Throughput Improvement by Mode Selection in Hybrid Duplex Wireless Networks

Hybrid duplex wireless networks, use half duplex (HD) as well as full duplex (FD) modes to utilize the advantages of both technologies. This paper tries to determine the proportion of the network nodes that should be in HD or FD modes in such networks, to maximize the overall throughput of all FD and HD nodes. Here, by assuming imperfect self-interference cancellation (SIC) and using ALOHA protocol, the local optimum densities of FD, HD and idle nodes are obtained in a given time slot, using Karush–Kuhn–Tucker (KKT) conditions as well as stochastic geometry tool. We also obtain the sub-optimal value of the signal-to-interference ratio (SIR) threshold constrained by fixed node densities, using the steepest descent method in order to maximize the network throughput. The results show that in such networks, the proposed hybrid duplex mode selection scheme improves the level of throughput. The results also indicate the effect of imperfect SIC on reducing the throughput. Moreover, it is demonstrated that by choosing an optimal SIR threshold for mode selection process, the achievable throughput in such networks can increase by around 5%

Using Synthetic Data to Enhance the Accuracy of Fingerprint-Based Localization: A Deep Learning Approach

Human-centered data collection is typically costly and implicates issues of privacy. Various solutions have been proposed in the literature to reduce this cost, such as crowd-sourced data collection, or the use of semisupervised algorithms. However, semisupervised algorithms require a source of unlabeled data, and crowd-sourcing methods require numbers of active participants. An alternative passive data collection modality is fingerprint-based localization. Such methods use received signal strength or channel state information in wireless sensor networks to localize users in indoor/outdoor environments. In this letter, we introduce a novel approach to reduce training data collection costs in fingerprint-based localization by using synthetic data. Generative adversarial networks (GANs) are used to learn the distribution of a limited sample of collected data and, following this, to produce synthetic data that can be used to augment the real collected data in order to increase overall positioning accuracy. Experimental results on a benchmark dataset show that by applying the proposed method and using a combination of 10% collected data and 90% synthetic data, we can obtain essentially similar positioning accuracy to that which would be obtained by using the full set of collected data. This means that by employing GAN-generated synthetic data, we can use 90% less real data, thereby reducing data-collection costs while achieving acceptable accuracy

Joint Optimization of Power and Location in Full-Duplex UAV Enabled Systems

Unmanned aerial vehicles (UAVs) can be used as aerial base stations (BSs) for future small cells. They can increase the spectral efficiency of the small cells due to their higher probability to have line-of-sight (LOS) connections and their mobility as a BS. In this article, in order to show the effectiveness of using full-duplex (FD) technology in UAV networks, we consider a UAV equipped with FD technology (FD-UAV) with imperfect self-interference cancelation as an aerial BS that serves both uplink (UL) and downlink (DL) users simultaneously in a small cell network. We aim to maximize DL sum-rate, whilst prescribing a certain quality of service for UL users, by optimizing the location of FD-UAV and available resources. The problem is nonconvex; so we propose an iterative method by exploiting the difference of convex functions programming to jointly optimize transmission power of users, FD-UAV location, and FD-UAV transmission power. Simulation results are illustrated to show the effectiveness of the proposed method for FD-UAV in comparison with ground BS, in both FD and half-duplex modes

Enhancing CSI-Based Human Activity Recognition by Edge Detection Techniques

Human Activity Recognition (HAR) has been a popular area of research in the Internet of Things (IoT) and Human–Computer Interaction (HCI) over the past decade. The objective of this field is to detect human activities through numeric or visual representations, and its applications include smart homes and buildings, action prediction, crowd counting, patient rehabilitation, and elderly monitoring. Traditionally, HAR has been performed through vision-based, sensor-based, or radar-based approaches. However, vision-based and sensor-based methods can be intrusive and raise privacy concerns, while radar-based methods require special hardware, making them more expensive. WiFi-based HAR is a cost-effective alternative, where WiFi access points serve as transmitters and users’ smartphones serve as receivers. The HAR in this method is mainly performed using two wireless-channel metrics: Received Signal Strength Indicator (RSSI) and Channel State Information (CSI). CSI provides more stable and comprehensive information about the channel compared to RSSI. In this research, we used a convolutional neural network (CNN) as a classifier and applied edge-detection techniques as a preprocessing phase to improve the quality of activity detection. We used CSI data converted into RGB images and tested our methodology on three available CSI datasets. The results showed that the proposed method achieved better accuracy and faster training times than the simple RGB-represented data. In order to justify the effectiveness of our approach, we repeated the experiment by applying raw CSI data to long short-term memory (LSTM) and Bidirectional LSTM classifiers

Genetic and antigenic analysis of type O and A FMD viruses isolated in Iran,

ABSTRACT FMD is one of the most highly contagious diseases of animals, caused by RNA virus belong to Picornaviridae family and Aphtovirus genus. A broad host range and occurrence of FMDV as seven serotypes and also intratypic antigenic variation without clear cut demarcations, which interferes with a concept of sub typing these factors make difficult conditions to diagnosis, control and eradication of disease. Therefore it is very important to characterize virus strains and monitoring the field virus to determine the relationship between field viruses and vaccine strains. The objective of this study was to characterize FMD type O, A, virus isolated from Iran between 2005 and 2006. 13 FMD type A and 6 type O viruses isolated from Iran between 2005 and 2006 were used in this study. All viruses adapted to IBRS2 cells and the clarified infected cell culture supernatants were used for typing by sandwich capture ELISA and extraction of viral RNA for RT-PCR reaction with the specific primer for each type. The PCR products were purified for sequencing. Sequence of 600 nucleotides at the 3` end of 1D gene of all samples subjected to phylogenic analysis and determine the antigenic relationship ("r" Value). All type A viruses that isolated from different province of Iran, sequenced in this study, were closely related to each other and A/iran/05 virus group. The sequencing results of type O isolated from Iran between 2005 and 2006 showed the close genetic relationship between field isolates and the Iranian vaccine strain. The result of average "r" Value detected by two dimensional virus neutralization test, for type A87IR was 0.46 (46%), type A05IR 0.78 (78%), type O Shabestar 0.81 (81%) and type O967 0.90 (90%)

Sociale veiligheid in de Nederlandse wetenschap: van papier naar praktijk

The politics and administration of institutional chang

Desperately constructing ethnic audiences: Anti-immigration discourses and minority audience research in the Netherlands

This article examines how minority ethnic audiences are measured, and thus constructed, in the Netherlands today. The analysis shows that this process is tightly woven into the dominant assimilationist and neoliberal discourse. This discourse portrays specific minority groups as deviant in relation to an essentialized notion of Dutchness. Furthermore, it presents social inclusion as an opportunity that is limited to well-adjusted, profitable consumers. Different attempts to represent minority audiences – including efforts to promote a more just minority representation in Dutch media – are compelled to accommodate to this dominant discourse. The article underscores the limited scope for contesting current hegemonic representations of minority groups and national belonging in the Netherlands