Improving the performance of data systems for join operations has long been a critical challenge. Recently, substantial attention has been focused on optimizing multi-way join performance, particularly in reducing the overhead caused by generating intermediate tuples that do not contribute to the final result. In this paper, we propose a novel algorithm called SieveJoin, which extends the established Bloomjoin approach to support multi-way joins. SieveJoin sets a new benchmark for the efficiency of join query execution. A key innovation of SieveJoin is its ability to propagate Bloom filters along the join path, allowing the system to terminate early and avoid producing superfluous intermediate results. The primary design objective of SieveJoin is to efficiently estimate join results using Bloom filters, while maintaining minimal memory overhead. We analyze the bottlenecks associated with deferred multi-way joins and detail how Bloom filters are utilized to suppress the creation of redundant intermediate tuples. To assess the effectiveness of SieveJoin, we conduct a comprehensive experimental evaluation using the TPC-H benchmark, citation datasets, and a synthetic dataset. Our results compare SieveJoin with a state-of-the-art column-store database and a worst-case optimal join algorithm, highlighting its advantages in both response time and memory usage.

The rapid emergence of smart homes offers convenience but also raises significant privacy concerns, such as challenges in comprehending privacy-related statuses, information, and settings. This study aims to mitigate these concerns by leveraging augmented reality technology to enhance privacy protection user experiences. Through interviews with experienced users in the Chinese culture context, we systematically identify and categorize three primary aspects of concerns encountered during smart home usage: sensor range and status, data transmission directions, and transparency of mode settings. Drawing on these insights, we design and implement a streamlined AR system for interacting with smart home devices. Our user studies demonstrate that our AR system offers significant advantages over traditional 2D smart home applications in terms of information representation, workload reduction (i.e., number of clicks), and enhanced privacy awareness. This research presents a notable step forward in privacy protection while suggesting pathways for AR-enhanced smart home systems.

In recent years, Artificial Intelligence (AI) has significantly enhanced road safety, with Explainable Artificial Intelligence (XAI) providing essential transparency and trust. Our research utilizes AI to improve Advanced Driving Assistance Systems (ADAS) by investigating the gap in Forward Collision Warning (FCW): the impact of previous negative warnings (false and nuisance warnings) on drivers’ response times to subsequent accurate FCWs. By integrating XAI methods, we offer insights into the factors affecting driver behavior and system trust. Utilizing extensive dataset that encompasses various driving scenarios and driver behaviors, we constructed a gradient-boosting machine model to forecast driver response times . To explain the underlying mechanics of the model, the Shapley Additive Explanations (SHAP) framework was employed, enabling a comprehensive interpretation of feature importance and inter-feature interactions. Key findings reveal that increased speeds heighten driver responsiveness due to amplified alertness, whereas slower speeds lead to delayed reactions. The influence of previous negative warnings, significantly extends response times to accurate warnings. Additionally, older drivers require longer response times. The relationship between the driving period and previous warning judgment profoundly affects subsequent driver responsiveness, indicating trust dynamics with FCW systems. By using interpretable machine learning, we provide insights into ADAS functionality, suggesting pathways for FCW responsiveness and contributing to the field of XAI applications. In the validation experiment, our approach improved driver response times, reducing the average time from 2.1 s to 1.6 s. The proportion of ignored warnings decreased from 12% to 6%, and the driver acceptance rate increased from 59% to 71%.

In the context of rapid urbanization, traditional manual guidance and static evacuation signs are increasingly inadequate for addressing complex and dynamic emergencies. This study proposes an innovative emergency evacuation framework that optimizes the crowd evacuation by integrating multiagent reinforcement learning (MARL) with adversarial reinforcement learning (ARL). The developed simulation environment models realistic human behavior in complex buildings and incorporates robotic navigation and intelligent path planning. A novel simulated human behavior model was integrated, capable of complex human–robot interaction, independent escape route searching, and exhibiting herd mentality and memory mechanisms. We also proposed a multiagent framework that combines MARL and ARL to enhance overall evacuation efficiency and robustness. Additionally, we developed a new ARL evaluation framework that provides a novel method for quantifying agents’ performance. Various experiments of differing difficulty levels were conducted, and the results demonstrate that the proposed framework exhibits advantages in emergency evacuation scenarios. Specifically, our ARLR approach increased survival rates by 1.8% points in low-difficulty evacuation tasks compared to the RLR approach using only MARL algorithms. In high-difficulty evacuation tasks, the ARLR approach raised survival rates from 46.7% without robots to 64.4%, exceeding the RLR approach by 1.7% points. This study aims to enhance the efficiency and safety of human–robot collaborative fire evacuations and provides theoretical support for evaluating and improving the performance and robustness of ARL agents.

