MODELING AND SPICE IMPLEMENTATION OF SILICON-ON-INSULATOR (SOI) FOUR GATE (G4FET) TRANSISTOR

As the device dimensions have reduced from micrometer to nanometer range, new bulk silicon devices are now facing many undesirable effects of scaling leading device engineers to look for new process technologies. Silicon-on-insulator (SOI) has emerged as a very promising candidate for resolving the major problems plaguing the bulk silicon technology. G4FET [G4FET] is a SOI transistor with four independent gates. Although G4FET has already shown great potential in different applications, the widespread adoption of a technology in circuit design is heavily dependent upon good SPICE (Simulation Program with Integrated Circuit Emphasis) models. CAD (Computer Aided Design) tools are now ubiquitous in circuit design and a fast, robust and accurate SPICE model is absolutely necessary to transform G4FET into a mainstream technology. The research goal is to develop suitable SPICE models for G4FET to aid circuit designers in designing innovative analog and digital circuits using this new transistor. The first phase of this work is numerical modeling of the G4FET where four different numerical techniques are implemented, each with its merits and demerits. The first two methods are based on multivariate Lagrange interpolation and multidimensional Bernstein polynomial. The third numerical technique is based on multivariate regression polynomial to aid modeling with dense gridded data. Another suitable alternative namely multidimensional linear and cubic spline interpolation is explored as the fourth numerical modeling approach to solve some of the problems resulting from single polynomial approximation. The next phase of modeling involves developing a macromodel combining already existing SPICE models of MOSFET (metal–oxide–semiconductor field-effect transistor) and JFET (junction-gate field-effect transistor). This model is easy to implement in circuit simulators and provides good results compared to already demonstrated experimental works with innovative G4FET circuits. The final phase of this work involves the development of a physics-based compact model of G4FET with some empirical fitting parameters. A model for depletion-all-around operation is implemented in circuit simulator based on previous work. Another simplified model, combining MOS and JFET action, is implemented in circuit simulator to model the accumulation mode operation of G4FET

Assorted, Archetypal and Annotated Two Million (3A2M) Cooking Recipes Dataset based on Active Learning

Cooking recipes allow individuals to exchange culinary ideas and provide food preparation instructions. Due to a lack of adequate labeled data, categorizing raw recipes found online to the appropriate food genres is a challenging task in this domain. Utilizing the knowledge of domain experts to categorize recipes could be a solution. In this study, we present a novel dataset of two million culinary recipes labeled in respective categories leveraging the knowledge of food experts and an active learning technique. To construct the dataset, we collect the recipes from the RecipeNLG dataset. Then, we employ three human experts whose trustworthiness score is higher than 86.667% to categorize 300K recipe by their Named Entity Recognition (NER) and assign it to one of the nine categories: bakery, drinks, non-veg, vegetables, fast food, cereals, meals, sides and fusion. Finally, we categorize the remaining 1900K recipes using Active Learning method with a blend of Query-by-Committee and Human In The Loop (HITL) approaches. There are more than two million recipes in our dataset, each of which is categorized and has a confidence score linked with it. For the 9 genres, the Fleiss Kappa score of this massive dataset is roughly 0.56026. We believe that the research community can use this dataset to perform various machine learning tasks such as recipe genre classification, recipe generation of a specific genre, new recipe creation, etc. The dataset can also be used to train and evaluate the performance of various NLP tasks such as named entity recognition, part-of-speech tagging, semantic role labeling, and so on. The dataset will be available upon publication: https://tinyurl.com/3zu4778y

Towards Automated Recipe Genre Classification using Semi-Supervised Learning

Sharing cooking recipes is a great way to exchange culinary ideas and provide instructions for food preparation. However, categorizing raw recipes found online into appropriate food genres can be challenging due to a lack of adequate labeled data. In this study, we present a dataset named the ``Assorted, Archetypal, and Annotated Two Million Extended (3A2M+) Cooking Recipe Dataset" that contains two million culinary recipes labeled in respective categories with extended named entities extracted from recipe descriptions. This collection of data includes various features such as title, NER, directions, and extended NER, as well as nine different labels representing genres including bakery, drinks, non-veg, vegetables, fast food, cereals, meals, sides, and fusions. The proposed pipeline named 3A2M+ extends the size of the Named Entity Recognition (NER) list to address missing named entities like heat, time or process from the recipe directions using two NER extraction tools. 3A2M+ dataset provides a comprehensive solution to the various challenging recipe-related tasks, including classification, named entity recognition, and recipe generation. Furthermore, we have demonstrated traditional machine learning, deep learning and pre-trained language models to classify the recipes into their corresponding genre and achieved an overall accuracy of 98.6\%. Our investigation indicates that the title feature played a more significant role in classifying the genre

