Time-domain diffuse optical imaging is a noninvasive technique that uses pulsed near-infrared light as the interrogation source to produce quantitative images displaying the variation in blood volume and oxygenation in the human brain. Measuring the times of flights of photons provides information on the photon pathlengths in tissue, which enables absolute concentrations of the oxygenated and deoxygenated forms of hemoglobin to be estimated. Recent advances in silicon electronics have enabled the development of time-domain systems, which are lightweight and low cost, potentially enabling the imaging technique to be applied to a far greater cohort of subjects in a variety of environments. While such technology usually depends on customized circuits, in this article, we present a system assembled from commercially available components, including a low-cost time-to-digital converter and a silicon photomultiplier detector. The system is able to generate histograms of photon flight times at a rate of 81–90 kS/s and with a sampled bin width of 54 ps. The linearity and performance of the system are presented, and its potential as the basis for a modular multi-detector imaging system is explored.

The growing demand for real-time video streaming in power-constrained embedded systems, such as drone navigation and remote surveillance, requires encoding solutions that prioritize low latency. In these applications, even small delays in video transmission can impair the operator’s ability to react in time, leading to instability in closed-loop control systems. To mitigate this, encoding must be lightweight and designed so that streaming can start as soon as possible, ideally even while frames are still being processed, thereby ensuring continuous and responsive operation. This paper presents the design of a hardware implementation of the Logarithmic Hop Encoding (LHE) algorithm on a Field-Programmable Gate Array (FPGA). The proposed architecture is deeply pipelined and parallelized to achieve sub-frame latency. It employs adaptive compression by dividing frames into regions of interest and uses a quantized differential system to minimize data transmission. Our design achieves an encoding latency of between 1.87 ms and 2.1 ms with a power consumption of only 2.7 W when implemented on an FPGA clocked at 150 MHz. Compared to a parallel GPU implementation of the same algorithm, this represents a 6.6-fold reduction in latency at approximately half the power consumption. These results show that FPGA-based LHE is a highly effective solution for low-latency, real-time video applications and establish a robust foundation for its deployment in embedded systems.

Virtual Reality (VR) setups connected to a computer offer a more realistic experience compared to standalone devices. However, these setups require high-performance hardware, making them expensive and less portable. A more cost-effective and portable alternative involves leveraging shared cloud or edge resources to render VR experiences. This work introduces NajVR, an architecture for remote rendering of VR applications that requires only commodity hardware on the client and includes LHE, a codec designed to minimize latency and resistant to packet loss that uses the user’s gaze data to improve the encoding process. NajVR does not require any modifications to the VR applications or their runtime environment. In NajVR an application on the client captures pose data and events generated by the VR hardware and transmits them to the server. On the server, a driver processes these data, simulating the VR hardware as if it were connected directly to it. This driver also captures the images to be displayed on the Head Mounted Display (HMD), encodes them, and transmits them to the client. NajVR supports an arbitrary number of trackers and takes advantage of the user’s gaze data to improve the perceived quality. The architecture round-trip latency was measured, and the LHE codec was compared with four different configurations of two commercially available codecs. The tests were carried out on both high-bandwidth and resource-constrained networks and in WAN setups. In the best-case scenario, latencies of just over 20 ms were achieved.