The provision of lane-level travel time information can enable accurate traffic control and route guidance in urban roads with distinctive traffic conditions among lanes. However, few studies in the literature have been conducted to estimate lane-level travel time distributions. This study proposes a new vehicle re-identification (V-ReID) method for estimating lane-level travel time distributions using video images from widely deployed surveillance cameras. In the proposed method, a lane-based bipartite graph matching is introduced to obtain optimal matches between upstream and downstream vehicles by considering lane-level traffic conditions and vehicles’ lane changing behaviors and visual features. A lane-based travel time estimation technique is introduced to real-time estimate full spectrum of lane-level distribution parameters, including not only the mean but also the standard deviation and the distribution type. A comprehensive case study is carried out on a congested urban road in Hong Kong. Results of case study show that the proposed method outperforms the state-of-the-art link-based V-ReID method and is capable for providing accurate lane-level travel time distribution information on congested urban roads.

The fractional amplitude of low-frequency fluctuations (fALFF) has been widely used as potential clinical biomarkers for resting-state functional-magnetic-resonance-imaging-based schizophrenia diagnosis. How-ever, previous studies usually measure the fALFF with specific bands from 0.01 to 0.08 Hz, which cannot fully delineate the complex variations of spontaneous fluctuations in the resting-state brain. In addition, fALFF data are intrinsically constrained by the brain structure, but most of the traditional methods have not consider it in feature selection. For addressing these problems, we propose a model to classify schizophrenia in multifrequency bands with tree-guided group sparse learning. In detail, we first acquire the fALFF data in multifrequency bands (i.e., slow-5: 0.01-0.027 Hz, slow-4: 0.027-0.073 Hz, slow-3: 0.073-0.198 Hz, and slow-2: 0.198-0.25 Hz). Then, we divide the whole brain into different candidate patches and select those significant patches related to schizophrenia using random forest-based important score. Moreover, we use tree-structured sparse learning method for feature selection with the above patch spatial constraint. Finally, considering biomarkers from multifrequency bands can reflect complementary information among multiple-frequency bands, we adopt the multikernel learning method to combine features of multifrequency bands for classification. Our experimental results show that these biomarkers from multifrequency bands can achieve a classification accuracy of 91.1% on 17 schizophrenia patients and 17 healthy controls, further demonstrating that the multifrequency bands analysis can better account for classification of schizophrenia.

A characterization of systematic network coding over multi-hop wireless networks is key towards understanding the trade-off between complexity and delay performance of networks that preserve the systematic structure. This paper studies the case of a relay channel, where the source's objective is to deliver a given number of data packets to a receiver with the aid of a relay. The source broadcasts to both the receiver and the relay using one frequency, while the relay uses another frequency for transmissions to the receiver, allowing for a full-duplex operation of the relay. We analyze the decoding complexity and delay performance of two types of relays: one that preserves the systematic structure of the code from the source; another that does not. A systematic relay forwards uncoded packets upon reception, but transmits coded packets to the receiver after receiving the first coded packet from the source. On the other hand, a non-systematic relay always transmits linear combinations of previously received packets. We compare the performance of these two alternatives by analytically characterizing the expected transmission completion time as well as the number of uncoded packets forwarded by the relay. Our numerical results show that, for a poor channel between the source and the receiver, preserving the systematic structure at the relay (i) allows a significant increase in the number of uncoded packets received by the receiver, thus reducing the decoding complexity, and (ii) preserves close to optimal delay performance.

The throughput benefits of random linear network codes have been studied extensively for wirelined and wireless erasure networks. It is often assumed that all nodes within a network perform coding operations. In energy-constrained systems, however, coding subgraphs should be chosen to control the number of coding nodes while maintaining throughput. In this paper, we explore the strategic use of network coding in the wireless packet erasure relay channel according to both throughput and energy metrics. In the relay channel, a single source communicates to a single sink through the aid of a half-duplex relay. The fluid flow model is used to describe the case where both the source and the relay are coding, and Markov chain models are proposed to describe packet evolution if only the source or only the relay is coding. In addition to transmission energy, we take into account coding and reception energies. We show that coding at the relay alone while operating in a rateless fashion is neither throughput nor energy efficient. Given a set of system parameters, our analysis determines the optimal amount of time the relay should participate in the transmission, and where coding should be performed.

We consider an Adaptive Random Convolutional Network Coding (ARCNC) algorithm to address the issue of field size in random network coding for multicast, and study its memory and decoding delay performances through both analysis and numerical simulations. ARCNC operates as a convolutional code, with the coefficients of local encoding kernels chosen randomly over a small finite field. The cardinality of local encoding kernels increases with time until the global encoding kernel matrices at the related sink nodes have full rank. ARCNC adapts to unknown network topologies without prior knowledge, by locally incrementing the dimensionality of the convolutional code. Because convolutional codes of different constraint lengths can coexist in different portions of the network, reductions in decoding delay and memory overheads can be achieved. We show that this method performs no worse than block linear network codes in terms of decodability, and can provide significant gains in terms of average decoding delay or memory in combination, shuttle and random geometric networks.

