Parasitic Layer-Based Reconfigurable Antenna and Array For Wireless Applications

Antenna is one of the most important components in wireless systems since signal transmission and reception are conducted through the antenna interface. Therefore, the signal quality is highly affected by the properties of the antenna. Traditional antennas integrated in devices such as laptops or cell phones have fixed radiation properties and can not be changed to adapt to different environments. Thus the performance of thefwhole system will be negatively affected since the antenna will not operate in the optimum status in different environments. To solve this problem, reconfigurable antenna, which can dynamically change its operation frequency, radiation pattern, and polarization, has gained a significant interest recently. Recongurable antennas are considered smart antennas, and can maximize the capacity of the wireless system. This dissertation focuses upon the theoretical analysis and design of smart antennas with recongurable radiation properties. The presented multi-functional reconfigurable antennas (MRAs) are aimed to applications in WLAN (wireless local area network) systems. The theoretical analysis of the MRA was rst investigated to validate the design concept, and then applied for practical applications. The multi-functional recongurable antenna array (MRAA), which is a new class of antenna array, is also created as a linear formation (4 1) of MRA, with theoretical analysis and design of the MRAA fully described. This work developed three MRA(A)s for practical implementation in WLAN systems. The rst design is the MRA operating in 802.11 b/g band (2.4-2.5 GHz), with nine beam steering directions in a parasitic layer-based MRA structure. The second is a MRA operating in 802.11ac band (5.17-5.83 GHz) with three beam steering directions in a simplied parasitic layer-based MRA structure. The third is a MRAA extension of the second design. The design process of these MRA(A)s is realized with the joint utilization of electromagnetic (EM) full-wave analysis and multi-objective genetic algorithm. All three MRA(A) designs have been fabricated and measured. The measured and simulated results agree well for both impedance and radiation characteristics. These prototypes can be directly employed in a WLAN system since practical limits have been taken into account with real switches and components implemented. Finally, this dissertation work concludes with plans for future work, which will focus on development of MRA(A)s with dual-frequency operation

Full-Duplex Systems Using Multi-Reconfigurable Antennas

Full-duplex systems are expected to achieve 100% rate improvement over half-duplex systems if the self-interference signal can be significantly mitigated. In this paper, we propose the first full-duplex system utilizing Multi-Reconfigurable Antenna (MRA) with ?90% rate improvement compared to half-duplex systems. MRA is a dynamically reconfigurable antenna structure, that is capable of changing its properties according to certain input configurations. A comprehensive experimental analysis is conducted to characterize the system performance in typical indoor environments. The experiments are performed using a fabricated MRA that has 4096 configurable radiation patterns. The achieved MRA-based passive self-interference suppression is investigated, with detailed analysis for the MRA training overhead. In addition, a heuristic-based approach is proposed to reduce the MRA training overhead. The results show that at 1% training overhead, a total of 95dB self-interference cancellation is achieved in typical indoor environments. The 95dB self-interference cancellation is experimentally shown to be sufficient for 90% full-duplex rate improvement compared to half-duplex systems.Comment: Submitted to IEEE Transactions on Wireless Communication

Composition of 'fast-slow' traits drives avian community stability over North America

1. Rapid biodiversity loss has triggered decades of research on the relationships between biodiversity and community stability. Recent studies highlighted the importance of species traits for understanding biodiversity-stability relationships. The species with high growth rates ('fast' species) are expected to be less resistant to environmental stress but recover faster if disturbed; in contrast, the species with slow growth rates ('slow' species) can be more resistant but recover more slowly if disturbed. Such a 'fast-slow' trait continuum provides a new perspective for understanding community stability, but its validity has mainly been examined in plant communities. Here, we investigate how 'fast-slow' trait composition, together with species richness and environmental factors, regulate avian community stability at a continental scale. 2. We used bird population records from the North American Breeding Bird Survey during 1988-2017 and defined avian community stability as the temporal invariability of total community biomass. We calculated species richness and the community-weighted mean (CWM) and functional diversity (FD) of four key life-history traits, including body size, nestling period (i.e. period of egg incubation and young bird fledging), life span and clutch size (i.e. annual total number of eggs). Environmental factors included temperature, precipitation and leaf area index (LAI). 3. Our analyses showed that avian community stability was mainly driven by the CWM of the 'fast-slow' trait. Communities dominated by 'fast' species (i.e. species with small body size, short nestling period and life span and large clutch size) were more stable than those dominated by 'slow' species (i.e. species with large body size, long nestling period and life span and small clutch size). Species richness and the FD of the 'fast-slow' trait explained much smaller proportions of variation in avian community stability. Temperature had direct positive effects on avian community stability, while precipitation and leaf area index affected community stability indirectly by influencing species richness and trait composition. 4. Our study demonstrates that composition of 'fast-slow' traits is the major biotic driver of avian community stability over North America. Temperature is the most important abiotic factor, but its effect is weaker than that of the 'fast-slow' trait. An integrated framework combining 'fast-slow' trait composition and temperature is needed to understand the response of avian communities in a changing environment.Peer reviewe

