Search Results (83)

Electric vehicles, or EVs, have taken the spotlight in recent years in attempts to minimize the negative environmental effects associated with conventional modes of transportation. The location of charging infrastructure is an significant roadblock to promoting EV adoption; it demands careful planning in order to ensure the sustainability of EV use. Models like the Flow Capturing Location Model (FCLM) and Flow Refueling Location Model (FRLM) address this by considering operational constraints, system features, and uncertainties to provide effective solutions. In this study, Using MATLAB, R2020a the FCLM and FRLM were applied in Al-Karkh, Baghdad. When combined, the results revealed three key outcomes: identification of the nodes most frequently connected by traffic flows, with the shortest path method used to exclude paths that could not be utilized due to vehicle range limitations, and determination of the best nodes located along the shortest feasible routes dependent on the number of stations that the models chose. Full article

Spaceborne high-resolution wide-area SAR image formation processing faces critical challenges induced by orbital curvature, Earth rotation, and spherical ground surfaces. The Spherical Geometry Algorithm (SGA) offers an effective solution to these problems. However, the standard SGA is inherently limited to spotlight mode SAR data processing and cannot be directly extended to other operational modes. To overcome this constraint, this paper proposes an enhanced SGA framework tailored for sliding spotlight mode SAR data processing. Firstly, this paper presents a rigorous analysis of time–frequency relationship variations during the classical SGA processing under sliding spotlight mode, and gives the reasons why the classical SGA can not be directly applied to the data processing in sliding spotlight mode. Then, a modified SGA processing framework is proposed to address the signal sampling ambiguity problem faced by the SGA in processing sliding spotlight mode data. The improved algorithm avoids the sampling ambiguity problem during azimuthal resampling and azimuthal IFFT by introducing an instantaneous Doppler central frequency correction processing before azimuthal resampling and a suitable amount of oversampling during azimuthal resampling. Finally, the effectiveness of the algorithm is verified by measured real data processing. Full article

Synthetic aperture radar (SAR) operating mode recognition plays a crucial role in SAR countermeasures and serves as the foundation for effective SAR interference. To address the limitations of current SAR operating mode recognition algorithms, such as low recognition rates, poor generalization, and limited engineering applicability under low signal-to-noise ratio (SNR) conditions, an enhanced algorithm named dual-input feature fusion ShuffleNet (DIFF-ShuffleNet) based on intercepted SAR signal data is proposed. First, the SAR signal is processed by combining pulse compression and time–frequency analysis technology to enhance anti-noise robustness. Then, an improved lightweight ShuffleNet architecture is designed to fuse range pulse compression (RPC) maps and azimuth time–frequency features, significantly improving recognition accuracy in low-SNR environments while maintaining practical deployability. Moreover, an improved coarse-to-fine search fractional Fourier transform (CFS-FRFT) algorithm is proposed to address the chirp rate estimation required for RPC. Simulations demonstrate that the proposed SAR operating mode recognition algorithm achieves over 95.00% recognition accuracy for SAR operating modes (stripmap, spotlight, sliding spotlight, and scan) at an SNR greater than −8 dB. Finally, four sets of measured SAR data are used to validate the algorithm’s effectiveness, with all recognition results being correct, demonstrating the algorithm’s practical applicability. Full article

To achieve on-board real-time processing for sliding-spotlight mode synthetic aperture radar (SAR), on the one hand, this paper proposes an adaptive and efficient imaging algorithm for the sliding-spotlight mode. On the other hand, a batch processing method was designed and optimized based on the AGX Orin platform to implement the algorithm effectively. Based on the chirp scaling (CS) algorithm, sliding-spotlight mode imaging can be achieved by adding Deramp preprocessing along with either zero-padding or performing an extra chirp scaling operation. This article analyzes the computational complexity of the two algorithms and provides a criterion called the Method Choice Indicator (MCI) for selecting the appropriate method. Additionally, the mathematical expressions for time–frequency transformation are derived, providing the theoretical basis for calculating the equivalent PRF and the azimuth width represented by a single pixel. To increase the size of the data that AGX Orin can process, the batch processing method was proposed to reduce peak memory usage during imaging, so that the limited memory could be better utilized. Meanwhile, this algorithm was also compatible with strip mode and TOPSAR (Terrain Observation by Progressive scans SAR) mode imaging. While batch processing increased data transfers, the integrated architecture of AGX Orin minimized the negative impact. Subsequently, through a series of optimizations of the algorithm, the efficiency of the algorithm was further improved. As a result, it took 19.25 s to complete the imaging process for sliding-spotlight mode data with a size of 42,966 × 27,648. Since satellite data acquisition time was 11.43 s, it can be considered that this method achieved near-real-time imaging. The experimental results demonstrate the feasibility of on-board processing. Full article

