Accurate Counting Research Articles

Smartwatch step counting: impact on daily step-count estimation accuracy.

The effect of displayed step count in smartwatches on the accuracy of daily step-count estimation and the potential underlying psychological factors have not been revealed. The study aimed for the following: (i) To investigate whether the counting and reporting of daily steps by a smartwatch increases the daily step-count estimation accuracy and (ii) to elucidating underlying psychological factors. A total of 34 healthy men and women participants wore smartwatches for 4 weeks. In week 1 (baseline), 3 (follow-up 1), and 8 (follow-up 2), the number of smartwatch displayed steps was blinded for each participant. In week 2 (Intervention), the number of steps was not blinded. During baseline and follow-ups 1 and 2, the participants were instructed to estimate their number of steps four times per day. During the 4-week wash-out period between follow-ups 1 and 2, no feedback was provided. The Body Awareness Questionnaire and the Body Responsiveness Questionnaire (BRQ) were used to elucidate the psychological facets of the assumed estimation accuracy. The mean absolute percentage error between the participants' steps count estimations and measured steps counts were 29.49% (at baseline), 0.54% (intervention), 11.89% (follow-up 1), and 15.14% (follow-up 2), respectively. There was a significant effect between baseline and follow-up 1 [t (61.7) = 3.433, p < 0.001] but not between follow-up 1 and follow-up 2 [t (60.3) = -0.288, p = 0.774]. Only the BRQ subscale "Suppression of Bodily Sensations" appeared to be significant at the Baseline (p = 0.012; Bonferroni adjusted p = 0.048) as a factor influencing step-count estimation accuracy. The counting and reporting of daily steps with a smartwatch allows improving the subjective estimation accuracy of daily step counts, with a stabilizing effect for at least 6 weeks. Especially individuals who tend to suppress their bodily sensations are less accurate in their daily step-count estimation before the intervention.

A Walkthrough of ActiGraph Counts

Activity counts have been used for over two decades with over 22,000 published scientific papers in public health and clinical research. ActiGraph recently released the algorithm for computing counts from raw accelerometer data as an open-source Python library, which is now ported by researchers to other languages, notably R. The current commentary presents historical overview of ActiGraph counts, and its development and evolution as a measure of physical activity. Further, we provide general recommendations on extracting counts from raw accelerometer data and discuss specific considerations with respect to device types, resampling, nonwear, axes orientations, and epoch length that may influence counts. Last, we provide a tutorial on how to use ActiGraph’s open-source Python library, agcounts, for consistent, accurate, and reproducible count. We expect this commentary will provide familiarity and transparency needed to adopt and produce activity counts in a consistent manner, allowing researchers to conduct statistical comparisons across multiple data sets and studies.

Phenotypic detection of flax plants based on improved Flax-YOLOv5.

Accurate detection and counting of flax plant organs are crucial for obtaining phenotypic data and are the cornerstone of flax variety selection and management strategies. In this study, a Flax-YOLOv5 model is proposed for obtaining flax plant phenotypic data. Based on the solid foundation of the original YOLOv5x feature extraction network, the network structure was extended to include the BiFormer module, which seamlessly integrates bi-directional encoders and converters, enabling it to focus on key features in an adaptive query manner. As a result, this improves the computational performance and efficiency of the model. In addition, we introduced the SIoU function to compute the regression loss, which effectively solves the problem of mismatch between predicted and actual frames. The flax plants grown in Lanzhou were collected to produce the training, validation, and test sets, and the detection results on the validation set showed that the average accuracy (mAP@0.5) was 99.29%. In the test set, the correlation coefficients (R) of the model's prediction results with the manually measured number of flax fruits, plant height, main stem length, and number of main stem divisions were 99.59%, 99.53%, 99.05%, and 92.82%, respectively. This study provides a stable and reliable method for the detection and quantification of flax phenotypic characteristics. It opens up a new technical way of selecting and breeding good varieties.

