Search Results (168)

To obtain more accurate information on using an inertial navigation system (INS)-based integrated localization system, an integrated filter with maximum correntropy criterion Kalman filter (mccKF) and finite impulse response (FIR) is proposed for the fusion of INS-based multisource sensor data in this work. In the realm of medical applications, precise localization is crucial for various aspects, such as tracking the movement of a medical instrument within the human body or monitoring its position in the human body during procedures. This study uses ultra-wideband (UWB) technology to rectify the position errors of the INS. In this method, the difference between the positions of the INS and UWB is used as the measurement of the filter. The main data fusion filter in this study is the mccKF, which utilizes the maximum correntropy criterion (mcc) method to enhance the robustness of the Kalman filter (KF). This filter is used for fusing data from multiple sources, including the INS. Moreover, we use the Mahalanobis distance to verify the performance of the mccKF. If the performance of the mccKF is lower than the preset threshold, the Allan Variance-assisted FIR filter is used to replace the mccKF, which is designed in this work. This adaptive approach ensures the resilience of the system in demanding medical environments. Two practical experiments were performed to evaluate the effectiveness of the proposed approach. The findings indicate that the mccKF/FIR integrated method reduces the localization error by approximately 32.43% and 37.5% compared with the KF and mccKF, respectively. These results highlight the effectiveness of the proposed approach. Full article

The computational complexity of existing multi-symbol detection (MSD) algorithms grows exponentially as the observation intervals increase, resulting in difficulties in algorithm implementation for detecting pulse code modulation/frequency modulation (PCM/FM) signals, especially for multi-channel signals. To address the challenges, we proposed a low-complexity MSD algorithm based on the averaged matched filtering. The proposed algorithm groups the local reference signals based on the different importance levels of the middle and edge bits in the correlation operations and averages the edge bits, leading to a considerable decrease in matching branches. Furthermore, it leverages the phase symmetry, and the proposed algorithm retains half of the averaged local reference signals for the matching operation, thus further reducing the matching branches. The proposed algorithm reduces the storage of the local signals and correlation operations to one-eighth compared to the traditional MSD algorithm under different observation lengths. Additionally, based on the structure of multiplexed FIR filters, the proposed algorithm optimizes single-channel single-coefficient FIR filters into four-channel double-coefficient FIR filters, further reducing the hardware resource consumption by approximately 25%. The simulation results showed that the proposed algorithm achieved demodulation performance comparable to the traditional MSD algorithms while reducing the computational complexity by 87.5%. Compared to the decision-feedback MSD algorithm, it achieves higher demodulation gain with a 75% complexity reduction. The Field Programmable Gate Array (FPGA) platform implementation results showed that the proposed algorithm reduces hardware resource consumption by nearly 90% compared with the traditional algorithm, and the hardware demodulation performance loss is less than 1 dB compared with the simulation results. Full article

In recent years, the macro–micro structure (servo platform for macro motion and galvanometer for micro motion) composed of a galvanometer and servo platform has been gradually applied to laser processing in order to address the increasing demand for high-speed, high-precision, and large-format precision machining. The research in this field has evolved from step-and-scan methods to linkage processing methods. Nevertheless, the existing linkage processing methods cannot make full use of the field-of-view (FOV) of the galvanometer. In terms of motion distribution, the existing methods are not suitable for continuous micro segments and generate the problem that the distribution parameter can only be obtained through experience or multiple experiments. In this research, a new laser linkage processing method for global trajectory smoothing of densely discretized paths is proposed. The proposed method can generate a smooth trajectory of the servo platform with bounded acceleration by the finite impulse response (FIR) filter under the global blending error constrained by the galvanometer FOV. Moreover, the trajectory of the galvanometer is generated by vector subtraction, and the motion distribution of macro–micro structure is accurately realized. Experimental verification is carried out on an experimental platform composed of a three-axis servo platform, a galvanometer, and a laser. Simulation experiment results indicate that the processing efficiency of the proposed method is improved by 79% compared with the servo platform processing only and 55% compared with the previous linkage processing method. Furthermore, the method can be successfully utilized on experimental platforms with good tracking performance. In summary, the proposed method adeptly balances efficiency and quality, rendering it particularly suitable for laser precision machining applications. Full article

