Multiple Testers Research Articles

System Residual Noise Reduction for Non-Contact Passive Intermodulation Measurements of Array Antennas Using Multiple Testers

System Residual Noise Reduction for Non-Contact Passive Intermodulation Measurements of Array Antennas Using Multiple Testers

Modeling the impact of resource allocation decisions on genomic prediction using maize multi‐environment data

AbstractIn a hybrid maize (Zea mays L.) breeding program that utilizes genomic selection, resource allocation used in phenotypic data acquisition must be balanced between population size, number of environments, and the number of testers used for generating hybrids. Plant breeders evaluate newly developed inbred lines using multi‐environment trials to account for genotype‐by‐environment interaction effects. The replication of hybrids across environments in these trials impacts the training data accuracy for developing genomic prediction models. This study examined the impact of resource allocation scenarios on genomic prediction accuracy using a multi‐environment trial dataset generated using inbred lines crossed to multiple testers. A total of 369 Stiff Stalk double haploid lines from a synthetic mapping population were testcrossed to three non‐Stiff Stalk inbred lines as testers, PHZ51, PHK76, and PHP02, and evaluated across 34 environments by the Genomes to Fields Initiative in 2020 and 2021. Resource allocation scenarios significantly impacted site‐specific genomic prediction accuracy for unobserved hybrids in unobserved environments. A training set with three to five environments that had the highest quality data produced similar prediction accuracy as data from 10 random environments for both observed and unobserved hybrids, indicating that strong prediction models can be built with a limited set of environments for both grain yield and plant height. We found that resource‐efficient prediction models that use data from one tester and three to five environments can effectively conduct selection of untested hybrids and in untested environments. Public research programs are often limited in testing capacity, and this study provides support for genomic selection in resource‐limited breeding programs.

Deep soft threshold feature separation network for infrared handprint identity recognition and time estimation

With the development of hardware devices, infrared technology has become an important detection method in criminal investigation, military and other fields. Infrared technology can capture the thermal infrared information of targets, achieve efficient and rapid individual identification, provide important technological support for crime investigation, improve case solving rate and public safety level. However, infrared technology still faces some difficulties. Due to factors such as environmental temperature changes and heat transfer, the infrared images captured over time gradually blur, resulting in highly aliased image features and difficulty in extracting effective information. This article addresses this issue by combining the specific tasks of infrared fingerprint identity recognition and time estimation. By analyzing the captured thermal traces to predict the target identity and departure time, a deep soft threshold feature separation (DSTFS) network is proposed. This network effectively improves the accuracy of identity recognition and time estimation by separating the characteristics of identity information and time information. Specifically, this article is based on the ResNet backbone network, extracting common features through shallow residual convolution blocks, extracting corresponding features for different tasks using branch residual convolution blocks, and introducing spatial and channel attention mechanisms to solve the problems of deep blur and feature separation. In order to enhance the model's expressive power, the soft threshold activation function SPRelu was adopted. By dynamically balancing the separation of features to learn weights, the weight balancing problem is solved, and periodic validation is used to evaluate task performance. According to the task requirements, we collected infrared fingerprint images of multiple testers and constructed an infrared hand heat trace dataset with labels such as departure time, gender, hand posture, and identity category as experimental data. Through the analysis of experimental results, our proposed deep feature separation network has better identity prediction results compared to other deep learning models in infrared fingerprint identity recognition and time estimation tasks. At the same time, it performs well in time estimation tasks, with lower error rates and higher stability.

PvCNN: Privacy-Preserving and Verifiable Convolutional Neural Network Testing

We propose a new approach for privacy-preserving and verifiable convolutional neural network (CNN) testing in a distrustful multi-stakeholder environment. The approach is aimed to enable that a CNN model <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">developer</i> convinces a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">user</i> of the truthful CNN performance over non-public data from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multiple testers</i> , while respecting model and data privacy. To balance the security and efficiency issues, we appropriately integrate three tools with the CNN testing, including collaborative inference, homomorphic encryption (HE) and zero-knowledge succinct non-interactive argument of knowledge (zk-SNARK). We start with strategically partitioning a CNN model into a private part kept locally by the model developer, and a public part outsourced to an outside server. Then, the private part runs over the HE-protected test data sent by a tester, and transmits its outputs to the public part for accomplishing subsequent computations of the CNN testing. Second, the correctness of the above CNN testing is enforced by generating zk-SNARK based proofs, with an emphasis on optimizing proving overhead for two-dimensional (2-D) convolution operations, since the operations dominate the performance bottleneck during generating proofs. We specifically present a new quadratic matrix program (QMP)-based arithmetic circuit with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a single multiplication gate</i> for expressing 2-D convolution operations between multiple filters and inputs in a batch manner. Third, we aggregate multiple proofs with respect to a same CNN model but different testers’ test data ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., different statements) into one proof, and ensure that the validity of the aggregated proof implies the validity of the original multiple proofs. Lastly, our experimental results demonstrate that our QMP-based zk-SNARK performs nearly 13.9× faster than the existing quadratic arithmetic program (QAP)-based zk-SNARK in proving time, and 17.6× faster in Setup time, for high-dimension matrix multiplication. Besides, the limitation on handling a bounded number of multiplications of QAP-based zk-SNARK is relieved.

