The rapid advancement of wireless networks towards Artificial Intelligence (AI)-driven solutions attracts many vendors to build resilient and intelligent capabilities for Open Radio Access Networks (O-RAN). However, besides the benefits of achieving flexibility and intelligence, openness in native AI-driven O-RAN functions is also the target of severe AI-related security threats, e.g., adversarial attacks. This work addresses the security matter for the AI-powered solutions in the physical layer of O-RAN, specifically within the context of deep reinforcement learning (DRL)-based resource allocation. We introduce a new adversarial attack variant that manipulates the environment parameters and misleads the agent's observation during the inference phase. The attack can cause incorrect allocation decisions and significant degradation in the transmission data rate. Our evaluation results show that the attack degrades user data and packet delivery rates by up to 40% and 77.74%, respectively, particularly in ultra-low-latency services. We also found that the major weakness of DRL-driven radio resource allocation is the environment observation stage, where a group of compromised users or jammers can spoof noises and signal power to mislead environment interaction. In our context, the proposed policy infiltration attack is the most efficient approach to cause sustained network inefficiencies or reduced throughput for benign users.

The debris flows caused by typhoon Morakot in August 2009 killed more than 600 people and caused US$ 500 million in damages to agriculture and forestry of Taiwan. Many lives can be saved if we can estimate the occurrence of debris flows and issue timely warnings to inhabitants. Previous literatures [1] have shown that rainfall is highly correlated with debris flows. Thus, the first step to debris flow monitoring and warning is to collect high-resolution, real-time precipitation in the debris-flow-prone areas. In this paper we discuss the considerations of designing a low-cost WSN-based rain gauge grid, which provides highresolution mapping of precipitation. Preliminary experimental results are presented.

This demo presents a mobile wireless sensor system for debris flow monitoring. The objective of this system is to realize long-term and effective debris flow surveillance using low cost wireless sensors. In the system, a set of robust wireless sensors are designed to deploy on riverbed and cooperatively observe the moving debris flows. Our mobile sensors are intended to be carried along by the debris flow. As the sensors move along, they are able to measure the internal parameters, such as vibration frequency, amplitude, moving direction and velocity, of the debris flow. By utilizing the proposed energy-saving mechanism on the WSN platform, the mobile sensors can continuously operate up to six months with merely two alkaline D cell batteries. The proposed system provides the abilities to collect high-fidelity data for civil engineering applications to analyze and determine the occurrence of debris flows, as well as estimate the damage.

In an event-monitoring wireless sensor network (WSN), the sensors must be waked up fast to engage in active and high-rate sensing when events of interest occur. However, to prolong the lifetime of the network, the sensors should be put into the standby mode as long as possible. There is thus a tradeoff between the standby duration and the wakeup latency. Though the tradeoff is important, what is more important is a flexible transmission schedule that allows the tradeoff to be exploited. In this research, we use debris flow monitoring as a case study to illustrate how the tradeoff between long standby and fast wakeup can be exploited systematically with a flexible transmission schedule.

This work is to augment a debris flow observation and early warning system with wireless sensor networks. Previously, the GIS at Fengchia University has constructed and deployed state-of-the-art, stationary and mobile types of observation systems at nearly 20 sites throughout Taiwan. These sites collect data from sensors ranging from rain gauges and tension cables to ultrasonic sensors and CCD cameras, and transmit them back to the GIS via a lower-orbit satellite uplink in real-time. A new wireless sensor network and middleware system are being designed and implemented to overcome several limitations with the current system. Wireless communication capabilities are being incorporated to enhance the coverage. Previously, most connections between the sensors and the server before the satellite uplink are wired or Wi-Fi with fixed topology and limited range. New wireless interfaces with a 500 m - 1 km range plus energy harvesting devices on the sensors reduces deployment effort and cost. More importantly, it is now becoming possible to construct and deploy brand new types of mobile sensor nodes that move with the debris flow along its path. Such sensor nodes are to be housed in pyramid-shaped, weather-proof capsules that contain motion sensors, GPS and other localization devices, energy harvesting and storage devices, and wireless transceivers. Normally in low-power or standby mode, these capsules would be deployed in the path of potential debris flows. They would stand steadily during normal weather conditions including wind, rain, and water flow. They would get triggered by a threshold motion detector or a rain gauge and start actively monitoring the flow. As it flows with the debris, these capsules transmit their sensor data wirelessly, via other relaying nodes if necessary. Based on the shape and mass of the capsule itself and the velocity, researchers can derive the direction and magnitude of the flow in brand new ways.

Mode decision is a kernel function of current video standards, especially while dealing with massive coding modes as the latest H.264/AVC variable block size partitions. The mode decision process is extreme time consuming because it contains the kernel motion estimation and intra prediction modules. By applying the concepts of using priority coding orders to encode a sequence, this paper concludes a set of mode decision strategies with observing among priority lists. A computation-aware mode decision scheme is then proposed to the H.264/AVC. As the results demonstrated, coding with priority orders can achieve computation-aware ability automatically and the priority info is very helpful for developing software-based computation-aware mode decision algorithms.

