Interference Signal Research Articles

Chemical shift-encoded multishot EPI for navigator-free prostate DWI.

DWI is an important contrast for prostate MRI to enable early and accurate detection of cancer. This study introduces a Dixon 3-shot-EPI protocol with structured low-rank reconstruction for navigator-free DWI. The aim is to overcome the limitations of single-shot EPI (ssh-EPI), such as geometric distortions and fat signal interference, while addressing the motion-induced phase variations of multishot EPI and simultaneously allowing water/fat separation. DWI data were acquired from 7 healthy volunteers using both Dixon 3-shot EPI and standard fat-suppressed ssh-EPI with similar scan times for comparison. Two readers evaluated image quality using a 5-point Likert scale regarding different aspects. The ADC values were quantitatively compared between protocols. To show feasibility in a clinical setting, the protocol was applied to two patients. From the reader scores, Dixon 3-shot EPI significantly reduced geometric distortion compared with ssh-EPI (p < 0.01), with no significant differences in edge definition, SNR, or overall image quality. There was no significant difference in ADC values between the two protocols. However, the Dixon multishot-EPI protocol offered advantages such as self-referenced B0 map-driven distortion correction, greater flexibility in imaging parameters, and superior fat suppression. In the patient data, the lesion could be clearly identified in both protocols and on the associated ADC maps. The proposed Dixon 3-shot-EPI protocol shows promise as an alternative to ssh-EPI for prostate DWI, providing reduced geometric distortions and improved fat suppression. It addresses common DWI issues based on EPI and enhances scanning flexibility, indicating potential for optimized imaging.

Signal recognition and prediction system based on random forest model

To accurately predict the occurrence of rock bursts during deep coal mining and ensure the safe and efficient operation of coal mines, a signal recognition and prediction system based on the Random Forest model is proposed. This system utilizes electromagnetic radiation (EMR) and acoustic emission (AE) signal data, employing feature extraction and point biserial correlation analysis to screen out the most relevant feature parameters. A Random Forest binary classification model is constructed to identify interference signals. Subsequently, by introducing new features such as moving average slope and exponentially weighted moving average (EWMA), a time series analysis of precursor feature signals is conducted. A real-time warning model based on the Random Forest algorithm is developed, dynamically calculating the probability of precursor feature signal occurrence by integrating historical data and real-time data changes. This approach improves the accuracy and recall rate of signal recognition and prediction, providing reliable data support for mine safety management.

IMPLEMENTASI SISTEM MONITORING KELEMBABAN TANAH, PH TANAH DAN INTENSITAS CAHAYA TANAMAN LAHAN TERBUKA DENGAN WSN BERBASIS MODUL NRF24L01

Soil conditions such as moisture, pH, and light intensity at BBPP Lembang are still conventionally monitored by testing soil samples in the laboratory. Therefore, this research aims to develop a monitoring system to determine soil moisture, soil pH, and light intensity in real-time. This research uses NRF24L01 module, which enables wireless communication between sensor nodes and gateway. Sensor tests for soil moisture, soil pH, and light intensity as well as data validation at the gateway and transmission range testing were conducted through ten experiments on sensor nodes and gateways and thirty experiments to measure transmission range at distances of 40 m, 100 m, 150 m, and 200 m with or without obstructions such as trees and buildings. The test results show that the soil moisture sensor has an average error of 5.765%, soil pH 2.515%, and light intensity 0.075%. Thus, the Gateway is able to transmit data with 0% error for soil moisture, soil pH, and light intensity. Test results at a distance of 40m showed 100% data transmission with and without obstructions. However, the received data dropped by about 7% at distances of 100m, 150m, and 200m, with no obstructions, and 27% - 47% when obstructions, such as trees and buildings, were present due to signal interference.

Signal processing for enhancing railway communication by integrating deep learning and adaptive equalization techniques.