Microscale damper prototype: A preliminary study on suppressing air flow oscillations within microchannels

This research introduces a novel micro-damper designed to mitigate pressure and velocity oscillations from a piezoelectric micropump in microfluidic environments. Unlike existing research focusing on damping in incompressible liquid flows with methods like elastic films and PDMS membranes, this study proposes a novel micro-damper prototype. Integrated into a microdevice for particle granulometric separation and detection, the damper connects to a piezoelectric micropump outlet and to a focusing microchannel inlet, followed by a capacitive sensor for size-based particle counting. Preliminary analysis determined an optimal airflow velocity at w = 0.5 m/s for accurate focusing and counting under laminar conditions. The micro-damper, constrained by the piezoelectric pump’s geometry, features a 27 µm high and 1000 µm wide cross section. Its outlet supports two potential focusing microchannel inlet configurations of 30 µm or 40 µm. Distinctively, it incorporates two symmetrical backward micro-channels connecting to the atmosphere, allowing direct piezometric contact between the main flow and an infinite compliant volume. OpenFOAM simulations confirm the damper’s effectiveness in maintaining laminar outlet flow and suppressing micropump disturbances. Thus, the proposed micro-damper ensures optimal inlet conditions for subsequent microchannel processes, enabling stable particle separation and detection in controlled airflow samples.

Comparative molecular study of Enterococcus faecalis in endodontic treatment pre and postdiode laser application

Abstract Background: Endodontic infections are polymicrobial, with the majority of bacteria being anaerobic and others being facultative. Objective: To detect and measure the level of Enterococcus faecalis in infected root canals during traditional cleaning techniques and after diode laser application by using real-time polymerase chain reaction (RT-PCR). Materials and Methods: Ninety samples were collected from 30 participant patients complaining of acute or chronic pulpitis with an age range of 18–50 years old; the teeth indicated endodontic treatment, including both anterior and posterior teeth. Thirty swabs were taken preinstrumentation, and then 30 swabs were taken after instrumentation and canal irrigation using sodium hypochlorite 5.25%, and then 30 swabs were taken in the same manner after the root canal sterilization with the diode laser. Results: This study included 30 patients with root canal infection presented with a mean age of 31.7 ± 8.6 years and a range of 18–50 years; Female patients were more than males (34.3% vs. 36.7%). The mean E. faecalis copy number was significantly reduced from precleaning status to postcleaning with sodium hypochlorite (5.25%) and postlaser therapy (P = 0.05). Conclusions: RT-PCR assay is a highly effective, dependable, and rapid technique for accurate detection and copy number counting of E. faecalis in the infected root canal; this molecular and clinical study supports the diode laser application of 940-nm WL as an adjuvant tool combined with sodium hypochlorite (5.25%) in canal disinfection during root canal treatment to reduce E. faecalis copy numbers.

CCANet: A Collaborative Cross-Modal Attention Network for RGB-D Crowd Counting

Presently, to obtain a more accurate density map and crowd number, existing methods often count by combining training RGB images and depth images. However, these methods are not ideal for capturing and fusing complementary features in RGB-D. Therefore, to solve the above problems, we propose a collaborative cross-modal attention network named CCANet for accurate RGB-D crowd counting. CCANet is mainly composed of the collaborative cross-modal attention module (CCAM) and the collaborative cross-modal fusion module (CCFM). Specifically, CCAM focuses on adaptive, interleaved RGB-D information through channel and spatial cross-modal attentions to fully capture complementary features in different modes. CCFM can adaptively integrate these features by weighing the importance of the above complementary features. A large number of experiments on the ShanghaiTechRGBD and MICC benchmarks have proven the effectiveness of CCANet in RGB-D crowd counting. In addition, our CCANet is generally applicable to multimodal crowd counting and has achieved superior counting performance on the RGBT-CC benchmark.

Crowd Counting Based on Multiscale Spatial Guided Perception Aggregation Network.

