Analog Filters Research Articles

Hybrid Protection of Digital FIR Filters

A digital finite impulse response (FIR) filter is a ubiquitous block in digital signal processing applications and its behavior is determined by its coefficients. To protect filter coefficients from an adversary, efficient obfuscation techniques have been proposed, either by hiding them behind decoys or replacing them by key bits. In this article, we initially introduce a query attack that can discover the secret key of such obfuscated FIR filters, which could not be broken by the existing prominent attacks. Then, we propose a first of its kind hybrid technique, including both hardware obfuscation and logic locking using a point function for the protection of parallel direct and transposed forms of digital FIR filters. Experimental results show that the hybrid protection technique can lead to FIR filters with higher security while maintaining the hardware complexity competitive or superior to those locked by prominent logic locking methods. It is also shown that the protected multiplier blocks and FIR filters are resilient to existing attacks. The results on different forms and realizations of FIR filters show that the parallel direct form FIR filter has a promising potential for a secure design.

Design and Application of Led Driver Based on Isolated DC-DC Cuk Converter by Comparing Power Led Models

Design and application of led driver with isolated cuk converter for 10W is realized in this paper especially for automotive purposes. The advantage of isolated cuk topology than conventional Cuk topology is to have electrical isolation with supply and load and providing same polarity output. Besides, power led model including constant voltage and exponential models is obtained and employed in simulation study for comparison. Furthermore, maximum current limitation of power led is provided via dsPIC30f4011 microcontroller. Also, power switch is operated by 100 kHz switching frequency, and connection of the power led driver to the source provided by LC with parallel damping filter, whose noise reduction shown by application as well. With experimental set up, proving the desired results achieved by the converter, led current-voltage, input voltage-current are measured. Simulation results also verifies application. Moreover, exponential power led model provides more accurate results than constant voltage model.

Pilot-scale removal of persistent and mobile organic substances in granular activated carbon filters and experimental predictability at lab-scale

Present knowledge about the fate of persistent and mobile (PM) substances in drinking water treatment is limited. Hence, this study assesses the potential of fixed-bed granular activated carbon (GAC) filters to fill the treatment gap for PM substances and the elimination predictability from lab-scale experiments. Two parallel pilot filters (GAC bed height 2 m, diameter 15 cm) with different GAC were operated for 1.5 years (ca. 47,000 BV throughput) alongside rapid small-scale column tests (RSSCT) designed based on the proportional diffusivity (PD) and the constant diffusivity (CD) approaches. Background dissolved organic matter (DOM) and a set of 17 target substances were investigated, among them 2-acrylamido-2-methylpropane sulfonate (AAMPS), adamantan-1-amine (ATA), melamine (MEL) and trifluoromethanesulfonic acid (TFMSA). Nine substances were predominantly present in the drinking water used as pilot filter influent (frequencies of detection above 80 %, median concentrations 0.003–1.868 μg/L) and their breakthrough behaviors could be observed: TFMSA was not retained at all, four substances including AAMPS and ATA reached complete breakthrough below 20,000 BV, three compounds were partially retained until the end of operation and oxypurinol was retained completely. The comparable PM candidate and DOM removal performances of both GAC aligns with their very similar surface characteristics and elemental compositions. The agreement of results between RSSCT with the pilot-scale filters were substance specific and no superior RSSCT design could be identified. However, CD-RSSCT provide a conservative removal prediction for most studied compounds. MEL adsorption was significantly underestimated by both RSSCT designs. Using the criterion of a carbon usage rate (with respect to 50 % breakthrough) below 25 mgGAC/Lwater for an economic retention by fixed-bed GAC filters, five (out of nine) substances met the requirement.

Topology Assessment for Dual-Band Filters Based on Acoustic Wave Resonators

This work assesses three different approaches to the design of dual-band filters with single-ended input and output based on acoustic wave (AW) technology together with the filter synthesis methodology behind each structure. First, the straightforward option, already on the market, is to connect two standalone ladder filters in parallel. This work provides a synthesis view to this option and also describes its strong limitations regarding achievable isolation. Nonetheless, departing from the classical ladder, an innovative topology is proposed in which series and shunt resonators of different sub-bands are concatenated forming a double-ladder topology. Although this latter structure is able to achieve better isolation levels, this work describes the particular constraints arising from this innovative solution in terms of response and technology requirements. Finally, the conventional ladder structure with a single signal path is proposed as a strong candidate to accommodate dual-band filtering responses for the first time. All three structures are discussed from a filter synthesis perspective, describing the particular constraints behind each topology. Also, each structure is reviewed and compared in technological terms to highlight its main advantages and disadvantages.

