Implementation Of Filter Research Articles

Implementation of FIR Filters through Inner product Units and Parallel Accumulations

Finite Impulse Response (FIR) filters are pivotal in digital signal processing, finding applications in diverse fields like audio processing, telecommunications, and biomedical signal analysis. This work presents an enhanced implementation methodology for FIR filters utilizing inner product computation and parallel accumulations. In the existing, FIR filters are typically implemented using convolution techniques, basic adders, and multipliers, which involve sequential processing and intensive computational resources. This method often leads to latency issues and limits real-time applications. Moreover, traditional implementations suffer from inefficiencies in utilizing hardware resources optimally, leading to suboptimal performance. The proposed methodology overcomes these limitations by leveraging inner product computations and parallel accumulation techniques. By exploiting inherent parallelism in the filtering process, the proposed method significantly reduces latency and enhances throughput.

Multi-phase time-zooming high-precision ultrasonic water flow meter

In this study, we have developed a high-precision ultrasonic water flow meter with a 0.18 μm standard CMOS technology. Three methods are proposed to improve the performance: implementation of an active bandpass filter, threshold voltage optimization, and integration of a multi-phase time-zooming technique. The active bandpass filter effectively reduces noise in the received signal, thereby contributing to the overall accuracy of propagation measurements. Based on a threshold voltage generator, our proposed system identifies the optimum threshold voltage, ensuring robust noise durability. Furthermore, we have extended the conventional time-zooming technique by incorporating multiple phases of internal clocks. This innovative multiphase time-zooming technique offers a theoretical improvement in time resolution. The results demonstrate a remarkable suppression of flow rate measurement variations, achieving an impressive reduction to 1/10 of the original values. This affirms the effectiveness of the developed ultrasonic water flow meter in ensuring precise and reliable flow rate measurements.

Hardware Implementation of FIR Filter for ECG Signal Processing: Design, Optimization, and Performance Analysis on an FPGA

Electrocardiogram (ECG) signals are commonly used to diagnose heart-related diseases. However, noise induced during the measurement process can affect the accuracy of the diagnosis. Digital filters, such as the Finite Impulse Response (FIR) filter, are widely used to filter out noise from the ECG signal. Nevertheless, the processing speed of software-based FIR filters is slow for large ECG datasets due to serial processing. This paper presents a hardware implementation of the FIR filter for ECG signal processing to overcome the processing speed issue. The filter is designed using the Kaiser Window method and implemented on the Intel Cyclone IV Field Programmable Gate Array (FPGA). The filter is first designed in MATLAB to obtain the filter coefficients where the ECG data were obtained from Physionet database. From the difference equation, we designed the signal flow graph (SDFG) and then mapped into hardware logics to enable parallel processing. Simulation results of the software (MATLAB) and hardware (FPGA) implementations are obtained and compared. The results show that the FPGA-based FIR filter can process the ECG signal up to 1,250 times faster than software implementations. To further optimize the design and reduce hardware cost, we introduce optimized designs by applying the operation scheduling and constrained resource allocation techniques. The maximum operating frequency, logic utilization, and power consumption of each design were analysed and compared. This study demonstrates that custom-designed hardware logic for digital signal processing can significantly outperform software implementations due to its parallel processing capabilities. The proposed optimization techniques reduce the hardware cost while maintaining high processing speed and accuracy. The hardware implementation of the FIR filter for ECG signal processing has numerous applications in diagnosing heart-related diseases and real-time monitoring of ECG signals in critical care settings.

Adaptive Gaussian Wiener Filter for CT-Scan Images with Gaussian Noise Variance

Medical imaging plays an important role in modern healthcare, with Computed Tomography (CT) being essential for high-resolution cross-sectional imaging. However, Gaussian noise often occurs within the CT scan images and makes it difficult for image interpretation and reduces the diagnostic accuracy, creating a significant obstacle to fully utilizing CT scanning technology. Existing denoising techniques have a hard time balance between noise reduction and preserving the important image details, failing to enable the optimal diagnostic precision. This study introduces Adaptive Gaussian Wiener Filter (AGWF), a novel filter aims to denoise CT scan images that have been corrupted with various Gaussian noise variance without compromising the image details. The AGWF combines the Gaussian filter for initial noise reduction, followed by the implementation of Wiener filter, which can adaptively estimate noise variance and signal power in localized regions. This approach not only outperforms other existing techniques but also showcases a remarkable balance between noise reduction and image detail preservation. The experiment evaluates 300 images from the dataset and each image is corrupted with Gaussian noise variance to ensure a comprehensive evaluation of the AGWF’s performance. The evaluation indicated that AGWF can improve the Signal-to-Noise Ratio (SNR) value and reduce the Root Mean Square Error (RMSE) and Mean Square Error (MSE) value, showing a qualitative improvement in CT scan imagery. The proposed method holds promising potential for advancing medical imaging technology with the implementation of deep learning.