Crowd counting has received extensive attention in the field of computer vision, and methods based on deep convolutional neural networks (CNNs) have made great progress in this task. However, challenges such as scale variation, nonuniform distribution, complex background, and occlusion in crowded scenes hinder the performance of these networks in crowd counting. In order to overcome these challenges, this article proposes a multiscale spatial guidance perception aggregation network (MGANet) to achieve efficient and accurate crowd counting. MGANet consists of three parts: multiscale feature extraction network (MFEN), spatial guidance network (SGN), and attention fusion network (AFN). Specifically, to alleviate the scale variation problem in crowded scenes, MFEN is introduced to enhance the scale adaptability and effectively capture multiscale features in scenes with drastic scale variation. To address the challenges of nonuniform distribution and complex background in population, an SGN is proposed. The SGN includes two parts: the spatial context network (SCN) and the guidance perception network (GPN). SCN is used to capture the detailed semantic information between the multiscale feature positions extracted by MFEN, and improve the ability of deep structured information exploration. At the same time, the dependence relationship between the spatial remote context is established to enhance the receptive field. GPN is used to enhance the information exchange between channels and guide the network to select appropriate multiscale features and spatial context semantic features. AFN is used to adaptively measure the importance of the above different features, and obtain accurate and effective feature representations from them. In addition, this article proposes a novel region-adaptive loss function, which optimizes the regions with large recognition errors in the image, and alleviates the inconsistency between the training target and the evaluation metric. In order to evaluate the performance of the proposed method, extensive experiments were carried out on challenging benchmarks including ShanghaiTech Part A and Part B, UCF-CC-50, UCF-QNRF, and JHU-CROWD ++ . Experimental results show that the proposed method has good performance on all four datasets. Especially on ShanghaiTech Part A and Part B, CUCF-QNRF, and JHU-CROWD ++ datasets, compared with the state-of-the-art methods, our proposed method achieves superior recognition performance and better robustness.

Transmigration of Surgical Gauze: A Rare Case of Intraluminal Gossypiboma

Gossypiboma, a rare but significant complication of retained surgical gauze, can lead to considerable morbidity and presents diagnostic challenges. We describe the case of a 40-year-old female who presented with a three-day history of generalized abdominal pain, accompanied by bilious vomiting and bowel obstruction. Her medical history included an open cholecystectomy seven months earlier. An abdominal X-ray suggested a linear radio-opaque foreign body, which was confirmed as gossypiboma by contrast-enhanced CT. During an exploratory laparotomy, a 30 x 30 cm surgical gauze was found within the ileum and successfully removed. This case highlights the ongoing risk of retained surgical items, even with modern advancements in surgical practice and technology. It underscores the importance of meticulous surgical technique, including accurate surgical counts, adherence to standardized protocols. This case serves as a critical reminder for all surgical teams to maintain heightened vigilance throughout all phases of surgery.

FIDMT-GhostNet: a lightweight density estimation model for wheat ear counting.

Wheat is one of the important food crops in the world, and the stability and growth of wheat production have a decisive impact on global food security and economic prosperity. Wheat counting is of great significance for agricultural management, yield prediction and resource allocation. Research shows that the wheat ear counting method based on deep learning has achieved remarkable results and the model accuracy is high. However, the complex background of wheat fields, dense wheat ears, small wheat ear targets, and different sizes of wheat ears make the accurate positioning and counting of wheat ears still face great challenges. To this end, an automatic positioning and counting method of wheat ears based on FIDMT-GhostNet (focal inverse distance transform maps - GhostNet) is proposed. Firstly, a lightweight wheat ear counting network using GhostNet as the feature extraction network is proposed, aiming to obtain multi-scale wheat ear features. Secondly, in view of the difficulty in counting caused by dense wheat ears, the point annotation-based network FIDMT (focal inverse distance transform maps) is introduced as a baseline network to improve counting accuracy. Furthermore, to address the problem of less feature information caused by the small ear of wheat target, a dense upsampling convolution module is introduced to improve the resolution of the image and extract more detailed information. Finally, to overcome background noise or wheat ear interference, a local maximum value detection strategy is designed to realize automatic processing of wheat ear counting. To verify the effectiveness of the FIDMT-GhostNet model, the constructed wheat image data sets including WEC, WEDD and GWHD were used for training and testing. Experimental results show that the accuracy of the wheat ear counting model reaches 0.9145, and the model parameters reach 8.42M, indicating that the model FIDMT-GhostNet proposed in this study has good performance.

Highly accurate measurement of the relative abundance of oral pathogenic bacteria using colony-forming unit-based qPCR.

