The GPUs that have been used by these previous studies are summarized in Table 1.Table 1.Comparison of GPUs view more which have been used for GPS SDRs in the literature. The specifications of GPUs are selected from Table A-1 of [36].Our GPS SDR for CRPA presented in this paper has 12 beamsteering channels for all-in-view satellite tracking (Figure 1), and it processes IF-sampled data at a 40 Msps rate (20 Msps inphase and quadrature samples) which enables future GPS L5 signal tracking whose bandwidth is ten times wider than the L1 C/A-code bandwidth. Further, our Inhibitors,Modulators,Libraries receiver processes digitized samples with 14-bit resolution. Although most commercial GPS receivers process 2-bit resolution data, more dynamic range is required for anti-jamming applications.
As a very Inhibitors,Modulators,Libraries rough comparison, the GPU-based SDR in [34] effectively processes 750 Msps (5 Msps data and 150 tracking channels), but our GPU-based SDR for CRPA effectively processes 2,400 Msps (40 Msps data and 60 tracking channels because 12 channels for each antenna element of a four-element array and additional 12 channels for beamsteering). In addition, our receiver calculates signal covariance matrix for adaptive interference rejection which requires additional computational Inhibitors,Modulators,Libraries power. To the authors�� knowledge, a real-time capable GPS SDR for CRPA with comparable functionalities on general-purpose CPU and GPU has not been previously demonstrated.This paper is organized as follows: Section 2 presents our GPS SDR architecture and discusses the computational challenges in detail.
Our SDR design with a real-time phase calibration feature considers a GPS reference station receiver under RFI. Since a CRPA array is more
Wireless sensor networks have various applications that include habit monitoring [1], wildfire monitoring Inhibitors,Modulators,Libraries [2], and navigation [3,4]. In today��s life, due to expanding social activities, people require reliable lighting during all hours of the day and night. Due to the limitations and rising cost of electrical energy production, it is becoming increasingly important to direct greater efforts into optimizing electrical energy utilization. Recently, wireless sensor networks have been applied to energy conservation applications such as light control [5�C10]. The logic of lighting control systems may include factors such as daylight intensity, which is measured by light-sensitive sensors [7].
In [6] the authors defined several user requirements and cost functions. Their goal was to adjust lights to minimize the total cost of energy supplied. GSK-3 However, the result was applied to entertainment and media production systems. selleck chem In [10], a tradeoff between energy consumption and users�� satisfaction in light control was studied. The authors applied utility functions which considered users�� location and lighting preferences so that illumination could be adjusted as to maximize the total utilities.