Smart Face Mask Based on an Ultrathin Pressure Sensor for Wireless Monitoring of Breath Conditions (Adv. Mater. 6/2022)

Wearable Healthcare Devices Convenient breath monitoring via wearable devices is helpful for personal healthcare, especially during the COVID-19 pandemic. In article number 2107758, Kenjiro Fukuda, Takao Someya, and co-workers develop a wearable smart face mask based on an ultrathin self-powered pressure sensor with high output ability, and various breath conditions from multiple testers are wirelessly detected and analyzed.

CrowdIoT: The Crowd-Sourcing Test System for IoT Devices Based on Blockchain

In view of some problems existing in traditional software crowdsourcing tests and Internet of Things device tests, we developed a CrowdIoT crowdsourcing test system for the Internet of Things devices based on a block chain. CrowdIoT uses the core technology of blockchain to focus its testing on physical devices on the Internet of Things. CrowdIoT uses two different testing methods for different testing needs: online testing and offline testing. Online remote testing is our key function and research object. By opening the testing interface of Internet of Things devices to testers, testers can test the devices in the CrowdIoT system interface without having to get the hardware. At the same time, CrowdIoT introduced multi-threaded parallel testing technology to solve the conflict problem of multiple testers competing for hardware resources in online testing. Offline testing, as a supplement to online testing, is to send Internet of Things devices to testers with high credibility in deposit guarantee, so that testers can fully test the hardware devices and dig out as many vulnerabilities of the devices as possible. CrowdIoT has its own complete and scientific credibility system, combined with relevant incentive mechanism, consensus mechanism and connection mechanism, which not only effectively solves the centralization problem existing in traditional crowdsourcing testing platforms, but also solves the pain point of the lack of remote testing of Internet of Things devices in the market. Then it solves the problems of equipment limitation and testing cost limits in the field of Internet of Things equipment testing, and provides a platform for security testing and use for the Internet of Things participants.

A deep learning based 2-dimensional hip pressure signals analysis method for sitting posture recognition

Abnormal sitting postures usually cause adolescents’ myopia, scoliosis, and degenerative diseases. Therefore, research on intelligent monitoring technology that can quickly and accurately identify irregular sitting postures is of profound significance to the healthy development of adolescents. Existing methods mostly use computer vision to recognize sitting posture, but the model is not only complicated but also easily interfered with by problems such as occlusion and light. This paper proposes a method based on the analysis of the pressure on the hip interface to identify the sitting postures. An array pressure sensor placed on the cushion collects the tester's hip pressure and obtains a pressure heat map. This paper uses traditional feature extraction and shallow classifier methods and popular end-to-end deep convolutional neural network (CNN) methods to identify different types of sitting postures. The method in this paper is verified on the data of multiple testers of different body types. Experimental results show that the classification accuracy based on CNN reaches 99.82%, which proves the effectiveness of the method in sitting posture recognition. The study indicated hip pressure distribution is closely related to the sitting posture, and compared with computer vision, it is less disturbed and easier to recognize. The time efficiency of feature extraction using CNN is nearly 30% higher than traditional methods. Therefore, in the practical application of real scenes, with the increase of data volume, the time benefit brought by CNN can be more considerable and our system can be embedded in the cushion and do real-time detection.

Smart Face Mask Based on an Ultrathin Pressure Sensor for Wireless Monitoring of Breath Conditions.

