Specific Emitter Identification (SEI) exploits hardware-induced Radio Frequency Fingerprinting (RFF) characteristics to authenticate transmitters, serving as critical infrastructure for wireless security and electromagnetic spectrum monitoring. However, conventional SEI approaches face challenges in cross-receiver deployment scenarios due to hardware-induced domain shifts that distort signal feature distributions. The coexistence of unknown-class emitters in target domains further exacerbates classification complexity. To address these dual challenges, we propose a Cross-Receiver Open-Set (CROS) SEI method based on Universal Domain Adaptation (UniDA) framework. The method learns the distributions of the target domain through self-supervised learning, clusters target samples around neighboring target samples or source domain prototypes via neighborhood clustering, while employs entropy optimization to separate known and unknown classes. The experimental results show that UniDA-based CROS SEI has an improvement in recognition accuracy by 3% ~ 6% on ADS-B and LoRa-Rffi datasets compared to threshold-based open-set SEI algorithm.

Abstract Fuzzy extractors allow for the extraction and reproduction of a nearly uniform string from a noisy and non-uniform source. Reusable and robust fuzzy extractors further require that the output string should remain pseudorandom under multiple extractions and any modification of public value should be detectable. Existing constructions of reusable and robust fuzzy extractors are all designed in the Common Reference String (CRS) model and work only for CRS-independent sources. In this work, we introduce a construction of reusable and robust fuzzy extractor for CRS-dependent sources. Our construction is built upon some well-studied cryptography, including a collision resistant hash function, a symmetric key encryption that keeps secure with respect to auxiliary input, a public key encryption and simulation-sound non-interactive zero-knowledge argument. We also present some instantiations of these primitives from Learning with Parity Noise (LPN) assumption and Short Integer Solution (SIS) assumption. These instantiations result in the first reusable and robust fuzzy extractor for CRS-dependent sources that can tolerate linear errors.

Abstract The group fuzzy extractor allows group users to extract and reproduce group cryptographic keys from their individual non-uniform random sources. It can be easily used in group-oriented cryptographic applications. However, current group fuzzy extractors are not dynamic, i.e. they spend a large cost when dealing with user revocation. In this work, we propose the formal definition and construction of dynamic group fuzzy extractor (DGFE) to address this issue. For the revocation, DGFE allows unrevoked group users to reproduce updated group keys from the existing group help data. Meanwhile, it prevents any revoked group user from generating new group keys using the previously authorized individual help data. We propose a DGFE construction based on the revocable group signature. Furthermore, we give formal proofs of reusability, anonymity and traceability of our construction.

We study the high dynamic range (HDR) imaging problem in dual-lens systems. Existing methods usually treat the HDR imaging problem as an image fusion problem and the HDR result is estimated by fusing the aligned short exposure image and long exposure image. However, the image fusion pipeline depends highly on the image alignment, which is difficult to be perfect. We propose to transfer the dual-lens HDR imaging problem into the disentangled enhancement of exposure correction and denoising for the short exposure image, guided by the long exposure image. In the guided exposure correction module, we make use of the guidance image and 3D color transformation to propose a guided 3D exposure CNN (GEC) to get the rough HDR result from the short exposure image. Then, in the guided denoising module, we make use of the cross-attention mechanism to propose a guided denoising transformer (GDT) to directly use the long exposure image as guidance to denoise the rough HDR result in a pyramid way. And in both modules, we bypass the difficult image alignment processing. Experimental results demonstrate the superiority of our method over the state-of-the-art ones.

Metagenomic research uses sequencing technologies to investigate the genetic biodiversity of microbiomes presented in various ecosystems or animal tissues. The composition of a microbial community is highly associated with the environment in which the organisms exist. As large amount of sequencing short reads of microorganism genomes obtained, accurately estimating the abundance of microorganisms within a metagenomic sample is becoming an increasing challenge in bioinformatics. In this paper, we describe a hierarchical taxonomy tree-based mixture model (HTTMM) for estimating the abundance of taxon within a microbial community by incorporating the structure of the taxonomy tree. In this model, genome-specific short reads and homologous short reads among genomes can be distinguished and represented by leaf and intermediate nodes in the taxonomy tree, respectively. We adopt an expectation-maximization algorithm to solve this model. Using simulated and real-world data, we demonstrate that the proposed method is superior to both flat mixture model and lowest common ancestry-based methods. Moreover, this model can reveal previously unaddressed homologous genomes.

Data is the core and emphasis of geographical information system (GIS). The operations such as information storage, management, inquiry, analysis and description all depend on data. Data collection is the basic work of GIS construction. Conventional methods can't satisfy the need of GIS development with some disadvantages such as low digital degree, high cost and long time consuming. Global Positioning System (GPS) provides three-dimension position, velocity and time information about any point on the earth with high precision, whole-day and continuous properties by receiving radio signals from satellites. GPS as one of the most mature orientation technologies has gradually become one of the most important data resources of GIS. This paper focuses on two aspects. The first one is the technology and method of GIS data collection based on Global Positioning System. GPS dynamic data communication, position data process method and data transformation technology is discussed in detail with resolving methods and flow charts. The second one is the technology and method of GIS database update. The weighted average method of update which considers the original GIS position data as one kind of measurement and the GPS position data as another kind of measurement computes the average of these two kinds of measurements to get the new position information. And these two kinds of measurement are considered with different weights. The study is undertaken on Zhalong Wetland in Qiqihaer City China and Malan River in Dalian City China. The investigation results prove that the data collection and database update of GIS based on GPS technology can orient changed areas rapidly and make the local database update more convenient and flexible. It is helpful in trend prediction of wetland for the sustainable development of regional economy and has some significance in wetland dynamic analysis, protection and exploiture.

Discusses some important problems in applying integration technology of GPS (Global Positioning System) and GIS (Geographical Information System) to wetland study. The study focuses on both dynamic data exchange of GPS and GIS technology and wetland resource investigation on current status using information integration technology of GPS and GIS. Study points' position in subject digital maps can be displayed and tracked using GPS and GIS integration technology. Ecological environment changes can be found. The GPS study points' position information and satellite status data can be displayed and be used to update the wetland database. GPS data communication technology, positioning data process method and data display techniques are presented in This work. A special resolving method, flow charts and the data processing method are discussed in detail. The GIS platform is Arc/Info 8.2 of ESRI. The data communication is realized by using MSComm Control. The study is undertaken on Zhalong Wetland, Qiqihaer City, Heilongjiang Province, China. The result of practice proves that the integration technology of GIS and GPS can orient changed areas accurately and reflect the current situation and historical background of wetland comprehensively. It is an efficient method in dynamic analysis of wetland protection and is helpful in trend prediction of wetland for the sustainable development of regional economy. It offers basic data for some macro adjustments and government policy.