JournalTOCs

Journal of Sensor Technology
Number of Followers: 2

This is an Open Access Journal

Open Access journal
ISSN (Print) 2161-122X - ISSN (Online) 2161-1238
Published by Scientific Research Publishing

[243 journals]

IEEE Journal on Emerging and Selected Topics in Circuits and Systems
publication information
- Pages: C2 - C2
  Abstract: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
  PubDate: Sept. 2021
  Issue No: Vol. 11, No. 3 (2021)
IEEE Circuits and Systems Society Information
- Pages: C3 - C3
  Abstract: Provides a listing of current committee members and society officers.
  PubDate: Sept. 2021
  Issue No: Vol. 11, No. 3 (2021)
[Blank page]
- Pages: C4 - C4
  Abstract: This page or pages intentionally left blank.
  PubDate: Sept. 2021
  Issue No: Vol. 11, No. 3 (2021)
Guest Editorial Circuits and Systems for Smart Agriculture and Healthy
Foods
- Authors: Danilo Demarchi;Julius Georgiou;Victor Grimblatt;Yosi Shacham-Diamand;
  Pages: 431 - 434
  Abstract: This Special Issue of the IEEE Journal on Emerging and Selected Topics in Circuits and Systems (JETCAS) is dedicated to circuits and systems applied to innovative products for the agriculture and food value chain.
  PubDate: Sept. 2021
  Issue No: Vol. 11, No. 3 (2021)
How to Feed a Growing Population—An IoT Approach to Crop Health and
Growth
- Authors: Victor Grimblatt;Christophe Jégo;Guillaume Ferré;Francois Rivet;
  Pages: 435 - 448
  Abstract: Humankind consumes the planet’s resources beyond what the Earth can provide with estimations according 9.2 billion human beings in 2050 consuming yearly almost twice the resources the Earth can provide. Crop production needs to increase by 70% to be able to feed all the population. A convenient way to address this issue is to produce efficiently respecting the planet. Non-invasive technology at a low cost is a solution to monitor crop growth and health, helping small to medium farmers to increase their productivity keeping in mind the conservation of the planet. Circuits and Systems offer large range of possibilities. We propose to investigate IoT with medium cost sensors to deliver real time, affordable and precise data processing for agriculture producing an Automated Decision-Making Systems (ADMS). This paper is a detailed analysis of plant parameters, sensors and their compatibility with affordable ADMS based IoT systems. Some experimentation of critical parameters show their impacts for plant growth and health. An overview of an IoT system under development is introduced.
  PubDate: Sept. 2021
  Issue No: Vol. 11, No. 3 (2021)
A Versatile, Stand-Alone, In-Field Sensor Node for Implementation in
Precision Agriculture
- Authors: Marios Sophocleous;Andreas Karkotis;Julius Georgiou;
  Pages: 449 - 457
  Abstract: The concept of Precision Agriculture has been conceived several decades ago however, it has never been fully implemented due to several technological constraints, such as the high cost of automated systems. In this paper, a versatile, low-cost and stand-alone sensing node with unique capabilities is presented, suitable for direct soil quality monitoring. The node is equipped with a potentiostat to run electrochemical measurements as well as a circuit for potentiometric measurements, a circuit to accommodate a wide variety of resistance thermometers and a circuit to detect impedance at 1 kHz. All analog circuits feature accuracies of less than 5% with linearity of more than R2 = 0.999. The system is battery-powered and can recharge through a solar panel or a power supply. Bluetooth, WiFi and LoRa communication technologies are supported making the system IoT compatible whilst maintaining a low cost. The designed instrumentation is versatile enough so that it can accommodate other sensors operating under the same principles with different sensitivities, as well as commercial sensors communicating through SPI and I2C protocols. All the modifications to the system can be done through software during sensor calibration.
  PubDate: Sept. 2021
  Issue No: Vol. 11, No. 3 (2021)
Automated Pest Detection With DNN on the Edge for Precision Agriculture
- Authors: Andrea Albanese;Matteo Nardello;Davide Brunelli;
  Pages: 458 - 467
  Abstract: Artificial intelligence has smoothly penetrated several economic activities, especially monitoring and control applications, including the agriculture sector. However, research efforts toward low-power sensing devices with fully functional machine learning (ML) on-board are still fragmented and limited in smart farming. Biotic stress is one of the primary causes of crop yield reduction. With the development of deep learning in computer vision technology, autonomous detection of pest infestation through images has become an important research direction for timely crop disease diagnosis. This paper presents an embedded system enhanced with ML functionalities, ensuring continuous detection of pest infestation inside fruit orchards. The embedded solution is based on a low-power embedded sensing system along with a Neural Accelerator able to capture and process images inside common pheromone-based traps. Three different ML algorithms have been trained and deployed, highlighting the capabilities of the platform. Moreover, the proposed approach guarantees an extended battery life thanks to the integration of energy harvesting functionalities. Results show how it is possible to automate the task of pest infestation for unlimited time without the farmer’s intervention.