This paper presents a novel cross-coupled current conveyor based CMOS transimpedance amplifier (TIA) design to obtain an input capacitive load insensitive and very low noise structure. The proposed structure is presented with an implementation in GlobalFoundries' 0.18- μm 1.8-V industry compatible CMOS technology. The whole TIA circuit consumes only 31.5 mW of dc power. Measured results show a -3 dB bandwidth of about 4 GHz with a 0.25 pF photodiode capacitance. The single-ended transimpedance gain for positive output port is 46 dB Ω. The measured single-ended input-referred noise current spectral density is kept below 18 pA/√{Hz} within the TIA frequency band. The optical sensitivity for a bit-error-rate of 10-12is -15 dBm with 4.25 Gb/s 231-1 proactive Reed-Solomon bypass data pattern. This cross-coupled structure also facilitates building an input-insensitive differential TIA. The simulation result shows a stable frequency response over a wide range of input capacitance from 0.05 to 0.5 pF.

A two-stage down-conversion architecture for 3.1–8 GHz ultra-wideband receiver front-end is designed which uses a local oscillator frequency equal to half the input frequency. The down-conversion technique is performed in two steps based on half-RF architecture to produce baseband signal. The proposed technique is implemented in 0.18 µm CMOS technology which achieves a conversion gain ranges from 36.1–32.4 dB and noise figure of 5.4–8.3 dB across the bandwidth.

We propose an efficient Adaptive Random Convolutional Network Coding (ARCNC) algorithm to address the issue of field size in random network coding. ARCNC operates as a convolutional code, with the coefficients of local encoding kernels chosen randomly over a small finite field. The lengths of local encoding kernels increase with time until the global encoding kernel matrices at related sink nodes all have full rank. Instead of estimating the necessary field size a priori, ARCNC operates in a small finite field. It adapts to unknown network topologies without prior knowledge, by locally incrementing the dimensionality of the convolutional code. Because convolutional codes of different constraint lengths can coexist in different portions of the network, reductions in decoding delay and memory overheads can be achieved with ARCNC.We show through analysis that this method performs no worse than random linear network codes in general networks, and can provide significant gains in terms of average decoding delay in combination networks.

Multi-resolution codes enable multicast at different rates to different receivers, a setup that is often desirable for graphics or video streaming. We propose a simple, distributed, two-stage message passing algorithm to generate network codes for single-source multicast of multi-resolution codes. The goal of this pushback algorithm is to maximize the total rate achieved by all receivers, while guaranteeing decodability of the base layer at each receiver. By conducting pushback and code assignment stages, this algorithm takes advantage of inter-layer as well as intra-layer coding. Numerical simulations show that in terms of total rate achieved, the pushback algorithm outperforms routing and intra-layer coding schemes, even with field sizes as small as 2^10(10 bits). In addition, the performance gap widens as the number of receivers and the number of nodes in the network increases. We also observe that naive inter-layer coding schemes may perform worse than intra-layer schemes under certain network conditions.

Automated estimation of DNA base-sequences is an important step in genomics and in many other emerging fields in biological and medical sciences. Current automated sequencers process single strands only. To improve the utility of existing technologies, we propose to mix two independent strands prior to electrophoresis, and base-call jointly by applying the sum-product algorithm on factor graphs. We first present a statistical model for DNA sequencing data and examine the model parameters. A practical heuristic is then proposed to estimate the peaks, which are then separated into two source sequences (Major/Minor) by passing messages on a factor graph. Simulation results show that joint base-calling can provide less accurate but valid results for the minor. The algorithm presented provides a basis for future investigation of joint sequencing techniques.

To improve the utility of existing technologies based on Sanger sequencing, this paper examines the possibility of base-calling two superposed DNA sequences jointly. This approach allows a single electrophoresis experiment to process two sequences, using the same quantity of reagents and machine hours as for a single sequence. A practical heuristic is proposed to first estimate the peak parameters, then separate them into two sequences (major/minor) by passing messages on a factor graph. Base-calling on the major alone yields accuracy commensurate with single sequence approaches, and joint base-calling provides results for the minor which, while being of lesser quality, incurs no additional cost and can be ultimately used in the genome assembly process.

A model development methodology for complex shaped on-wafer interconnects is presented. The equivalent circuit of the entire interconnect is obtained by cascading basic subsegment models. The extracted parameters are formulated into empirical expressions. Thus, the proposed model can be easily incorporated with commercial electronic design automation (EDA) tools. The accuracy of the model is validated by the on-wafer measurements up to 20 GHz.

This paper investigates the properties of the on-wafer interconnects built in a 0.18-/spl mu/m CMOS technology for RF applications. A scalable equivalent circuit model is developed. The model parameters are extracted directly from the on-wafer measurements and formulated into empirical expressions. The expressions are in functions of the length and the width of the interconnects. The proposed model can be easily implemented into commercial RF circuit simulators. It provides a novel solution to include the frequency-variant characteristics into a circuit simulation. The silicon-verified accuracy is proved to be up to 25 GHz with an average error less than 2%. Additionally, equivalent circuit model for longer wires can be obtained by cascading smaller subsections together. The scalability of the propose model is demonstrated.