With the increasing amount of data in railway communication system, the conventional wireless high-frequency communication technology cannot meet the requirements of modern communication and needs to be improved. In order to meet the requirements of high-speed signal processing, a high-speed communication signal processing method based on visible light is developed and studied. This method combines the adaptive equalization algorithm with deep learning and is applied to railway communication signal processing. In this research, the wavelength division multiplexing (WDM) and orthogonal frequency division multiplexing (OFDM) techniques are used, and fuzzy C equalization algorithm is used to softly divide the received signals, reduce signal distortion and interference suppression. The experimental results showed that increasing the step size could reduce the equalization effect, while increasing the modulation parameter will increase the bit error rate. Through deep learning to achieve channel equalization, visible light communication could effectively mitigate multi-path transmission and reflection interference, thereby reducing the bit error rate to the level of 0.0001. Under various signal-to-noise ratios, the system using the channel compensation method achieved the lowest bit error rate. This outcome was achieved by implementing hybrid modulation scheme, including Wavelength division multiplexing (WDM) and direct current-biased optical orthogonal frequency division multiplexing (DCO-OFDM) techniques. It has been proved that this method can effectively reduce the channel distortion when the receiver is moving. This study develops a dependable communication system, which enhances signal recovery, reduces interference, and improves the quality and transmission efficiency of railway communication. The system has practical application value in the field of railway communication signal processing.

A Comprehensive Review of Electromyography in Rehabilitation: Detecting Interrupted Wrist and Hand Movements with a Robotic Arm Approach

Electromyography (EMG) is a diagnostic technique that measures the electrical activity generated by skeletal muscles. Utilizing electrodes placed either on the skin (surface EMG) or inserted directly into the muscle(intramuscular EMG), it detects electrical signals produced during muscle contractions. EMG is widely employed in clinical and research settings to assess muscle function, diagnose neuromuscular disorders,and guide rehabilitation therapy. Over the years, EMG has evolved from a basic measurement tool into an essential technology within clinical and research environments, propelled by advances in recording techniques and digital innovations. The integration of wearable technology and artificial intelligence (AI) has significantly expanded its applications, particularly in rehabilitation and sports science. By capturing muscle electrical activity through surface or intramuscular electrodes, EMG benefits from enhanced signal processing that improves accuracy and data analysis. Despite challenges such as signal interference and the complexities of movement patterns—especially in wrist and hand rehabilitation—EMG combined with robotic systems offers real-time feedback for precise and personalized therapy. However, obstacles like cost, complexity, and variability among patients still remain. Future advancements aim to make EMG more accessible and to integrate AI for tailored rehabilitation strategies, alongside improvements in sensors and wireless communication to enhance reliability and performance. This review explores various facets of EMG,from its fundamental principles to its application in detecting disrupted wrist and hand movements through robotic approaches. It provides a comprehensive analysis of EMG’s historical and technological evolution, recent innovations like AI and wearable devices, and its extensive applications in rehabilitation and sports science. Detailed case studies illustrate its effectiveness in areas such as stroke recovery and spinal cord injury rehabilitation. Additionally, the review addresses challenges like technical limitations and patient variability while emphasizing the integration of EMG with robotic systems for personalized therapy. It also discusses the significance of real-time feedback, future enhancements in AI and sensor technology, and the pressing need for more affordable, user-friendly solutions to improve therapeutic outcomes.

Flexible SERS chips for rapid on-site detection of tricyclazole pesticide in agricultural products.

A flexible, ultrasensitive, and practical SERS chip is presented based on a paper/f-TiO2/Ag structure. The chip enhances the self-assembly of Ag nanoparticles on a cellulose fiber matrix, facilitated by smart functionalized TiO2 nanomaterials (f-TiO2). This design enables superior detection of the hazardous pesticide tricyclazole (TCZ) on crops using an advanced, simple, and efficient analytical method.Despite its straightforward fabrication process via a solvent immersion method, the intrinsic smart surface properties of the TiO2 bridging material -both hydrophilic and hydrophobic-enable the uniform and dense self-assembly of hydrophilic Ag nanoparticles (NPs) on the cellulose fiber paper substrate. This innovative design provides superior sensing efficiency for TCZ molecules with a detection limit reaching 2.1 × 10-9M, a remarkable improvement compared to Paper/Ag substrates lacking f-TiO2 nanomaterials, which register at 10-5M. This flexible SERS substrate also exhibits very high reliability as indicated by its excellent reproducibility and repeatability with relative standard deviations (RSD) of only 5.93% and 4.73%, respectively. Characterized by flexibility and a water-attractive yet non-soluble surface, the flexible Paper/f-TiO2/Ag chips offer the convenience of direct immersion into the analytical sample, facilitating seamless target molecule collection while circumventing interference signals. Termed the "dip and dry" technique, its advantages in field analysis are indisputable, boasting in situ deployment, simplicity, and high efficiency, while minimizing interference signals to negligible levels. Through the application of this advanced technique, we have successfully detected TCZ in two high-value crops, ST25 rice and dragon fruit, achieving excellent recovery values ranging from 90 to 128%. This underscores its immense potential in ensuring food quality and safety. As a proof of concept, flexible Paper/f-TiO2/Ag SERS chips, with a simple fabrication process, advanced analytical technique, and superior sensing efficiency, bring SERS one step closer to field applications beyond the laboratory.

