We propose a meta-learning-enhanced BiLSTM autoencoder architecture for robust one-bit error correction coding, designed to dynamically adapt to diverse channel conditions without requiring explicit retraining. The proposed method fuses a channel-aware meta-discriminator into an adversarial training framework, allowing the system to generalize across Rician, Rayleigh, and AWGN channels by adapting its decision boundaries based on temporal signal statistics. The meta-discriminator, realized as a lightweight Transformer-encoder with cross-attention, computes channel-specific embeddings from the received signal, which modulate the adversarial loss and guide the reconstruction process. Furthermore, the BiLSTM encoder-decoder utilizes bidirectional layers with residual connections to capture long-range dependencies, while a learnable one-bit quantizer with adaptive thresholds ensures efficient signal representation. The training objective combines reconstruction loss, adversarial loss, and a meta-regularization term, which stabilizes updates and refines adaptation. The meta-discriminator performs real-time parameter adjustments using a single gradient step during inference to make the system resilient to unseen channel impairments. The experiments demonstrate significant improvements in BER and MSE across various fading channels and data sizes. The Rician channel exhibits the lowest values of BER and MSE of 0.032 and 0.031, respectively, when considering a data size of 2500 symbols. The proposed work shows its dual capability to learn error-correcting codes through BiLSTMs, apart from exploiting meta-learning for channel adaptation.

The imbalanced surveillance video dataset consists of majority and minority classes as normal and anomalous instances in the nonlinear and non-Gaussian framework. The normal and anomalous instances cause majority and minority samples or particles associated with high and low probable regions when considering the standard particle filter. The minority particles tend to be at high risk of being suppressed by the majority particles, as the proposal probability density function (pdf) encourages the highly probable regions of the input data space to remain a biased distribution. The standard particle filter-based tracker afflicts with sample degeneration and sample impoverishment due to the biased proposal pdf ignoring the minority particles. The difficulty in designing the correct proposal pdf prevents particle filter-based tracking in the imbalanced video data. The existing methods do not discuss the imbalanced nature of particle filter-based tracking. To alleviate this problem and tracking challenges, this paper proposes a novel fractional whale particle filter (FWPF) that fuses the fractional calculus-based whale optimization algorithm (FWOA) and the standard particle filter under weighted sum rule fusion. Integrating the FWPF with an iterative Gaussian mixture model (GMM) with unbiased sample variance and sample mean allows the proposal pdf to be adaptive to the imbalanced video data. The adaptive proposal pdf leads the FWPF to a minimum variance unbiased estimator for effectively detecting and tracking multiple objects in the imbalanced video data. The fractional calculus up to the first four terms makes the FWOA a local and global search operator with inherent memory property. The fractional calculus in the FWOA oversamples minority particles to be diversified with multiple imputations to eliminate data distortion with low bias and low variance. The proposed FWPF presents a novel imbalance evaluation metric, tracking distance correlation for the imbalanced tracking over UCSD surveillance video data and shows greater efficacy in mitigating the effects of the imbalanced nature of video data compared to other existing methods. The proposed method also outshines the existing methods regarding precision and accuracy in tracking multiple objects. The consistent tracking distance correlation near zero values provides efficient imbalance reduction through bias-variance correction compared to the existing methods.