowd-sensing under critical battery levels. We use sociability profiles that were obtained in our previous work and use those values to simulate the sociability behavior of a large pool of participants in an MCS environment. Through simulations, we show that SOBER-MCS is able to introduce battery savings up to 18.5% while improving user and platform utilities by 12% and 20%, respectively. Full article

In this paper, we mainly study a cooperative search and coverage algorithm for a given bounded rectangle region, which contains several unknown stationary targets, by a team of unmanned aerial vehicles (UAVs) with non-ideal sensors and limited communication ranges. Our goal is to minimize the search time, while gathering more information about the environment and finding more targets. For this purpose, a novel cooperative search and coverage algorithm with controllable revisit mechanism is presented. Firstly, as the representation of the environment, the cognitive maps that included the target probability map (TPM), the uncertain map (UM), and the digital pheromone map (DPM) are constituted. We also design a distributed update and fusion scheme for the cognitive map. This update and fusion scheme can guarantee that each one of the cognitive maps converges to the same one, which reflects the targets’ true existence or absence in each cell of the search region. Secondly, we develop a controllable revisit mechanism based on the DPM. This mechanism can concentrate the UAVs to revisit sub-areas that have a large target probability or high uncertainty. Thirdly, in the frame of distributed receding horizon optimizing, a path planning algorithm for the multi-UAVs cooperative search and coverage is designed. In the path planning algorithm, the movement of the UAVs is restricted by the potential fields to meet the requirements of avoiding collision and maintaining connectivity constraints. Moreover, using the minimum spanning tree (MST) topology optimization strategy, we can obtain a tradeoff between the search coverage enhancement and the connectivity maintenance. The feasibility of the proposed algorithm is demonstrated by comparison simulations by way of analyzing the effects of the controllable revisit mechanism and the connectivity maintenance scheme. The Monte Carlo method is employed to validate the influence of the number of UAVs, the sensing radius, the detection and false alarm probabilities, and the communication range on the proposed algorithm. Full article