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The increasing number of Internet of things (IoT) objects has been a growing challenge of the current spectrum supply. To handle this issue, the IoT devices should have cognitive capabilities to access the unoccupied portion of the wideband spectrum. However, most IoT devices are difficult to perform wideband spectrum sensing using either conventional Nyquist sampling system or sub-Nyquist sampling system since both the power-hungry sampling components and intricate sub-Nyquist sampling hardware are unrealistic in the power-constrained IoT paradigm. In this paper, we propose a blind joint sub-Nyquist sensing scheme by utilizing the surround IoT devices to jointly sample the spectrum based on the multi-coset sampling theory. Thus, only the off-the-shelf low-rate analog-to-digital converters (ADCs) on the IoT devices are required to form coset samplers and only the minimum number of coset samplers are adopted without the prior knowledge of the number of occupied channels and signal-to-noise ratios. Moreover, to further reduce the number of coset samplers and transfer part of the computational burden from the IoT devices to the core network, we adopt the data from geo-location database when applicable. The experimental results on both the simulated and real-world signals verify the theoretical results and the effectiveness of the proposed scheme. At the meanwhile, it is shown that the adaptive number of coset samplers could be adopted without causing the degradation of the detection performance and the number of coset samplers could be further reduced with the assists from geolocation database even when the obtained information is partially correct.

A previously unidentified chicken parvovirus (ChPV) and turkey parvovirus (TuPV) strain, associated with runting-stunting syndrome (RSS) and poultry enteritis and mortality syndrome (PEMS) in turkeys, is now prevalent among chickens in China. In this study, a large-scale surveillance of parvoviruses in chickens and turkeys using conserved PCR assays was performed. We assessed the prevalence of ChPV/TuPV in commercial chicken and turkey farms in China between 2014 and 2019. Parvoviruses were prevalent in 51.73% (1,795/3,470) of commercial chicken and turkey farms in Guangxi, China. The highest frequency of ChPV positive samples tested by PCR occurred in chickens that were broiler chickens 64.18% (1,041/1,622) compared with breeder chickens 38.75% (572/1,476) and layer hens 38.89% (112/288), and TuPV was detected in 70/84 (83.33%). Native and exotic chicken species were both prevalent in commercial farms in southern China, and exotic broiler chickens had a higher positive rate with 88.10% (148/168), while native chickens were 50.00% (1,465/2,930). The environmental samples from poultry houses tested positive for ChPV and TuPV were 47.05% (415/874). Samples from open house flocks had higher prevalence rates of ChPV than those of closed house flocks ( Table 5 ), among which those from the open house showed 84.16% (85/101) positivity, those from litter showed 62.86% (44/70) positivity, and those from drinking water showed 50.00% (56/112) positivity, whereas those from the closed house litter were 53.57% (60/112), those from swabs were 50.18% (138/275), and those from drinking water were 15.69% (32/204). Samples collected during spring were more frequently ChPV/ TuPV positive than those collected during other seasons. This study is the first report regarding the epidemiological surveillance of ChPV and TuPV in chicken/turkey flocks in Guangxi, China. Our results suggest that ChPV and TuPV are widely distributed in commercial fowl in Guangxi. These findings highlight the need for further epidemiological and genetic research on ChPV and TuPV in this area.

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