Quantitative polymerase chain reaction (qPCR) has recently been employed to measure the number of bacterial cells by quantifying their DNA fragments. However, this method can yield inaccurate bacterial cell counts because the number of DNA fragments varies among different bacterial species. To resolve this issue, we developed a novel optimized qPCR method to quantify bacterial colony-forming units (CFUs), thereby ensuring a highly accurate count of bacterial cells. To establish a new qPCR method for quantifying 6 oral bacteria namely, Porphyromonas gingivalis, Treponema denticola, Tannerella forsythia, Prevotella intermedia, Fusobacterium nucleatum, and Streptococcus mutans, the most appropriate primer-probe sets were selected based on sensitivity and specificity. To optimize the qPCR for predicting bacterial CFUs, standard curves were produced by plotting bacterial CFU against Ct values. To validate the accuracy of the predicted CFU values, a spiking study was conducted to calculate the recovery rates of the predicted CFUs to the true CFUs. To evaluate the reliability of the predicted CFU values, the consistency between the optimized qPCR method and shotgun metagenome sequencing (SMS) was assessed by comparing the relative abundance of the bacterial composition. For each bacterium, the selected primer-probe set amplified serial-diluted standard templates indicative of bacterial CFUs. The resultant Ct values and the corresponding bacterial CFU values were used to construct a standard curve, the linearity of which was determined by a coefficient of determination (r²) >0.99. The accuracy of the predicted CFU values was validated by recovery rates ranging from 95.1% to 106.8%. The reliability of the predicted CFUs was reflected by the consistency between the optimized qPCR and SMS, as demonstrated by a Spearman rank correlation coefficient (ρ) value of 1 for all 6 bacteria. The CFU-based qPCR quantification method provides highly accurate and reliable quantitation of oral pathogenic bacteria.

CSNet: A Count-Supervised Network via Multiscale MLP-Mixer for Wheat Ear Counting.

Wheat is the most widely grown crop in the world, and its yield is closely related to global food security. The number of ears is important for wheat breeding and yield estimation. Therefore, automated wheat ear counting techniques are essential for breeding high-yield varieties and increasing grain yield. However, all existing methods require position-level annotation for training, implying that a large amount of labor is required for annotation, limiting the application and development of deep learning technology in the agricultural field. To address this problem, we propose a count-supervised multiscale perceptive wheat counting network (CSNet, count-supervised network), which aims to achieve accurate counting of wheat ears using quantity information. In particular, in the absence of location information, CSNet adopts MLP-Mixer to construct a multiscale perception module with a global receptive field that implements the learning of small target attention maps between wheat ear features. We conduct comparative experiments on a publicly available global wheat head detection dataset, showing that the proposed count-supervised strategy outperforms existing position-supervised methods in terms of mean absolute error (MAE) and root mean square error (RMSE). This superior performance indicates that the proposed approach has a positive impact on improving ear counts and reducing labeling costs, demonstrating its great potential for agricultural counting tasks. The code is available at http://csnet.samlab.cn.

Impact of Dead Fish Degradation on the Monitoring of Fish Losses in the Pregrowing and Growing Phase of Caged European Sea Bass (Dicentrarchus labrax)

The interplay between environmental, biological, and physical factors often leads to the deterioration of dead fish in marine cages prior to their removal. Depending on the weight of the dead fish and the frequency of their removal, deterioration can progress to a stage where visual identification by divers becomes challenging, thereby disrupting accurate counting of dead fish. This study presents a practical precision tool for monitoring the number of dead fish during the pregrowth and growth phases of caged European sea bass (Dicentrarchus labrax). To improve the assessment of collected mortality, experiments were conducted in farming cages with various fish weights. Identifiable fish rates (I, %) were calculated every 24 hr and classified into four weight classes: WC1 (4–15 g), WC2 (15–30 g), WC3 (30–80 g), and WC4 (&gt;80 g). The corrected number of dead fish (M) was calculated by dividing the collected number (C) by a correction factor (McR), which was determined based on the adopted removal frequency. The possible mortality removal frequencies per week (Fn) included operations such as F7 (daily), F3 (3 times), F2 (2 times), and F1 (once). The smallest correction denominator was 22% for WC1 at a frequency of once per week, whereas the maximum was 100% for WC3 and WC4 daily. The results revealed a high negative significant correlation between Fn and uncollected degraded fish rate (UR) (r = –0.841, p &lt; 0.05). Applying corrections to mortality collected in three finished batches (B2, B3, and B7) led to an increase in the mortality rate by 3.9% ± 1.5%, 5.5% ± 0.7%, and 5.0% ± 0.5%, respectively. This explained 16.8% ± 4.7%, 65.5% ± 26.7%, and 30.3% ± 3.7% of fish disappearances in B2, B3, and B7, respectively. The significance of this study lies in its practical applicability to fish farms as a precise tool for monitoring fish raised in marine cages.