This study introduces a mechatronic biomedical device engineered for concurrent acquisition and analysis of four cardiac non-invasive signals: Electrocardiogram (ECG), Phonocardiogram (PCG), Impedance Cardiogram (ICG), and Photoplethysmogram (PPG). The system enables assessment of individual and simultaneous waveforms, allowing for detailed scrutiny of cardiac electrical and mechanical dynamics, encompassing heart rate variability, systolic time intervals, pre-ejection period (PEP), and aortic valve opening and closing timings (ET) through an application programmed with MATLAB App Designer, which applies derivative filters, smoothing, and FIR digital filters and evaluates the delay of each one, allowing the synchronization of all signals. These metrics are indispensable for deriving critical hemodynamic indices such as Stroke Volume (SV) and Cardiac Output (CO), paramount in the diagnostic armamentarium against cardiovascular pathologies. The device integrates an assembly of components including five electrodes, operational and instrumental amplifiers, infrared opto-couplers, accelerometers, and advanced filtering subsystems, synergistically tailored for precision and fidelity in signal processing. Rigorous validation utilizing a cohort of healthy subjects and benchmarking against established commercial instrumentation substantiates an accuracy threshold below 4.3% and an Interclass Correlation Coefficient (ICC) surpassing 0.9, attesting to the instrument’s exceptional reliability and robustness in quantification. These findings underscore the clinical potency and technical prowess of the developed device, empowering healthcare practitioners with an advanced toolset for refined diagnosis and management of cardiovascular disorders. Full article

Passive pulse shaping at frequencies above 1 GHz is mainly achieved through frequency-domain processing using filters. Unfortunately, the conventional frequency domain approach does not allow precise control of the impulse response of the filter, therefore, setting limitations to the pulse shaping accuracy. Sub-nanosecond pulse expansion that preserves steep pulse transitions is one of the ultra-wideband (UWB) applications where frequency domain approaches do not provide satisfactory results. This paper proposes a highly innovative approach based on time-domain signal processing using a set of parallel microstrip delay lines connected in a network accompanied by a splitter at the input and a combiner at the output. The proposed design, analogous to finite impulse response (FIR) filters in digital signal processing (DSP), provides fine-grained control over time-domain characteristics and supports the implementation of complex functions, including pulse expansion. This paper presents a detailed analysis of previous work and theoretical considerations regarding the advantages and limitations of UWB pulse time-domain processing. Moreover, detailed HFSS simulations of components, such as a microstrip pulse splitter, delay lines, a combiner, and their combinations, are presented. Finally, the results of the experimental validation of the device, fabricated on an FR-4 substrate, are presented. Technology for effective implementation of a pulse splitter, delay lines, and a pulse combiner, as well as their matching, can be considered as key findings of the given research. Limitations associated with matching and delay estimation for pulsed UWB signals are highlighted. Full article

In this work, an analytical method in the frequency domain is proposed for the design of two-dimensional digital FIR differentiators. This technique uses an approximation based on two methods: the Chebyshev series and the Fourier series, which, finally, lead to a trigonometric polynomial, which is a remarkably precise approximation of the transfer function of the ideal differentiator. The digital differentiator is applied to three test images, one greyscale image and two binary images, and simulation results show its performance in the processing task. Also, based on the fact that this 2D differentiator is separable on the two frequency axes, we propose an efficient implementation at the system level, using polyphase filtering. The designed digital differentiator is very accurate and efficient, having a high level of parallelism and reduced computational complexity. Full article

This paper presents a highly efficient, low-power, compact mixed-signal sinusoidal current generator (CG) integrated circuit (IC) designed for bioelectrical impedance spectroscopy (BIS) with low total harmonic distortion (THD). The proposed system employs a 9-bit sine wave lookup table (LUT) which is simplified to a 4-bit data stream through a third-order digital delta–sigma modulator (ΔΣM). Unlike conventional analog low-pass filters (LPF), which statically limit bandwidth, the finite impulse response (FIR) filter attenuates high-frequency noise according to the operating frequency, allowing the frequency range of the sinusoidal signal to vary. Additionally, the output of the FIR filter is applied to a 6-bit capacitive digital-to-analog converter (CDAC) with data-weighted averaging (DWA), enabling dynamic capacitor matching and seamless interfacing. The sinusoidal CG IC, fabricated using a 65 nm CMOS process, produces a 5 μA amplitude and operates over a wide frequency range of 0.6 to 20 kHz. This highly synthesizable CG achieves a THD of 0.04%, consumes 19.2 μW of power, and occupies an area of 0.0798 mm². These attributes make the CG IC highly suitable for compact, low-power bio-impedance applications. Full article

