DeepAoANet: Learning Angle of Arrival From Software Defined Radios With Deep Neural Networks

Direction finding and positioning systems based on RF signals are significantly impacted by multipath propagation, particularly in indoor environments. Existing algorithms (e.g MUSIC) perform poorly in resolving Angle of Arrival (AoA) in the presence of multipath or when operating in a weak signal regime. We note that digitally sampled RF frontends allow for the easy analysis of signals, and their delayed components. Low-cost Software-Defined Radio (SDR) modules enable Channel State Information (CSI) extraction across a wide spectrum, motivating the design of an enhanced AoA solution. We propose a Deep Learning approach for deriving AoA from a single snapshot of the SDR multichannel data. We compare and contrast deep-learning based angle classification and regression models, to estimate up to two AoAs accurately. We have implemented the inference engines on different platforms to extract AoAs in real-time, demonstrating the computational tractability of our approach. To demonstrate the utility of our approach we have collected IQ (In-phase and Quadrature components) samples from a four-element Universal Linear Array (ULA) in various Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) environments, and published the dataset. Our proposed method demonstrates excellent reliability in determining number of impinging signals and realized mean absolute AoA errors less than 2◦

Illumination-Aware Hallucination-Based Domain Adaptation for Thermal Pedestrian Detection

Thermal imagery is emerging as a viable candidate for 24-7, all-weather pedestrian detection owning to thermal sensors’ robust performance for pedestrian detection under different weather and illumination conditions. Despite the promising results obtained from combining visible (RGB) and thermal cameras in multi-spectral fusion techniques, the complex synchronization requirements, including alignment and calibration of sensors, impede their deployment in real-world scenarios. In this paper, we introduce a novel approach for domain adaptation to enhance the performance of pedestrian detection based solely on thermal images. Our proposed approach involves several stages. Firstly, we use both thermal and visible images as input during the training phase. Secondly, we leverage a thermal-to-visible hallucination network to generate feature maps that are similar to those generated by the visible branch. Finally, we design a transformer-based multi-modal fusion module to integrate the hallucinated visible and thermal information more effectively. The thermal-to-visible hallucination network acts as domain adaptation, allowing us to obtain pseudo-visual and thermal features using solely thermal input. Based on the experimental results, it is observed the mean average precision (mAP) increases by 4.72% and the miss rate decreases by 7.56% on the KAIST dataset when compared to the baseline model

Expression of Human Frataxin Is Regulated by Transcription Factors SRF and TFAP2

Friedreich ataxia is an autosomal recessive neurodegenerative disease caused by reduced expression levels of the frataxin gene (FXN) due to expansion of triplet nucleotide GAA repeats in the first intron of FXN. Augmentation of frataxin expression levels in affected Friedreich ataxia patient tissues might substantially slow disease progression.We utilized bioinformatic tools in conjunction with chromatin immunoprecipitation and electrophoretic mobility shift assays to identify transcription factors that influence transcription of the FXN gene. We found that the transcription factors SRF and TFAP2 bind directly to FXN promoter sequences. SRF and TFAP2 binding sequences in the FXN promoter enhanced transcription from luciferase constructs, while mutagenesis of the predicted SRF or TFAP2 binding sites significantly decreased FXN promoter activity. Further analysis demonstrated that robust SRF- and TFAP2-mediated transcriptional activity was dependent on a regulatory element, located immediately downstream of the first FXN exon. Finally, over-expression of either SRF or TFAP2 significantly increased frataxin mRNA and protein levels in HEK293 cells, and frataxin mRNA levels were also elevated in SH-SY5Y cells and in Friedreich ataxia patient lymphoblasts transfected with SRF or TFAP2.We identified two transcription factors, SRF and TFAP2, as well as an intronic element encompassing EGR3-like sequence, that work together to regulate expression of the FXN gene. By providing new mechanistic insights into the molecular factors influencing frataxin expression, our results should aid in the discovery of new therapeutic targets for the treatment of Friedreich ataxia

Accelerating a ray launching model using GPU with CUDA

The high computational cost of accurate deterministic wave propagation models often prevent them from being used in channel modelling. In this work, we present our experience of attempts to accelerate our 2.5D ray launching model using GPUs (Graphic Processing Units), which continue to grow in popularity due to their vast computation capability. At the heart of this trial is an implementation of a ray-surface intersection detection function, which was found to be the bottleneck of serial CPU computation, using NVIDIA's CUDA (Compute Unified Device Architecture). Various optimization efforts are made to obtain the best overall performance. The intersection detection function executes seven times faster on a large urban scenario after acceleration on a modest laptop GPU. This paper details the implementation of the CUDAbased intersection detection function and presents the acceleration results for different environments

UAV-aided source localization in urban environments based on ray launching simulation

A novel source localization approach suitable for urban environments is proposed. The reliability against noise and other practical issues are investigated via simulation. A fingerprint database of transmitter and receiver candidate locations is obtained using an efficient ray launching simulator developed in earlier work. Following a pre-described path, a UAV (unmanned aerial vehicle) equipped with a radio receiver is simulated to generate time series of observations such as signal strength and angle-of-arrival for all possible transmitter candidate locations. The localisation algorithm uses an approach based on the DFT (discrete Fourier transform) and dynamic time warping (DTW). Using the fingerprint database and the measured time-series the best matching transmitter location is found. The algorithm is found to be robust to significant measurement errors and also to signification deviations in the actual path flown by the UAV

OdomBeyondVision: An Indoor Multi-modal Multi-platform Odometry Dataset Beyond the Visible Spectrum

This paper presents a multimodal indoor odometry dataset, OdomBeyondVision, featuring multiple sensors across the different spectrum and collected with different mobile platforms. Not only does OdomBeyondVision contain the traditional navigation sensors, sensors such as IMUs, mechanical LiDAR, RGBD camera, it also includes several emerging sensors such as the single-chip mmWave radar, LWIR thermal camera and solid-state LiDAR. With the above sensors on UAV, UGV and handheld platforms, we respectively recorded the multimodal odometry data and their movement trajectories in various indoor scenes and different illumination conditions. We release the exemplar radar, radar-inertial and thermal-inertial odometry implementations to demonstrate their results for future works to compare against and improve upon. The full dataset including toolkit and documentation is publicly available at: https://github.com/MAPS-Lab/OdomBeyondVision