Synthetic aperture radar (SAR) technology is one of the imaging radar technologies receiving the most attention worldwide. The main purpose is to detect targets in the area of interest in different settings, such as day/night, various weather conditions, etc. Phase gradient autofocusing (PGA) algorithms have been widely used for autofocus in SAR imaging. Conventional PGA methods in stripmap SAR apply dechirping to switch the range-compressed phase history-domain signal to a form equivalent to that in spotlight mode. However, this switching method has inherent limitations in phase error estimation, leading to degraded autofocusing performance. To address this issue, we introduce an FrFT-based switching method that provides more precise and fast autofocus. Additionally, this method enables effective detection and extraction of moving targets in the environment where moving targets are present. Moving targets introduce additional phase errors that hinder accurate autofocus, making it essential to isolate and process them separately. We carried out practical experiments with an X-band chirp pulse SAR system to verify the proposed method and mount the system on an automobile. Full article

Spotlight/sliding spotlight modes can achieve higher resolution than the other imaging modes and are widely used in object detection and recognition applications. This paper reviews the progress of the spaceborne spotlight/sliding spotlight SAR imaging field. The three steps of the current spaceborne spotlight/sliding spotlight SAR imaging algorithm framework are discussed in this paper. These include the following: eliminating the azimuth spectral aliasing by azimuth deramp preprocessing; implementing imaging processing using imaging kernels (RD, CS, RMA, etc.); and degrading the back-folded phenomenon in the final focused image domain by reference function multiplication post-processing. The different imaging kernels, consisting of RD, CS, RMA, BAS, FS, and PFA, are presented. The phase errors in high-resolution spaceborne spotlight/sliding spotlight SAR imaging, especially the stop-and-go error, curved orbit error, and tropospheric delay error, are analyzed in detail. Furthermore, the autofocus methods are described. In addition, some new imaging SAR systems based on spotlight/sliding spotlight SAR mode, which have more advantages than the classic spaceborne spotlight/sliding spotlight SAR imaging, were shown in this paper. These include FMCW-based systems, multichannel systems, varying-PRF systems, and bistatic systems. Full article

After a boom that coincided with the advent of the internet, digital cameras, digital video and audio storage and playback devices, the research on data compression has rested on its laurels for a quarter of a century. Domain-dependent lossy algorithms of the time, such as JPEG, AVC, MP3 and others, achieved remarkable compression ratios and encoding and decoding speeds with acceptable data quality, which has kept them in common use to this day. However, recent computing paradigms such as cloud computing, edge computing, the Internet of Things (IoT), and digital preservation have gradually posed new challenges, and, as a consequence, development trends in data compression are focusing on concepts that were not previously in the spotlight. In this article, we try to critically evaluate the most prominent of these trends and to explore their parallels, complementarities, and differences. Digital data restoration mimics the human ability to omit memorising information that is satisfactorily retrievable from the context. Feature-based data compression introduces a two-level data representation with higher-level semantic features and with residuals that correct the feature-restored (predicted) data. The integration of the advantages of individual domain-specific data compression methods into a general approach is also challenging. To the best of our knowledge, a method that addresses all these trends does not exist yet. Our methodology, COMPROMISE, has been developed exactly to make as many solutions to these challenges as possible inter-operable. It incorporates features and digital restoration. Furthermore, it is largely domain-independent (general), asymmetric, and universal. The latter refers to the ability to compress data in a common framework in a lossy, lossless, and near-lossless mode. COMPROMISE may also be considered an umbrella that links many existing domain-dependent and independent methods, supports hybrid lossless–lossy techniques, and encourages the development of new data compression algorithms. Full article