Multiplexed immunolabelling of cancer using bioconjugated plasmonic gold-silver alloy nanoparticles.

Reliable protein detection methods are vital for advancing biological research and medical diagnostics. While immunohistochemistry and immunofluorescence are commonly employed, their limitations underscore the necessity for alternative approaches. This study introduces immunoplasmonic labelling, utilizing plasmonic nanoparticles (NPs), specifically designed gold and gold-silver alloy NPs (Au:Ag NPs), for multiplexed and quantitative protein detection. These NPs, when coupled with antibodies targeting proteins of interest, enable accurate counting and evaluation of protein expression levels while overcoming issues such as autofluorescence. In this study, we compare two nanoparticle functionalization strategies-one coating based on thiolated PEG and one coating based on calix[4]arenes-on gold and gold-silver alloy nanoparticles of varying sizes. Overall results tend to demonstrate a greater versatility for the calix[4]arene-based coating. With this coating and using the classical EDC/sulfo-NHS cross-linking procedure, we also demonstrate the successful multiplexed immunolabelling of Her2, CD44, and EpCAM in breast cancer cell lines (SK-BR-3 and MDA-MB-231). Furthermore, we introduce a user-friendly software for automatic NP detection and classification by colour, providing a promising proof-of-concept for the practical application of immunoplasmonic techniques in the quantitative analysis of biopsies in the clinical setting.

Using drones equipped with thermal cameras to locate and count quail individuals and coveys: A case study using Northern Bobwhite Colinus virginianus in Mississippi, USA

Abstract The presence and abundance of Northern Bobwhite Colinus virginianus often relies on imprecise methods (e.g. fall covey counts), which can lead to inaccurate estimates resulting in poor management practices. Drones equipped with thermal sensors have proven beneficial for monitoring wildlife but still require sufficient field validation regarding precision. We tested the application of thermal drones for locating and counting Northern Bobwhite in Mississippi, USA. We conducted flights over known locations of Northern Bobwhite representing three individual, radio‐marked birds during the breeding season and seven covey locations during the wintering season shortly after dusk. Individuals and coveys were located in 67% and 86% of flights, respectively. In some scenarios, bobwhite could be seen from altitudes of 120 m because heat signatures of multiple individuals grouped together in the covey provided an easily recognisable feature, in contrast to individual birds alone. Counts were accurate for two coveys and underestimated remaining coveys, because individual birds were sometimes visually obstructed from above. The small body size of bobwhite results in few pixels in thermal images and depends on ground sampling distance as a function of flight altitude. Our flights were tested at known vicinities of bobwhite locations, yet our method shows promise for locating unknown coveys as well. Future systematic methods may benefit from automated image recognition using computer vision and machine learning to improve count accuracy and save time, costs and labor.

Improving Dysarthric Speech Segmentation With Emulated and Synthetic Augmentation.