Research of the efficiency of Kravchenko weighting functions and combinations based on them in the problem of narrow band interference rejection

The influence of weight functions (WFs) of preliminary weighting on the quality of narrowband interference rejection based on direct and inverse discrete Fourier transforms has been studied. Classical and modern WFs are considered: Kravchenko, Kravchenko–Dolph–Chebyshev, Kravchenko–Gauss, Kravchenko–Bernstein–Rogozinsky. Quantitative estimates of efficiency were obtained – the coefficients of interference suppression and signal transmission, as well as their product – the total efficiency coefficient. Graphic examples of the behavior of the total efficiency coefficient depending on the interference frequency are shown. Estimates of the indicated quality indicators are presented when a fixed number of frequency samples are removed from the spectrum. The families of dependences of the probabilities of correctly performing a search for a spread spectrum signal on the “interference/signal” ratio when using modern WFs to weigh the implementations of an additive mixture of signal, interference and noise are presented. The significant advantage of modern WFs formed through combinations with Kravchenko functions in the problem of interference rejection has been demonstrated and confirmed.

ADAPTIVE METHOD OF FORMING COMPLEX SIGNALS ENSEMBLES BASED ON MULTI-LEVEL RECURRENT TIME-FREQUENCY SEGMENT MODELING

The article investigates the implementation of an adaptive method for forming ensembles of complex signals based on multilevel recurrent time-frequency segmentation. It addresses the key challenges faced by cognitive wireless networks operating in dynamic radio frequency environments with high levels of interference, necessitating rapid adaptation to changes in the spectral characteristics of signals. The study substantiates the need for adaptive filters and specific transformations to enhance signal processing quality, particularly in environments with high variability in frequency characteristics and significant noise interference. The proposed method of multilevel recurrent time-frequency segmentation allows for the modification of time segment durations and the use of segments of varying lengths, providing flexibility in signal processing and adaptation to current conditions. This adaptability enables optimal signal processing for each individual case, taking into account short-term impulses, long-term fluctuations, and various types of noise and distortions. This approach effectively separates frequency components and reduces interference between them, which is crucial for maintaining high signal quality and communication stability in cognitive networks. It has been proven that the use of adaptive filters such as LMS (Least Mean Squares) and RLS (Recursive Least Squares), as well as fast Fourier Transform (STFT), wavelet, and Hilbert transforms at different stages of multilevel time-frequency segmentation, significantly enhances signal interference resistance and energy efficiency. Comparative analysis of signal metrics before and after filtering and transformation shows an increase in signal quality by 14,3–24,5% and a reduction in noise levels by 21,7–29,6%. The wavelet transform, in particular, proved to be highly effective, allowing for precise extraction of useful frequency components from the noise background and improving signal parameters through dynamic adjustment to specific radio environment conditions. Experimental results confirm the effectiveness of the proposed method, demonstrating its ability to ensure consistently high-quality processing of complex signal ensembles even in dynamic cognitive radio environments.

A novel superposition coding scheme based on polar code for multi‐user detection in underwater acoustic OFDM communication system

SummarySuperposition coding (SC) is a non‐orthogonal multiple access (NOMA) scheme for the downlink communication system, which allows signals of different users to be stacked and forwarded simultaneously in the same frequency band. The codeword for each user is assigned a different weight at the transmitter to avoid signal conflicts and interference after stacking, and successive interference cancellation (SIC) is adopted to decode. A novel SC scheme based on polar code, suitable for the downlink multi‐user underwater acoustic (UWA) communication system using orthogonal frequency division multiplexing (OFDM), is proposed in this paper. Polar code is utilized to create a nested code structure that is divided into several subsets. We exploit this feature of the polar codes and assign each subset to the corresponding user in the channel coding process. The information bits are placed on the independent subset for each user, and the random bits are placed on the subset of other users while all the users share the same set of frozen bits. Then, the codewords are stacked with different weights. The selection range of power factors can be expanded through the double superposition of the coding and the power domains, and higher system throughput and fairness can be achieved. The simulation and tank experiment results significantly verify the effectiveness of the proposed scheme, making it most suitable for the downlink UWA multi‐user communication systems.