For ultra-high-resolution (UHR) squint spotlight airborne synthetic aperture radar (SAR), the severe range-azimuth coupling caused by squint mode and the spatial and frequency dependence of the motion error brought by ultra-wide bandwidth both make it difficult to obtain satisfactory imaging results. Although some autofocus methods for squint airborne SAR have been presented in the published literature, their practical applicability in UHR situations remains limited. In this article, a new 2D wavenumber domain autofocus method combined with the Omega-K algorithm dedicated to UHR squint spotlight airborne SAR is proposed. First, we analyze the dependence of range envelope shift error (RESE) and range defocus on the squint angle and then propose a new spectrum modification strategy, after which the spectrum transforms into a quasi-side-looking one. The accuracy of estimation and compensation can be improved significantly in this way. Then, the 2D phase error can be calculated with the 1D estimated error by the mapping relationship, and after that the 2D compensation is performed in the wavenumber domain. Furthermore, the image-blocking technique and range-dependent motion error compensation method are embedded to accommodate the spatial-variant motion error for UHR cases. Simulations are carried out to verify the effectiveness of the proposed method. Full article

The present article responds to the food engineering community’s growing interest in an emerging and lauded approach to food preservation, popularised by its developers as ‘isochoric freezing’. A strong campaign in the scientific literature and mass media has recently promoted this technique as a universal replacement for traditional food freezing and the frozen supply chain by highlighting a number of alleged advantages of ‘isochoric freezing’. Some of these claims therefore require a more neutral and critical assessment against the background of the today’s state of the art in food freezing technologies. Hence, this article spotlights several concerns regarding the plausibility, energy expenditure, resource efficiency, process rate, throughput and safety of ‘isochoric freezing’, as well as the correct use of food refrigeration terminology. The aspects considered are intended to make food scientists, technologists and engineers more aware of the real capabilities and the application perspectives of this still immature mode of refrigerated food processing. Full article

FAM46C is a well-established tumour suppressor with a role that is not completely defined or universally accepted. Although FAM46C expression is down-modulated in several tumours, significant mutations in the FAM46C gene are only found in multiple myeloma (MM). Consequently, its tumour suppressor activity has primarily been studied in the MM context. However, emerging evidence suggests that FAM46C is involved also in other cancer types, namely colorectal, prostate and gastric cancer and squamous cell and hepatocellular carcinoma, where FAM46C expression was found to be significantly reduced in tumoural versus non-tumoural tissues and where FAM46C was shown to possess anti-proliferative properties. Accordingly, FAM46C was recently proposed to function as a pan-cancer prognostic marker, bringing FAM46C under the spotlight and attracting growing interest from the scientific community in the pathways modulated by FAM46C and in its mechanistic activity. Here, we will provide the first comprehensive review regarding FAM46C by covering (1) the intracellular pathways regulated by FAM46C, namely the MAPK/ERK, PI3K/AKT, β-catenin and TGF-β/SMAD pathways; (2) the models regarding its mode of action, specifically the poly(A) polymerase, intracellular trafficking modulator and inhibitor of centriole duplication models, focusing on connections and interdependencies; (3) the regulation of FAM46C expression in different environments by interferons, IL-4, TLR engagement or transcriptional modulators; and, lastly, (4) how FAM46C expression levels associate with increased/decreased tumour cell sensitivity to anticancer agents, such as bortezomib, dexamethasone, lenalidomide, pomalidomide, doxorubicin, melphalan, SK1-I, docetaxel and norcantharidin. Full article

Operating mode identification is an important prerequisite for precise deceptive jamming technology against synthetic aperture radar (SAR). In order to solve the problems of traditional spaceborne SAR operating mode identification, such as low identification accuracy, poor timeliness, and limitation to main lobe reconnaissance, an efficient identification method based on sidelobe reconnaissance and machine learning is proposed in this paper. It can identify four classical SAR operating modes, including stripmap, scan, spotlight and ground moving target indication (GMTI). Firstly, the signal models of different operating modes are presented from the perspective of sidelobe reconnaissance. By setting the parameters differently, such as the SAR trajectory height, antenna length, transmit/receive gain and loss, signal–noise ratio, and so on, the feature samples based on multiple parameters can be obtained, respectively. Then, based on the generated database of feature samples, the initialized neural network can be pre-trained. As a result, in practice, with the intercepted sidelobe signal and the pre-trained network, we can precisely infer the SAR operating mode before the arrival of the main lobe beam footprint. Finally, the effect of SNR and the jammer’s position on the identification accuracy is experimentally detailed in the simulation. The simulation results show that the identification accuracy can reach above 91%. Full article