Acoustic features extracted from speech can help with the diagnosis of neurological diseases and monitoring of symptoms over time. Temporal segmentation of audio signals into individual words is an important pre-processing step needed prior to extracting acoustic features. Machine learning techniques could be used to automate speech segmentation via automatic speech recognition (ASR) and sequence to sequence alignment. While state-of-the-art ASR models achieve good performance on healthy speech, their performance significantly drops when evaluated on dysarthric speech. Fine-tuning ASR models on impaired speech can improve performance in dysarthric individuals, but it requires representative clinical data, which is difficult to collect and may raise privacy concerns. This study explores the feasibility of using two augmentation methods to increase ASR performance on dysarthric speech: 1) healthy individuals varying their speaking rate and loudness (as is often used in assessments of pathological speech); 2) synthetic speech with variations in speaking rate and accent (to ensure more diverse vocal representations and fairness). Experimental evaluations showed that fine-tuning a pre-trained ASR model with data from these two sources outperformed a model fine-tuned only on real clinical data and matched the performance of a model fine-tuned on the combination of real clinical data and synthetic speech. When evaluated on held-out acoustic data from 24 individuals with various neurological diseases, the best performing model achieved an average word error rate of 5.7% and a mean correct count accuracy of 94.4%. In segmenting the data into individual words, a mean intersection-over-union of 89.2% was obtained against manual parsing (ground truth). It can be concluded that emulated and synthetic augmentations can significantly reduce the need for real clinical data of dysarthric speech when fine-tuning ASR models and, in turn, for speech segmentation.

Rancang Bangun Alat Sortir Jeruk Medan Berdasarkan Luas Lingkar Berbasis Mikrokontroler ATMega2560

The increasing demand in the agricultural sector demands efficient fruit sorting solutions. This study considered the design and implementation of a orange fruit sorter combining ultrasonic sensor technology, servo motor, and infrared proximity. This tool aims to measure small to large dimensions of orange fruits. The use of infrared proximity aims to counter the fruit in each path where the orange runs to its container, and then the calculated information is displayed on the LCD screen to provide clear information for farmers. The main advantage of this tool is its ability to automatically classify orange fruits by size and amount. The integration of ultrasonic sensors and infrared proximity provides accuracy in distance measurement and fruit counting. This tool innovation provides unprecedented solutions and potential in orange fruit sorting, and can increase productivity in the agricultural industry. This research uses a technology design and implementation approach by integrating ultrasonic sensors, servo motors, IR proximity, on microcontrollers. The designed tool is able to classify orange fruits automatically by size and number. The design of this orange sorter promises to increase efficiency in the agricultural industry. The implementation of sensor technology and automation systems has the potential to provide the right solution for fruit sorting needs.

Novel Colorimetric and Light Scatter Methods to Identify and Manage Peritoneal Dialysis-Associated Peritonitis at the Point-of-Care

IntroductionPeritoneal dialysis-related peritonitis is a common cause of transfer to haemodialysis, patient morbidity and is a risk factor for mortality. Associated patient anxiety can deter selection of PD for renal replacement therapy. Diagnosis relies on hospital laboratory tests, however this might be achieved earlier if such information was available at the point-of-care, thereby significantly improving outcomes. The presence of culturable microbes and the concentration of leukocytes in effluent both aid peritonitis diagnosis, as specified in the International Society for Peritoneal Dialysis diagnostic guidelines. Here we report the development of two new methods providing such information in simple point-of-care tests. MethodsOne approach uses a tetrazolium-based chemical reporting system, primarily focused on detecting bacterial contamination and associated vancomycin-sensitivity. The second approach uses a novel forward light-scatter device (QuickCheck) to provide an instant quantitative cell count directly from PD patient effluent. ResultsThe tetrazolium approach detected and correctly distinguished lab isolates, taking 10h to provide non-quantitative results. We compared the technical performance of the light scatter leukocyte counting approach with spectrophotometry, haemocytometer counting and flow cytometry (Sysmex) using patient effluent samples. QuickCheck had high accuracy (94%) and was the most precise (coefficient of variation <4%), showing minimal bias, overall performing similarly to flow cytometry. ConclusionsThese complementary new approaches provide a simple means to obtain information to assist diagnosis at the point-of-care. The first provides antibiotic sensitivity following ten hours incubation, whereas the second optical approach (QuickCheck), provides instant accurate total leukocyte count.

