Hybrid TRP-UE Sensing for Enhanced Target Localization

Integrated Sensing and Communication (ISAC) refers to the capability for the network to provide communications services whilst also being able to sense the environment in a scalable manner. One of the key functions of ISAC is the accurate localization of passive and mobile sensing targets. This paper introduces a novel hybrid TRP-UE sensing mechanism that improves network-based sensing performance. Evaluation results are provided using 3GPP-compliant ISAC channel models. The results demonstrate the significant benefit in complimenting TRP-based sensing with UE-assisted sensing in challenging propagation environments such as indoor factory.

Spaceborne SAR Change Detection and Coherence Analysis for Maritime Port Monitoring

Spaceborne synthetic aperture radar (SAR) provides coherent microwave imagery suitable for maritime infrastructure monitoring under illumination-independent and weather-independent acquisition conditions. An academic conference-style analysis is presented for SAR amplitude and geocoded multitemporal data over Tianjin Port, China. The processing chain includes amplitude visualization, radiometric scaling, view-direction interpretation, range and azimuth resolution assessment, speckle reduction, amplitude-based change mapping, GeoTIFF export for geographic inspection, and interferometric coherence estimation. Histogram-guided display limits improve the interpretability of the complex SAR magnitude images, while zoomed inspection of shadows and bright layover responses supports qualitative interpretation of illumination geometry. A two-dimensional Fourier analysis is used to characterize dominant spectral content and to estimate an approximate range resolution of 0.42 m and an azimuth angular separation of 0.19 degrees under the available image-coordinate calibration. Multitemporal master and slave images are subsequently compared through filtered amplitude differences and coherence maps computed with multiple spatial averaging windows. The results highlight the relevance of SAR amplitude and coherence products for detecting structural and surface-condition variations in dense port environments characterized by vessels, storage tanks, quay structures, industrial yards, and water-land transitions.

Experimental Analysis of Neural Network-Based Image Classification on the CIFAR-10 Dataset

An experimental investigation of neural image classification on the CIFAR-10 benchmark is presented through fully connected and convolutional network formulations. The analysis emphasizes the complete learning pipeline: image vectorization, normalization, one-hot class encoding, supervised loss minimization, learning-rate selection, mini-batch training, convolutional feature extraction, max-pooling, and validation-based generalization assessment. A convolutional architecture with six convolutional layers and three max-pooling stages is evaluated for ten training epochs using a batch size of 128 and an Adam optimizer with a learning rate of 0.001. The validation accuracy reaches approximately 74.77%, while the validation loss begins to increase after the middle of training despite continued reduction in training loss. The resulting behavior illustrates the practical difference between representation learning and memorization, and it provides a compact experimental baseline for future studies on regularization, data augmentation, deeper architectures, and reproducible image-classification education.

Reference-Based Recursive Least-Squares Mitigation of Real Interference in Stereo Audio Recordings

Reference-based adaptive interference cancellation is evaluated for stereo audio recordings corrupted by real train noise and environmental background. The observed signal is modeled as a clean stereo program contaminated by an additive disturbance generated by an external acoustic source through unknown propagation paths. A second stereo recording, representing another filtered observation of the same physical noise source, is used as the reference input of a multi-reference recursive least-squares (RLS) estimator. The estimated train-interference component is subtracted from the noisy audio and followed by a finite-impulse-response low-pass postfilter. Three 74.01 s real audio sequences sampled at 11.025 kHz are processed under identical algorithmic parameters. Since clean ground truth is not available, performance is assessed with no-reference indicators: waveform behavior, Welch spectral estimates, RMS change, and residual normalized correlation with the reference. With 30 taps per reference channel, 15 anti-causal taps, and forgetting factor 0.999, the maximum reference correlation is reduced from 0.386--0.832 before processing to 0.011--0.016 after processing. The corresponding correlation-ratio reduction is approximately 30.6--34.1 dB, while the output RMS decreases by 1.8--4.8 dB depending on section and stereo channel. The results demonstrate that real train interference, including environmental acoustic effects, can be substantially attenuated when a correlated reference recording is available.