Sustainable modes of transport are being promoted to achieve global climate goals. The perceived user experience is decisive for the choice of transport mode. To increase the share of sustainable transport in total traffic, the user experience is placed into the spotlight, raising the need for appropriate exploration methods. Machine learning (ML) techniques have become increasingly popular in the transport domain, but the black-box nature of ML models poses significant challenges in interpreting the relationship between model input and output. Explainable AI methods (XAI) can fill this gap by providing post hoc interpretation methods for black-box models. The aim of the present work was therefore to assess the potential of XAI to explore user experience in transport. The introduced method was based on a popular XAI method named SHAP (SHapley Additive exPlanations). Applied to the use case of e-bikes, we aimed to explore factors influencing the riding experience on e-bikes. We applied Gaussian process regression to data collected in a cycling study from 55 e-bike riders including rider behaviour, motor power and riding dynamics. Applying SHAP, we compared the riding experience of four rider types identified by hierarchical cluster analysis. The results provide insights into the riding experience on e-bikes: motor power, rider behaviour and riding dynamics were found to be meaningful predictors differing in their impact between rider types. Our results can be regarded as a proof of concept and demonstrate the potential of XAI to enhance the understanding of user experience in transport. Full article

Conventional squinted sliding spotlight synthetic aperture radar (SAR) imaging suffers from substantial swath width reduction and complex processing requirements due to the continuous variation in the squint angle and the large range cell migration (RCM) throughout the data acquisition interval. A novel two-dimensional (2D) beam scanning mode for high-resolution wide swath (HRWS) imaging is proposed. The key to the novel imaging mode lies in the synchronous scanning of azimuth and range beams, allowing for a broader and more flexible imaging swath with a high geometric resolution. Azimuth beam scanning from fore to aft was used to improve the azimuth resolution, while range beam scanning was adopted to illuminate the oblique wide swath to avoid the large RCM and the serious swath width reduction. Compared with the conventional sliding spotlight mode, both the swath width and swath length could be extended. According to the echo model of this imaging mode, an echo signal preprocessing approach is proposed. The key points of this approach are range data extension and azimuth data upsampling. A designed system example with a resolution of 0.5 m, swath width of 60 km, and azimuth coverage length of 134 km is presented. Furthermore, a simulation experiment on point targets was carried out. Both the presented system example and imaging results of point targets validated the proposed imaging mode. Full article

The detection performance of power transmission towers in mountainous areas using SAR amplitude images is obviously influenced by the strong layover background (mainly including vegetation and soil) clutter interference around the towers. In this paper, power transmission tower detection in a mountainous layover area, using single-baseline SAR interferometry coherence images, which show better feature enhancement effectiveness compared to SAR amplitude images, is presented. Moreover, a novel feature enhancement method, that of generating multi-baseline SAR interferometry-correlated synthesis images for power transmission tower detection in a mountain layover area, is proposed. It demonstrates better feature enhancement (layover background cluster suppression) than that using single-baseline SAR interferometry coherence images. Theoretical analysis illustrates that the mountainous layover background clutter interference can be suppressed in the proposed single-baseline/multi-baseline SAR interferometry-correlated synthesis image. Experiments including over 12 repeat-pass TerraSAR-X staring spotlight mode acquisitions were conducted, and the results demonstrate that the detection performance with the use of multi-baseline SAR interferometry-correlated synthesis images showed an improvement of more than 43.6%, compared with the traditional method of using SAR amplitude images when benchmark deep learning-based detectors are used, i.e., Faster RCNN and YOLOv7. Full article

The transmitter and receiver of unmanned aerial vehicle (UAV) bistatic synthetic aperture radar (Bi-SAR) are respectively carried on different UAV platforms, which has the advantages of flexible movement and strong concealment, and has broad application prospects in remote sensing fields. However, the range cell migration (RCM) and azimuth non-linear phase (ANP) of UAV Bi-SAR are seriously spatially variant along the range and azimuth directions, while the UAV Bi-SAR has a short operating range, complex trajectory and wide azimuth beam. Aiming at the problem that the RCM and ANP of UAV Bi-SAR in spotlight mode are difficult to correct and equalize due to the severe two-dimensional (2D) spatial variation, an RCM correction (RCMC) and ANP equalization (ANPE) method based on Doppler domain blocking is proposed. First, the azimuth spatial variance of RCM is eliminated by Doppler blocking, and the range spatial variant RCMC is realized by RNCS. Second, by combining Doppler blocking with azimuth nonlinear chirp scaling (ANCS), this method can adapt to ANPE with larger width and more severe spatial variation. At last, the criteria of Doppler blocking are given in detail, and the effectiveness of the proposed method is verified by UAV Bi-SAR real data and computer simulation. Full article