Novel CCII-based Field Programmable Analog Array and its Application to a Sixth-Order Butterworth LPF

In this paper, a field programmable analog array (FPAA) is proposed. The proposed FPAA consists of seven configurable analog blocks (CABs) arranged in a hexagonal lattice such that the CABs are directly connected to each other. This structure improves the overall frequency response of the chip by decreasing the parasitic capacitances in the signal path. The CABS of the FPAA is based on a novel fully differential digitally programmable current conveyor (DPCCII). The programmability of the DPCCII is achieved using digitally controlled three-bit MOS ladder current division network. No extra biasing circuit is required to generate specific analog control voltage signals. The DPCCII has constant standby power consumption, offset voltage, bandwidth and harmonic distortions over all its programming range. A sixth-order Butterworth tunable LPF suitable for WLAN/WiMAX receivers is realized on the proposed FPAA. The filter power consumption is 5.4mW from 1V supply; it’s cutoff frequency is tuned from 5.2 MHz to 16.9 MHz. All the circuits are realized using 90nm CMOS technology from TSMC. All simulations are carried out using Cadence

Exploring the public's beliefs, emotions and sentiments towards the adoption of the metaverse in education: A qualitative inquiry using big data

The metaverse is rapidly reshaping our understanding of education, yet identifying the public's beliefs, emotions and sentiments towards its adoption in education remains largely uncharted empirically. Grounded in the Technology Acceptance Model (TAM) and Digital Diffusion Theory (DOI), this paper aims to fill this gap using a big-data approach and machine learning to scrape comments made by social media users on recent popular posts or videos related to adopting the metaverse in education from three prominent social media platforms. The cleaning process narrowed down 11,024 comments to 4277, then analysed them using thematic, emotion and sentiment analysis techniques. The thematic analysis revealed that adopting the metaverse in education evokes a complex range of public beliefs: (1) innovative learning methods; (2) accessibility and inclusion; (3) concerns about quality and effectiveness; (4) technological challenges and the digital divide; (5) the future of work and skills; and (6) privacy and security concerns. Integrating these themes with emotion and sentiment analyses reveals a landscape of a significant portion of neutral sentiments that corroborates enthusiasm attenuated by caution. This careful consideration stresses the urgent need for a balanced approach to adopting the metaverse in education to ensure that resulting educational advancements benefit all learners equitably. As one of the first studies to offer a multidimensional view of the public's beliefs about metaverse education using big data, this research not only contributes to TAM and DOI but also provides critical insights that could inform policy, enhance educational practices and guide future scholarship in this emerging field

Medical image enhancement using threshold decomposition driven adaptive morphological filter

One of the most common degradations in medical images is their poor contrast quality. This suggests the use of contrast enhancement methods as an attempt to modify the intensity distribution of the image. In this paper, a new edge detected morphological filter is proposed to sharpen digital medical images. This is done by detecting the positions of the edges and then applying a class of morphological filtering. Motivated by the success of threshold decomposition, gradientbased operators are used to detect the locations of the edges. A morphological filter is used to sharpen these detected edges. Experimental results demonstrate that the detected edge deblurring filter improved the visibility and perceptibility of various embedded structures in digital medical images. Moreover, the performance of the proposed filter is superior to that of other sharpener-type filters

Behavioural pattern identification and prediction in intelligent environments

In this paper, the application of soft computing techniques in prediction of an occupant's behaviour in an inhabited intelligent environment is addressed. In this research, daily activities of elderly people who live in their own homes suffering from dementia are studied. Occupancy sensors are used to extract the movement patterns of the occupant. The occupancy data is then converted into temporal sequences of activities which are eventually used to predict the occupant behaviour. To build the prediction model, different dynamic recurrent neural networks are investigated. Recurrent neural networks have shown a great ability in finding the temporal relationships of input patterns. The experimental results show that non-linear autoregressive network with exogenous inputs model correctly extracts the long term prediction patterns of the occupant and outperformed the Elman network. The results presented here are validated using data generated from a simulator and real environments

The oscillatory distribution of distances in random tries

We investigate \Delta_n, the distance between randomly selected pairs of nodes among n keys in a random trie, which is a kind of digital tree. Analytical techniques, such as the Mellin transform and an excursion between poissonization and depoissonization, capture small fluctuations in the mean and variance of these random distances. The mean increases logarithmically in the number of keys, but curiously enough the variance remains O(1), as n\to\infty. It is demonstrated that the centered random variable \Delta_n^*=\Delta_n-\lfloor2\log_2n\rfloor does not have a limit distribution, but rather oscillates between two distributions.Comment: Published at http://dx.doi.org/10.1214/105051605000000106 in the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org

Type of Tomato Classification Using Deep Learning

Abstract: Tomatoes are part of the major crops in food security. Tomatoes are plants grown in temperate and hot regions of South American origin from Peru, and then spread to most countries of the world. Tomatoes contain a lot of vitamin C and mineral salts, and are recommended for people with constipation, diabetes and patients with heart and body diseases. Studies and scientific studies have proven the importance of eating tomato juice in reducing the activity of platelets in diabetics, which helps in protecting them from developing deadly blood clots. A tomato classification approach is presented with a data set containing approximately 5,266 images with 7 species belonging to tomatoes. The Neural Network Algorithms (CNN), a deep learning technique applied widely in image recognition, is used for this task