VeriSAT is the first modern SAT solver implemented entirely in synthesizable SystemVerilog, leveraging FPGA architecture for hardware acceleration. This paper introduces the design of VeriSAT, focusing on hardware-specific optimizations that significantly improve performance over traditional software-based solvers. By rethinking key SAT components for hardware, VeriSAT introduces custom data structures and parallelized processes to accelerate solving efficiency.Central to VeriSAT's architecture are hardware-optimized data structures. A linked-list based literal-watching mechanism, enhanced with cached watching literals, reduces latency in unit propagation. Additionally, a concurrent propagation tree enables simultaneous traversal for faster conflict detection. The solver's pipelined clause learning further boosts throughput, allowing rapid conflict analysis without compromising performance.Extensive benchmarking demonstrates that VeriSAT outperforms two other FPGA-based solvers, SAT-Hard and SAT-Accel, by a factor of 1044x and is 18x faster, respectively, on curated benchmark instances. Additionally, VeriSAT shows a 30x speedup over the popular CPU-based MiniSat solver for specific datasets, validating the effectiveness of our design optimizations.VeriSAT represents a significant leap forward in FPGA-based SAT solving, offering unprecedented efficiency through its hardware-tailored design. This work lays the foundation for future advancements in hardware-accelerated SAT solvers and paves the way for more scalable, high-performance solutions in both academic and industrial applications.

Touch fastening structures are widely used to quickly assemble and disassemble an object with multiple parts. However, such structures are under-explored in the context of additive manufacturing for personal fabrication. We proposed Touch-n-Go, a method for designing touch-fastening structures with customizable mechanical properties such as holding capacities or shearing strength. Additionally, the customization of fastener patterns enables both static and dynamic connections, and the dynamic connections grant the freedom of rotation and translation. To facilitate the customization process, we developed a design tool that allows the integration of fastening structures on the surface of a 3D-printed object. Furthermore, we validated the fastening properties of Touch-n-Go through a series of experiments, and the result exhibits performances that match or even surpass off-the-shelf fasteners. Finally, we demonstrated the implementation of Touch-n-Go through a collection of applications.

"Like poles repel, unlike poles attract" is a fundamental principle of magnetism commonly used in instantaneous haptic interaction. Through the assembly design of basic five magnets, MagneChase creates a potential barrier between repulsion and attraction. This allows MagneChase modules to change the direction of their interacting force when brought closer. Applying this interaction principle, we provide application examples of kinetic roleplay to enhance tangible play experiences. A preliminary user study suggests that children were captivated by the magnetic phenomenon and derived pleasure from engaging with MagneChase. We also discuss the potential for MagneChase as a tangible kit to promote enlightenment learning in gameplay.

Interest-based communities, where users may have diversified interests and consequently cause communities overlapping, exist extensively in social networks. However, this will inevitably introduce more opportunities for malware spreading, whose propagation model is fundamentally different from that in currently widely-studied contact-based social networks. To address this issue, we firstly investigate the basic differences between interest-based communities and contact-based social networks. Then, the problem is formulated, and a model for malware propagation in interest-based overlapping communities is put forward. The proposed model fully considers the characters of such environment and reveals the malware spreading rules in randomly overlapping interest communities. Moreover, the model is transformed into a lightweight computational complexity mode so as to be easily utilized in practice. Finally, our model is verified with a simulation which is based on real-world dataset from Youtube interest communities, showing the theoretical results match the simulation results very well.

