Uvod: Jednofotonska emisiona tomografija (SPECT) značajno je unaprijedila nuklearno-medicinsku dijagnostiku. Nedostatak SPECT metode su relativno niski signal-šum odnos (SNR) kao i kontrast-šum odnos (CNR). Nizak broj fotona slike predstavlja veliki problem kod SPECT snimanja zbog smanjenja signala uz povećanje šuma (pozadine slike). Zadnjih godina veliki broj naučnika ističe značaj SPECT snimanja niskog broja signala korištenjem savremenih detektorskih sistema i naprednih softverskih modaliteta. Najznačajniji napredak rekonstrukcije SPECT snimaka predstavlja primjena metode oporavka rezolucije (RR) kod snimanja miokarda (Myovation Evolution) i koštanog sistema (Evolution for Bone) jer dugotrajna SPECT akvizicija smanjuje komfor uz pojavu artefakata micanja i smanjenje kvaliteta slike. Savremeni metod zasnovan je na principu “Half-Time SPECT” uz jednaku senzitivnost i specifičnost tomografske studije. Materijal i metode: Istraživanje je provedeno kao prospektivna klinička eksperimentalna studija na uzorku od 100 pacijenata sa potvrđenom onkološkom dijagnozom. Izvršena je komparacija snimaka pune i skraćene dužine akvizicije (skraćenje za 25 i 50% od pune akvizicije). Svi snimci su rekonstruisani sa tri različita protokola. Ukupno je ispitano 9 različitih SPECT protokola. Na svim snimcima nivo šuma, odnos signal-šum kao i odnos kontrast-šum su mjereni za isto anatomsko područje. Rezultati: Skraćenjem trajanja akvizicije zabilježen je porast vrijednosti šuma, najblaži porast zabilježen je korištenjem Evolution for Bone protokola. Smanjenjem trajanja akvizicije SNR i CNR značajno su smanjeni, dok je protokol koji je koristio RR modalitet zabilježio veću vrijednost SNR i CNR na 50% akvizicije u odnosu na punu akviziciju bez RR modaliteta. Zaključci: Analizom snimaka skraćene i pune akvizicije uz primjenu parametara rekonstrukcije došlo se do zaključka da RR modalitet omogućava skraćenje trajanja SPECT akvizicije bez značajnog uticaja na povećanje vrijednosti šuma. SNR i CNR primjenom RR modaliteta i dalje mogu ostati visoki pri čemu je omogućena jasna detekcija patoloških lezija kao i kod pune SPECT akvizicije.

To establish more intelligent cellular networks for future ubiquitous access and heterogeneous devices, we need to obtain channel state information (CSI) in a more agile and economical manner, especially for frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) architectures. Unlike conventional CSI feedback or limited feedback methods, we can predict downlink CSI by leveraging channel reciprocity between uplink and downlink. The downlink CSI prediction can be formulated as a data-driven deep learning task, however, there exist isolated data silos and online adaptation problem for the offline trained neural network-based models. In this article, we propose an interacting federated and transfer learning (IFTL) based framework for downlink CSI prediction and online update, where several factors including asynchrony of different clients are considered, and light heterogeneity of diverse cells can be tolerated. Both model-level and link-level simulations are conducted under standardized FDD massive MIMO scenarios. The results outline promising prospect and potential of the utilization of federated learning and transfer learning in physical layer of wireless communications.

In this article, we propose a few-shot indoor position method based on Triplet Matchnet, which transforms coordinate positioning into channel state information (CSI) similarity matching problem. Triplet loss is designed to train and learn hidden correspondence between CSI features and physical space positions, with emphasis on minimizing distance or angle-based triplet loss. Then, according to pre-trained network with best similarity match, a similarity score map of CSI with unknown coordinates is constructed to predict position precisely. Experimental results show that angle-based triplet loss can obtain more accurate CSI fingerprint similarity matching accuracy. Compared with existing methods, experiment results confirm that our proposed method can achieve excellent positioning performance with few-shot datasets.

