A NEW MULTIFUNCTION SWITCHED-CURRENT FILTER

A new switched-current(SI)biquadratic filter is presented which can simultaneouslyprovide the low-pass,high-pass and band-pass filters in a single building block.A symbol torepresent the universal SI integrator is also introduced.It can simplify the analysis and synthesisof SI networks.

SWITCHED-CURRENT FILTER DESIGN USING CASCADED SECTIONS

A new analogue sampled-data active device, named as a switched-current operationalamplifier (SIOA), is presented. The use of active circuit elements may simplify drawing the circuitdiagram significantly greatly and may permit easier analysis and synthesis of SI networks. Anumber of all pole and elliptic (second-or third-order) switched-current (SI) filters are derivedfrom the switched capacitor prototypes. These can be used as simple self-contained filters or asfilter sections in the cascaded realizations of a higher order transfer functions. To illustrate theapproach, a fifth-order low-pass filter is designed.

Discrete-Time Chaotic Circuits for Implementation of Tent Map and Bernoulli Map

Discrete-time chaotic circuit implementations of a tent map and a Bernoulli map using switched-current （SI） techniques are presented. The two circuits can be constructed with 16 MOSFET＇s and 2 capacitors. The simulations and experiments built with commercially available IC＇s for the circuits have demonstrated the validity of the circuit designs. The experiment results also indicate that the proposed circuits are integrable by a standard CMOS technology. The implementations are useful for studies and applications of chaos.

Test Mismatch in Switched-Current Circuits Using Wavelet Analysis

Errors of mismatch and currents calibration caused by channel geometrical variety in switchedcurrent are investigated in this paper. The relation and computing of mismatch and sensitivity are discussed also, and then a measure method of switched current mismatch using wavelet decomposition is proposed. A selected group of same transconductance is choosing as a cohort firstly, and the sensitivities of cohort in relation to the variation of transconductance are computed. Compared with the nominal deviation and tolerance borderline, the optimization and testing can be performed. As an example, a sixth order chebyshev low-pass filter is simulated and tested. The results have justified the reliability and feasibility of the method.

Threshold logic based low-level vision sparse object features

Purpose-The real-time generation of feature descriptors for object recognition is a challenging problem.In this research,the purpose of this paper is to provide a hardware friendly framework to generate sparse features that can be useful for key feature point selection,feature extraction,and descriptor construction.The inspiration is drawn from feature formation processes of the human brain,taking into account the sparse,modular,and hierarchical processing of visual information.Design/methodology/approach-A sparse set of neurons referred as active neurons determines the feature points necessary for high-level vision applications such as object recognition.A psycho-physical mechanism of human low-level vision relates edge detection to noticeable local spatial stimuli,representing this set of active neurons.A cognitive memory cell array-based implementation of low-level vision is proposed.Applications of memory cell in edge detection are used for realizing human vision inspired feature selection and leading to feature vector construction for high-level vision applications.Findings-True parallel architecture and faster response of cognitive circuits avoid time costly and redundant feature extraction steps.Validation of proposed feature vector toward high-level computer vision applications is demonstrated using standard object recognition databases.The comparison against existing state-of-the-art object recognition features and methods shows an accuracy of 97,95,69 percent for Columbia Object Image Library-100,ALOI,and PASCAL VOC 2007 databases indicating an increase from benchmark methods by 5,3 and 10 percent,respectively.Originality/value-A hardware friendly low-level sparse edge feature processing system isproposed for recognizing objects.The edge features are developed based on threshold logic of neurons,and the sparse selection of the features applies a modular and hierarchical processing inspired from the human neural system.