Assessment of Smart Mechatronics Applications in Agriculture: A Review

Smart mechatronics systems in agriculture can be traced back to the mid-1980s, when research into automated fruit harvesting systems began in Japan, Europe, and the United States. Impressive advances have been made since then in developing systems for use in modern agriculture. The aim of this study was to review smart mechatronics applications introduced in agriculture to date, and the different areas of the sector in which they are being employed. Various literature search approaches were used to obtain an overview of the current state-of-the-art, benefits, and drawbacks of smart mechatronics systems. Smart mechatronics modules and various networks applied in the processing of agricultural products were examined. Finally, relationships in the data retrieved were tested using a one-way analysis of variance on keywords and sources. The review revealed limited use of sophisticated mechatronics in the agricultural industry in practice at a time of falling production rates and a dramatic decline in the reliability of the global food supply. Smart mechatronics systems could be used in different agricultural enterprises to overcome these issues

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Theory, Design and Implementation of Energy-Efficient Biotelemetry using Ultrasound Imaging

This dissertation investigates the fundamental limits of energy dissipation in establishing a communication link with implantable medical devices using ultrasound imaging-based biotelemetry. Ultrasound imaging technology has undergone a revolution during the last decade due to two primary innovations: advances in ultrasonic transducers that can operate over a broad range of frequencies and progresses in high-speed, high-resolution analog-to-digital converters and signal processors. Existing clinical and FDA approved bench-top ultrasound systems cangenerate real-time high-resolution images at frame rates as high as 10000 frames per second. On the other end of the spectrum, portable and hand-held ultrasound systems can generate high-speed real-time scans, widely used for diagnostic imaging in non-clinical environments. This dissertation’s fundamental hypothesis is to leverage the massive data acquisition and computational bandwidth afforded on these devices to establish energy-efficient bio-telemetry links with multiple in-vivo implanted devices. In the first part of the dissertation, I investigate using a commercial off-the-shelf (COTS) diagnostic ultrasound reader to achieve reliable in-vivo wireless telemetry with millimeter-sized piezoelectric crystal transducers. I propose multi-access biotelemetry methods in which several of these crystals simultaneously transmit the data using conventional modulation and coding schemes. I validated the feasibility of in-vivo operation using two piezoelectric crystals tethered to the tricuspid valve and the skin’s surface in a live ovine model. I demonstrated data rates close to 800 Kbps while consuming microwatts of power even in the presence of respiratory and cardiac motion artifacts. In the second part of the dissertation, I investigate the feasibility of energy harvesting from cardiac valvular perturbations to self-power the wireless implantable device. In this study, I explored using piezoelectric sutures implanted in proximity to the valvular regions compared to the previous studies involving piezoelectric patches or encasings attached to the cardiac or aortic surface to exploit nonlinearity in the valvular dynamics and self-power the implanted device. My study shows that power harvested from different annular planes of the tricuspid valve could range from nano-watts to milli-watts. In the final part of this dissertation, I investigate beamforming in B-scan ultrasound imaging to further reduce the biotelemetry energy-budget. In this context, I will study variance-based informatics in which the signal representation takes a form of signal variance instead of the signal mean for encoding and decoding. Using a modeling study, I show that compared to the mean-based logic representation, the variance-based representation can theoretically achieve a superior performance trade-off (in terms of energy dissipation) when operating at fundamental limits imposed by thermal-noise. I will then discuss how to extend variance-based representation to higher signal dimensions. I show that when applying variance-based encoding/decoding to B-scan biotelemetry, the power-dissipation requirements can be reducedto 100 pW even while interrogating from depths greater than 10 cm in a water medium