Resource-Efficient Transfer Learning From Speech Foundation Model Using Hierarchical Feature Fusion

Self-supervised pre-training of a speech foundation model, followed by supervised fine-tuning, has shown impressive quality improvements on automatic speech recognition (ASR) tasks. Fine-tuning separate foundation models for many downstream tasks are expensive since the foundation model is usually very big. Parameter-efficient fine-tuning methods (e.g. adapter, sparse update methods) offer an alternative paradigm where a small set of parameters are updated to adapt the foundation model to new tasks. However, these methods still suffer from a high computational memory cost and slow training speed because they require backpropagation through the entire neural network at each step. In the paper, we analyze the performance of features at different layers of a foundation model on the speech recognition task and propose a novel hierarchical feature fusion method for resource-efficient transfer learning from speech foundation models. Experimental results show that the proposed method can achieve better performance on speech recognition task than existing algorithms with fewer number of trainable parameters, less computational memory cost and faster training speed. After combining with Adapters at all layers, the proposed method can achieve the same performance as fine-tuning the whole model with $97\%$ fewer trainable encoder parameters and $53\%$ faster training speed

New Probabilistic Multi-Graph Decomposition Model to Identify Consistent Human Brain Network Modules

Many recent scientific efforts have been devoted to constructing the human connectome using Diffusion Tensor Imaging (DTI) data for understanding large-scale brain networks that underlie higher-level cognition in human. However, suitable network analysis computational tools are still lacking in human brain connectivity research. To address this problem, we propose a novel probabilistic multi-graph decomposition model to identify consistent network modules from the brain connectivity networks of the studied subjects. At first, we propose a new probabilistic graph decomposition model to address the high computational complexity issue in existing stochastic block models. After that, we further extend our new probabilistic graph decomposition model for multiple networks/graphs to identify the shared modules cross multiple brain networks by simultaneously incorporating multiple networks and predicting the hidden block state variables. We also derive an efficient optimization algorithm to solve the proposed objective and estimate the model parameters. We validate our method by analyzing both the weighted fiber connectivity networks constructed from DTI images and the standard human face image clustering benchmark data sets. The promising empirical results demonstrate the superior performance of our proposed method

Functional traits and environment jointly determine the spatial scaling of population stability in North American birds

Understanding the spatial scaling of population stability is critical for informing conservation strategies. A recently proposed metric for quantifying how population stability varies across scales is the invariability-area relationship (IAR), but the underlying drivers shaping IARs remain unclear. Using 15-year records of 249 bird species in 1035 survey transects in North America, we derived the IAR for each species by calculating population temporal invariability at different spatial scales (i.e., number of routes) and investigated how species IARs were influenced by functional traits and environmental factors. We found that species with faster life history traits and reduced flight efficiency had higher IAR intercepts (i.e., locally more stable), whereas migratory species exhibited higher IAR slopes (i.e., a faster gain of stability with increasing spatial scale). In addition, spatial correlation in temperature and vegetation structure synchronized bird population dynamics over space and thus decreased IAR slopes. By demonstrating the joint influence of functional traits and environmental factors on bird population stability across scales, our results highlight the need for dynamic conservation strategies tailored to particular types of species in an era of global environmental changes.Peer reviewe

Habitat Use and Activity Patterns of Mammals and Birds in Relation to Temperature and Vegetation Cover in the Alpine Ecosystem of Southwestern China with Camera-Trapping Monitoring

The high-altitude ecosystem of the Tibetan Plateau in China is a biodiversity hotspot that provides unique habitats for endemic and relict species along an altitudinal gradient at the eastern edge. Acquiring biodiversity information in this area, where the average altitude is over 4000 m, has been difficult but has been aided by recent developments in non-invasive technology, including infrared-triggered camera trapping. We used camera trapping to acquire a substantial number of photographic wildlife records in Wolong National Nature Reserve, Sichuan, China, from 2013 to 2016. We collected information of the habitat surrounding the observation sites, resulting in a dataset covering 37 species and 12 environmental factors. We performed a multivariate statistical analysis to discern the dominant environmental factors and cluster the mammals and birds of the ecosystem in order to examine environmental factors contributing to the species’ relative abundance. Species were generalized into three main types, i.e., cold-resistant, phyllophilic, and thermophilic, according to the identified key environmental drivers (i.e., temperature and vegetation) for their abundances. The mammal species with the highest relative abundance were bharal (Pseudois nayaur), Moupin pika (Ochotona thibetana), and Himalayan marmot (Marmota himalayana). The bird species with highest relative abundance were snow partridge (Lerwa lerwa), plain mountain finch (Leucosticte nemoricola), Chinese monal (Lophophorus lhuysii), and alpine accentor (Prunella collaris)

Advances and Open Problems in Federated Learning

Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data decentralized. FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges

Advances and Open Problems in Federated Learning

Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g. service provider), while keeping the training data decentralized. FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches. Motivated by the explosive growth in FL research, this paper discusses recent advances and presents an extensive collection of open problems and challenges.Comment: Published in Foundations and Trends in Machine Learning Vol 4 Issue 1. See: https://www.nowpublishers.com/article/Details/MAL-08