Joint Direction-of-Arrival and Range Estimation for Millimeter-Wave Uniform Linear Array Radar

An FFT-based direction-of-arrival (DOA) and range-estimation framework for a monostatic uniform linear array (ULA) operating at 77 GHz is presented. A narrowband sinusoidal waveform is used to derive the spatial phase model, determine an aliasing-free inter-element spacing, and select the aperture required to obtain a boresight angular resolution of 2 degree. The resulting design uses an element spacing of 0.97 mm and 58 antenna elements, corresponding to an aperture length of 56.42 mm. Numerical results show accurate angular estimation for a single target at 30 degree and for multiple simultaneous targets. The analysis is further extended to two-dimensional localization by replacing the narrowband waveform with a 1 GHz sinc-modulated signal, which provides an approximate range resolution of 0.15 m. Additional simulations quantify the effects of additive complex Gaussian noise, increased antenna spacing, and target decorrelation on the DOA response.

Communication Modeling of Long-Distance Abscisic Acid Signaling in Plant Vascular Systems

Abscisic acid (ABA) is a central plant hormone for coordinating responses to drought, salinity, cold stress, pathogen attack, wounding, and developmental aging. This paper reviews the biological stimuli that increase ABA biosynthesis, the main production sites and pathways, and the long-distance movement of ABA through plant vascular tissues. It then discusses experimental quantification approaches, including gas-liquid chromatography with electron-capture detection and high-performance liquid chromatography with ultraviolet detection. Finally, the paper presents a molecular-communication-inspired model of ABA transport in which root-side ABA release is represented as a transmitter, the xylem pathway as a bounded channel, and soybean tissue as a receiver. MATLAB Brownian-motion simulations are used to evaluate the effects of released molecule quantity and receiver radius on the detected ABA signal. The results show that higher release quantities produce smoother and stronger reception trends, while larger receivers increase molecule-capture probability.

Pilot-Aided MIMO Channel Identification and Linear Deconvolution in Correlated Gaussian Noise

This paper presents a pilot-aided study of multiple-input multiple-output (MIMO) channel identification and linear deconvolution under spatially correlated Gaussian noise. A real-valued $4\times4$ baseband model is analyzed for both memoryless and finite-impulse-response channels. The noise process is generated from a Toeplitz covariance matrix, the channel is estimated from pilot symbols through maximum-likelihood/least-squares formulations, and the empirical mean-square error is compared with the Cramer--Rao bound. The estimated channel is then used for data-symbol recovery through maximum-likelihood zero-forcing and linear minimum-mean-square-error deconvolution. The results show that sufficiently long and well-conditioned pilot blocks allow the channel estimator to approach the theoretical lower bound, whereas short training intervals cause rank and conditioning limitations, especially for the four-tap model. The deconvolution experiments further show that MMSE regularization provides a more stable inverse than unregularized zero forcing at low signal-to-noise ratios and for inaccurate channel estimates.

Communication Channel Modelling of Unmanned Aerial Vehicles

Wireless communication channel characterization for unmanned aerial vehicles (UAVs) is essential for reliable control, data transmission, and mission performance in civil, industrial, and defence applications. Channel behaviour is examined using a measurement-based approach that captures both large-scale propagation effects, represented by path loss, and small-scale characteristics, represented by the channel impulse response (CIR) and power delay profile (PDP). An SDR-based channel sounding system is employed to collect and process in-phase and quadrature (IQ) data, enabling the extraction of key channel parameters. Following system verification, measurements are conducted in ground-to-ground (G2G), air-to-ground (A2G), and air-to-air (A2A) scenarios. The results demonstrate that path loss alone is insufficient to describe UAV communication channels, as CIR and PDP provide additional insight into multipath propagation and delay-domain behaviour. The findings indicate that realistic UAV channel models should incorporate both large-scale and small-scale channel statistics. Further improvements may be achieved through increased sounding bandwidth, enhanced synchronization, measurements in a wider range of environments, and more detailed analysis of Doppler effects.