Miracle-dz/UniFAR-FTUs huggingface dataset:
StreamZip: Compressed Sliding-Windows for Stream Aggregation

High performance stream aggregation is critical for many emerging applications that analyze massive volumes of data. Incoming data needs to be stored in a sliding-window before processing, in case the aggregation functions cannot be computed incrementally. Updating the window with new incoming values and reading it to feed the aggregation functions are the two primary steps in stream aggregation. Although window updates can be supported efficiently using multi-level queues, frequent window aggregations remain a performance bottleneck as they put tremendous pressure on the memory bandwidth and capacity. This paper addresses this problem by introducing StreamZip, a dataflow stream aggregation engine that is able to compress the sliding-windows. StreamZip deals with a number of data and control dependency challenges to integrate a compressor in the stream aggregation pipeline and alleviate the memory pressure posed by frequent aggregations. In doing so, StreamZip offers higher throughput as well as larger effective window capacity to support larger problems. StreamZip supports diverse compression algorithms offering both lossless and lossy compression to integers as well as floating point numbers. Compared to designs without compression, StreamZip lossless and lossy designs achieve up to 7× and 22× higher throughput, while improving the effective memory capacity by up to 5× and 23×, respectively.
Adaptive NN-based Root Cause Analysis in Volume Diagnosis for Yield Improvement

Root Cause Analysis (RCA) is a critical technology for yield improvement in integrated circuit manufacture. Traditional RCA prefers unsupervised algorithms such as Expectation Maximization based on Bayesian models. However, these methods are severely limited by the weak predictive capability of statistical models and can’t effectively transfer the yield learning experience from old designs and processes to the new ones. Motivated by recent advancements of deep learning, in this paper we propose a Neural-Network-based adaptive framework for RCA in yield improvement. The proposed framework consists of an inference module and a self-adaptive module. The former receives volume diagnosis reports and predicts the root cause distributions. The latter is able to adapt the inference module to new designs and processes based on a few of targeted samples without any manual adjustment. Experimental results show that a relatively large improvement on accuracy is achieved by the proposed framework on simulated diagnosis data. Furthermore, the transferring capability of the self-adaptive module is also validated by the results.
APIR-DSP: An approximate PIR-DSP architecture for error-tolerant applications

In error-tolerant applications such as low-precision DNNs and digital filters, approximate arithmetic circuits can significantly reduce hardware resource utilization. In this work we propose an embedded block for field-programmable gate arrays, called APIR-DSP, which incorporates an approximate 9×9 hard multiplier based on the PIR-DSP architecture to improve speed and reduce area. In addition, a DSP unit evaluation platform based on Yosys and VPR which packs multiply accumulate operations into DSP blocks is developed. Using this tool we synthesis designs from Verilog implementations of matrix multiplication in DeepBench and the DoReFaNet low-precision neural network and show that APIR-DSP significantly reduces DSP resources and improves hardware utilization and performance compared with the Xilinx DSP48E2 embedded block. Compared with exact multiplication, it is shown that accuracy loss is optimized with the SNR of an FIR filter being reduced by 1.03 dB. For DNNs, accuracy loss for AlexNet is 0.31% on CIFAR10 dataset and no accuracy loss for LeNet on MNIST dataset is observed. Synthesis results show that the APIR-DSP enjoys an area reduction of 21.60%, critical path reduction of 4.85% and power consumption is reduced by 2.80%, compared with PIR-DSP.
Imitation Learning for Simultaneous Escape Routing

This paper proposes a novel problem-solving strategy for simultaneous escape routing (SER) by imitation learning (IL) which is beneficial to exclude human experts' domain knowledge for designing heuristic solvers. The SER is a crucial task for printed circuit boards (PCB) layout design, affecting signal integrity (SI). However, SER is NP-hard, heuristic solver that provides a fair solution in a reasonable time is needed. We utilize deep neural network (DNN) as a learn-able heuristic solver trained by IL. To be specific, a simple search-based expert policy generates a guiding solution; the DNN learns to imitate the guiding solution. Extensive experiments show that the proposed method significantly outperforms baseline SER methods, including genetic algorithm (GA).
Power Swing Phenomena and Comparative Study of Its Detection on Transmission Line

Power systems are subject to a wide range of small or large disturbances during operating conditions. Small changes in loading conditions occur continually. The power system must adjust to these changing conditions and continue to operate satisfactorily and within the desired limits of voltage and frequency. The power system should be designed to survive larger types of disturbances, such as faults, loss of a large generator, or line switching. Certain system disturbances may cause loss of synchronism between a generator and rest of the utility system, or between interconnected power systems of neighboring utilities. If such a loss of synchronism occurs, it is imperative that the generator or system areas operating asynchronously are separated immediately to avoid widespread outages and equipment damage. Here it is described to distinguish between power swing and real fault. It is also discussed recent enhancements in the design of out of step tripping and blocking protection functions that improve the security and reliability of the power system. In addition to that the behavior of distance relay element during power swing and during fault is simulated using MATLAB and SIMULINK simulations.
Point Cloud Object Segmentation Using Multi Elevation-Layer 2D Bounding-Boxes

