How Far Is the Nanocellulose Chip and Its Production in Reach? A Literature Survey.

The slowdown of Moore's Law necessitates an exploration of novel computing methodologies, new materials, and advantages in chip design. Thus, carbon-based materials have promise for more energy-efficient computing systems in the future. Moreover, sustainability emerges as a new concern for the semiconductor industry. The production and recycling processes associated with current chips present huge environmental challenges. Electronic waste is a major problem, and sustainable solutions in computing must be found. In this review, we examine an alternative chip design based on nanocellulose, which also features semiconductor properties and transistors. Our review highlights that nanocellulose (NC) is a versatile material and a high-potential composite, as it can be fabricated to gain suitable electronic and semiconducting properties. NC provides ideal support for ink-printed transistors and electronics, including green paper electronics. Here, we summarise various processing procedures for nanocellulose and describe the structure of exclusively nanocellulose-based transistors. Furthermore, we survey the recent scientific efforts in organic chip design and show how fully automated production of such a full NC chip could be achieved, including a Process Design Kit (PDK), expected variation models, and a standard cell library at the logic-gate level, where multiple transistors are connected to perform basic logic operations-for instance, the NOT-AND (NAND) gate. Taking all these attractive nanocellulose features into account, we envision how chips based on nanocellulose can be fabricated using Electronic Design Automation (EDA) tool chains.

Applying andragogy for integrating a MOOC into a formal online learning experience in computer engineering.

In this paper, we present the results of a research experience of implementing andragogy in a learning environment designed to better meet the needs of adult learners studying part-time at a distance university. The learning environment was composed of a learning experience on a formal distance university online course that has been enriched with a non-formal component based on students' participation in a Massive Online Open Course (MOOC) related to the same topic. The non-formal experience was designed to consolidate the learning of specific content that involved difficult concepts and foster collaborative skills. The university online course is in the field of computer science and human-computer interaction. The instructional design, including the course assignments, has been guided by Knowles' principles of andragogy. Results from the data analysis of five years of academic results and student satisfaction has helped to understand the learning experience from including a MOOC in adult distance formal learning.

MRNG: Accessing Cosmic Radiation as an Entropy Source for a Non-Deterministic Random Number Generator.

Privacy and security require not only strong algorithms but also reliable and readily available sources of randomness. To tackle this problem, one of the causes of single-event upsets is the utilization of a non-deterministic entropy source, specifically ultra-high energy cosmic rays. An adapted prototype based on existing muon detection technology was used as the methodology during the experiment and tested for its statistical strength. Our results show that the random bit sequence extracted from the detections successfully passed established randomness tests. The detections correspond to cosmic rays recorded using a common smartphone during our experiment. Despite the limited sample, our work provides valuable insights into the use of ultra-high energy cosmic rays as an entropy source.

A novel adaptive resampling for sequential Bayesian filtering to improve frequency estimation of time-varying signals.

This paper presents a new algorithm for adaptive resampling, called percentile-based resampling (PBR) in a sequential Bayesian filtering, i.e., particle filter (PF) in particular, to improve tracking quality of the frequency trajectories under noisy environments. Since the conventional resampling scheme used in the PF suffers from computational burden, resulting in less efficiency in terms of computation time and complexity as well as the real time applications of the PF. The strategy to remedy this issue is proposed in this work. After state updating, important high particle weights are used to formulate the pre-set percentile in each sequential iteration to create a new set of high quality particles for the next filtering stage. The number of particles after PBR remains the same as the original. To verify the effectiveness of the proposed method, we first evaluated the performance of the method via numerical examples to a complex and highly nonlinear benchmark system. Then, the proposed method was implemented for frequency estimation for two time-varying signals. From the experimental results, via three measurement metrics, our approach delivered better performance than the others. Frequency estimates obtained by our method were excellent as compared to the conventional resampling method when number of particles were identical. In addition, the computation time of the proposed work was faster than those recent adaptive resampling schemes in literature, emphasizing the superior performance to the existing ones.

An assessment of student satisfaction with e-learning: An empirical study with computer and software engineering undergraduate students in Turkey under pandemic conditions.

As COVID-19 reached Turkey in March 2020, all universities switched to e-learning in a very short period. Computer and software engineering (CE/SE) undergraduate students studying at university campuses have switched to e-learning. This paper seeks to understand the e-learning experience of CE/SE undergraduate students. A questionnaire was created and applied to CE/SE undergraduate students in Turkish universities. The data were analyzed using quantitative and qualitative techniques. The questionnaire received 290 usable responses. The highlights from the findings include: the participants (1) used video recordings intensively for e-learning and found them useful; (2) found face-to-face lectures more beneficial compared to digital live lectures; (3) used external online resources to improve their learning performance in courses; (4) thought that the materials and methods utilized for assessment should be adapted to e-learning for a better and fair evaluation; (5) perceived significantly less instructor support and classmate interaction and collaboration in e-learning compared to on-campus education settings; (6) rated their perceived satisfaction from e-learning as 2.85, slightly under the mid-level of the 5-point Likert scale; (7) perceived instructor support, student interaction and collaboration, and student autonomy as noteworthy factors in high-quality e-learning.