This paper presents a programmable digital filtering unit dedicated to operating with signals from infrared (IR) detection modules. The designed device is quite useful for increasing the signal-to-noise ratio due to the reduction in noise and interference from detector–amplifier circuits or external radiation sources. Moreover, the developed device is flexible due to the possibility of programming the desired filter types and their responses. In the circuit, an advanced field-programmable gate array FPGA chip was used to ensure an adequate number of resources that are necessary to implement an effective filtration process. The proposed circuity was assisted by a 32-bit microcontroller to perform controlling functions and could operate at frequency sampling of up to 40 MSa/s with 16-bit resolution. In addition, in our application, the sampling frequency decimation enabled obtaining relatively narrow passband characteristics also in the low frequency range. The filtered signal was available in real time at the digital-to-analog converter output. In the paper, we showed results of simulations and real measurements of filters implementation in the FPGA device. Moreover, we also presented a practical application of the proposed circuit in cooperation with an InAsSb mid-IR detector module, where its self-noise was effectively reduced. The presented device can be regarded as an attractive alternative to the lock-in technique, artificial intelligence algorithms, or wavelet transform in applications where their use is impossible or problematic. Comparing the presented device with the previous proposal, a higher signal-to-noise ratio improvement and wider bandwidth of operation were obtained. Full article

Recently, solar and wind power generation have gained attention as pathways to achieving carbon neutrality, and Renewable Energy Resource Management System (RERMS) technology has been developed to monitor and control small-scale, distributed renewable energy resources. In this work, we present an Event-Triggered Transmission (ETT) algorithm for RERMS, which transmits sensor measurements to the base station only when necessary. The ETT algorithm helps prevent congestion in the communication channel between RERMS and the base station, avoiding time delays or packet loss caused by the excessive transmission of sensor measurements. We design a hybrid state estimation algorithm that combines Kalman and Finite Impulse Response (FIR) filters to enhance the estimation performance, and we propose a new ETT algorithm based on this design. We evaluate the performance of the proposed algorithm through experiments that transmit actual sensor measurements from a photovoltaic power generation system to the base station, demonstrating that it outperforms existing algorithms. Full article

Fading in communication channels presents eminently stochastic characteristics and is a significant challenge, especially at millimeter wave (mmW) frequencies, where the need for lines of sight and the high attenuation of obstacles complicate transmission. This article presents a model based on Bayesian fundamentals intended to improve the description and simulation of stochastic fading effects in these channels. It also includes the use of signal processing techniques to simulate and reconstruct the received signal, simulating the communication channel with an FIR filter. The results obtained by simulating the model show its ability to efficiently capture rapid and profound variations in the signal, typical of those that occur in urban and suburban environments and transmissions in the mmW spectrum. It also provides greater uniformity in signal reconstruction compared to the traditional models that are in use. Using Bayesian fundamentals, which allow dynamic adaptation to change in channel behavior, can improve the efficiency and reliability of networks, especially modern smart networks. Compared to traditional models, the proposed model offers improved signal reconstruction and fading mitigation accuracy, with prospects for future integration in smart communication systems. The better capacity in signal reconstruction presents itself as a differentiator of the model, suggesting greater precision in data transmission. Full article

In this paper, a multichannel FIR filter design based on the Time Division Multiplex (TDM) approach that incorporates one multiply and add unit, regardless of the variable coefficient length and varying channels, by associating the resource sharing doctrine is suggested. A multiplier based on approximate distributed arithmetic (DA) circuits is employed for effective resource optimization. Although no explicit multiplication was conducted in this realization, the radix-8 and radix-4 Booth algorithms are utilized in the DA framework to curtail and optimize the partial products (PPs). Furthermore, the input stream is truncated with an erratum mending unit to roughly construct the partial products. For an aggregation of PPs, an approximate Wallace tree is taken into consideration to further minimize hardware expenses. Consequently, the suggested design’s latency, utilized area, and power usage are largely reduced. The Xilinx Vertex device is expedited, given the synthesis of the suggested multichannel realization with 16 taps, which is simulated using the Verilog formulary. It is observed that the filter structure with one channel produced the desired results, and the system’s frequency can support up to 429 MHz with a reduced area. Utilizing TSMC 180 nm CMOS technology and the Cadence RC compiler, cell-level performance is also achieved. Full article

This paper describes a revolutionary design paradigm for monitoring aquatic life. This unique methodology addresses issues such as limited memory, insufficient bandwidth, and excessive noise levels by combining two approaches to create a comprehensive predictive filtration system, as well as multiple-transfer route analysis. This work focuses on proposing a novel filtration learning approach for underwater sensor nodes. This model was created by merging two adaptive filters, the finite impulse response (FIR) and the adaptive line enhancer (ALE). The FIR integrated filter eliminates unwanted noise from the signal by obtaining a linear response phase and passes the signal without distortion. The goal of the ALE filter is to properly separate the noise signal from the measured signal, resulting in the signal of interest. The cluster head level filters are the adaptive cuckoo filter (ACF) and the Kalman filter. The ACF assesses whether an emitter node is part of a set or not. The Kalman filter improves the estimation of state values for a dynamic underwater sensor networking system. It uses distributed learning long short-term memory (LSTM-CNN) technology to ensure that the anticipated value of the square of the gap between the prediction and the correct state is the smallest possible. Compared to prior methods, our suggested deep filtering–learning model achieved 98.5% of the sensory filtration method in the majority of the obtained data and close to 99.1% of an adaptive prediction method, while also consuming little energy during lengthy monitoring. Full article