A smart face mask that can conveniently monitor breath information is beneficial for maintaining personal health and preventing the spread of diseases. However, some challenges still need to be addressed before such devices can be of practical use. One key challenge is to develop a pressure sensor that is easily triggered by low pressure and has excellent stability as well as electrical and mechanical properties. In this study, a wireless smart face mask is designed by integrating an ultrathin self-powered pressure sensor and a compact readout circuit with a normal face mask. The pressure sensor is the thinnest (totally compressed thickness of ≈5.5µm) and lightest (total weight of ≈4.5mg) electrostatic pressure sensor capable of achieving a peak open-circuit voltage of up to ≈10V when stimulated by airflow, which endows the sensor with the advantage of readout circuit miniaturization and makes the breath-monitoring system portable and wearable. To demonstrate the capabilities of the smart face mask, it is used to wirelessly measure and analyze the various breath conditions of multiple testers.

The use of dried tube specimens of Plasmodium falciparum in an external quality assessment programme to evaluate health worker performance for malaria rapid diagnostic testing in healthcare centres in Togo

BackgroundThe use of rapid diagnostic tests (RDTs) to diagnose malaria is common in sub-Saharan African laboratories, remote primary health facilities and in the community. Currently, there is a lack of reliable methods to ascertain health worker competency to accurately use RDTs in the testing and diagnosis of malaria. Dried tube specimens (DTS) have been shown to be a consistent and useful method for quality control of malaria RDTs; however, its application in National Quality Management programmes has been limited.MethodsA Plasmodium falciparum strain was grown in culture and harvested to create DTS of varying parasite density (0, 100, 200, 500 and 1000 parasites/µL). Using the dried tube specimens as quality control material, a proficiency testing (PT) programme was carried out in 80 representative health centres in Togo. Health worker competency for performing malaria RDTs was assessed using five blinded DTS samples, and the DTS were tested in the same manner as a patient sample would be tested by multiple testers per health centre.ResultsAll the DTS with 100 parasites/µl and 50% of DTS with 200 parasites/µl were classified as non-reactive during the pre-PT quality control step. Therefore, data from these parasite densities were not analysed as part of the PT dataset. PT scores across all 80 facilities and 235 testers was 100% for 0 parasites/µl, 63% for 500 parasites/µl and 93% for 1000 parasites/µl. Overall, 59% of the 80 healthcare centres that participated in the PT programme received a score of 80% or higher on a set of 0, 500 and 1000 parasites/ µl DTS samples. Sixty percent of health workers at these centres recorded correct test results for all three samples.ConclusionsThe use of DTS for a malaria PT programme was the first of its kind ever conducted in Togo. The ease of use and stability of the DTS illustrates that this type of samples can be considered for the assessment of staff competency. The implementation of quality management systems, refresher training and expanded PT at remote testing facilities are essential elements to improve the quality of malaria diagnosis.

Reliability and Normative Data of Computerized Dynamic Visual Acuity Tests

Purpose: First, to create a standard for testing DVA using all three testing protocols (head still, object moving; head moving, object still; head and object moving). Second, to examine reliability of the tests. Third, provide normative data. Methods: Participants (n = 138) were males (n = 83) and females (n = 55) between the ages of 18-44 years old. All participants were tested on the RightEye Dynamic Visual Acuity Tests to determine reliability via Cronbach’s Alpha and Intraclass Correlation Coefficients (ICC). Age related differences were also examined across variables for two groups (under 30 and over 30) using t-test analysis. Results: All Cronbach’s Alphas were above an acceptable 0.7 level for reliability, ranging from .703-.953, thus demonstrating strong reliability across all tests. All ICC’s were statistically significant. There were no significant differences for age group on all DVA variables (p &gt; .05). Normative data for each variable report means, standard deviations and ranges for participants aged 18-44 years. Conclusions: Overall conclusions show that the RightEye Dynamic Visual Acuity tests show strong reliability and can therefore be used confidently as a measure of dynamic acuity. Data was collected with multiple testers and still showed excellent reliability. Future studies should examine non-healthy populations as well as young children and older adults.

Multienvironment Validation of the Effectiveness of Phenotypic and Genomewide Selection within Biparental Maize Populations

ABSTRACTPublished information is lacking on whether genomewide selection, based on a single tester and a single year of testing, can identify maize (Zea mays L.) lines that would perform well in multiple subsequent years and with multiple testers. Our objectives were to determine (i) if phenotypic selection or genomewide selection is more predictive of maize performance in future environments and with different testers, (ii) if combining both marker and phenotypic information is advantageous in selection, and (iii) the upward bias in correlations between marker‐predicted values and phenotypic values (rMP) when cross‐validation across individuals and across environments is not performed. We evaluated four elite populations, each with 150 or 250 doubled haploid (DH) lines, in 18 environments in the US Corn Belt. The DH lines were genotyped with 3072 single nucleotide polymorphism markers. The rMP values were 0.14 to 0.66 for grain yield and 0.49 to 0.66 for moisture. Phenotypic selection was always as effective as or more effective than genomewide selection. The rMP was lower when different testers were used for the training and test populations. Selection based on marker and phenotypic information was slightly more effective than genomewide selection or phenotypic selection alone. The correlation between predicted and observed performance was higher when cross‐validation across individuals and years was not performed than when cross‐validation across individuals and years was performed. For genomewide selection to be superior to phenotypic selection, the gains must not be measured in terms of the per‐generation response with equal population sizes and selection intensities.