The huge computational complexity of motion estimation (ME) process of a standard video codec has resulted in numerous researches on ME VLSI architecture designs. Especially, the new features, variable-block-size (VBS) partitions and multiple-reference-frames (MRF), in the H.264/AVC bring new challenges and opportunities. Though many VLSI designs have been proposed to deal with the new VBS ME feature, there is still no efficient solution for reducing the MRF complexity without drably repeating the whole ME modules. In this paper, an efficient MRF architecture based on a VBS merging scheme is presented. Based on the previous merging scheme, the new architecture can support up to 16 reference frames with the same ME component requirements as a simple 16x16 fixed-block-size ME architecture pluses a few merging logics and five memory latch chains. The proposed architecture is pipelining designed to achieve fully hardware utilization, whereas using the least hardware cost with our previous proposed embedded merging scheme.

The variable block size motion estimation (VBSME) technique can improve the coding efficiency greatly and has been adopted into the latest international video coding standard H.264/AVC. However, the VBSME technique introduces excessive computations comparing to fix block size ones. In H.264/AVC, the possible macroblock partition modes can have up to 259 different combinations, which make the design of an efficient VLSI architecture for H.264/AVC a challenging task. Many VLSI designs for the general motion estimation modules were presented in past decades, and some of them were also implemented as commercial products. Thereby, instead of developing a newly motion estimation module for H.264/AVC, this paper proposes an embedded merging scheme to extend the applicability of existing VLSI architectures. More specifically, the proposed embedded merging scheme only adds a few components into the existing VLSI architecture modules but can empower them to deal with the new H.264/AVC applications.

Content with rapid, moderate, and slow motion is frequently mixed together in real video sequences. Until now, no fast block-matching algorithm (FBMA), including the well-known three-step search (TSS), the block-based gradient descent search (BBGDS), and the diamond search (DS), can efficiently remove the temporal redundancy of sequences with wide range motion content. This paper proposes an adaptive FBMA, called A-TDB, to solve this problem. Based on the characteristics of a proposed predicted profit list, the A-TDB can adaptively switch search patterns among the TSS, DS, and BBGDS, according to the motion content. Experimental results reveal that the A-TDB successfully adopts the search patterns to remove the temporal redundancy of sequences with slow, moderate and rapid motion content.

The fast block-matching algorithm (FBMA), includes the well-known three-step search (TSS), the block-based gradient descent search (BBGDS) and the diamond search (DS), have been developed for motion estimation in designing a video codec. In this paper, an adaptive FBMA, named A-TDB, to solve the block matching problem for varying motions is proposed. Based on the characteristics of a proposed predicted profit list, the A-TDB can adaptively switch the search patterns among the TSS, DS, and BBGDS according to the motion contents.

A simple and efficient motion-vector-based search engine supporting query by clip is presented for indexing and retrieving of videos. We propose a technique to filter and reconstruct noisy motion vectors to improve the fidelity of the motion activity. In addition to the motion features of intensity and direction, a new motion descriptor, named representative spectrum, extracted by a vector quantization expressing the spatial-temporal distribution of motion activity, is introduced for video indexing. A dynamic programming scheme is also utilized to measure the similarity of two videos based on the concept of common subsequence. Furthermore, a 2-step search of similar videos is employed to reduce the computational complexity. The experimental results indicate that the proposed search engine performs well and the obtained recall and precision values are also high enough.

The emerging H.264/AVC video coding standard takes 50% performance improvement than the H.263. One of the key successful factors is to employee the variable block size matching algorithm in the motion compensation stage. However, the highly computational complexity leads the codec become too complex and hard to implement in some real time applications. Again, large memory requirement to save the middle results limits its usage, too. In this paper, we proposed an embedded merging scheme for fast full search algorithm of H.264/AVC. The proposed scheme cannot only reduce the computation complexity but also provides a better way to be implemented in VLSI hardware.

The purpose of the rate controller in a MPEG encoder is to allocate bit budget for each picture, and adjusts the quantization scale of each macroblock to control the actual used bits to close to the allocated bit budget for a picture. Multi-pass coding can allocate sufficient bit budget for each picture, but cannot be utilized in the real-time single-pass application. In the proposed algorithm, the complexity of the current picture can be easily obtained from the motion estimation, and therefore can allocate the bit budget for the picture more precisely to maintain a uniform visual quality from picture to picture. An initial quantization scale can also be estimated to increase the coding quality. The extremely simple structure of this framework can be easily implemented as a real-time video coding system by hardware as well as software.

Summary form only given. Region-based fractal image compression (Chang et al., 1997) is an attractive variation of fractal image compression algorithm. By segmenting an image into variable sizes of quadtree blocks and then merging these blocks into regions, this algorithm has the potential to encode a still image with a very low bit rate. However, highly visible artifacts become annoying when the coding bit rate is low. A large number of studies have been done in the literature to improve the reconstructed image quality at low bit rates. In this paper, an adaptive image enhancement algorithm for region-based fractal image compression is proposed. By utilizing an algorithm to determine which pixels have to be smoothed, then applying different smoothing filters for various image areas, the proposed algorithm can significantly eliminate the annoying blocking artifacts for images coded with low bit rate.