Segmentation of point clouds is a necessary pre-processing technique when object discrimination is needed for scene understanding. In this paper, we propose a segmentation technique utilizing 2D bounding-box data obtained via the orthographic projection of 3D points onto a plane at multiple elevation layers. Connected components is utilized to obtain bounding-box data, and a consistency metric between bounding-boxes at various elevation layers helps determine the classification of the bounding-box to an object of the scene. The merging of point data within each 2D bounding-box results in an object-segmented point cloud. Our method conducts segmentation using only the topological information of the point data within a dataset, requiring no extra computation of normals, creation of an octree or k-d tree, nor a dependency on RGB or intensity data associated with a point. Initial experiments are run on a set of point cloud datasets obtained via photogrammetric means, as well as some open-source, LIDAR-generated point clouds, showing the method to be capture agnostic. Results demonstrate the efficacy of this method in obtaining a distinct set of objects contained within a point cloud.
A unified accelerator design for LiDAR SLAM algorithms for low-end FPGAs

A fast and reliable LiDAR (Light Detection and Ranging) SLAM (Simultaneous Localization and Mapping) system is the growing need for autonomous mobile robots, which are used for a variety of tasks such as indoor cleaning, navigation, and transportation. To bridge the gap between the limited processing power on such robots and the high computational requirement of the SLAM system, in this paper we propose a unified accelerator design for 2D SLAM algorithms on resource-limited FPGA devices. As scan matching is the heart of these algorithms, the proposed FPGA-based accelerator utilizes scan matching cores on the programmable logic part and users can switch the SLAM algorithms to adapt to performance requirements and environments without modifying and re-synthesizing the logic part. We integrate the accelerator into two representative SLAM algorithms, namely particle filter-based and graph-based SLAM. They are evaluated in terms of resource utilization, processing speed, and quality of output results with various real-world datasets, highlighting their algorithmic characteristics. Experiment results on a Pynq-Z2 board demonstrate that scan matching is accelerated by 13.67–14.84x, improving the overall performance of particle filter-based and graph-based SLAM by 4.03–4.67x and 3.09–4.00x respectively, while maintaining the accuracy comparable to their software counterparts and even state-of-the-art methods.
Convolution and Long Short-Term Memory Neural Network for PECVD Process Quality Prediction

Plasma Enhanced Chemical Vapor Deposition (PECVD) is the most important process in the manufacturing of solar panels, and the deposition effect determines the energy conversion efficiency of solar panels. In this process, multi-sensor data is collected by the system for quality prediction and control. Existing research on quality prediction only pays attention to one of temporal characteristics and spatial characteristics but ignores the other. The multi-sensor data collected in the PECVD process has the characteristics of time series data, and the data collected by different sensors is also relevant, so it is necessary to integrate the temporal characteristics and the spatial characteristics. This paper proposes a hybrid prediction network model and uses Convolutional-LSTM Network to establish a regression model between multi-sensor data and deposition quality, then evaluates the prediction accuracy of the hybrid model and the single model. In the proposed hybrid prediction model, the spatial characteristics of the data are extracted through the convolutional neural network (CNN), and then the temporal characteristics of the data are extracted through the long short-term memory network (LSTM). The results show that compared with existing prediction models, the proposed CNN-LSTM method reduces the prediction error by at least 31% without significantly improving the convergence time.
Low Frequency Simulations for Ambisonics Auralization of a Car Sound System

In this paper, a technique is described for obtaining the High Order Ambisonics (HOA) Impulse Responses (IRs) of an automotive infotainment system, relying on Finite Elements Method (FEM) simulations performed in COMSOL Multiphysics. The resulting HOA IRs are employed for auralizing the car sound system, either inside an Ambisonics listening room with a loudspeaker rig or with binaural rendering on a Head Mounted Display (HMD), benefiting from head-tracking and personalized Head Related Transfer Functions (HRTFs). This allows performing subjective tests before the prototype is built and preserving the auditory experience with a degree of realism unattainable with the static binaural approach. Measurements performed in a prototype vehicle with a spherical microphone array are compared to FEM simulations. A good agreement between numerical and experimental methods have been demonstrated.
A dataset of higher-order Ambisonic room impulse responses and 3D models measured in a room with varying furniture

