Fashion image analysis has a lot of trouble working with noisy data, especially when there aren't any good training examples to use. This happens because standard auto encoders can't keep class separability when the noise level changes. This paper introduces Structured Auto encoders (SAE), which combine convolutional encoder-decoder architectures with geometric constraints in the latent space. This strategy uses greedy layer-wise pre-training, and then it fine-tunes the model using two loss functions: structured latent space regularization and reconstruction error. The encoder has convolutional layers with 3×3 filters and ReLU functions that only turn on when they are needed. During testing, it was tested on Fashion-MNIST with noise levels ranging from 20% to 70%. It was also tested on MNIST, DeepFashion2, and 3D human pose datasets. The results of the experiment show that SAE can classify 85.4% of the time with only five samples labeled for each group. This is much better than the standard auto encoders (68.4%) and the baseline support vector machine (SVM) methods (84.32%). With the best setup, which has 1,000 hidden nodes and a learning rate of 0.1, images with up to 70% noise can be reconstructed well With 80% classification accuracy on clean test data, the latent space structured method allows for the solution of basic issues that are related to introducing geometric constraints between the classes so that the class boundaries remain intact even in a noisy environment.The proposed method achieves a classification accuracy of 85.4% ± 1.2%under standard evaluation settings, with robustness validated under noise levels of up to 70%. All results are obtained using standard training–testing splits and are averaged over multiple experimental runs to ensure reliability and reproducibility. The proposed semi-supervised performance is enhanced by increasing the number of discriminative features through the use of a highly structured representation of the data, as opposed to using an unstructured representation, which results in performance enhancement. The author presents an original framework capable of simultaneously providing robust image classification and denoising capability through geometric constraints established in the framework, which enhances the learning of discriminative features through the use of limited amounts of labeled data. The implementation of this framework in the real-world fashion industry would facilitate the processing of fashion images with respect to noise resistance and the ability to annotate images quickly.

Hand-drawn circuit diagrams must be manually converted into hardware description languages (HDLs) for digital design workflows. This manual conversion is time-consuming and error-prone and there has been little focus on hardware validation along the entire end-to-end circuit design process (such as circuit recognition and code generation). In response to these challenges, we present Sketic-FPGA, an end-to-end machine learning-based automated framework for converting hand-drawn logic circuits into functionally verified implementations on FPGA devices. The Sketic-FPGA system operates in a six-stage pipeline consisting of: adaptive image preprocessing, gate detection using an improved Faster R-CNN with ResNet-50 backbone, topology extraction, synthesis-aware Verilog code generation, automated FPGA implementation using Xilinx Vivado toolchain, and hardware-level validation. The proposed model was trained with 800 annotated samples across eight classes of logic gates, utilizing rotation-aware detection and curriculum learning to improve robustness. When evaluated against a dataset of 200 previously-unseen, test circuits, Sketic-FPGA produced 99.2% detection accuracy and 98.8% classification accuracy. All designs generated with Sketic-FPGA were successfully synthesized and implemented onto actual FPGA hardware, achieving functional correctness across the entire test circuit dataset using LED testing, Integrated Logic Analyzer (ILA) waveform verification, and exhaustive truth-table validation. On average, processing each circuit took 29.4 seconds from start to finish which has reduced the time required for a designer to create a circuit manually. An examination of how long it took students to design a circuit revealed that they spent 67% less time across multiple design iterations. Although we have only demonstrated the effectiveness of our framework on combinational circuits and in a controlled environment, our results indicate that there are many opportunities for rapid prototyping and automated hardware design as well as support for digital educational methods.