Design and Implementation of Efficient IIR Low Pass Filter Based On Vedic Multiplier Algorithm

Abstract: This article presents a state-of-the-art design for a limitless impulse response (IIR) filter that can be easily reconfigured for use in real-time software. Using a vedic multiplication strategy, this work demonstrates the excellent general performance of a recursive or Infinite Impulse Response (IIR) filter. The number of repeats may be kept to a minimum and the processor's overall performance is improved by the reduction of computational delay and hardware. This research investigates the impact of different filter architectures on the development process and overall performance. To enforce and evaluate the planned filter's functionality, simulation is used across three independent platforms. The first system is MATLAB/ SIMULINK, the software package utilised to implement the IIR filter. The second strategy is called "HDL - Cosimulation," and it involves using SIMULINK's already-present tools in order to translate the formulated filter-out method into VHDL, the language used to describe very fast integrated circuits. The third approach uses Xilinx System Generator's pre-existing building components to physically realise the filter design. The method shown here allows the proposed filter to be implemented locally inside the FPGA device of interest

Implementation of a New Method for an Improved Voltage Dips Evaluation by the Italian Power Quality Monitoring System in Presence of VT Saturation Effects

The paper deals with a recent measurement improvement introduced into the Italian power quality monitoring system, concerning voltage dips detection and their proper evaluation in the presence of voltage transformer VT saturation phenomena. Saturation effects, essentially on network operated with isolated neutral, can give origin to “false” voltage dips, which can affect the on-line voltage monitoring during a few specific events (i.e. single line-to-ground faults). The “false dip” problem, on the other hand, can be considered negligible in compensated neutral networks. A new criterion has been developed in cooperation with the Polytechnic of Turin, based on the detection of a second harmonic component in the measured voltages, which, once provided with the proper setting parameters, can succeed in identifying “false” dips. The criterion effectiveness has been evaluated on field. The major advantage of the filter implementation in the monitoring system is that, from now on, network operated with isolated neutral could be considered quite similar to those operated with neutral compensation from the point of view of voltage dip performance.

An efficient equalization filter design approach for the accurate sound reproduction in a room environment with wide-band content

In some sound reproduction applications, a desired sound field needs to be reproduced by remote loudspeakers in a room with sufficient accuracy over a wide frequency band. When low-frequency room modes exist and the low-frequency content is required to be reproduced accurately, an equalization filter with a long time-domain response may be necessary, resulting in a filter with a significantly high filter order, especially when the sampling rate is high for the high-frequency content. This imposes computational challenges on both filter design and implementation. In this article, a convex formulation is proposed for the wide-band sound reproduction filter design. To address issues due to the low-frequency room modes, a reduced-order technique is then proposed based on a sub-band filter structure where a low sampling rate is used to equalize the low-frequency room mode characteristics, and a high sampling rate is used for high-frequency features which usually has a short time span in response. The computational time can be reduced by several orders of magnitude and the numerical stability can be improved when the proposed order reduction method is used to eliminate redundant orders. Simulation and experimental results confirm the accuracy and efficiency advantages of the proposed method.

An Adaptive Unscented Kalman Filter for the Estimation of the Vehicle Velocity Components, Slip Angles, and Slip Ratios in Extreme Driving Manoeuvres.

This paper presents a novel unscented Kalman filter (UKF) implementation with adaptive covariance matrices (ACMs), to accurately estimate the longitudinal and lateral components of vehicle velocity, and thus the sideslip angle, tire slip angles, and tire slip ratios, also in extreme driving conditions, including tyre-road friction variations. The adaptation strategies are implemented on both the process noise and measurement noise covariances. The resulting UKF ACM is compared against a well-tuned baseline UKF with fixed covariances. Experimental test results in high tyre-road friction conditions show the good performance of both filters, with only a very marginal benefit of the ACM version. However, the simulated extreme tests in variable and low-friction conditions highlight the superior performance and robustness provided by the adaptation mechanism.