The Ride-Sharing Services in Bangladesh: Current Status, Prospects, and Challenges

The study reveals the current status, prospects, and challenges of ride-sharing services in Bangladesh. The current status and prospects are analyzed from the viewpoint of customers and users with a sample of 200 and the challenges have been traced from the business perspective with a sample of 10 executives of different ride-sharing organizations. There are 177 and 8 respondents from the sample of 200 and 10 respectively. It has gained an immense acceptance among the young generations and businesses due to its distinct prospects, for instance, real-time response, more convenient, on-demand location, user-friendliness, and so on. The major challenges have been identified as fund shortage, lack of logistic support, lack of awareness, adverse government policies, intense competition and so forth. Keywords: Ride-sharing services, prospects, challenges, Bangladesh. DOI: 10.7176/EJBM/11-31-05 Publication date: November 30th 201

A cross-cultural analysis of ridesharing intentions and compliance with COVID-19 health guidelines: The roles of social trust, fear of COVID-19, and trust-in-God

Ridesharing services such as Uber and Lyft have been substantially affected by the ongoing COVID-19 pandemic. Drawing on social capital theory, the current research investigates how social trust relates to three types of trust in compliance with COVID-19 guidelines and consumers\u27 ridesharing intentions. Analyzing data from two economically and culturally distinct countries, the results suggest that social trust positively affects trust in platform companies\u27 compliance with COVID-19 guidelines (TPC), but not (or to a lesser extent) trust in drivers\u27 (TDC) and other riders (TRC) compliance with COVID-19 guidelines in both the United States and Bangladesh. Importantly, TPC, TDC, and TRC are positively related with consumers\u27 ridesharing intentions in the United States but not in Bangladesh. Furthermore, the analysis reveals two counterintuitive moderating effects of fear of COVID-19 and trust in God. The results provide important insights on factors affecting the ridesharing industry in the context of the COVID-19 pandemic, and they emphasize the importance of considering cultural context in understanding consumers’ intentions to engage in the sharing economy

A 2D Chaotic Oscillator for Analog IC

In this paper, we have proposed the design of an analog two-dimensional (2D) discrete-time chaotic oscillator. 2D chaotic systems are studied because of their more complex chaotic behavior compared to one-dimensional (1D) chaotic systems. The already published works on 2D chaotic systems are mainly focused either on the complex analytical combinations of familiar 1D chaotic maps such as Sine map, Logistic map, Tent map, and so on, or off-the-shelf component-based analog circuits. Due to complex hardware requirements, neither of them is feasible for hardware-efficient integrated circuit (IC) implementations. To the best of our knowledge, this proposed work is the first-ever report of an analog 2D discrete-time chaotic oscillator design that is suitable for hardware-constrained IC implementations. The chaotic performance of the proposed design is analyzed with bifurcation plots, the transient response, 2D Lyapunov exponent, and correlation coefficient measurements. It is demonstrated that the proposed design exhibits promising chaotic behavior with low hardware cost. The real-world application of the proposed 2D chaotic oscillator is presented in a random number generator (RNG) design. The applicability of the RNG in cryptography is verified by passing the generated random sequence through four standard statistical tests namely, NIST, FIPS, TestU01, and Diehard

Design, Analysis, and Application of Flipped Product Chaotic System

In this paper, a novel method is proposed to build an improved 1-D discrete chaotic map called flipped product chaotic system (FPCS) by multiplying the output of one map with the output of a vertically flipped second map. Two variants, each with nine combinations, are shown with trade-off between computational cost and performance. The chaotic properties are explored using the bifurcation diagram, Lyapunov exponent, Kolmogorov entropy, and correlation coefficient. The proposed schemes offer a wider chaotic range and improved chaotic performance compared to the constituent maps and several prior works of similar nature. Wide chaotic window and improved chaotic complexity are two desired characteristics for several security applications as these two characteristics ensure enhanced design space with elevated entropic properties. We present a general Field-Programmable Gate Array (FPGA) design framework for the hardware implementation of the proposed flipped-product schemes and the results show good qualitative agreement with the numerical results from MATLAB simulation. Finally, we present a new Pseudo Random Number Generator (PRNG) using the two variants of the proposed chaotic map and validate their excellent randomness property using four standard statistical tests, namely NIST, FIPS, TestU01, and Diehard