  PubDate: Sept. 2021
  Issue No: Vol. 11, No. 3 (2021)
A Low-Cost, Low-Power and Real-Time Image Detector for Grape Leaf Esca
Disease Based on a Compressed CNN
- Authors: Laura Falaschetti;Lorenzo Manoni;Romel Calero Fuentes Rivera;Danilo Pau;Gianfranco Romanazzi;Oriana Silvestroni;Valeria Tomaselli;Claudio Turchetti;
  Pages: 468 - 481
  Abstract: Esca is one of the most common grape leaf diseases that seriously affect grape yield, causing a loss of global production in the range of 20%–40%. Therefore, a timely and effective identification of the disease could help to develop an early treatment approach to control its spread while reducing economic losses. For this purpose the use of computer vision and machine learning techniques for recognizing plant diseases have been extensively studied in recent years. The aim of this paper is to propose an image detector based on a high-performance convolutional neural network (CNN) implemented in a low cost, low power platform, to monitor the Esca disease in real-time. To meet the severe constraints typical of an embedded system, a new low-rank CNN architecture (LR-Net) based on CANDECOMP/PARAFAC (CP) tensor decomposition has been developed. The compressed CNN network so obtained has been trained on a specific dataset and implemented in a low-power, low-cost Python programmable machine vision camera for real-time classification. An extensive experimentation has been conducted and the results achieved show the superiority of LR-Net with respect to the state-of-the-art networks both in terms of inference time and memory occupancy.
  PubDate: Sept. 2021
  Issue No: Vol. 11, No. 3 (2021)
On the Interpretation of Four Point Impedance Spectroscopy of Plant
Dehydration Monitoring
- Authors: Lee Bar-On;Yosi Shacham-Diamand;
  Pages: 482 - 492
  Abstract: In this paper, we present a study of in-vivo four-point impedance spectroscopy (FPIS) for plant monitoring. FPIS was measured vs. time under hydration/dehydration cycles in tobacco plants. The plants were set up in a gravimetric system and grown in sand for better control of water drainage. Data was gathered at fixed time intervals, every few minutes, collecting the complex impedance values at a few hundred frequencies in the 40 Hz to 2MHz range. Due to the large amount of the collected data, the issues of data representation and interpretation became a challenge. These two issues are discussed examining system orientated approaches, along with possible methods representing and interpreting the data. The results of the electrical measurements and the various analysis methods were compared to those of the well-established agriculture gravimetry measurements. The FPIS results show that the impedance depends on the plant status, similar to the value expressed in the gravimetry system, thus the plant status can be interpreted from the electrical measurements. Each of the data interpretation methods examines the change and is suggested for further device development.
  PubDate: Sept. 2021
  Issue No: Vol. 11, No. 3 (2021)
LoRaWAN ESL for Food Retail and Logistics
- Authors: Alfredo Arnaud;Martin Marioni;Mario Ortiz;Gisselle Vogel;Matías R. Miguez;
  Pages: 493 - 502
  Abstract: Electronic shelf labels (ESL, or electronic price tag) are used by retailers for displaying product pricing on shelves substituting printed price tags; each ESL can display not only the price, but also text, and images, and can be remotely updated, configured, and programmed from a central server. Additionally, the emerging IoT paradigm, allows to expand the application horizon. Firstly, this work presents an analysis on how the use of Cloud-connected, intelligent, ESLs can improve food logistics in the complex scenarios of medium and large cities. Then, a low-cost ESL device, using an electronic ink (e-ink) display and the LoRaWAN protocol to communicate with the central server, aimed at price display and innovative applications in food retail and logistics, is presented. Several current consumption and communication distance measurements are shown, and the design and configuration space are explored to optimize battery life and communication reliability in the different food distribution scenarios. The proposed ESL is powered with a 500mA $cdot text{h}$ LiMnO2 battery that can last for >8 years in a typical application, considering it communicates with a central server once per hour. The developed price tag can dynamically update the price and information for the customer, which in the case of food can be used to minimize spoilage and can easily upgraded to incorporate sensors or smartphone interaction to develop innovative food logistics and marketing practices. Finally, a LoRaWAN time-slot allocation strategy is presented, to minimize collisions when thousand nodes are connected to a single gateway, with a minimum power consumption overhead.
  PubDate: Sept. 2021
  Issue No: Vol. 11, No. 3 (2021)
Machine-Learning-Based Microwave Sensing: A Case Study for the Food
Industry
- Authors: Marco Ricci;Bernardita Štitić;Luca Urbinati;Giuseppe Di Guglielmo;Jorge A. Tobón Vasquez;Luca P. Carloni;Francesca Vipiana;Mario R. Casu;
  Pages: 503 - 514
