This paper presents an HLS-based algorithm-hardware co-design methodology and complete FPGA hardware accelerator for multi-user massive MIMO-OFDM receivers operating in contested tactical environments with jamming suppression capabilities. We develop a systematic bi-directional co-design methodology using high-level synthesis (HLS) where algorithms provide functional verification constraints while hardware synthesis feedback drives algorithmic complexity reduction, enabling efficient transformation from signal processing algorithms to optimized circuit implementations on the Xilinx ZCU111 radio frequency system-on-chip (RFSoC) platform. The primary contributions include: 1) hardware architecture innovations featuring QR decomposition-based synchronization achieving 330 MHz post-route operation and optimized frequency-domain minimum mean square error (FD-MMSE) equalization with systematic loop restructuring for data dependency removal in substitution modules, reducing hardware resources by 56% lookup tables (LUTs), 58% flip-flops (FFs), and 67% digital signal processing (DSP) blocks while maintaining real-time throughput; 2) systematic HLS-based co-design framework enabling automated architecture exploration with hardware-oriented algorithm adaptations including silent-period frame structure and Cholesky-based decision-feedback equalization; 3) complete system integration validated through field trials demonstrating 8 dB jamming suppression improvement with reliable spatial division multiple access (SDMA) communications. The presented methodology provides insights applicable to broader signal processing systems with stringent real-time constraints.

In this paper, we have proposed a fingerprint orientation model based on 2D Fourier expansions (FOMFE) in the phase plane. The FOMFE does not require prior knowledge of singular points (SPs). It is able to describe the overall ridge topology seamlessly, including the SP regions, even for noisy fingerprints. Our statistical experiments on a public database show that the proposed FOMFE can significantly improve the accuracy of fingerprint feature extraction and thus that of fingerprint matching. Moreover, the FOMFE has a low-computational cost and can work very efficiently on large fingerprint databases. The FOMFE provides a comprehensive description for orientation features, which has enabled its beneficial use in feature-related applications such as fingerprint indexing. Unlike most indexing schemes using raw orientation data, we exploit FOMFE model coefficients to generate the feature vector. Our indexing experiments show remarkable results using different fingerprint databases.

TCP is known to have bandwidth fairness problems for flows of different RTT. Highspeed TCP, which adapts TCP's congestion avoidance to grow faster, and thus be able to scale to high bandwidth environments, aggravates the RTT fairness problem since the congestion window increases at a rate proportional to the RTT and the current window size. We propose a method to eliminate the RTT fairness problem for competing high bandwidth flows. We decouple the rate of window growth in Highspeed TCP from the current window size, such that competing flows adjust their window size, instead, according to their perceived byte sending rate. We derive condiitions and a normalisation scheme to ensure that high bandwidth competing flows converge to fair bandwidth allocation regardless of the RTT or segment size used, while maintaining Highspeed TCP's ability to scale to very high bandwidth environments. We introduce features to preserve TCP friendliness in low bandwidth environments such that, the RTT fairness is no worse than that of standard TCP in scenarios of moderate packet loss. We have conducted simulations to validate the abilities of our proposal to eliminate the RTT bias from Highspeed TCP.

It is known that analytic modelling for TCP latency is a non trivial task. Recently, some significant progress has been made, such as the comprehensive result by Sikdar et al. However, models similar to these often rely on trial and error methods such as "data fitting". This can lead to a very limited scope for the resulting model and also large estimation error. In this paper, we propose improvements to Sikdar's SACK TCP model. A new delayed acknowledgement slow start model is developed that is analytically derived from the slow start algorithm which provides a novel mechanism to model the relationship between RTT and the delayed acknowledgement timer. We introduce a simple mechanism to include time taken to send an HTTP get request to broaden the scope of our SACK TCP model to Website file transfer. Simulation and live Internet experimentation has validated our scheme.