Smart agriculture is an enabling technology addressing the increasing challenges of efficiency, sustainability, and quality of food production. It requires rich data from the farming area at high spatial and temporal resolution. Although remote sensing systems have become readily available recently, in-situ sensing is still required to capture important properties of soil, crops, and their close environment. Ubiquitous sensor networks (USNs) provide a seamless and real-time in-situ sensing infrastructure that could overcome some limitations of smart agriculture. Resource efficiency is essential for USNs due to (1) the expected long operation time in typically resource-constrained environments, (2) the vast amount of captured and processed data, and (3) the ever-increasing application requirements. This survey comprehensively analyzes resource optimization techniques for USNs along three USN layers: the sensing, the communication & connectivity, and the processing & analysis layer. It discusses the application of these techniques in the smart agriculture domain and identifies current challenges and open research issues.

Stable grasps are essential for robots handling objects. This is especially true for “robotized” large industrial machines as heavy and bulky objects that are unintentionally dropped by the machine can lead to substantial damages and pose a significant safety risk. Consequently, adding a proximity and tactile sensing to such large industrial machinery can help to mitigate this problem. In this paper, we present a sensing system for proximity/tactile sensing in gripper claws of a forestry crane. In order to avoid difficulties with respect to the installation of cables (in particular in retrofitting of existing machinery), the sensors are truly wireless and can be powered using energy harvesting, leading to autarkic, i.e., self-contained, sensors. The sensing elements are connected to a measurement system which transmits the measurement data to the crane automation computer via Bluetooth low energy (BLE) compliant to IEEE 1451.0 (TEDs) specification for eased logical system integration. We demonstrate that the sensor system can be fully integrated in the grasper and that it can withstand the challenging environmental conditions. We present experimental evaluation of detection in various grasping scenarios such as grasping at an angle, corner grasping, improper closure of the gripper and proper grasp for logs of three different sizes. Results indicate the ability to detect and differentiate between good and poor grasping configurations.

Uncertainty evaluation plays a key role in assessing and comparing measurement results, e.g. towards the design of experiments and the design of measurement systems. Nevertheless, it is frequently neglected by engineers particularly in early design phases, which may cause problems during the design process or even lead to system failures. In prior work, the use of software tools for uncertainty calculation in measurement science education has been suggested in order to raise awareness and increase the use of such tools to obtain GUM conform uncertainty estimates. However, we found that uncertainty of complex-valued quantities represents a major challenge in this context. Therefore, we present a proposal to extend the teaching concept towards such quantities, based directly on the utilization of an "Uncertainty Toolbox" for MATLAB maintained by our research group. The approach evaluates the uncertainty in complex parameters considering the real and imaginary components separately, with potential correlations between them arising from shared input quantities. As teaching example we study the Maxwell-Wien Bridge, as it is commonly taught in measurement science courses and brings in aspects such as above mention correlations and different representations of the measurement result (magnitude/phase, real/complex part of impedance). Based on this example, advantages and disadvantages of the presented teaching philosophy are discussed, emphasizing how problems arising from uncertainty may be identified in early design phases also considering complex-valued quantities.

The reconstruction of frequency-sparse signals from a low number of samples using a compressed sensing concept is a widely studied problem and solutions based on greedy and relaxation methods are available. However, their performance is degraded in presence of spectral leakage, which affects the sparsity of the signal representation and consequently, its estimation accuracy. In this paper a two-stage optimization approach, called Opti2, is proposed for the reconstruction of periodic signals that can be expressed in terms of fundamental frequency and harmonics. The estimation provided by basis pursuit denoising (BPDN) sparse optimization approach is computed in the first stage and used as initial guess for the second stage, where a constrained non-linear optimization problem is solved in an iterative fashion, aiming to improve the estimation of the signal parameters. The evaluation of the proposed method with simulated and experimental data demonstrates that it outperforms existing approaches in term of accuracy, showing its robustness to noise, compression rate and dictionary refinement factor.