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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.

Mobile technology is currently trending toward supporting multiple communication standards on a single device. This means that some reconfigurable techniques must be the foundation of their design. The two essential requirements of channel filters are minimized complexity and reconfigurability. In this research, a novel extension of Frequency Response Masking (FRM) was investigated by employing Time Division Multiplexing (TDM)-based single Multiply and Accumulate (MAC) architecture using the principle of resource sharing to realize multiple sharp filter responses from a single prototype constant group delay low pass filter. This paper uses a single multiply and add units regardless of the quantity of channels and taps. The suggested reconfigurable filter was synthesized on technology based on 0.18-µm CMOS and put into practice. Further trials were carried out on Virtex-II 2v3000ff1152-4 FPGA device. The outcomes revealed that the suggested channel filter, which was synthesized using FPGA, provides 21.36% of the area curtail and 14.88% of power scaling down on average and put into practice using ASIC provides 5.18% of the area reduction and 9.08% of power scaling down on average. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Skin cancer diagnosis greatly benefits from advanced machine learning techniques, particularly fine-tuned deep learning models. In our research, we explored the impact of traditional machine learning and fine-tuned deep learning approaches on prediction accuracy. Our findings reveal significant improvements in predictability and accuracy with fine-tuning, particularly evident in deep learning models. The CNN, SVM, and Random Forest Classifier achieved high accuracy. However, fine-tuned deep learning models such as EfficientNetB0, ResNet34, VGG16, Inception _v3, and DenseNet121 demonstrated superior performance. To ensure comparability, we fine-tuned these models by incorporating additional layers, including one flatten layer and three densely interconnected layers. These layers play a crucial role in enhancing model efficiency and performance. The flatten layer preprocesses multidimensional feature maps, facilitating efficient information flow, while subsequent dense layers refine feature representations, capturing intricate patterns and relationships within the data. Leveraging LeakyReLU activation functions in the dense layers mitigates the vanishing gradient problem and promotes stable training. Finally, the output dense layer with a sigmoid activation function simplifies decision making for healthcare professionals by providing binary classification output. Our study underscores the significance of incorporating additional layers in fine-tuned neural network models for skin cancer classification, offering improved accuracy and reliability in diagnosis. [ABSTRACT FROM AUTHOR]

Rheumatoid Arthritis (RA) is a chronic autoimmune illness that occurs in the joints, resulting in inflammation, pain, and stiffness. X-ray examination is one of the most common diagnostic procedures for RA, but manual X-ray image analysis has limitations because it is a time-consuming procedure and is prone to errors. A specific algorithm aims to a lay stable and accurate segmenting of carpal bones from hand bone images, which is vitally important for identifying rheumatoid arthritis. The algorithm demonstrates several stages, starting with Carpal bone Region of Interest (CROI) specification, dynamic thresholding, and Gray Level Co-occurrence Matrix (GLCM) application for texture analysis. To get the clear edges of the image, the component is first converted to the greyscale function and thresholding is carried out to separate the hand from the background. The pad region is identified to obtain the contours of it, and the CROI is defined by the bounding box of the largest contour. The threshold value used in the CROI method is given a dynamic feature that can separate the carpal bones from the surrounding tissue. Then the GLCM texture analysis is carried out, calculating the number of pixel neighbors, with the specific intensity and neighbor relations of the pixels. The resulting feature matrix is then employed to extract features such as contrast and energy, which are later used to categorize the images of the affected carpal bone into inflamed and normal. The proposed technique is tested on a rheumatoid arthritis image dataset, and the results show its contribution to diagnosis of the disease. The algorithm efficiently divides carpal bones and extracts the signature parameters that are critical for correct classification of the inflammation in the cartilage images. [ABSTRACT FROM AUTHOR]

As smart grid advance, Power Load Forecasting (PLF) has become a research hotspot. As the foundation of the forecasting model, the Power Load Monitoring (PLM) data takes on great importance due to its completeness, reliability and accuracy. However, monitoring equipment failures, transmission channel congestion and anomalies result in missing PLM data, which directly affects the performance of the PLF model. To address this issue, this paper proposes an L1-and-L2-Regularized Nonnegative Tensor Factorization (LNTF) model to impute PLM missing data. Its main idea is threefold: (1) combining L1 and L2 norms to achieve effective feature extraction and improve the model's robustness; (2) incorporating two temporal-dependent linear biases to describe the fluctuations of PLM data; (3) adding nonnegative constraints to precisely define the nonnegativity of PLM data. Extensive empirical studies on two publicly real-world PLM datasets with 1,569,491 and 413,357 known entries and missing rates of 93.35% and 96.75% demonstrate that the proposed LNTF improves 14.04%, 59.31%, and 71.43% on average over the state-of-the-art imputation models in terms of imputation error, convergence rounds, and time cos, respectively. Its high computational efficiency and low imputation error make practical sense for PLM data imputation. [ABSTRACT FROM AUTHOR]

Recently, community detection has emerged as a prominent research area in the analysis of complex network structures. Community detection models based on non-negative matrix factorization (NMF) are shallow and fail to fully discover the internal structure of complex networks. Thus, this article introduces a novel constrained symmetric non-negative matrix factorization with deep autoencoders (CSDNMF) as a solution to this issue. The model possesses the following advantages: (1) By integrating a deep autoencoder to discern the latent attributes bridging the original network and community assignments, it adeptly captures hierarchical information. (2) Introducing a graph regularizer facilitates a thorough comprehension of the community structure inherent within the target network. (3) By integrating a symmetry regularizer, the model's capacity to learn undirected networks is augmented, thereby facilitating the precise detection of symmetry within the target network. The proposed CSDNMF model exhibits superior performance in community detection when compared to state-of-the-art models, as demonstrated by eight experimental results conducted on real-world networks. [ABSTRACT FROM AUTHOR]

Difficulty in visualizing anatomical structures has been identified as a challenge in anatomy learning and the emergence of three‐dimensional printed models (3DPMs) offers a potential solution. This study evaluated the effectiveness of 3DPMs for learning the arterial supply of the head and neck region. One hundred eighty‐four undergraduate medical students were randomly assigned to one of four learning modalities including wet specimen, digital model, 3DPM, and textbook image. Posttest scores indicated that all four modalities supported participants' knowledge acquisition, most significantly in the wet specimen group. While the participants rated 3DPMs lower for helping correct identification of structures than wet specimens, they praised 3DPMs for their ability to demonstrate topographical relationships between the arterial supply and adjacent structures. The data further suggested that the biggest limitation of the 3DPMs was their simplicity, thus making it more difficult for users to recognize the equivalent structures on the wet specimens. It was concluded that future designs of 3DPMs will need to consider the balance between the ease of visualization of anatomical structures and the degree of complexity required for successful transfer of learning. Overall, this study presented some conflicting evidence of the favorable outcomes of 3DPMs reported in other similar studies. While effective for anatomy learning as a standalone modality, educators must identify the position 3DPM models hold relative to other modalities in the continuum of undergraduate anatomy education in order to maximize their advantages for students. [ABSTRACT FROM AUTHOR]