Measurement and Control of Body Pressure Towards Smart Bed System

This research paper introduces a novel methodology for optimizing pressure dispersion in interactive bed systems, aiming to enhance sleep comfort while considering variegated body shapes and sleeping positions. By controlling the spring constant with precision, which is further optimized by a secondary differential filter, the proposed methodology assures ideal pressure distribution across the bed surface. At the heart of the proposed methodology lies the design of an interactive bed system that effectively responds to the unconscious postural shifts of the user during sleep. The implementation of a secondary differential filter in modulating the spring constant is an integral part of this approach, facilitating the crafting of a responsive bedding surface that promptly adapts to pressure alterations. The effectiveness of this novel method is verified through finite element method (FEM) analysis, which confirms successful pressure dispersion across the bed surface, an essential factor in enhancing sleep comfort. The research also proposes potential enhancements to this methodology, such as incorporating air-pressure control mechanisms, thereby introducing additional pressure control axes akin to those present in existing technologies. This study represents a significant stride forward in the advancement of interactive bed systems by presenting a new method for optimizing pressure dispersion, and hence, enhancing sleep comfort. The employment of FEM analysis not only validates the effectiveness of the proposed methodology but also highlights the potential for the future development of personalized and adaptive bedding solutions.

A Micro-architecture that supports the Fano–Elias encoding and a hardware accelerator for approximate membership queries

We present the first hardware design that supports operations over the Fano–Elias encoding (FE-encoding). Our design is a combinational circuit (i.e., single clock cycle) that supports insertions, deletions, and queries. FE-encoding allows one to store f binary strings, each of length ℓ+logm using a string that is m+f+fℓ bits long (rather than f(ℓ+logm)). The asymptotic gate-count of the circuit is Θ((m+f)⋅lgm+f⋅ℓ). The asymptotic delay is Θ(lgm+lgf+lgℓ). We implemented our design on an FPGA with four combinations of parameters in which the FE-encoding fits in 512 or 1024 bits.We present the first hardware design for a dynamic filter that maintains a set subject to insertions, deletions, and approximate membership queries. The design contains four main blocks: two memory banks that store FE-encodings and two combinational circuits for FE-encoding. Additional logic deals with double buffering and forwarding.We implemented the dynamic filter on an FPGA with the following parameters: (1) Elements in the dataset are 32-bit strings. (2) The supported dataset can contain up to nmax=45⋅214=737,280 elements. (3) The latency is 2-4 clock cycles. (4) Fixed (i.e., constant and stable) throughput. A new operation can be issued every clock cycle. (5) We prove that the probability of a false-positive error is bounded by 0.385⋅10−2. (6) We prove that the expected number of insertion failures is less than 1 for every 75 million insertions.Synthesis of our filter on a Xilinx Alveo U250 FPGA achieves a clock rate of 100 MHz (the critical path is due to the memory access). We measure a fixed throughput of 97.7 million operations per second (the loss of 2.3% in the throughput is due to instabilities in the bandwidth of the AXI4 Lite I/O channel).A unique feature of our filter implementation is that the throughput is stable and constant for all benchmarks and loads. Namely, the combination of operations does not influence the throughput and the throughput does not depend on the number of elements in the dataset (as long as the cardinality of the dataset is bounded by nmax). Previous dynamic filter implementations in software (implemented on x86 or GPU’s) do not exhibit stable and constant throughputs.

How Python brings efficiency to chatbots: enhancing user experience with magic filters in Aiogram