Time Truncated Testing Strategy using Multiple Testers: Lognormal Distributed Lifetime

In this study, group acceptance sampling plan proposed by Aslam et al. (2011) is reconsidered when the lifetime variant of the test item follows lognormal distribution. The optimal plan parameters are obtained by considering various pre-specified parameters. The plan parameters are obtained using the non-linear optimization solution using two points approach. The advantage of the proposed plan is discussed over the existing plan using the single point approach and the proposed plan is more efficient than the existing plan.

Evaluation of Reliability of Automatic System for Measuring Sizing Degree by Basis Weight Variation of Paper

ABSTRACT Reliability of the novel automatic system capable of measuring both Stockigt sizing degree and contact angle at a time was evaluated through the calculation of its repeatability and reproducibility based on TAPPI Standard Method T 1200. As the basis weight of paper specimen increased, the repeatability and reproducibility of the automatic system became more improved than those from Hercules sizing test and contact angle test designated on TAPPI Standard Method T 530 and T 558. The more improved repeat-ability and reproducibility implies that a single tester can obtain the similar results under similar test con-ditions in spite of repetitive testing using the automatic system, and likewise that multiple testers can re-spectively reproduce similar data without big variation from the same paper specimen using the auto-matic system. In conclusion, this study is greatly m eaningful in having developed the world’s first auto-matic system to measure both Stockigt sizing degree and contact angle simultaneously with excellent repeatability and reproducibility.

Reliability of equipment for measuring the ground hardness and traction

The aim of this paper is to report the inter-rater reliabilities and intra-rater reliabilities of the Clegg hammer, penetrometer, and studded-boot apparatus used for measuring the mechanical properties of natural turf, and to determine whether the level of experience influences the reliability. Three experienced and three novice testers measured the surface hardness and rotational traction at nine locations on a community-level Australian football oval. A repeated-measures analysis of variance tested for significant differences between the six testers for all equipment, and intra-class correlation coefficients (ICCs) were calculated to determine the inter-rater reliabilities and intra-rater reliabilities. The ICCs for the reliability between the six testers ranged between 0.77 and 0.87 for the Clegg hammer, ranged between 0.55 and 0.73 for the penetrometer, and equalled 0.51 for the studded-boot apparatus. The inter-rater reliabilities and intra-rater reliabilities were greater for the experienced testers than for the novice testers for the Clegg hammer and penetrometer but the novice testers produced greater inter-rater reliabilities for the studded-boot apparatus. This study highlights the potential variability that can exist between testers using the ground hardness and traction equipment, which has implications for future research involving multiple testers both in agronomic-based studies and in linking the surface properties to the injury risk across multiple venues.

Multiple UIO-based test sequence generation for distributed systems

In developing distributed systems, conformance testing is required to determine whether an implementation under test (IUT) conforms to its specification. With distributed test architectures involving multiple remote testers, testing approaches may become more complicated because of issues known as controllability and observability problems. Based on a finite state machine (FSM) representation of the system’s specification, this paper proposes a new method to generate a test sequence utilizing multiple UIO sequences. The method is essentially guided by the way of minimizing the use of external coordination messages and input/output operations. Experiments are given to evaluate the proposed method.

Timing issues in distributed testing

The objective of conformance testing is to determine whether an implementation under test (IUT) conforms to its specification. In distributed test architecture where there are multiple remote testers, the objective can be complicated by the fact that testers may encounter controllability and observability problems during the application of a test sequence. A certain amount of work has been done in the area of generating test sequence that is free from these problems. However, few researchers investigate them from the aspect of test execution. This work studies the test execution phase when test sequences are applied to the implementation and it is pointed out that controllability and observability problems can be resolved if and only if the test system implements some timing constraints. When determining these constraints, the dynamic time information during test is taken into account, which reduces the test execution time and improves test efficiency further.

