Convolutional neural networks (CNNs) have been extensively employed in remote sensing image detection and have exhibited impressive performance over the past few years. However, the abovementioned networks are generally limited by their complex structures, which make them difficult to deploy with power-sensitive and resource-constrained remote sensing edge devices. To tackle this problem, this study proposes a lightweight remote sensing detection network suitable for edge devices and an energy-efficient CNN accelerator based on field-programmable gate arrays (FPGAs). First, a series of network weight reduction and optimization methods are proposed to reduce the size of the network and the difficulty of hardware deployment. Second, a high-energy-efficiency CNN accelerator is developed. The accelerator employs a reconfigurable and efficient convolutional processing engine to perform CNN computations, and hardware optimization was performed for the proposed network structure. The experimental results obtained with the Xilinx ZYNQ Z7020 show that the network achieved higher accuracy with a smaller size, and the CNN accelerator for the proposed network exhibited a throughput of 29.53 GOPS and power consumption of only 2.98 W while consuming only 113 DSPs. In comparison with relevant work, DSP efficiency at an identical level of energy consumption was increased by 1.1–2.5 times, confirming the superiority of the proposed solution and its potential for deployment with remote sensing edge devices.

The development process of a glass-fabric composite wind turbine blade is presented. A finite element model is constructed for the design and stress analysis of the wind blade. The wind blade parts were fabricated using the vacuum-bag molding technique. The assembling process of the wind blade is described in detail. The wind blade was tested to validate the suitability of the design. The finite element method is used to predict the failure wind loads of the wind blade.

A WSN enabled Home Energy Management System (HEMS) can achieve user behaviour change by providing real time feedback of domestic energy consumption. However, features in collecting nodes within HEMS Systems need to change to allow new working patterns to be established which adapt to ad-hoc demands for energy data retrieval, e.g. different acquisition frequencies, and light-weight quality control of retrieved data. In this paper, a code dissemination protocol, called EECD (Excellence Estimation Code Dissemination) is presented to implement remote reprogramming of WSN nodes, without any manual replacement of them. Compared to Deluge, the default wireless reprogramming protocol in the TinyOS, the EECD can obtain a performance improvement of 24% in terms of completion time.

This paper presents a tracking algorithm for joint estimation of direction of arrival (DOA) and polarization parameters, which exhibit dynamic behavior due to the movement of signal source carriers. The proposed algorithm addresses the challenge of real-time estimation in multi-target scenarios with an unknown number. This algorithm is built upon the Multi-target Multi-Bernoulli (MeMBer) filter algorithm, which makes use of a sensor array called Circular Orthogonal Double-Dipole (CODD). The algorithm begins by constructing a Minimum Description Length (MDL) principle, taking advantage of the characteristics of the polarization-sensitive array. This allows for adaptive estimation of the number of signal sources and facilitates the separation of the noise subspace. Subsequently, the joint parameter Multiple Signal Classification (MUSIC) spatial spectrum function is employed as the pseudo-likelihood function, overcoming the limitations imposed by unknown prior information constraints. To approximate the posterior distribution of MeMBer filters, Sequential Monte Carlo (SMC) method is utilized. The simulation results demonstrate that the proposed algorithm achieves excellent tracking accuracy in joint DOA-polarization parameter estimation, whether in scenarios with known or unknown numbers of signal sources. Moreover, the algorithm demonstrates robust tracking convergence even under low Signal-to-Noise Ratio (SNR) conditions.