User experience is of immense importance, especially when it comes to chatbots. Continuous and intuitive interaction with a chatbot can significantly elevate customer satisfaction and loyalty. Conversely, a poor user experience can lead to disappointment and abandonment, ultimately harming the reputation and effectiveness of the chatbot. To ensure a positive user experience, chatbot developers need to focus on designing conversations that are natural, context-aware, and efficient. Chatbots have become a key element of customer service in the digital environment, and this article explores how the Python programming language, particularly through the aiogram library, allows the implementation of magic filters and how they can alter the functionality of any chatbot. The article discusses issues and limitations to consider, as well as the potential and possibilities of using magic filters in aiogram for future developments. Magic filters enhance user interaction, recognize entities, analyze moods, and determine the language of messages. The research aims to explore the possibilities of using Python and magic filters to enhance chatbots using aiogram. The goal is to create a guide on using magic filters and their effective implementation. It's essential to consider the complexity of implementing magic filters, the need for quality data, and understanding linguistic and cultural nuances. These aspects can pose challenges for developers. Recommendations include understanding the target audience, testing, and maintaining a balance between automation and human intervention. Developing chatbots using Python and aiogram with magic filters opens up broad possibilities for improving interaction and functionality. Understanding limitations and implementing best practices are key to success. The implementation of magic filters in chatbots brings numerous benefits for businesses and users. Personalized interaction, time and cost savings, improved scalability, and data-driven statistics collection are key advantages that can significantly enhance chatbot functionality and user satisfaction.

Design and Implementation of Quart-Torus Waveguide Filters for Layout Optimization Using Additive Manufacturing Techniques

Design and Implementation of Quart-Torus Waveguide Filters for Layout Optimization Using Additive Manufacturing Techniques

Wireless HVAC compressor diagnostics using state of the art machine learning-based signal analysis Z-freq 2D

One of the priorities in detecting a faulty car engine is through a method known as diagnostic, and it is very crucial as each diagnostic able to provide information and assessment to identify problems in the car A/C compressor. Early detection of a compressor malfunction, is a fast way to prevent any heavy maintenance of vehicles either in the short or long term. This paper introduced a new statistical method to find the faulty in the vehicle’s A/C compressor which is known as Z-freq 2D. The foundation of Z-freq 2D is involving the implementation of a Z-notch frequency domain filter. This approach was enhanced by using a special sensor that can detect two axial axes known as the Phantom Vibration Sensor specifically designed to detect and monitor the performance of the A/C compressor of the vehicle. Using the sensor, the data were recorded at numerous parameter sets of compressor speed. The analyzed data shows that Z-freq 2D coefficient is increase as the speed of the compressor increase over the duration of time. Z-freq 2D can be used to detect the malfunction and the irregularities of vibration signals, which may be indicated that the compressor is failing.

A Variable Fractional Delay Filter Based True-Time-Delay Array Architecture for Wideband Beamforming

True-time delay (TTD) units are critical in Wideband Large-Scale Antenna Systems (LSASs) for resolving beam squint and frequency selective fading. In this paper, we propose a TTD array architecture for wideband beamforming that addresses the challenges posed by LSASs by utilizing variable fractional delay (VFD) filters and time delay estimations (TDEs). This study introduces a convex optimization problem to develop a novel method for designing VFD filters that can achieve high precision time delay resolution while maintaining an acceptable frequency response peak error. Using these VFD filters, we present a TDE approach with Newton-Raphson iteration update that corrects the difference in arrival time between sensors. Due to the implementation of VFD filters and fast TDE modules, the new architecture can complete beam steering and tracking in 10 ms with MATLAB simulation. Furthermore, an example was designed to demonstrate that the proposed architecture is capable of producing precise beam pointing while maintaining the bit error rate (BER) that is extremely close to the theoretical curve over the entire bandwidth.

Real-Time Continuous Locomotion Mode Recognition and Transition Prediction for Human With Lower Limb Exoskeleton.

Real-time continuous locomotion mode recognition and seamless timely transition detection is critical for the exoskeleton robot. This study aims to present a comprehensive and innovative framework for locomotion mode recognition and transition prediction, exclusively utilizing inertial measurement unit (IMU) signals from the exoskeleton. In this framework, a CNN-BiLSTM model was developed and trained to be the classifier and a novel majority filter was designed to reduce the transition misjudgment rate. Moreover, a comprehensive evaluation system encompassing eight dimensions for the classifier, incorporating evaluation metrics specifically for transition misjudgment, was proposed. We collected locomotion motion data from six subjects wearing a rigid exoskeleton robot using six IMU sensors on the exoskeleton. The proposed method achieves a high level of recognition accuracy, with an overall average of 99.58% for the five steady locomotion modes (level ground walking (LG), stair ascent/descent (SA/SD), and ramp ascent/descent (RA/RD)) across six subjects following the transition decision. All transitions are recognizable, and the majority can be predicted in advance, with an average prediction time of 353ms. Furthermore, the implementation of majority filter resulted in an average 87.04% reduction in the transition misjudgment rate among six subjects, thereby decreasing the average transition misjudgment rate to 4.82%. Finally, the model was tested on a Jetson Nano to verify its real-time performance. The results presented above were obtained under the condition where either leg could function as the first transition leg and revealed that the developed system was capable of achieving precise locomotion mode recognition and timely transition prediction, with high real-time performance.