High-Resolution Ultrasound Characterization of Local Scattering in Cancer Tissue.

Ultrasound (US) has afforded an approach to tissue characterization for more than a decade. The challenge is to reveal hidden patterns in the US data that describe tissue function and pathology that cannot be seen in conventional US images. Our group has developed a high-resolution analysis technique for tissue characterization termed H-scan US, an imaging method used to interpret the relative size of acoustic scatterers. In the present study, the objective was to compare local H-scan US image intensity with registered histologic measurements made directly at the cellular level. Human breast cancer cells (MDA-MB 231, American Type Culture Collection, Manassas, VA, USA) were orthotopically implanted into female mice (N=5). Tumors were allowed to grow for approximately 4 wk before the study started. In vivo imaging of tumor tissue was performed using a US system (Vantage 256, Verasonics Inc., Kirkland, WA, USA) equipped with a broadband capacitive micromachined ultrasonic linear array transducer (Kolo Medical, San Jose, CA, USA). A 15-MHz center frequency was used for plane wave imaging with five angles for spatial compounding. H-scan US image reconstruction involved use of parallel convolution filters to measure the relative strength of backscattered US signals. Color codes were applied to filter outputs to form the final H-scan US image display. For histologic processing, US imaging cross-sections were carefully marked on the tumor surface, and tumors were excised and sliced along the same plane. By use of optical microscopy, whole tumor tissue sections were scanned and digitized after nuclear staining. US images were interpolated to have the same number of pixels as the histology images and then spatially aligned. Each nucleus from the histologic sections was automatically segmented using custom MATLAB software (The MathWorks Inc., Natick, MA, USA). Nuclear size and spacing from the histology images were then compared with local H-scan US image features. Overall, local H-scan US image intensity exhibited a significant correlation with both cancer cell nuclear size (R2 > 0.27, p < 0.001) and the inverse relationship with nuclear spacing (R2 > 0.17, p < 0.001).

Computer model of a signal filtering device for a monitoring earth station

Satellite communication systems have an advantage over other communication systems, as they have a large coverage of the territory. The Earth station can be located anywhere in this territory and can be either a legitimate user or a satellite pirate (an illegitimate user). The developers are faced with the task of determining the coordinates of an illegitimate user illegally occupying a frequency resource. The article proposes a block diagram of a signal filtering device, which is part of a device for determining the coordinates of an illegitimate user, a monitoring Earth station. The purpose, requirements and main task of each composite block of the filtration device are defined. The search and analysis of analog filters and signal filtering methods was carried out. In the MatLab Simulink software environment, a computer model of a signal filtering device has been developed according to a block diagram with the possibility of determining the phase run, as well as the settings of the filtering device blocks are disclosed, which can be adjusted during the study of the model. For the bandpass filters included in the filtering device, the phase frequency characteristics of the phase change of the received signal on the frequency of this signal are constructed. A phase lock loop system has been formed with the possibility of changing its parameters. Calculations of the phase incursion through the elements of the filtration device are made.

Fault Voiceprint Signal Diagnosis Method of Power Transformer Based on Mixup Data Enhancement.

A voiceprint signal as a non-contact test medium has a broad application prospect in power-transformer operation condition monitoring. Due to the high imbalance in the number of fault samples, when training the classification model, the classifier is prone to bias to the fault category with a large number of samples, resulting in poor prediction performance of other fault samples, and affecting the generalization performance of the classification system. To solve this problem, a method of power-transformer fault voiceprint signal diagnosis based on Mixup data enhancement and a convolution neural network (CNN) is proposed. First, the parallel Mel filter is used to reduce the dimension of the fault voiceprint signal to obtain the Mel time spectrum. Then, the Mixup data enhancement algorithm is used to reorganize the generated small number of samples, effectively expanding the number of samples. Finally, CNN is used to classify and identify the transformer fault types. The diagnosis accuracy of this method for a typical unbalanced fault of a power transformer can reach 99%, which is superior to other similar algorithms. The results show that this method can effectively improve the generalization ability of the model and has good classification performance.