Recent studies show that Wi-Fi interference has been a major problem for low power urban sensing technology ZigBee networks. Existing approaches for dealing with such interferences often modify either the ZigBee nodes or Wi-Fi nodes. However, massive deployment of ZigBee nodes and uncooperative Wi-Fi users call for innovative cross-technology coexistence without intervening legacy systems. In this work, we investigate the Wi-Fi and ZigBee coexistence when ZigBee is the interested signal. Typically, the duration of transmitting a ZigBee data packet is longer than that of a Wi-Fi packet. Mitigating short duration Wi-Fi interference (calledflash) in long duration ZigBee data (calledsmog) is challenging. To address these challenges, we propose ZIMO: a sink-based MIMO design for harmony coexistence of ZigBee and Wi-Fi networks with the goal of protecting the ZigBee data packets from being interfered by high-power cross-technology signals. The key insight is to properly exploit opportunities resulted from differences between Wi-Fi and ZigBee, and bridge the gap between interested data and cross technology signals. Also, extracting the channel coefficient of Wi-Fi and ZigBee will enhance other coexistence technologies such as TIMO[1]. We implement a prototype in GNURadio-USRP N200, and our extensive evaluations under real wireless conditions show that ZIMO can improve ZigBee network throughput up to 1.9$\times$, with 1.5$\times$in media, and 1.1$\times$to 1.9$\times$for Wi-Fi network as byproduct in ZigBee signal recovery.

In this work, we investigate the distributed and balanced task reassignment in mobile social networks. Previous studies have shown the power of random choice in load balancing with random walking model. Inspired by the `2-choice' paradigm in `ball and bin' theory, we evaluate this simple but effective scheme with real trace data `MobiClique'. According to the preliminary evaluation results, we find that, social relationship significantly differs from pure random walk model, and will bring challenges in task reassignment in the followings: First, friendships are relatively stable, which will lead to imbalanced task assignment. Second, some users meet quite infrequently, which will lead to intolerable time delay and uneven task distribution. In tackling with these challenges, we propose `iTop-K', leveraging the basic concept, i.e., your friends are more powerful than you, which encourages mobile users to assign tasks among intimate friends instead of pure random assignment. With the selection of `top-K' friends, we can achieve load balancing and guaranteed network performance at the same time. Experimental studies verify our scheme and show the effectiveness. In typical working scenario, where real-trace driven simulation is applied, ours outperforms the conventional random choice up to 15×, and the social relationship assignment without priority method up to 9×.

We present SmartLoc, a localization system to estimate the location and the traveling distance by leveraging the lower-power inertial sensors embedded in smartphones as a supplementary to GPS. To minimize the negative impact of sensor noises, SmartLoc exploits the intermittent strong GPS signals and uses the linear regression to build a prediction model which is based on the trace estimated from inertial sensors and the one computed from the GPS. Furthermore, we utilize landmarks (e.g., bridge, traffic lights) detected automatically and special driving patterns (e.g., turning, uphill, and downhill) from inertial sensory data to improve the localization accuracy when the GPS signal is weak. Our evaluations of SmartLoc in the city demonstrates its technique viability and significant localization accuracy improvement compared with GPS and other approaches: the error is approximately 20m for 90% of time while the known mean error of GPS is 42.22m.

Recent studies show that WiFi interference has been a major problem for low power urban sensing technology ZigBee networks. Existing approaches for dealing with such interferences often modify either the ZigBee nodes or WiFi nodes. However, massive deployment of ZigBee nodes and uncooperative WiFi users call for innovative cross-technology coexistence without intervening legacy systems. In this work we investigate the WiFi and ZigBee coexistence when ZigBee is the interested signal.Mitigating short duration WiFi interference (called flash) in long duration ZigBee data (called smog) is challenging, especially when we cannot modify the WiFi APs and the massively deployed sensor nodes. To address these challenges, we propose ZIMO, a sink-based MIMO design for harmony coexistence of ZigBee and WiFi networks with the goal of protecting the ZigBee data packets.The key insight of ZIMO is to properly exploit opportunities resulted from differences between WiFi and ZigBee, and bridge the gap between interested data and cross technology signals. Also, extracting the channel coefficient of WiFi and ZigBee will enhance other coexistence technologies such as TIMO [1]. We implement a prototype for ZIMO in GNURadio-USRP N200, and our extensive evaluations under real wireless conditions show that ZIMO can improve up to 1.9x throughput for ZigBee network, with median gain of 1.5x, and 1.1x to 1.9x for WiFi network as byproduct in ZigBee signal recovery.