Minimum Eigenvalue Detection for Spectrum Sensing in Cognitive Radio

Spectrum sensing is a key task for cognitive radio. Our motivation is to increase the probability of detection for spectrum sensing in cognitive radio. In this paper, we proposed a new semi blind method which is based on minimum Eigenvalue of a covariance matrix. The ratio of the minimum eigenvalue to noise power is used as the test statistic. The method does not need channel and signal information as prior knowledge. Eigenvalue based algorithm perform better than energy detection for correlated signal. Our proposed method is better than the maximum eigenvalue and energy detection for correlated signal. We perform Simulation which is based on digital TV signal. In all tests, our method performs better than maximum eigenvalue detection and energy detection.DOI:http://dx.doi.org/10.11591/ijece.v4i4.622

Online monitoring instantaneous 2D temperature distributions in a furnace using acoustic tomography based on frequency division multiplexing

The online and accurate capture of dynamic changes in furnace temperature distribution is crucial for production efficiency improvement and international environmental policy compliance in power plants. To achieve this, a measurement system with a reliable online reconstruction capability and high temporal resolution is necessary. This paper presents a novel technique that can improve the temporal resolution of the currently existing acoustic tomography (AT) system using frequency division multiplexing (FDM). This method allows for concurrent transmissions of acoustic signals in several different frequency bands instead of a sequential manner, which leads to more efficient channel utilization and allows all acoustic signals to be acquired at the same time, so that a better temporal uniformity of multipath acoustic signals can be realized. Theoretical analysis and experiments have been conducted to verify the effectiveness of this technique. The results prove that the proposed method can significantly improve the temporal resolution of the AT system while maintaining the accuracy and robustness of the reconstruction

A stability and spatial-resolution enhanced laser absorption spectroscopy tomographic sensor for complex combustion flame diagnosis

A novel stable laser absorption spectroscopy (LAS) tomographic sensor with enhanced stability and spatial resolution is developed and applied to complex combustion flame diagnosis. The sensor reduces the need for laser collimation and alignment even in extremely harsh environments and improves the stability of the received laser signal. Furthermore, a new miniaturized laser emission module was designed to achieve multi-degree of freedom adjustment. The full optical paths can be sampled by 8 receivers, with such arrangement, the equipment cost can be greatly reduced, at the same time, the spatial resolution is improved. In fact, 100 emitted laser paths are realized in a limited space of 200mm×200 mm with the highest spatial resolution of 1.67mm×1.67 mm. The stability and penetrating spatial resolution of the LAS tomographic sensor were validated by both simulation and field experiments on the afterburner flames. Tests under two representative experiment states, i.e., the main combustion and the afterburner operation states, were conducted. Results show that the error under the main combustion state was about 4.32% and, 5.38% at the afterburner operation state. It has been proven that this proposed sensor can provide better tomographic measurements for combustion diagnosis, as an effective tool for improving performances of afterburners

Protected agriculture matters: Year-round persistence of Tuta absoluta in China where it should not

Tuta absoluta (Lepidoptera: Gelechiidae) originates from the South American tropics but has become a major invasive pest of tomato and other Solanaceae crops worldwide. Agricultural protected facilities (APFs) such as greenhouses and plastic tunnels may provide thermal conditions that allow the survival of T. absoluta in temperate zones with cold winters. In this study, a CLIMEX model was used to investigate the dual effects of increasing use of APFs and climate warming on the potential distribution and seasonal dynamics of T. absoluta in China. Our model showed that the northern boundary for year-round population persistence in China, ignoring APFs, was approximately 30°N, covering about 21% of China’s area suitable under current climate. The modelled suitable area increased to 31% and northern boundary for year-round population persistence shifted to 40°N in 2080 under global warming. When APF refuges are included, the potential suitable area was 78% under the current climate and 79% under global warming. This suggests that, in the future, the increasing use of APFs will increase the areas at risk of T. absoluta invasion significantly more than global warming because APFs effectively protect T. absoluta from harsh northern winters. In addition, vegetable production in surrounding open fields will be at risk of invasion during milder seasons when APFs are opened and T. absoluta can disperse. Therefore, the micro-climate of APFs should be considered as part of the invasion process, and Integrated Pest Management should be simultaneously implemented inside and outside APFs for the rational management T. absoluta.This work was supported by National Key R&D program of China (2021YFD1400200). CERCA Program / Generalitat de Catalunya provided funding to JA, and ND was funded in part by the Horizon Europe project ADOPT-IPM (n◦101060430).info:eu-repo/semantics/publishedVersio

Robust and efficient aggregate query processing in wireless sensor networks

Wireless sensor networks have been widely used in many applications, such as soil temperature monitoring for plant growth and abnormal event detection of industrial parameters. Among these applications, aggregate queries, such as SUM, COUNT, AVERAGE, MIN and MAX are often used to collect statistical data. Due to the low quality sensing devices or random environmental disturbances, sensor data are often noisy. Hence, the idea of moving average, which computes the average over consecutive aggregate data, is introduced to offset the effect. The high link loss rate, however, makes the result after averaging still inaccurate. To address this issue, we propose a PCM-based data transmission scheme to "make up" the possibly lost data. Specifically, we focus on obtaining robust aggregate results under high link loss rate. In order to reduce the communication traffic that dominates the energy consumption of the sensor network, we also design an intelligent path selection algorithm for our scheme. Our extensive simulation results have shown that this technique outperforms its counterparts under various sensor network conditions

A Geography-free Routing Protocol for Wireless Sensor Networks

Abstract--- In wireless sensor networks, it is critical to provide the data delivery services between sensors and the data collection unit (called sink). Some of the existing approaches require location or ID information which is quite expensive. For those without location or ID information, the usage of the collected information is rather limited. In this paper, we propose a geography-free coordinate system, called GREENWIS, to assist routing. In GREENWIS, sensors are identified by a 4-tuple with which messages are universally transmitted. Our analysis and experiments show that GREENWIS is reliable and power efficient compared with existing approaches. I