Intratester and Intertester Reliability of Clinical Measures of Lower Extremity Anatomic Characteristics: Implications for Multicenter Studies

To determine whether multiple examiners could be trained to measure lower extremity anatomic characteristics with acceptable reliability and precision, both within (intratester) and between (intertester) testers. We also determined whether testers trained 18 months apart could perform these measurements with good agreement. University's Applied Neuromechanics Research Laboratory. Sixteen, healthy participants (7 men, 9 women). Six investigators measured 12 anatomic characteristics on the right lower extremity in the Fall of 2004. Four testers underwent training immediately preceding the study, and measured subjects on 2 separate days to examine intratester reliability. Two testers trained 18 months before the study (Spring 2002) measured each subject on day 1 to examine the consistency of intertester reliability when testers are trained at different times. Knee laxity, genu recurvatum, quadriceps angle, tibial torsion, tibiofemoral angle, hamstring extensibility, pelvic angle, navicular drop, femur length, tibial length, and hip anteversion. With few exceptions, all testers consistently measured each variable between test days (intraclass correlation coefficient>or=0.80). Intraclass correlation coefficient values were lower for intertester reliability (0.48 to 0.97), and improved from day 1 to day 2. Intertester reliability was similar when comparing testers trained 18 months before those trained immediately before the study. Absolute measurement error varied considerably across individual testers. Multiple investigators can be trained at different times to measure anatomic characteristics with good to excellent intratester reliability. Intratester reliability did not always ensure acceptable intertester reliability or measurement precision, suggesting more training (or more experience) may be required to achieve acceptable measurement reliability and precision between multiple testers.

Constructing checking sequences for distributed testing

Abstract The objective of testing is to determine whether an implementation under test conforms to its specification. In distributed test architectures involving multiple remote testers, this objective can be complicated by the fact that testers may encounter coordination problems relating to controllability (synchronization) and observability during the application of tests. Based on a finite state machine (FSM) specification of the externally observable behaviour of a distributed system and a distinguishing sequence, this paper proposes a method for constructing a checking sequence where there is no potential controllability or observability problems, and where the use of external coordination message exchanges among testers is minimized. The proposed method does not assume a reliable reset feature in the implementations of the given FSM to be tested by the resulting checking sequence.

Indirect Test Architecture for SoC Testing

A generic model for test architectures in the core-based system-on-chip (SoC) designs consists of source/sink, wrapper, and test access mechanism (TAM). Current test architectures for digital cores assume a direct connection between the core and the tester. In these architectures, the tester establishes a physical link between itself and the core, such that it can directly control the core's design-for-testability (DFT), such as the scan chains or primary inputs. This direct connection undermines the modularity in the generic test architecture by tightly coupling its elements. In this paper, we propose a network-oriented indirect and modular architecture (NIMA) for postfabrication test in an SoC design methodology. In NIMA, test stimuli and expected results for digital cores are first compiled into new formats and subsequently encapsulated into packets. These packets are augmented with control and address bits such that they can autonomously be transmitted to their destination through a switching fabric. Owing to the indirect nature of the connection, embedded autonomous blocks at each core are used to apply the test to the core and compare the test results with expected values. This indirect access to the core decouples test data processing at the core from its communication providing the basis for flexible and modular test design and programming. Moreover, NIMA facilitates remote-access of single or multiple testers to an SoC, and enables the sending of test data to an SoC in-field in order to test the chip in its target system. Finally, NIMA serves in contributing toward the development of new test architectures that benefit from network-centric SoCs. We present a first implementation of NIMA when applied to a number of SoC benchmarks.

An upper bound on the weight-balanced testing procedure with multiple testers

This paper presents the performance of the Weight-Balanced Testing (WBT) algorithm with multiple testers. The WBT algorithm aims to minimize the expected number of (round of) tests and has been proposed for coding, memory storage, search and testing applications. It often provides reasonable results if used with a single tester. Yet, the performance of the WBT algorithm with multiple testers and particularly its upper bound have not been previously analyzed, despite the large body of literature that exists on the WBT algorithm, and the recent papers that suggest its use in various testing applications. Here we demonstrate that WBT algorithm with multiple testers is far from being the optimal search procedure. The main result of this paper is the generalization of the upper bound on the expected number of tests previously obtained for a single-tester WBT algorithm. For this purpose, we first draw an analogy between the WBT algorithm and alphabetic codes; both being represented by the same Q-ary search tree. The upper bound is then obtained on the expected path length of a Q-ary tree, which is constructed by the WBT algorithm. Applications to the field of testing and some numerical examples are presented for illustrative purposes.