This paper presents Motus, a new dataset of higher-order Ambisonic room impulse responses. The measurements took place in a single room while varying the amount and placement of furniture. 830 different room configurations were measured with four source-to-receiver configurations, resulting in 3320 room impulse responses in total. The dataset features various furniture object placements, including non-uniform distributions of absorptive material and cases with occluded direct paths between source and receiver. All acoustic measurements are accompanied by matching 3D models and 360°-photographs of the room. After describing the dataset, we demonstrate its usage with a reverberation time analysis. The analysis reveals that most of our measurements follow the expected relationship between absorption area and reverberation time. Some exceptional cases feature particular room acoustic phenomena, such as non-uniform absorption area distributions or multi-slope decays. Additionally, we show with a large number of measurements that furniture placement can significantly affect the reverberation time of a room. The dataset can be used to investigate room acoustic topics such as the acoustic effects of absorber placements or the decay behavior of rooms.
ac2SLAM: FPGA Accelerated High-Accuracy SLAM with Heapsort and Parallel Keypoint Extractor

In order to fulfill the rich functions of the application layer, robust and accurate Simultaneous Localization and Mapping (SLAM) technique is very critical for robotics. However, due to the lack of sufficient computing power and storage capacity, it is challenging to delpoy high-accuracy SLAM in embedded devices efficiently. In this work, we propose a complete acceleration scheme, termed ac2SLAM, based on the ORB-SLAM2 algorithm including both front and back ends, and implement it on an FPGA platform. Specifically, the proposed ac2SLAM features with: 1) a scalable and parallel ORB extractor to extract sufficient keypoints and scores for throughput matching with 4% error, 2) a PingPong heapsort component (pp-heapsort) to select the significant keypoints, that could achieve single-cycle initiation interval to reduce the amount of data transfer between accelerator and the host CPU, and 3) the potential parallel acceleration strategies for the back-end optimization. Compared with running ORB-SLAM2 on the ARM processor, ac2SLAM achieves 2.1 × and 2.7 × faster in the TUM and KITTI datasets, while maintaining 10% error of SOTA eSLAM. In addition, the FPGA accelerated front-end achieves 4.55 × and 40 × faster than eSLAM and ARM. The ac2SLAM is fully open-sourced at https://github.com/SLAM-Hardware/acSLAM.
A Policy Iteration Approach for Flock Motion Control

The flocking motion control is concerned with managing the possible conflicts between local and team objectives of multi-agent systems. The overall control process guides the agents while monitoring the flock-cohesiveness and localization. The underlying mechanisms may degrade due to overlooking the unmodeled uncertainties associated with the flock dynamics and formation. On another side, the efficiencies of the various control designs rely on how quickly they can adapt to different dynamic situations in real-time. An online model-free policy iteration mechanism is developed here to guide a flock of agents to follow an independent command generator over a time-varying graph topology. The strength of connectivity between any two agents or the graph edge weight is decided using a position adjacency dependent function. An online recursive least squares approach is adopted to tune the guidance strategies without knowing the dynamics of the agents or those of the command generator. It is compared with another reinforcement learning approach from the literature which is based on a value iteration technique. The simulation results of the policy iteration mechanism revealed fast learning and convergence behaviors with less computational effort.
Prior Aided Streaming Network for Multi-task Affective Analysis

Automatic affective recognition has been an important research topic in the human-computer interaction (HCI) area. With the recent development of deep learning techniques and large-scale in-the-wild annotated datasets, facial emotion analysis is now aimed at challenges in real world settings. In this paper, we introduce our submission to the 2nd Affective Behavior Analysis in-the-wild (ABAW2) Competition. In dealing with different emotion representations, including Categorical Expression (EXPR), Action Units (AU), and Valence Arousal (VA), we propose a multitask streaming network by a heuristic that the three representations are intrinsically associated with each other. Besides, we leverage an advanced facial expression embedding model as prior knowledge, which is capable of capturing identity-invariant expression features while preserving the expression similarities, to aid the down-streaming recognition tasks. In order to enhance the generalization ability of our model, we generate reliable pseudo labels for unsupervised training and adopt external datasets for fine-tuning. In the official test of ABAW2 Competition, our method ranks first in the EXPR and AU tracks and second in the VA track. The extensive quantitative evaluations, as well as ablation studies on the Aff-Wild2 dataset, prove the effectiveness of our proposed method.
An Architecture for the AES-GCM Security Standard