Adaptive parallel filter method for active cancellation of road noise inside vehicles

Active noise cancellation (ANC) is an efficient and effective method for suppressing low frequency disturbances generated by tyre–road interaction in a vehicle. There are significant differences in road noise inside the vehicle under different driving conditions. However, fixed-filter ANC strategies are unable to achieve superior noise attenuation for different types of noise, and conventional adaptive algorithms require prolonged adaptation which is detrimental to dynamic noise reduction. Thus, this study proposes an adaptive parallel filter method (APFM) that quickly responds to and suppresses varying vehicle interior road noises. The APFM is divided into two phases: the tuning phase and control phase. In the tuning phase, a set of potential noises is adopted to train the corresponding optimal fixed filters. During the control phase, the most appropriate parallel control filter is first selected for each frame based on a selection mechanism to minimise the energy of the estimated error signal. Then, a delayless variable step size normalised frequency-domain block adaptive filter algorithm is adopted to update the selected control filter. In addition, the computational complexity of the proposed APFM is analysed. Numerous simulations and real time in-vehicle experiments conducted using multi-channel ANC headrests have confirmed the feasibility and effectiveness of the proposed algorithm. It is evident that the APFM achieves fast noise reduction response and low steady-state error in attenuating varying in-vehicle road noise, and retains superior performance in the presence of non-pre-trained disturbances.

Signal processing algorithm and modeling of digital and analog filters for solving various technical problems

The article describes an algorithm for processing an audio signal in Matlab CAD, presents the results of filtering it using digital and analog filters and their comparison by specified characteristics. Practical significance of the work: studying the signal processing algorithm, writing digital filter code in Matlab and gaining skills in using Micro-Cap 12 CAD for modeling passive and active filters. This work can be used to teach students of technical universities in the study of software and principles of operation of analog and digital filters, for use as a methodological guide when performing calculations in homework or laboratory work using specialized software Matlab and Micro-Cap 12. An active low-pass filter is implemented and used as a stand for practical classes at the university.

Soft error detection and correction for parallel digital filters using Hamming code

Soft error detection and correction for parallel digital filters using Hamming code

An Approach for Classification of Alzheimer’s Disease Using Deep Neural Network and Brain Magnetic Resonance Imaging (MRI)

Alzheimer’s disease (AD) is a deadly cognitive condition in which people develop severe dementia symptoms. Neurologists commonly use a series of physical and mental tests to diagnose AD that may not always be effective. Damage to brain cells is the most significant physical change in AD. Proper analysis of brain images may assist in the identification of crucial bio-markers for the disease. Because the development of brain cells is so intricate, traditional image processing algorithms sometimes fail to perceive important bio-markers. The deep neural network (DNN) is a machine learning technique that helps specialists in making appropriate decisions. In this work, we used brain magnetic resonance scans to implement some commonly used DNN models for AD classification. According to the classification results, where the average of multiple metrics is observed, which includes accuracy, precision, recall, and an F1 score, it is found that the DenseNet-121 model achieved the best performance (86.55%). Since DenseNet-121 is a computationally expensive model, we proposed a hybrid technique incorporating LeNet and AlexNet that is light weight and also capable of outperforming DenseNet. To extract important features, we replaced the traditional convolution Layers with three parallel small filters (1×1,3×3, and 5×5). The model functions effectively, with an overall performance rate of 93.58%. Mathematically, it is observed that the proposed model generates significantly fewer convolutional parameters, resulting in a lightweight model that is computationally effective.

Performance Analysis of Twelve Lead ECG Based on Delivery Distance Using Bluetooth Communication

Based on data from Basic Health Research (Riskesdas) in 2018, the incidence of heart and blood vessel disease is increasing from year to year. At least 15 out of 1000 people or about 2,784,064 individuals in Indonesia suffer from heart disease. Therefore, cardiovascular health care can make a better diagnosis through continuous monitoring. The purpose of this study was to develop a 12-lead circuit, a lead selector (Wilson Central Terminal), an instrumentation booster, an analog filter (Notch Filter 50Hz), Arduino UNO, a Bluetooth module, and Delphi7 application to display electrocardiograph signals. The results show that the Bluetooth module cannot send a signal at a distance of 20 meters if there is no obstacle, cannot send a signal at a distance of 10 meters if there is an obstacle in the form of a wall, and cannot send a signal at a distance of 16 meters if there is an obstacle in the form of wood (doors).

Modeling and control of rate-dependent hysteresis characteristics of piezoelectric actuators based on analog filters