Use of computational intelligence techniques to predict flooding in places adjacent to the Magdalena River.

Floods are one of the worst natural disasters in the world. Colombia is a country that has been greatly affected by this disaster. For example, in the years 2010 and 2011 there was a heavy rainy season, which caused floods that affected at least two million people and there were economic losses of 6.5 million dollars, which is equivalent to 5.7% of the country's Gross Domestic Product (GDP) at that time. The Magdalena River is the most important since 128 municipalities and 43 cities with a population of 6.3 million people, which is 13% of the total population of the country, are located in its basins. For this reason, the objective of the research is to design and implement a model that helps predict flooding over the Magdalena River by examining three techniques of artificial intelligence (Artificial Neuronal Networks, Adaptive Neuro Fuzzy Inference System, Support Vector Machine), and thus determining which of these techniques are the most effective according to the case study. The research was limited only to these three types, due to limitations of time, data, human and financial resources, and technological infrastructure. In the end, it is concluded that the Artificial Neural Networks technique is a suitable option to implement the predictive system as long as it is not very complex and does not require high processing machine. However, to establish a model based on rules to achieve a better interpretability of the floods, the ANFIS model can be used.

Fast calibration for parameters of an inertial measurement unit fixed to a standard walker.

A system consisting of an inertial measurement unit (IMU) mounted on a walker is proposed. The objective of this system is to monitor a user's walking. The relationship between the walker and the IMU, which cannot be easily measured manually, plays an important role in the system. There are various relationships because of the different types of walkers, as well as adjustments made to the height of the walker legs for comfortable usage. In this study, we propose a simple procedure for fast calibration, which consists of the attitude and the position of the IMU with respect to the walker coordinate system. The procedure includes slightly tilting the walker to the front, back, right, and left. A Kalman filter based on the inertial navigation system is used to estimate the trajectory of the IMU during tilting movements. The relationship can be calibrated using the estimated trajectory and geometric characteristics of walkers. The results of the experiments show that the proposed method achieves acceptable accuracy (97% of distance and position) and convenience.

Bio-inspired wideband antenna for wireless applications based on perturbation technique.

The evolution of advancement in communication technologies and ever-increasing demand by users for compact communication devices has necessitated a shift in the design approach to achieve antenna structures that are compact and robust. Owing to the diverse communication requirements, antenna systems operating across wide bands have become a necessity. An antenna that is capable of working effectively in several bands is called wideband antenna. In this work, a bio-inspired microstrip antenna (Bi-MPA) for wideband application is proposed and simulated. The radiating patch of the proposed Bi-MPA is the shape of Carica Papaya leaf. The structure was realized through the perturbation of the circular shape patch. The proposed antenna has an impedance bandwidth of 4.3 GHz (1.9 GHz-6.2 GHz) at a return loss of 10 dB while it exhibits a narrow band at 7.2 GHz (6.99-7.44 GHz) and 9.3 GHz (9.15-9.35 GHz) bands. The gain of the proposed antenna is between 2.60 dB and 10.22 dB and the radiation pattern is quasi-omnidirectional. The proposed Bi-MPA is compact and suitable for global system for mobile communication (GSM1900), Universal Mobile Telecommunication System (UMTS), Wireless Local Area Network (WLAN), Long Term Evolution (LTE2300 and LTE2600), Worldwide Interoperability for Microwave Access (WiMAX), C-band, X-band, and sub6 GHz fifth-generation (5G) band. Our contribution to the scientific community in this work is that we have proposed a single antenna structure that is suitable for communication in all the bands mentioned in order to ensure compactness in the mobile devices as compared to base station antennas.

Design of a pneumatic soft actuator controlled via eye tracking and detection.

This work describes the control of a pneumatic soft robotic actuator via eye movements. The soft robot is actuated using two supply sources: a vacuum pump and an air supply pump for both negative and positive air supply sources respectively. Two controlled states are presented: the actuation of the vacuum and air pump. Through eye positioning and tracking on the graphical user interface to actuate either pump, a control command is directed to inflate or deflate the pneumatic actuator. The potential of this application is in rehabilitation, whereby eye movements are used to control a rehabilitation-based assistive soft actuator rather than ON/OFF electronics. This is demonstrated in this work using an elbow based rehabilitation soft actuator.

An automatic visible-range video weed detection, segmentation and classification prototype in potato field.