I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required final revisions, as accepted by my examiners. I understand that my thesis may be made electronically available to the public. Abstract The forth recommendation of symmetric block cipher mode of operation SP800-38D, Galois/Counter Mode of Operation (GCM) was developed by David A McGrew and John Viega. GCM uses an approved symmetric key block cipher with a block size of 128 bits and a universal hashing over a binary Galois field to provide confidentiality and authentication. It is built specifically to support very high data rates as it can take advantage of pipelining and parallel processing techniques. Before GCM, SP800-38A only provided confidentiality and SP800-38B provided authentication. SP800-38C provided confidentiality using the counter mode and authentication. However the authentication technique in SP800-38C was not parallelizable and slowed down the throughput of the cipher. Hence, none of these three recommendations were suitable for high speed network and computer system applications. With the advent of GCM, authenticated encryption at data rates of several Gbps is now practical, permitting high grade encryption and authentication on systems which previously could not be fully protected. However there have not yet been any published results on actual architectures for this standard based on FPGA technology. This thesis presents a fully pipelined and parallelized hardware architecture for AES-GCM which is GCM running under symmetric block cipher AES on a FPGA multi-core platform corresponding to the IPsec ESP data flow.
Dual Transformation and Manifold Distances Voting for Outlier Rejection in Point Cloud Registration

We present a novel outlier rejection scheme for point cloud registration using SE(3) voting on local transformation estimates with a dual consensus constraint. Point cloud registration is commonly performed by matching key-points in both point clouds and estimating the transformation parameters from these matches. In the presented method, each putative matching pair of points is equipped with a local transformation estimate using the Rigid Transformation Universal Manifold Embedding. Putative matching pairs with similar local estimates are then clustered together and the global transformation between point clouds is estimated for each cluster. Finally, the cluster with the majority of the votes such that the average of local transformations agrees with its associated global transformation is selected for completing the registration. This approach successfully deals with up to 99.5% outliers where state of the art fails.
Cross-modal Relational Reasoning Network for Visual Question Answering

Visual Question Answering (VQA) is a challenging task that requires a cross-modal understanding of images and questions with relational reasoning leading to the correct answer. To bridge the semantic gap between these two modalities, previous works focus on the word-region alignments of all possible pairs without attending more attention to the corresponding word and object. Treating all pairs equally without consideration of relation consistency hinders the model’s performance. In this paper, to align the relation-consistent pairs and integrate the interpretability of VQA systems, we propose a Cross-modal Relational Reasoning Network (CRRN), to mask the inconsistent attention map and highlight the full latent alignments of corresponding word-region pairs. Specifically, we present two relational masks for inter-modal and intra-modal highlighting, inferring the more and less important words in sentences or regions in images. The attention interrelationship of consistent pairs can be enhanced with the shift of learning focus by masking the unaligned relations. Then, we propose two novel losses ℒCMAM and ℒSMAM with explicit supervision to capture the fine-grained interplay between vision and language. We have conduct thorough experiments to prove the effectiveness and achieve the competitive performance for reaching 61.74% on GQA benchmark.
Leaf Area Estimation by Semantic Segmentation of Point Cloud of Tomato Plants

Growth monitoring is an essential task in agriculture for obtaining good crops and sustainable management of cultivation. Though it is essential, it is also a hard task requiring much labor and working time, and many automation approaches have been proposed. We present an attempt to estimate the leaf area of the tomatoes grown in a sunlight-type plant factory. We scanned tomato plants by an RGB-D sensor that moves vertically to scan one side of the plants from the pathway. We built a point cloud by merging the scanned data, and we segmented it into four classes (Stem, Leaf, Fruit, and Other) based on annotation. With a limited amount of data, we estimated the stem from Stem points, and from the number of Leaf points around the stem, we estimate the leaf area of a specific tomato plant in a plant factory with the relative error of about 20%.
Introduction to the Phasor Dual Induction Tool

Automatic environmental corrections to the induction log have been an elusive goal for many years. Recent work in induction response has been combined with modern signal processing theory to derive an automatic shouldereffect correction algorithm, but the method requires measurement of the induction quadrature or X-signals. The Phasor* dual induction tool incorporates these measurements and makes automatic shoulder corrections for induction a reality.
Leveraging Temporal Information for 3D Trajectory Estimation of Space Objects

This work presents a new temporally consistent space object 3D trajectory estimation from a video taken by a single RGB camera. Understanding space objects’ trajectories is an important component of Space Situational Awareness, especially for applications such as Active Debris Removal, On-orbit Servicing, and Orbital Maneuvers. Using only the information from a single image perspective gives temporally inconsistent 3D position estimation. Our approach operates in two subsequent stages. The first stage estimates the 2D location of the space object using a convolution neural network. In the next stage, the 2D locations are lifted to 3D space, using temporal convolution neural network that enforces the temporal coherence over the estimated 3D locations. Our results show that leveraging temporal information yields smooth and accurate 3D trajectory estimations for space objects. A dedicated large realistic synthetic dataset, named SPARK-T, containing 3 spacecrafts, under various sensing conditions, is also proposed and will be publicly shared with the research community.