This paper presents the outcomes of the PETS2025 challenge, held in conjunction with AVSS 2025 and sponsored by the EU-funded EURMARS project. The challenge introduces a novel maritime surveillance dataset comprising image sequences captured by diverse multi-altitude, multimodal sensors, reflecting the real-world multi-authority environment. The key tasks include: (1) object detection using various sensors across different platforms (ground-based and low-altitude aerial) and spectral ranges (visible, thermal, ultraviolet (UV), and short-wave infrared (SWIR)); (2) long-term tracking of targets in maritime environments spanning both sea and land; and (3) approximating target geolocations by using sensor imagery and telemetry data. Performance evaluations of results submitted by 12 international participants are discussed. The results show the effectiveness of these submissions and highlight ongoing challenges posed by heterogeneous sensors and complex environments. These challenges emphasise the need to further improve detection, tracking, and geolocation approximation for maritime and coastal surveillance.

The Boolean satisfiability problem (SAT) is an NP-complete combinatorial optimization problem, where fast SAT solvers are useful for various smart society applications. Since these edge-oriented applications require time-critical control, a high speed SAT solver on FPGA is a promising approach. Here the authors propose a novel FPGA implementation of a bio-inspired stochastic local search algorithm called ‘AmoebaSAT’ on a Zynq board. Previous studies on FPGA-AmoebaSATs tackled relatively smaller-sized 3-SAT instances with a few hundred variables and found the solutions in several milli seconds. These implementations, however, adopted an instance-specific approach, which requires synthesis of FPGA configuration every time when the targeted instance is altered. In this paper, a slimmed version of AmoebaSAT named ‘AmoebaSATslim,’ which omits the most resource-consuming part of interactions among variables, is proposed. The FPGA-AmoebaSATslim enables to tackle significantly larger-sized 3-SAT instances, accepting 30,000 variables with 130, 800 clauses. It achieves up to approximately 24 times faster execution speed than the software-AmoebaSATslim implemented on a CPU of the x86 server.

In Internet of Things (IoT) networks, devices use various application protocols, such as MQ telemetry transport (MQTT), constrained application protocol (CoAP), and extensible messaging and presence protocol (XMPP). However, because of the various protocols used, devices adopting different protocols cannot communicate mutually; thus, service interoperability issues arise. To enhance service interoperability, protocol conversion is required. Because different protocols are used in IoT networks, they are required to correspond to various protocols and have high extensibility for protocol conversion. In this paper, a protocol conversion method that satisfies these requirements is proposed. The proposed method converts packets into a middle format before converting them into target protocols. Conversion rules are described for each protocol; thus, the proposed method has high extensibility. We confirmed that clients that use the proposed conversion method could successfully connect to other devices using different protocols and communicate mutually through servers. The protocols included MQTT, CoAP, XMPP, and SMTP. Furthermore, the throughput degradation caused by the conversion process is small.

One of the possible causes of the phantom limb pain (PLP), which have not been clarified medically, is said that a brain is unable to adapt to the loss of limbs. Even though a brain sends a signal to move limbs, the feedback on the signal is not given because the limbs to which a brain sent the signal are no longer in existence. Recently, some kinds of treatment are introduced, which is making patients feel that their phantom limbs are in existence and they are able to move the phantom limbs actually by their own will, but each of them has some difficulty. In this research, we propose the method of PLP treatment using an optical see-through Head Mounted Display (HMD) that can adapt to the telescoping of phantom limbs and make it easy for patients to communicate with people around them.