The hysteresis nonlinearity and rate dependence of piezoelectric actuators seriously affect their positioning accuracy in precise positioning. Proper compensation needs to be given to improve the positioning accuracy of the actuator. In this paper, the static hysteresis characteristics are described by improving the BP neural network model based on the adaptive particle swarm optimization algorithm, and the PI model is used as the hysteresis factor by the extended space method to realize the multi-valued mapping of hysteresis nonlinearity. And input three types of voltage signals, compare and analyze the experimental data and model prediction output, and use the maximum relative error and root mean square error as the evaluation indicators to prove the accuracy of the built model. Based on the improved BP neural network model, a second-order low-pass analog filter is introduced to construct a rate-dependent hysteresis model. Comparing and analyzing the rate-dependent hysteresis model and the static hysteresis model, the experimental results show that when the signal frequency is 10 Hz, 40 Hz, and 100 Hz, the predicted output of the rate-dependent hysteresis model is compared with that of the static hysteresis model, and the root mean square error decreases by 46.3%. %, 59.6%, and 65.1%, the model can more accurately describe the hysteresis characteristics of piezoelectric actuators. Finally, a rate-dependent hysteresis inverse model is established, and the rate-dependent hysteresis inverse model is used as a feedforward combined with a single neuron network PID control to form a composite controller, and the experimental verification is carried out. The results show that the maximum error between the composite controller and the actual expectation is only 2.15%. Therefore, the established model can accurately express the rate-dependent hysteresis characteristics of piezoelectric actuators, and the use of a composite controller can effectively reduce the accuracy error caused by the hysteresis nonlinear characteristics of piezoelectric actuators.

Comparative Performance of UPQC Control System Based on PI-GWO, Fractional Order Controllers, and Reinforcement Learning Agent

In this paper, based on a benchmark on the performance of a Unified Power Quality Conditioner (UPQC), the improvement of this performance is presented comparatively by using Proportional Integrator (PI)-type controllers optimized by a Grey Wolf Optimization (GWO) computational intelligence method, fractional order (FO)-type controllers based on differential and integral fractional calculus, and a PI-type controller in tandem with a Reinforcement Learning—Twin-Delayed Deep Deterministic Policy Gradient (RL-TD3) agent. The main components of the UPQC are a series active filter and an Active Parallel Filter (APF) coupled to a common DC intermediate circuit. The active series filter provides the voltage reference for the APF, which in turn corrects both the harmonic content introduced by the load and the VDC voltage in the DC intermediate circuit. The UPQC performance is improved by using the types of controllers listed above in the APF structure. The main performance indicators of the UPQC-APF control system for the controllers listed above are: stationary error, voltage ripple, and fractal dimension (DF) of the VDC voltage in the DC intermediate circuit. Results are also presented on the improvement of both current and voltage Total harmonic distortion (THD) in the case of, respectively, a linear and nonlinear load highly polluting in terms of harmonic content. Numerical simulations performed in a MATLAB/Simulink environment demonstrate superior performance of UPQC-APF control system when using PI with RL-TD3 agent and FO-type controller compared to classical PI controllers.

0.5 V Versatile Voltage- and Transconductance-Mode Analog Filter Using Differential Difference Transconductance Amplifier

In this work, a new versatile voltage- and transconductance-mode analog filter is proposed. The filter, without requiring resistors, employs three differential-difference transconductance amplifiers (DDTAs) and two grounded capacitors, which is suitable for integrated circuit implementation. Unlike previous works, the proposed filter topology provides: (1) high-input and low-output impedances for a voltage-mode (VM) analog filter, that is desirable in a cascade method of realizing higher order filters, and (2) high-input and high-output impedances for a transconductance-mode (TM) analog filter without any circuit modification. Moreover, a quadrature oscillator is obtained by simply adding a feedback connection. Both VM and TM filters provide five standard filtering responses such as low-pass, high-pass, band-pass, band-stop and all-pass responses into single topology. The natural frequency and the condition of oscillation can be electronically controlled. The circuit operates with 0.5 V supply voltage. It was designed and simulated in the Cadence program using 0.18 µm CMOS technology from TSMC.

On the Design Flow of the Fractional-Order Analog Filters Between FPAA Implementation and Circuit Realization

This work explicitly states the design flows of the fractional-order analog filters that researchers throughout the literature have used. Two main flows are studied: the FPAA implementation and the circuit realization. Partial-fraction expansion representation is used to prepare the approximated fractional-order response for implementation on FPAA. The generalization of the second-order active RC analog filters based on opamp from the integer-order domain to the fractional-order domain is presented. The generalization is studied from both mathematical and circuit realization points of view. It is found that the great benefit of the fractional-order domain is that it adds more degrees of freedom to the filter design process. Simulation and experimental results match the expected theoretical analysis.

