CMOS low-power bandpass IF filter for Bluetooth

Design of a CMOS 18th-order IF (intermediate frequency) bandpass filter for integrated low-IF Bluetooth receivers is presented. The centre frequency and bandwidth of the filter are 3 and 1 MHz, respectively. The proposed filter is based on unity gain fully differential voltage buffers and provides efficient, low power and a small area design solution. The filter, including its automatic tuning circuit, occupies an area of 0.6 mm² in a standard 0.5 mum-CMOS chip. Experimental results show that the filter satisfies the selectivity and dynamic range requirements of Bluetooth while operating from a total supply current of 0.9 mA     

High-transconductance n-type Si/SiGe modulation-doped field-effecttransistors

The authors report on the fabrication and the resultant device characteristics of the first 0.25-μm gate-length field-effect transistor based on n-type modulation-doped Si/SiGe. Prepared using ultrahigh vacuum/chemical vapor deposition (UHV/CVD), the mobility and electron sheet charge density in the strained Si channel are 1500 (9500) cm²/V-s and 2.5×10¹² (1.5×10¹² ) cm^-2 at 300 K (77 K). At 77 K, the devices have a current and transconductance of 325 mA/mm and 600 mS/mm, respectively. These values far exceed those found in Si MESFETs and are comparable to the best results achieved in GaAs/AlGaAs modulation-doped transistors     

Climate-related electricity demand-side management in oil-exporting countries—the case of the United Arab Emirates

The oil crisis of the 1970s has increased the concern about the continuity of oil imports flow to major oil-importing developed countries. Numerous policy measures including electricity demand-side management (DSM) programs have been adopted in such countries. These measures aim at reducing the growing need for electricity power that increases the dependency on imported foreign oil and damages the environment. On the other hand, the perception that energy can be obtained at very low cost in oil-rich countries led to less attention being paid to the potential of DSM policies in these countries. This paper discusses such potential using the case of the United Arab Emirates (UAE). Since air conditioning is a major source of electric energy consumption, the relationship between climate conditions and electric energy consumption is considered. An electricity demand model is constructed using time series techniques. The fitted model seems to represent these relationships rather well. Forecasts for electricity consumption using the estimated model indicate that a small reduction in cooling degrees requirement might induce a significant reduction in electric energy demand. Hence, a DSM program is proposed with policy actions to include, among others, measures to reduce cooling degrees requirement.     

CMOS scaling into the nanometer regime

Starting with a brief review on 0.1-μm (100 nm) CMOS status, this paper addresses the key challenges in further scaling of CMOS technology into the nanometer (sub-100 nm) regime in light of fundamental physical effects and practical considerations. Among the issues discussed are: lithography, power supply and threshold voltage, short-channel effect, gate oxide, high-field effects, dopant number fluctuations and interconnect delays. The last part of the paper discusses several alternative or unconventional device structures, including silicon-on-insulator (SOI), SiGe MOSFET's, low-temperature CMOS, and double-gate MOSFET's, which may lead to the outermost limits of silicon scaling     

A novel Jacob spectral method for multi-dimensional weakly singular nonlinear Volterra integral equations with nonsmooth solutions

The main purpose of this work is to develop a spectrally accurate collocation method for solving weakly singular integral equations of the second kind with nonsmooth solutions in high dimensions. The proposed spectral collocation method is based on a multivariate Jacobi approximation in the frequency space. The essential idea is to adopt a smoothing transformation for the spectral collocation method to circumvent the curse of singularity at the beginning of time. As such, the singularity of the numerical approximation can be tailored to that of the singular solutions. A rigorous convergence analysis is provided and confirmed by numerical tests with nonsmooth solutions in two dimensions. The results in this paper seem to be the first spectral approach with a theoretical justification for high-dimensional nonlinear weakly singular Volterra type equations with nonsmooth solutions.

     

An evolutionary lion optimization algorithm‐based image compression technique for biomedical applications

Recently, medical image compression becomes essential to effectively handle large amounts of medical data for storage and communication purposes. Vector quantization (VQ) is a popular image compression technique, and the commonly used VQ model is Linde–Buzo–Gray (LBG) that constructs a local optimal codebook to compress images. The codebook construction was considered as an optimization problem, and a bioinspired algorithm was employed to solve it. This article proposed a VQ codebook construction approach called the L2‐LBG method utilizing the Lion optimization algorithm (LOA) and Lempel Ziv Markov chain Algorithm (LZMA). Once LOA constructed the codebook, LZMA was applied to compress the index table and further increase the compression performance of the LOA. A set of experimentation has been carried out using the benchmark medical images, and a comparative analysis was conducted with Cuckoo Search‐based LBG (CS‐LBG), Firefly‐based LBG (FF‐LBG) and JPEG2000. The compression efficiency of the presented model was validated in terms of compression ratio (CR), compression factor (CF), bit rate, and peak signal to noise ratio (PSNR). The proposed L2‐LBG method obtained a higher CR of 0.3425375 and PSNR value of 52.62459 compared to CS‐LBG, FA‐LBG, and JPEG2000 methods. The experimental values revealed that the L2‐LBG process yielded effective compression performance with a better‐quality reconstructed image.     

Automatic Channel Detection Using DNN on 2D Seismic Data

Geologists interpret seismic data to understand subsurface properties and subsequently to locate underground hydrocarbon resources. Channels are among the most important geological features interpreters analyze to locate petroleum reservoirs. However, manual channel picking is both time consuming and tedious. Moreover, similar to any other process dependent on human intervention, manual channel picking is error prone and inconsistent. To address these issues, automatic channel detection is both necessary and important for efficient and accurate seismic interpretation. Modern systems make use of real-time image processing techniques for different tasks. Automatic channel detection is a combination of different mathematical methods in digital image processing that can identify streaks within the images called channels that are important to the oil companies. In this paper, we propose an innovative automatic channel detection algorithm based on machine learning techniques. The new algorithm can identify channels in seismic data/images fully automatically and tremendously increases the efficiency and accuracy of the interpretation process. The algorithm uses deep neural network to train the classifier with both the channel and non-channel patches. We provide a field data example to demonstrate the performance of the new algorithm. The training phase gave a maximum accuracy of 84.6% for the classifier and it performed even better in the testing phase, giving a maximum accuracy of 90%.