  Abstract: Despite the meticulous attention of food industries to prevent hazards in packaged goods, some contaminants may still elude the controls. Indeed, standard methods, like X-rays, metal detectors and near-infrared imaging, cannot detect low-density materials. Microwave sensing is an alternative method that, combined with machine learning classifiers, can tackle these deficiencies. In this paper we present a design methodology applied to a case study in the food sector. Specifically, we offer a complete flow from microwave dataset acquisition to deployment of the classifiers on real-time hardware and we show the effectiveness of this method in terms of detection accuracy. In the case study, we apply the machine-learning based microwave sensing approach to the case of food jars flowing at high speed on a conveyor belt. First, we collected a dataset from hazelnut-cocoa spread jars which were uncontaminated or contaminated with various intrusions, including low-density plastics. Then, we performed a design space exploration to choose the best MLPs as binary classifiers, which resulted to be exceptionally accurate. Finally, we selected the two most light-weight models for implementation on both an ARM-based CPU and an FPGA SoC, to cover a wide range of possible latency requirements, from loose to strict, to detect contaminants in real-time. The proposed design flow facilitates the design of the FPGA accelerator that might be required to meet the timing requirements by using a high-level approach, which might be suited for the microwave domain experts without specific digital hardware skills.
  PubDate: Sept. 2021
  Issue No: Vol. 11, No. 3 (2021)
Non-Destructive Assessment of Kiwifruit Flesh Firmness by a Contactless
Waveguide Device and Multivariate Regression Analyses
- Authors: Annachiara Berardinelli;Eleonora Iaccheri;Leonardo Franceschelli;Marco Tartagni;Luigi Ragni;
  Pages: 515 - 522
  Abstract: Non-destructive and cheap methods to evaluate the slow ripening process with possible on-line applications are highly required by the industry to enhance critical post-harvest management. After a brief review of the literature, we present the potentiality of an electronic contactless device for the non-destructive assessment of the Magness-Taylor flesh firmness (Mtf) of Hayward kiwifruits. The technique combines spectral information acquired in the microwave range by an open-ended aluminum waveguide containing TX and RX antennas, placed above the sample, with the features of the multivariate analysis. The electronic controller comprises a VCO, a low noise amplifier, a gain-phase comparator, and a serial interface governed by an MCU. Partial Least Squares regression analysis (PLS) was used to build predictive models starting from gain and phase waveforms raw data in the 947-1900 MHz frequency range. The main results evidenced that explored spectra variability is related to changes occurring in the fruit during the maturity process and particularly to the cell wall degradation. PLS regression models show, in prediction, R2 values of 0.726 (RMSE = 5 N) for the estimation of the Mtf starting from gain waveforms. A lower accuracy was observed for the model setup by considering phase waveforms. These results demonstrate that the proposed non-invasive solution combined with the PLS is a grounded starting point for estimating kiwifruit firmness with an acceptable level of accuracy.
  PubDate: Sept. 2021
  Issue No: Vol. 11, No. 3 (2021)
Introducing IEEE Collabratec
- Pages: 523 - 523
  Abstract: Advertisement, IEEE. IEEE Collabratec is a new, integrated online community where IEEE members, researchers, authors, and technology professionals with similar fields of interest can network and collaborate, as well as create and manage content. Featuring a suite of powerful online networking and collaboration tools, IEEE Collabratec allows you to connect according to geographic location, technical interests, or career pursuits. You can also create and share a professional identity that showcases key accomplishments and participate in groups focused around mutual interests, actively learning from and contributing to knowledgeable communities. All in one place! Learn about IEEE Collabratec at ieeecollabratec.org.
  PubDate: Sept. 2021
  Issue No: Vol. 11, No. 3 (2021)
TechRxiv: Share Your Preprint Research with the World!
- Pages: 524 - 524
  Abstract: Advertisement: TechRxiv is a free preprint server for unpublished research in electrical engineering, computer science, and related technology. TechRxiv provides researchers the opportunity to share early results of their work ahead of formal peer review and publication. Benefits: Rapidly disseminate your research findings; Gather feedback from fellow researchers; Find potential collaborators in the scientific community; Establish the precedence of a discovery; and Document research results in advance of publication. Upload your unpublished research today!
  PubDate: Sept. 2021
  Issue No: Vol. 11, No. 3 (2021)
IEEE Access
- Pages: 525 - 525
  Abstract: Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.
  PubDate: Sept. 2021
  Issue No: Vol. 11, No. 3 (2021)
IEEE Journal on Emerging and Selected Topics in Circuits and Systems
information for authors
- Pages: 526 - 526
  Abstract: These instructions give guidelines for preparing papers for this publication. Presents information for authors publishing in this journal.
  PubDate: Sept. 2021
  Issue No: Vol. 11, No. 3 (2021)

HOME > Browse the 3359 Publishers covered by JournalTOCs	1 2 3 4 5 6 7 8
Publisher	Total Journals
	1 2 3 4 5 6 7 8