Generalized sidelobe canceller based adaptive multiple-input multiple-output radar array beamforming under scenario mismatches

It is well known that the performance of an adaptive MIMO radar array fully depends on the precise steering control and is deteriorated by even a small scenario mismatch. This paper presents an advanced generalized sidelobe canceller (AGSC) based adaptive MIMO radar array beamformer with robustness against the effect due to scenario mismatches. A new signal blocking matrix is developed for effectively blocking the desired signal when the adaptive beamforming is performed under multiple scenario mismatches. The novelty of the new signal blocking matrix is that it contains two additional matrix components in addition to the conventional blocking matrix. The first one is a matrix made up of the basis orthogonal to some appropriately designed derivative constraint vector. It avoids the possible leakage of the desired signal due to scenario mismatches. The other one is a matrix made up of the dominant eigenvectors associated with the correlation matrix of the blocked data vector at the output of the first matrix component. It is employed to preserve all of the interference signals. As a result, the whole blocking operation can delete the desired signal and save the interference signals under multiple scenario mismatches. Hence, the AGSC based adaptive MIMO radar beamformer effectively deals with the performance degradation caused by scenario mismatches without resorting to any robust optimization algorithms. Performance analysis and complexity evaluation regarding the AGSC based adaptive MIMO radar beamformer are presented. Simulation results are also provided for confirmation and comparison.

Novel CRM cosine similarity mapping strategy for simultaneous in-situ visual profiling lignocellulose in plant cell walls

Confocal Raman microscopy (CRM) is a promising in-situ visual technique that provides detailed insights into multiple lignocellulosic components and structures in plant cell walls at the micro-nano scale. In this study, we propose a novel CRM cosine similarity (CS) mapping strategy for the simultaneous in-situ visual profiling of lignin, cellulose, and hemicellulose in plant cell walls. The main stages of this strategy include: 1) a modified Otsu algorithm for extracting the regions of interest (ROI); 2) a modified subtraction method for cleaning the background signals in the ROI spectra; 3) a lignin signal subtraction method based on the pixel correction factor for eliminating the interference of strong lignin signals with weak cellulose and hemicellulose signals in the Raman full spectra of the cell walls; 4) second-order derivative spectral preprocessing for enhancing the discrimination between the characteristic peaks of cellulose and hemicellulose; 5) a CS mapping algorithm for simultaneous in-situ profiling of lignin, cellulose, and hemicellulose in plant cell walls. The effectiveness of the strategy is verified by characterizing the Brittle Culm1 (BC1) gene-mutant rice stem (IL349-BC1-KO) with known bioinformatics. This approach provides methodological support for in-situ visualization and analysis in fields such as plant or crop science at the micro-nano scale.

An Interference Cancelation and Signal Decoding Method for Non-orthogonal Multiple Access Based on Deep Learning

In wireless communication, spectral efficiency is one of the key performance parameters. The available limited resources must be effectively shared among different applications. Due to the tremendous demand for wireless applications, an effective technique could be used for sharing the resources (time/frequency). Non-orthogonal Multiple Access (NOMA), is a wireless network technique aimed at boosting spectral efficiency. In NOMA, Users in the downlink have received a super-imposed signal from a Base Station (BS) with varying power allocations. Depending on the power allocation, the receiver employs interference cancelation techniques to decode its own data. The effectiveness of interference cancelation hinges on achieving a perfect channel condition, which is challenging to achieve in practical scenarios. This study introduces an unsupervised Deep Learning (DL) approach known as autoencoder, aimed at interference cancelation to ensure signal decoding from super-imposed signal. In contrast to the traditional approach, simulation results of the proposed structure show that the DL-based receiver achieves superior performance in correctly decoding the signal.

Close Range and High-Resolution Detection of Vibration by Ultrasonic Wave Using Silicon-on-Nothing PMUTs.