Specific emitter identification (SEI) is a potential physical layer authentication technology, which is one of the most critical complements of upper layer authentication. Radio frequency fingerprint (RFF)-based SEI is to distinguish one emitter from each other by immutable RF characteristics from electronic components. Due to the powerful ability of deep learning (DL) to extract hidden features and perform classification, it can extract highly separative features from massive signal samples, thus enabling SEI. Considering the condition of limited training samples, we propose a novel few-shot SEI (FS-SEI) method based on interpolative metric learning (InterML) which gets rid of the dependence on auxiliary dataset. Specifically, InterML is designed to mine more implicit samples in the sample space to improve generalization, and constrain the feature distance in the feature space to improve discriminability. The proposed InterML-based FS-SEI method is evaluated on a real-world Wi-Fi dataset. The simulation results show that the proposed method achieves better identification performance, higher feature discriminability and more stable performance than five latest FS-SEI methods. In the 10 shot scenario, the identification accuracy of InterML is 91.48%, compared to the comparison methods, the accuracy is improved by 0.62%–31.29%.

To provide seamless wireless coverage, the air-to-ground (A2G) heterogeneous wireless network is considered as one of the most promising solutions. In this article, we develop a novel A2G communication-caching-charging (3C) integrated network based on non-orthogonal multiple access (NOMA). As a significant participant of A2G network, unmanned aerial vehicle (UAV), which harvests energy from the base station (BS) with the aid of wireless power transfer (WPT), is utilized as content server to cache files and serve users. To be specific, we first propose a resource allocation strategy to enhance the quality of service (QoS) of ground users. The goal is to minimize the transmission latency of ground users, which is decomposed into sub-problems, such as user pairing, files' power allocation and users' power allocation. Firstly, we propose a NOMA user pairing algorithm based on K-means to deploy UAVs and pair users. Then, the closed-form solution for files' power allocation with the goal of maximizing the duration for energy harvesting is formulated. Finally, we apply the genetic algorithm (GA) to obtain power allocation factors to increase users' rate and the reminder time of content delivery phase is utilized for energy harvesting. Simulation results validate the advantage of the proposed strategy in reducing user delay than benchmark schemes.

Previous research has established that during all phases of a crisis, people resort to different means of communication in order to get more information (McIntyre et al., 2012, Nelson et al., 2009, Lachlan et al., 2009), in order to reduce uncertainty ( Lachlan et al., 2010), and to gain a sense of control over the situation (Lachlan et al., 2016). At the beginning of the 21st century, mass communication is taking on new forms. The exponential growth and affirmation of the Internet as a very important channel for communication has minimized the influence of traditional media. Digitization processes, interactivity, multimedia, connection and networking of a large number of people and expediency in the dissemination of information enabled the wide use of social networks in times of crisis. In the first part of the paper, previous research on the use of social networks in crisis communication was synthesized, through the presentation of best practices for effective communication. The second part of the paper provides a detailed analysis of the use of social networks on the example of the war in Ukraine, answering two important questions: 1. how are social networks used to spread competing national narratives and disinformation in times of crisis? and 2. what is the role of social media owners and government policies in limiting disinformation?

Bats are a natural host for a number of viruses, many of which are zoonotic and thus present a threat to human health. RNA viruses of the family Filoviridae, many of which cause disease in humans, have been associated with specific bat hosts. Lloviu virus is a Filovirus which has been connected to mass mortality events in Miniopterus schreibersii colonies in Spain and Hungary, and some studies have indicated its immense zoonotic potential. A die-off has been recorded among Miniopterus schreibersii in eastern Bosnia and Herzegovina for the first time, prompting the investigation to determine the causative agent. Bat carcasses were collected and subjected to pathological examination, after which the lung samples with notable histopathological changes, lung samples with no changes and guano were analyzed using metagenomic sequencing and RT-PCR. A partial Lloviu virus genome was sequenced from lung samples with histopathological changes and found to be closely related to Hungarian and Italian virus sequences. Further accumulation of mutations on the GP gene, coding the glycoprotein responsible for cell tropism and host preference, enhances the need for further characterization and monitoring of this virus to prevent spillover events and protect human health.