Very Long Instruction Word (VLIW) processors can expose substantial instruction-level parallelism (ILP), yet compiler-generated schedules often fail to fully utilize available functional units, leaving performance on the table and forcing costly manual tuning. We present GRASP (Graph Reinforcement Assembly Scheduling Platform), an assembly-level post-pass optimizer that improves VLIW instruction scheduling and packing via graph reinforcement learning. GRASP represents each basic block as an Instruction Dependency Graph (IDG) that explicitly encodes dependence, latency, and machine resource constraints, and trains a GNN-based reinforcement learning agent to iteratively select legal scheduling decisions and construct high-throughput issue packets. By operating after compilation, GRASP requires no source-code changes and does not modify compiler internals, making it easy to integrate into existing toolchains. Across a suite of AI and HPC kernels, GRASP accelerates execution by up to 1.38 × (1.22 × on average), narrowing the gap between compiler output and expert-tuned VLIW code.

Smart eyeglasses are emerging as a new medium for human-computer interaction. Existing solutions typically rely on cameras or touchpads, raising privacy invasion concerns or requiring users to physically interact with the glass frames. Here, we present, a novel mid-air gesture interaction system for eyeglasses, based on radio frequency identification (RFID). The design of our system involves embedding a conformal RFID tag into the eyeglass frame, where the received signals change with user gestures. We optimize the radiation gain of the tag to avoid view blockage while preserving a long working range. To discriminate different gestures, we propose an adaptively weighted multi-channel fusion network to extract their respective distinctive features. To allow efficient adaptation of the pre-trained network to new users, we also introduce a novel diagonal dot-product attention in our contrastive learning framework to uncover the feature similarities of different users. The proposed system was evaluated through extensive experiments, demonstrating an average recognition accuracy of 98.86% across twenty users and a cross-user accuracy of 98.75%.

With the rapid development of the Internet of Things (IoT), we have seen a larger number of devices deployed with different wireless communication protocols (i.e., WiFi, ZigBee, Bluetooth). Working in the same place opens a new opportunity for these devices to communicate directly with each other, leveraging on Cross-technology Communication (CTC). However, since these devices operate in the same frequency band which results in the competition against each other for network resources, severe interfere may arise. In this paper, we explore pseudo-random sequence (PR sequence) to design a novel CTC protocol that enables low-cost direct communication between WiFi and ZigBee in noisy indoor environments. Pseudorandom sequence offers a unique statistical feature to accomplish both information transmission and synchronization between heterogeneous devices. We design a dynamic synchronous decoding strategy to handle interference coexisted among different wireless protocols. Our system does not require any modification of communication protocol and underlying hardware and firmware. We implement our system on commercial devices (Intel 5300 WiFi NIC and MicaZ CC2420), and conduct extensive experiments to evaluate the system performance in three typical scenarios. The experimental results show that the synchronization time of our approach is lower than 0.5 ms, and the accuracy is greater than 84% while the channel occupancy is as high as 50%.

Active antenna system (AAS) is a promising technology to boost the capacity of next generation wireless communication systems. As a key feature of AAS, vertical sectorization can help form new sub-sectors vertically in a conventional macro cell, facilitates reusing the frequency resources for multiple users, and thus has the potential to improve the system peroformance. In this paper, we investigate the performance of vertical sectorization by optimizing the antenna downtilt and transmit power in LTE-A downlink systems. We first derive the achievable data rate of a downlink wireless communication system considering vertical sectorization and then formulate the problem based on the derived data rate. Finally, antenna downtilt and transmit power are optimized to improve the performance of vertical sectorization. The simulation results demonstrate the effectiveness of the proposed algorithm.

Many emerging applications drive the needs of device-free localization (DfL), in which the target can be localized without any device attached. Because of the ubiquitousness of WiFi infrastructures nowadays, the widely available Received Signal Strength (RSS) information at the WiFi Access points are commonly employed for localization purposes. However, current RSS based DfL systems have one main drawback hindering their real-life applications. That is, the RSS measurements (fingerprints) vary slowly in time even without any change in the environment and frequent updates of RSS at each location lead to a high human labor cost. In this paper, we propose an RSS based low cost DfL system named TafLoc which is able to accurately localize the target over a long time scale. To reduce the amount of human labor cost in updating the RSS fingerprints, TafLoc represents the RSS fingerprints as a matrix which has several unique properties. Based on these properties, we propose a novel fingerprint matrix reconstruction scheme to update the whole fingerprint database with just a few RSS measurements, thus the labor cost is greatly reduced. Extensive experiments illustrate the effectiveness of TafLoc, outperforming the state-of-the-art RSS based DfL systems.

Passive localization is fundamental for many applications such as activity monitoring and real-time tracking. Existing received signal strength (RSS)-based passive localization approaches have been proposed in the literature, which depend on dense deployment of wireless communication nodes to achieve high accuracy. Thus, they are not cost-effective and scalable. This paper proposes the RSS distribution-based localization (RDL) technique, which can achieve high localization accuracy without dense deployment. In essence, RDL leverages the RSS and the diffraction theory to enable RSS-based passive localization in sensor networks. Specifically, we analyze the fine-grained RSS distribution properties at a variety of node distances and reveal that the structure of the triangle is efficient for low-cost passive localization. We further construct a unit localization model aiming at high accuracy localization. Experimental results show that RDL can improve the localization accuracy by up to 50%, compared to existing approaches when the error tolerance is less than 1.5 m. In addition, we apply RDL to facilitate the application of moving trajectory identification. Our moving trajectory identification includes two phases: an offline phase where the possible locations can be estimated by RDL and an online phase where we precisely identify the moving trajectory. We conducted extensive experiments to show its effectiveness for this application - the estimated trajectory is close to the ground truth.

The Radio Signal Strength Indicator (RSSI)-based localization algorithm is an effective solution for passive object localization. However, the localization accuracy of the existing methods highly depends on the transceiver distance and deployment density. Generally speaking, to obtain higher accuracy, we need a denser sensor node deployment, which results in a higher deployment cost and more communication overheads. In this paper, we investigate this problem based on extensive measurements. According to the measurement results, we propose to localize objects using an RSSI distribution based localization (RDL) model to identify the object location by different RSSI distributions of the communicating links. Experimental results show that the RDL method can achieve higher localization accuracy with less sensor nodes.

In a sensor network, the nodes that have a packet loss rate greater than a given threshold are called lossy nodes. Lossy nodes adversely affect network performance and collectively cause breakdown of network service in many cases. In this paper, we propose a new and simple approach to identify the lossy nodes from passive end-to-end measurements. Specifically, we consider the case of identifying lossy nodes during the aggregation of data from a collection of the sensor nodes to a sink node. The proposed method is based on the hamming distance of sequences on receipt/loss of aggregated data between each pair of parent-child node. With prior knowledge of the reverse data aggregation topology, our approach can identify all lossy nodes effectively. Finally, we validate the proposed approach