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

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

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

Structured codebook design in CELP

Codebook Excited Linear Protection (CELP) is a popular analysis by synthesis technique for quantizing speech at bit rates from 4 to 6 kbps. Codebook design techniques to date have been largely based on either random (often Gaussian) codebooks, or on known binary or ternary codes which efficiently map the space of (assumed white) excitation codevectors. It has been shown that by introducing symmetries into the codebook, good complexity reduction can be realized with only marginal decrease in performance. Codebook design algorithms are considered for a wide range of structured codebooks

Real-time speech encoding based on Code-Excited Linear Prediction (CELP)

This paper reports on the work proceeding with regard to the development of a real-time voice codec for the terrestrial and satellite mobile radio environments. The codec is based on a complexity reduced version of code-excited linear prediction (CELP). The codebook search complexity was reduced to only 0.5 million floating point operations per second (MFLOPS) while maintaining excellent speech quality. Novel methods to quantize the residual and the long and short term model filters are presented