Automatic Tube Counter Monitoring System using Infrared Sensor based on NODEMCU ESP8266

Technology is needed in every industry because it can simplify the production process, improve production quality, and enhance the company's reputation in the sight of consumers. The cylinder counting activity at PT Batuah Energi Indonesia is still done manually, involving time and standardized estimation of LPG cylinder loads, which faces inaccuracy issues. In fact, PT Batuah Energi Indonesia has facilities that handle many LPG cylinders from various users and providers of LPG cylinders. While accurate cylinder counts are beneficial to the industry, companies need technology that can automatically calculate the number of filled LPG cylinders. Therefore, this study was carried out to demonstrate to students how to develop automatic tube counters using an infrared E18-D80NK as a tube detector, NodeMCU microcontrollers ESP8266 as controllers, Arduino IDE for developing programs, and IoT for remote monitoring. The developed device approach, specifically using the E18-D80NK infrared proximity sensor based on the NodeMCU ESP8266, can be coded using the Arduino IDE compiler. For the detection of the number of tubes, the E18-EN80K infrared sensor is used and data transmission wirelessly utilizes the Blynk application. The results of the automatic tube counter monitoring tool were successfully tested with a 100% accuracy rate, utilizing the E18-D80NK infrared sensor and NodeMCU microcontroller ESP8266, and can be monitored remotely using Blynk.

Tomography of a Number-Resolving Detector by Reconstruction of an Atomic Many-Body Quantum State.

The high-fidelity analysis of many-body quantum states of indistinguishable atoms requires the accurate counting of atoms. Here we report the tomographic reconstruction of an atom-number-resolving detector. The tomography is performed with an ultracold rubidium ensemble that is prepared in a coherent spin state by driving a Rabi coupling between the two hyperfine clock levels. The coupling is followed by counting the occupation number in one level. We characterize the fidelity of our detector and show that a negative-valued Wigner function is associated with it. Our results offer an exciting perspective for the high-fidelity reconstruction of entangled states and can be applied for a future demonstration of Heisenberg-limited atom interferometry.

YOLO_MRC: A fast and lightweight model for real-time detection and individual counting of Tephritidae pests

Tephritidae pests severely affect the quality and safety of various melons, fruits and vegetable crops. However, many agricultural managers lack an adequate understanding of the level of pest occurrence, resulting in the misuse of pesticides, which ultimately leads to environmental pollution and economic loss. Therefore, real-time detection and counting of Tephritidae pests are important for timely pest spotting and control. This work helps quickly determine the distribution and abundance of pests in the current environment, thus providing data on pest conditions for agricultural management to optimize pesticide use. Nevertheless, the fast speed, high accuracy, and lightweight performance of real-time detection and counting are difficult to balance. To address this problem, based on the YOLOv8n model, this paper takes Bactrocera cucurbitae pests as the detection target and proposes a fast and lightweight real-time detection and individual counting model for Tephritidae pests, named YOLO_MRC. This paper introduces three key improvements: (1) Constructing a new module called Multicat into the neck network increases the focus on the detection target by incorporating an attention mechanism; (2) Reducing the original three detection heads to two and then adjusting their sizes to decrease the number of parameters in the network model; (3) Devising a novel module, C2flite, to enhance the deep feature extraction capability of the backbone network. According to the above points, this paper conducts ablation experiments to compare the performances of different models. Experiments showed that the Multicat module significantly offsets the large increase in GFLOPs and processing time caused by reducing the detection head and can further reduce the number of parameters and improve the accuracy when combined with the C2flite module. On our Bactrocera cucurbitae pest dataset, the mAP@0.5 of the YOLO_MRC model reached 99.3%. Simultaneously, as the number of parameters decreases by 63.68%, GFLOPs is reduced by 19.75%, and the processing time is shortened by 5%. To ensure the validity of the model, YOLO_MRC is compared with four excellent detection models by using manual counting results as the benchmark. YOLO_MRC achieves an average pest counting accuracy of 94%, demonstrating superior performance in terms of model size and processing time. To further explore the performance of YOLO_MRC in multiclass insect detection tasks, we choose the public dataset Pest_24_640 for comparison with four state-of-the-art models. YOLO_MRC achieves a 3.6 ms processing time and 70.4% accuracy with only a 2.4 MB model size, which demonstrates the potential of YOLO_MRC in multiclass pest detection.