This article presents a novel method for close-range, high-resolution ultrasonic time-of-flight (ToF) ranging using piezoelectric micromachined ultrasonic transducers (PMUTs) operating below the device resonance in air. The proposed method involves cross correlation techniques to accurately detect the reflected echo signals despite the presence of ringdown signal interference. For the experiments, a high fill-factor array of silicon-on-nothing (SON) PMUTs was used to enhance the signal-to-noise ratio (SNR). A thorough investigation was conducted to determine the optimal driving frequency for below-resonance ToF ranging, to improve resolution and minimize detection errors. The results of the experiments showed that the system was able to accurately measure sub- μ m vibrations of a metal plate placed 13 mm away from the PMUT array. The system exhibited the ability to detect target object vibrations with a peak-to-peak displacement under 6 μ m and sub- μ m floor noise. Moreover, the maximum detectable vibration frequency reached up to 1 kHz. This study highlights the potential of the proposed ToF ranging method in noncontact vibration monitoring applications across various fields, such as robotics and predictive maintenance.

Selected applications of satellite technologies in rail transport

Global Navigation Satellite Systems (GNSS) are increasingly being used in various modes of transport, including rail transport. When this technology is applied to railway traffic control, it is essential to ensure a high level of safety and reliability. The approval of a railway traffic control system requires a safety analysis, which includes hazard analysis and risk analysis. This also includes GNSS-based solutions in terms of their compliance with safety integrity requirements, i.e. THR (Tolerable Hazard Rate) and SIL (Safety Integrity Level) parameters, as defined in normative documents. In the case of railway traffic control systems, the level of dependability of the determined train position, referred to as position integrity, is very important in ensuring safety. Position integrity is affected by many factors, including: errors due to SIS (Signal-In-Space) propagation, multipath errors, signal interference or GNSS receiver errors. In order to improve position integrity, among other things, new data processing methods can be used to improve the accuracy and reliability of measurements. The paper presents the concept of satellite signal processing for precise determination of the position of objects in selected railway systems. The Kalman filter based model of satellite signal filtration and its selected applications, which were tested in the real condition, was presented. The application of Kalman filtration indicated in the paper is a universal method that improves the estimated measurement parameters and can be used in many applications of satellite systems for railway tasks. The applicability of satellite systems to automatic train operation, defect positioning in automatic and manual flaw detection tests, determining track spatial orientation and train integrity control have been considered. The conducted tests confirmed the correctness of the adopted concept and the model of satellite signals filtration developed for this purpose. According to the authors, the described methods can also be used in many other tasks related to rail transport.

Design and Development of Lightning Detection System Utilizing Slow Atmospheric Electric Field Waveform at Legoland Malaysia

Conventional lightning localization and detection techniques, including Magnetic Direction Finder (MDF), Time of Arrival (TOA), Interferometer (ITF), and Distance of Arrival (DOA), predominantly rely on fast atmospheric electric fields, magnetic fields, and very high frequency (VHF) signals. This paper pioneers a novel approach by delving into the analysis of the slow atmospheric electric field, aiming to develop a waveform analysis utilizing this field to estimate the distance and radius of lightning occurrences at LEGOLAND Malaysia Resort. The newly implemented lightning detection system at LEGOLAND leverages a straightforward and cost-effective setup, incorporating a capacitive antenna, slow and fast atmospheric electric field sensors, and dedicated data analysis software. The system's efficacy and accuracy have undergone a rigorous comparison with LEGOLAND's existing online lightning detection service. Achieving accurate data necessitates proper grounding and isolation from electrical noise, as signal interference from power lines, towers, or machinery can potentially trigger false signals. This research has contributed detailed documentation on the analysis of slow atmospheric electric field data, encompassing waveform patterns and key characteristics. This documentation is expected to serve as a valuable resource for future research endeavors and the continuous refinement of lightning detection systems. The comparative evaluation between the novel system and the current online service at LEGOLAND Malaysia Resort has shed light on the efficiency and capabilities of the newly introduced methodology.

Phytophthora zoospores display klinokinetic behaviour in response to a chemoattractant.

