FPGA Implementation of Spectral Subtraction for In-Car Speech Enhancement and Recognition

The use of speech recognition in noisy environments requires the use of speech enhancement algorithms in order to improve recognition performance. Deploying these enhancement techniques requires significant engineering to ensure algorithms are realisable in electronic hardware. This paper describes the design decisions and process to port the popular spectral subtraction algorithm to a Virtex-4 field-programmable gate array (FPGA) device. Resource analysis shows the final design uses only 13% of the total available FPGA resources. Waveforms and spectrograms presented support the validity of the proposed FPGA design

High throughput spatial convolution filters on FPGAs

Digital signal processing (DSP) on field- programmable gate arrays (FPGAs) has long been appealing because of the inherent parallelism in these computations that can be easily exploited to accelerate such algorithms. FPGAs have evolved significantly to further enhance the mapping of these algorithms, included additional hard blocks, such as the DSP blocks found in modern FPGAs. Although these DSP blocks can offer more efficient mapping of DSP computations, they are primarily designed for 1-D filter structures. We present a study on spatial convolutional filter implementations on FPGAs, optimizing around the structure of the DSP blocks to offer high throughput while maintaining the coefficient flexibility that other published architectures usually sacrifice. We show that it is possible to implement large filters for large 4K resolution image frames at frame rates of 30–60 FPS, while maintaining functional flexibility

PCA-RECT: An Energy-efficient Object Detection Approach for Event Cameras

We present the first purely event-based, energy-efficient approach for object detection and categorization using an event camera. Compared to traditional frame-based cameras, choosing event cameras results in high temporal resolution (order of microseconds), low power consumption (few hundred mW) and wide dynamic range (120 dB) as attractive properties. However, event-based object recognition systems are far behind their frame-based counterparts in terms of accuracy. To this end, this paper presents an event-based feature extraction method devised by accumulating local activity across the image frame and then applying principal component analysis (PCA) to the normalized neighborhood region. Subsequently, we propose a backtracking-free k-d tree mechanism for efficient feature matching by taking advantage of the low-dimensionality of the feature representation. Additionally, the proposed k-d tree mechanism allows for feature selection to obtain a lower-dimensional dictionary representation when hardware resources are limited to implement dimensionality reduction. Consequently, the proposed system can be realized on a field-programmable gate array (FPGA) device leading to high performance over resource ratio. The proposed system is tested on real-world event-based datasets for object categorization, showing superior classification performance and relevance to state-of-the-art algorithms. Additionally, we verified the object detection method and real-time FPGA performance in lab settings under non-controlled illumination conditions with limited training data and ground truth annotations.Comment: Accepted in ACCV 2018 Workshops, to appea

Channel Sounding for the Masses: Low Complexity GNU 802.11b Channel Impulse Response Estimation

New techniques in cross-layer wireless networks are building demand for ubiquitous channel sounding, that is, the capability to measure channel impulse response (CIR) with any standard wireless network and node. Towards that goal, we present a software-defined IEEE 802.11b receiver and CIR estimation system with little additional computational complexity compared to 802.11b reception alone. The system implementation, using the universal software radio peripheral (USRP) and GNU Radio, is described and compared to previous work. By overcoming computational limitations and performing direct-sequence spread-spectrum (DS-SS) matched filtering on the USRP, we enable high-quality yet inexpensive CIR estimation. We validate the channel sounder and present a drive test campaign which measures hundreds of channels between WiFi access points and an in-vehicle receiver in urban and suburban areas