1. “Comparison of Time Delay Estimation Methods Used for Fast Pipeline Leak Localization in High-Noise Environment”
by Kousiopoulos, G.-P.; Papastavrou, G.-N.; Kampelopoulos, D.; Karagiorgos, N. and Nikolaidis, S.
Technologies 2020, 8(2), 27; https://doi.org/10.3390/technologies8020027
Available online: https://www.mdpi.com/2227-7080/8/2/27

Highlights:

• An acoustic method is described for pipeline leak localization;
• Cross-Correlation and Generalized Cross-Correlation techniques are used;
• Accelerometer sensors are used;
• The paper studies the relationship between the measurement duration and execution time of the method;
• The authors develop a statistical algorithm to deal with the stochastic (non-deterministic) nature of leak signals.

2. “Analog Realization of Fractional-Order Skin-Electrode Model for Tetrapolar Bio-Impedance Measurements”
by Alimisis, V.; Dimas, C.; Pappas, G. and Sotiriadis, P. P.
Technologies 2020, 8(4), 61; https://doi.org/10.3390/technologies8040061
Available online: https://www.mdpi.com/2227-7080/8/4/61

Highlights:

• Cole models are employed for the electrode and skin cells;
• Inverse Follow the Leader Feedback (IFLF) is employed and a custom versatile topology proposed;
• Bio-impedance behaviors up to 10 kHz are captured;
• A tunable active integrated circuitry block (90 nm technology) is built to emulate the tetrapolar bio-impedance measurement setup.

3. “The Influence of Smart Manufacturing towards Energy Conservation: A Review”
by Terry, S.; Lu, H.; Fidan, I.; Zhang, Y.; Tantawi, K.; Guo, T. and Asiabanpour, B.
Technologies 2020, 8(2), 31; https://doi.org/10.3390/technologies8020031
Available online: https://www.mdpi.com/2227-7080/8/2/31

Highlights:

• Smart Manufacturing combines multiple technologies, including Cyber-Physical Systems, Internet of Things, Robotics/Automation, Big Data Analytics, and Cloud Computing;
• Today's products made using Smart Manufacturing Technologies require 50% to 75% less energy than large-scale manufactured goods;
• Digital thread/twin technology provides manufacturers with the information needed to form intelligent solutions to reduce energy use.

4. “ExerTrack—Towards Smart Surfaces to Track Exercises”
by Fu, B.; Jarms, L.; Kirchbuchner, F. and Kuijper, A.
Technologies 2020, 8(1), 17; https://doi.org/10.3390/technologies8010017
Available online: https://www.mdpi.com/2227-7080/8/1/17

Highlights:

• The paper proposes embedded capacitive proximity sensing integrated into a fitness mat to track and monitor exercise, which successfully recognize up to eight different whole-body workout exercises;
• An evaluation study involving nine participants is conducted as a proof-of-concept. Data augmentation methods are introduced that can increase data diversity and broader generalizability for human-activity recognition in quantified-self exercises, and the authors further justify the model architecture and training strategy for reproducibility.

5. “Design, Construction and Tests of a Low-Cost Myoelectric Thumb”
by Ayvali, M.; Wickenkamp, I. and Ehrmann, A.
Technologies 2021, 9(3), 63; https://doi.org/10.3390/technologies9030063
Available online: https://www.mdpi.com/2227-7080/9/3/63

Highlights:

• The authors develop a low-cost myoelectric thumb;
• Freely available 3D printing models and software can serve as a basis for further research;
• By implementing feedback, the myoelectric thumb can be used as a low-cost prosthesis.

6. “Investigation of Methods to Extract Fetal Electrocardiogram from the Mother’s Abdominal Signal in Practical Scenarios”
by Sarafan, S.; Le, T.; Naderi, A. M.; Nguyen, Q.-D.; Kuo, B. T.-Y.; Ghirmai, T.; Han, H.-D.; Lau, M. P. H. and Cao, H.
Technologies 2020, 8(2), 33; https://doi.org/10.3390/technologies8020033
Available online: https://www.mdpi.com/2227-7080/8/2/33 

Highlights:

• Various methods, including the Extended Kalman Filter (EKF), template subtraction (TS), independent component analysis (ICA), and their combinations, are rigorously investigated using data from the PhysioNet 2013 Challenge;
• Data with added Gaussian and motion noise, which mimic a practical scenario, are utilized to examine the performance of different algorithms;
• Different algorithm combinations are proposed and tested, yielding promising results;
• A comprehensive performance metric, including the F1 score, computational complexity (i.e., execution time and allocated memories), and noise robustness, is used to assess performance.