Air Levitation State Feedback Control Using Pole Placement and Kalman Filter

State feedback controller design and implementation of Kalman filter for Air levitation system is studied in simulation environment (Matlab/Simulink) and on real laboratory plant, low cost Floatshield device developed recently at our University and having an open source support, Takács (2022). Nonlinear model and its linearized state space representation are used for a pole placement controller design. The main focus is on Kalman filter implementation via its equations, which can help better understanding of its principle. Simulation results received for linear and nonlinear systems are compared with real plant experiments. The authors believe that the presented control design topics and corresponding tasks can contribute to developing students’ skills in control design and further enhance educational potential of hands-on laboratories using low cost air levitation device.

Approaches to Synthesis of High-Frequency Filters of Finite Impulse Response Type Based on Convolution Algorithm

Digital filters are widely used in audio and video processing, telecommunications, and other data processing industries. They enable the removal of unwanted components from signals, improve signal quality, and solve various signal processing tasks. This article covers the basic principles of digital filtering, including the stages from design to implementation of a digital filter. The text provides a comprehensive overview of the filtering process, covering parameterization and analysis of real data at the filter output. The authors discuss various types of digital filters, such as infinite impulse response (IIR) and finite impulse response (FIR) filters, each with its own unique characteristics, advantages, and disadvantages. This information will assist in selecting the most appropriate filter for a given task. The article discusses the use of digital filters for signal processing tasks, including noise reduction, in various real-world scenarios. The language used is clear, concise, and objective, with a formal register and precise word choice. The article discusses the use of digital filters for signal processing tasks, including noise reduction, in various real-world scenarios. The sentences and paragraphs have a logical flow of information with causal connections between statements. The text is free from grammatical errors, spelling mistakes, and punctuation errors. It aims to help readers understand the functionality of digital filters, how to select the appropriate filter for a specific task, and how to use them to enhance signal quality. The text adheres to conventional academic structure and formatting, with consistent citation and footnote style. No changes in content have been made to the original text. It is an important resource for technicians, engineers, students, and anyone interested in signal processing.

Model Based Navigation for a Twin Thruster Autonomous Surface Catamaran

The paper describes the design, implementation, and performance assessment of a navigation system for a small-size robotic twin thruster autonomous surface vessel (ASV). The navigation solution is designed building on a first principles maneuvering model that includes sources of nonlinearity and asymmetry of this class of catamaran robots. The paper illustrates how the proposed model is exploited in the navigation filter design and implementation and it discusses its impact on the overall navigation performance. Experimental results relative to the ULISSE ASV are included and illustrated.

Review of State-of-the-Art Microwave Filter Tuning Techniques and Implementation of a Novel Tuning Algorithm Using Expert-Based Hybrid Learning

Review of State-of-the-Art Microwave Filter Tuning Techniques and Implementation of a Novel Tuning Algorithm Using Expert-Based Hybrid Learning

On the Reconfiguration of Coupling Matrix

In this article, a general coupling matrix reconfiguration approach is proposed considering arbitrary topology, frequency-dependent couplings (FDCs), and nonresonating nodes (NRNs). The principle is developed in two steps: 1) a systematic procedure is introduced to reconfigure an arbitrary coupling matrix into the transversal topology using the similarity transformation and the eigen decomposition of the coupling matrix; an analytical solution with excellent accuracy is achieved in this process and 2) a framework of coupling matrix reconfiguration is constructed as a much simpler inverse problem, which can be optimized with low computational cost. Both local and global optimization approaches are exploited for this problem. The proposed framework is general and applicable to various scenarios. To verify the proposed algorithm, three traditional coupling matrix examples, two FDCs-incorporated coupling matrix examples, an NRNs-incorporated coupling matrix example, and a five-pole waveguide filter implementation are presented, respectively.