Microswimmers are single-celled bodies powered by flagella. Typical examples are zoospores, dispersal agents of oomycete plant pathogens that are used to track down hosts and infect. Being motile, zoospores presumably identify infection sites using chemical cues such as sugars, alcohols and amino acids. With high-speed cameras we traced swimming trajectories of Phytophthora zoospores over time and quantified key trajectory parameters to investigate chemotactic responses. Zoospores adapt their native run-and-tumble swimming patterns in response to the amino acid glutamic acid by increasing the rate at which they turn. Simulations predict that tuneable tumble frequencies are sufficient to explain zoospore aggregation, implying positive klinokinesis. Zoospores thus exploit a retention strategy to remain at the plant surface once arriving there. Interference of G-protein mediated signalling affects swimming behaviour. Zoospores of a Phytophthora infestans G⍺-deficient mutant show higher tumbling frequencies but still respond and adapt to glutamic acid, suggesting chemoreception to be intact.

Design of a Technology Verification Platform for Space Electromagnetic Interference Signal Testing and Analysis

This paper designs a space electromagnetic interference signal test and analysis technology verification platform. The article firstly introduces the general scheme of the technical verification platform and then describes each component unit of the hardware and the overall structure of the software in detail. The platform can achieve a 10 MHz ~ 50 GHz working frequency band, 1.2 GHz acquisition and real-time recording bandwidth, 6 GB/s recording rate, and 12 TB recording capacity.

Radio Frequency Interference Detection and Automatic Modulation Recognition Based on Mask RCNN

The increasing complexity of wireless communication conditions presents noteworthy obstacles to the adaptability and flexibility of cognitive radio systems. The latter can alleviate the problem of spectrum shortages through proper spectrum management. However, cognitive radio has difficulty distinguishing between signals of interest and interference, which can be a general enemy since it is undesirable because it influences the signal's quality. Efficient detection and characterization of interference in wireless communication networks is critical for ensuring strong security. However, in this research, we use the Mask R-CNN methodology to present a new concept for automatic modulation recognition and radio frequency interference detection. Moreover, this algorithm can segment, detect, and recognize several types of interference that can affect wireless communication systems, such as chirp interference (CI), continuous wave interference (CWI), and multiple continuous wave interference (MCWI) within the signal of the interest (SOI), as well as the modulation kind present in the SoI. Overall, the combination of these distinct techniques can be very valuable in the field of signal processing, especially in anti-jamming strategies in wireless networks. Moreover, the proposed approach showcases exceptional performance in the validation dataset. For radio frequency interference detection, it achieves a mean average precision (mAP) of 0.946 and a mean average recall (mAR) of 0.954. For automatic modulation recognition, it attains a mAP of 0.898 and a mAR of 0.916.

A complex-valued convolutional fusion-type multi-stream spatiotemporal network for automatic modulation classification

Automatic Modulation Classification (AMC) is crucial in non-cooperative communication systems as it facilitates the identification of interference signals with minimal prior knowledge. Although there have been significant advancements in Deep Learning (DL) within the field of AMC, leveraging the inherent relationships between In-phase (I) and Quadrature-phase (Q) components, and enhance recognition accuracy under low signal-to-noise ratio (SNR) conditions remains a challenge. This study introduces a complex-valued convolutional fusion-type multi-stream spatiotemporal network (CC-MSNet) for AMC, which combines spatial and temporal feature extraction modules for modulation recognition. Experimental results demonstrate that the CC-MSNet performs well on three benchmark datasets, RML2016.10a, RML2016.10b, and RML2016.04c, with average recognition accuracy of 62.86%, 65.08%, and 71.12%. It also performs excellently in low SNR environments below 0dB, significantly outperforming other networks.

From molecules to materials: SHG-CD microscopy of structured chiral films

The interplay between molecular and structural chirality as a function of local sample morphology determines the nonlinear optical properties of many organic and hybrid organic–inorganic thin films. Here, we used second harmonic generation circular dichroism (SHG-CD) microscopy of thin molecular films of 1,1′-bi-2-naphthol (R-BINOL) as a research model. Our results show that the SHG signal measured at frequencies close to the electronic transition of BINOL molecules is resonantly enhanced by more than an order of magnitude compared to the non-resonant case. The extracted resonant SHG-CD signal is dominated by the chiral response of the molecule. In contrast, structural chirality determines the non-resonant SHG-CD images. We see clear evidence that the interference of the SHG signals caused by the molecular and structural chirality can lead to a decrease in the overall SHG intensity when both SHG signals are out of phase. These findings highlight the intricate relationship between molecular and structural chirality on the one hand and structural morphology on the other hand and pave the way for novel applications by exploiting the chiroptic properties of thin films.