Weeds might be defined as destructive plants that grow and compete with agricultural crops in order to achieve water and nutrients. Uniform spray of herbicides is nowadays a common cause in crops poisoning, environment pollution and high cost of herbicide consumption. Site-specific spraying is a possible solution for the problems that occur with uniform spray in fields. For this reason, a machine vision prototype is proposed in this study based on video processing and meta-heuristic classifiers for online identification and classification of Marfona potato plant (Solanum tuberosum) and 4299 samples from five weed plant varieties: Malva neglecta (mallow), Portulaca oleracea (purslane), Chenopodium album L (lamb's quarters), Secale cereale L (rye) and Xanthium strumarium (coklebur). In order to properly train the machine vision system, various videos taken from two Marfona potato fields within a surface of six hectares are used. After extraction of texture features based on the gray level co-occurrence matrix (GLCM), color features, spectral descriptors of texture, moment invariants and shape features, six effective discriminant features were selected: the standard deviation of saturation (S) component in HSV color space, difference of first and seventh moment invariants, mean value of hue component (H) in HSI color space, area to length ratio, average blue-difference chrominance (Cb) component in YCbCr color space and standard deviation of in-phase (I) component in YIQ color space. Classification results show a high accuracy of 98% correct classification rate (CCR) over the test set, being able to properly identify potato plant from previously mentioned five different weed varieties. Finally, the machine vision prototype was tested in field under real conditions and was able to properly detect, segment and classify weed from potato plant at a speed of up to 0.15 m/s.

Flexible robotic teleoperation architecture for intelligent oil fields.

Teleoperation virtual platforms allow people to send their skills and capacities into machines located in either relative close (few meters away) or far (different continents) locations. With the use of lightweight protocols, people can remotely control the actions and movements of robots so they can avoid physical interaction with dangerous or risky places. Oil and gas well-pads stations are working zones considered hazardous due to the various chemical substances used in their daily processes. This characteristic makes these places the perfect candidates for the implementation of teleoperation solutions in order to reduce the direct interaction of humans with different chemicals and risky situations. The following investigation focuses on the development of a base teleoperation scheme to perform inspection and maintenance tasks in the inside one of these hydrocarbon facilities. The proposed system aims to generate an easily scalable teleoperation solution using distributed control schemes and a lightweight communication protocol to remotely manipulate a KUKA mobile manipulator. As the first stage of this investigation, the main result focuses on the development of the generic control and communication functions that allow the physical testing of the system using a KUKA YouBOT mobile manipulator and the help of a qualified operator of the station.

Quality of Service (QoS) improving schemes in optical networks.

In optical networks, such as OPS/OBS, the network results into significant loss in the network layer. When the loss significantly deteriorates the QoS by increasing the Bit Error Rate (BER), a viable approach can be used to increase the performance. This paper presents state of the art of Quality of Service (QoS) schemes used for improving the performance of optical networks. Furthermore, some possible applications and performance data are summarized based on Packet Loss Rate (PLR), secrecy, survivability and other parameters. The different states of art methods proposed by several authors are compared with Coded Packet Transport (CPT) scheme. We believe that this study is valuable to researchers envisaging a novel approach to enhance the performance of optical networks for telecommunications networks of the future.

Cross-spectral iris recognition using phase-based matching and homomorphic filtering.

In cross-spectral iris recognition, different spectral bands are used to obtain rich information of the human iris. Previous studies on cross-spectral iris recognition are based primarily on feature-based approaches, which are prone to the changes in parameters in the feature extraction process, such as spatial position and iris image acquisition conditions. These parameters can degrade iris recognition performance. In this paper, we present a phase-based approach for cross-spectral iris recognition using phase-only correlation (POC) and band-limited phase-only correlation (BLPOC). A phase-based iris recognition system recognizes an iris using the phase information contained in the iris image; therefore, its performance is not affected by feature extraction parameters. However, the performance of a phase-based cross-spectral iris recognition is strongly influenced by specular reflection. Different illumination conditions may produce different iris images from the same subject. To overcome this challenge, we integrate a photometric normalization technique -homomorphic filtering- with phase-based cross-spectral iris recognition. The experimental results reveal that the proposed technique achieved an excellent matching performance with an equal error rate of 0.59% and a genuine acceptance rate of 95%.

Evaluating Impact on Motivation and Academic Performance of a Game-Based Learning Experience Using Kahoot.

Gamification methods adapt the mechanics of games to educational environments for the improvement of the teaching-learning process. Serious games play an important role as tools for gamification, in particular in the context of software engineering courses because of the idiosyncratic nature of the topic. However, the studies on the improvement of student performance resulting from the use of gamification and serious games in courses with different contexts are not conclusive. More empirical research is thus needed to obtain reliable results on the effectiveness, benefits and drawbacks. The overall objective of this work is to study the benefits generated by serious games in the teaching-learning process of Computer Engineering degrees, analyzing the impact on the motivation and student satisfaction, as well as on the learning outcomes and results finally achieved. To this end, an intervention is proposed in the subject of Computer Architecture based on two components covering theoretical and practical sessions. In the theoretical sessions, a serious game experience using Kahoot has been introduced, complementing the master classes and class exercises. For the practical sessions, the development of projects with groups of students has been proposed, whose results in terms of computer performance can be compared through a competition (hackathon). Evaluation of the serious game-based intervention has been approached in terms of student satisfaction and motivation, as well as improved academic performance. In order to assess student satisfaction, surveys have been used to assess the effect on student motivation and satisfaction. For the evaluation of academic performance, a comparative analysis between an experimental and a control group has been carried out, noting a slight increase in the experimental group students' marks.