Finite impulse response (FIR) filters are explicitly used in decisive applications such as communication and signal processing areas. Advancement in the latest technologies necessitates specific designs with optimal characteristics. This research work proposes the realization of an efficient distributed arithmetic adaptive FIR filter (DAAFA) architecture using radix-4 and radix-8 computation. Distributed arithmetic (DA) is extensively used to calculate the sum of products without involving a multiplier. The proposed fixed-point realization of a single multiply and accumulate (MAC) FIR adaptive filter is implemented with minimum complex design. The total longest-way computation time is a combination of the delay that occurred in the error calculation module and the delay involved in updating the filter weights. The longest-way computation time of the filter structure is higher, which results in increased latency. In addition, the approximate design of the radix DA multiplier structure is constructed using Booth recoding, partial product formation block and shifting-based accumulation block. Further, the approximate design of DA offers a reduction in complexity and area with respect to the number of slices and enhances the operating speed. The partial product is created using shifters and efficient adders, which further enhances the performance of the realization. This work is implemented in Xilinx and Altera devices and is compared with the present literature. From the synthesis results, it is observed that the propounded design outperforms in terms of complexity, slice delay product and ultimate speed of exertion. The suggested architecture was found to be decisive in terms of area, delay and complexity abatement. The results indicate that the propounded design achieves area reduction (slices) of about 92.03% compared to the existing design. Also, a speed enhancement of about 90.7% is accomplished for the proposed architecture. Nonetheless, the devised architecture utilizes the least means square approach, which enhances the convergence rate notably. Full article

A digitizer is considered one of the fundamental components of an earthquake monitoring system. In this paper, we design and implement a high accuracy seismic digitizer. The implemented digitizer consists of several blocks, i.e., the analog-to-digital converter (ADC), GPS receiver, and microprocessor. Three finite impulse response (FIR) filters are used to decimate the sampling rate of the input seismic data according to user needs. A graphical user interface (GUI) has been designed for enabling the user to monitor the seismic waveform in real time, and process and adjust the parameters of the acquisition unit. The system casing is designed to resist harsh conditions of the environment. The prototype can represent the three component sensors data in the standard MiniSEED format. The digitizer stream seismic data from the remote station to the main center is based on TCP/IP connection. This protocol ensures data transmission without any losses as long as the data still exist in the ring buffer. The prototype was calibrated by real field testing. The prototype digitizer is integrated with the Egyptian National Seismic Network (ENSN), where a commercial instrument is already installed. Case studies shows that, for the same event, the prototype station improves the solution of the ENSN by giving accurate timing and seismic event parameters. Field test results shows that the event arrival time and the amplitude are approximately the same between the prototype digitizer and the calibrated digitizer. Furthermore, the frequency contents are similar between the two digitizers. Therefore, the prototype digitizer captures the main seismic parameters accurately, irrespective of noise existence. Full article

Motor imagery brain–computer interface (MI-BCI) systems hold the potential to restore motor function and offer the opportunity for sustainable autonomous living for individuals with a range of motor and sensory impairments. The feature extraction and classification of motor imagery EEG signals related to motor imagery brain–computer interface systems has become a research hotspot. To address the challenges of difficulty in feature extraction and low recognition rates of motor imagery EEG signals caused by individual variations in EEG signals, a classification algorithm for EEG signals based on multi-feature fusion and the SVM-AdaBoost algorithm was proposed to improve the recognition accuracy of motor imagery EEG signals. Initially, the electroencephalography (EEG) signals are preprocessed using Finite Impulse Response (FIR) filters, and a multi-wavelet framework is constructed based on the Morlet wavelet and the Haar wavelet. Subsequently, the preprocessed signals undergo multi-wavelet decomposition to extract energy features, Common Spatial Patterns (CSP) features, Autoregressive (AR) features, and Power Spectral Density (PSD) features. The extracted features are then fused, and the fused feature vector is normalized. Following that, classification is implemented within the SVM-AdaBoost algorithm. To enhance the adaptability of SVM-AdaBoost, the Grid Search method is employed to optimize the penalty parameter and kernel function parameter of the SVM. Concurrently, the Whale Optimization Algorithm is utilized to optimize the learning rate and number of weak learners within the AdaBoost ensemble, thereby refining the overall performance. In addition, the classification performance of the algorithm is validated using a brain-computer interface (BCI) dataset. In this study, it was found that the classification accuracy reached 95.37%. Via the analysis of motor imagery electroencephalography (EEG) signals, the activation patterns in different regions of the brain can be detected and identified, enabling the inference of user intentions and facilitating communication and control between the human brain and external devices. Full article