Novel Double Modular Redundancy Based Fault-Tolerant FIR Filter for Image Denoising

In signal processing and communication systems, digital filters are widely employed. In some circumstances, the reliability of those systems is crucial, necessitating the use of fault tolerant filter implementations. Many strategies have been presented throughout the years to achieve fault tolerance by utilising the structure and properties of the filters. As technology advances, more complicated systems with several filters become possible. Some of the filters in those complicated systems frequently function in parallel, for example, by applying the same filter to various input signals. Recently, a simple strategy for achieving fault tolerance that takes advantage of the availability of parallel filters was given. Many fault-tolerant ways that take advantage of the filter’s structure and properties have been proposed throughout the years. The primary idea is to use structured authentication scan chains to study the internal states of finite impulse response (FIR) components in order to detect and recover the exact state of faulty modules through the state of non-faulty modules. Finally, a simple solution of Double modular redundancy (DMR) based fault tolerance was developed that takes advantage of the availability of parallel filters for image denoising. This approach is expanded in this short to display how parallel filters can be protected using error correction codes (ECCs) in which each filter is comparable to a bit in a standard ECC. “Advanced error recovery for parallel systems,” the suggested technique, can find and eliminate hidden defects in FIR modules, and also restore the system from multiple failures impacting two FIR modules. From the implementation, Xilinx ISE 14.7 was found to have given significant error reduction capability in the fault calculations and reduction in the area which reduces the cost of implementation. Faults were introduced in all the outputs of the functional filters and found that the fault in every output is corrected.

Shunt active power filter with a three-phase neutral-point-clamped inverter for power quality improvement based on fractional order proportional integral control

The quality of the electrical wave is far from being perfect, due to the use of nonlinear loads that generate current harmonics and consume reactive power. Knowing the origins and the effects of pollution harmonics on the electrical networks, this study is to implement a system of active filters with three levels inverter hysteresis-based control which injects into the network a current equal to those absorbed by the polluting load and out of phase, thus leading the supply current to be sinusoidal. The regulation and the stability of the power supply of the filter during a variation of the load is ensured by a classical proportional integral (PI) controller and then by a fractional order PI controller (FOPI). Several cases of simulation accompanied by a temporal analysis and a spectral analysis were carried out starting from the model of the supply network, nonlinear load and parallel active filter. The simulation results of the proposed methods show that the parallel active filter can effectively suppress the current harmonic to force the supply current to be sinusoidal and compensate the reactive power generated by the nonlinear load.

CO3Net: Coordinate-Aware Contrastive Competitive Neural Network for Palmprint Recognition

Palmprint recognition achieves high discrimination for identity verification. Compared with handcrafted local texture descriptors, convolutional neural networks (CNNs) can spontaneously learn optimal discriminative features without any prior knowledge. To further enhance the features’ representation and discrimination, we propose a coordinate-aware contrastive competitive neural network (CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> Net) for palmprint recognition. To extract the multi-scale textures, CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> Net consists of three parallel learnable Gabor filters (LGF)-based texture extraction branches that learn the discriminative and robust ordering features. Due to the heterogeneity of palmprints, the effects of different textures on the final recognition performance are inconsistent, and dynamically focusing on the textures is beneficial to the performance improvement. Then, CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> Net introduces the attention modules to explore the spatial information, and selects more robust and discriminative textures. Specifically, coordinate attention is embedded into CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> Net to adaptively focus on the important textures from the positional information. Since it is difficult for the cross-entropy loss to build a compact intra-class and separate inter-class feature space, the contrastive loss is employed to jointly optimize the network. CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> Net is validated on four public datasets, and the results demonstrate the remarkable recognition performance of the proposed CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> Net compared to other state-of-the-art methods.

A Novel Framework for Melanoma Lesion Segmentation Using Multiparallel Depthwise Separable and Dilated Convolutions with Swish Activations.

Skin cancer remains one of the deadliest kinds of cancer, with a survival rate of about 18-20%. Early diagnosis and segmentation of the most lethal kind of cancer, melanoma, is a challenging and critical task. To diagnose medicinal conditions of melanoma lesions, different researchers proposed automatic and traditional approaches to accurately segment the lesions. However, visual similarity among lesions and intraclass differences are very high, which leads to low-performance accuracy. Furthermore, traditional segmentation algorithms often require human inputs and cannot be utilized in automated systems. To address all of these issues, we provide an improved segmentation model based on depthwise separable convolutions that act on each spatial dimension of the image to segment the lesions. The fundamental idea behind these convolutions is to divide the feature learning steps into two simpler parts that are spatial learning of features and a step for channel combination. Besides this, we employ parallel multidilated filters to encode multiple parallel features and broaden the view of filters with dilations. Moreover, for performance evaluation, the proposed approach is evaluated on three different datasets including DermIS, DermQuest, and ISIC2016. The finding indicates that the suggested segmentation model has achieved the Dice score of 97% for DermIS and DermQuest and 94.7% for the ISBI2016 dataset, respectively.