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Millimeter-wave radar-based identification technology has a wide range of applications in persistent identity verification, covering areas such as security production, healthcare, and personalized smart consumption systems. It has received extensive attention from the academic community due to its advantages of being non-invasive, environmentally insensitive and privacy-preserving. Existing identification algorithms mainly rely on a single signal, such as breathing or heartbeat. The reliability and accuracy of these algorithms are limited due to the high similarity of breathing patterns and the low signal-to-noise ratio of heartbeat signals. To address the above issues, this paper proposes an algorithm for multimodal fusion for identity recognition. This algorithm extracts and fuses features derived from phase signals, respiratory signals, and heartbeat signals for identity recognition purposes. The spatial features of signals with different modes are first extracted by the residual network (ResNet), after which these features are fused with a spatial-channel attention fusion module. On this basis, the temporal features are further extracted with a time series-based self-attention mechanism. Finally, the feature vectors of the user's vital sign modality are obtained to perform identity recognition. This method makes full use of the correlation and complementarity between different modal signals to improve the accuracy and reliability of identification. Simulation experiments show that the algorithm identity recognition proposed in this paper achieves an accuracy of 94.26% on a 20-subject self-test dataset, which is much higher than that of the traditional algorithm, which is about 85%. [ABSTRACT FROM AUTHOR]

In this paper, a novel input impedance analysis methodology based on Babinet's principle to broaden bandwidth is proposed, and a broadband multiple-input and multiple-output (MIMO) antenna system is designed, fabricated, and measured for fifth-generation (5G) and Wireless Fidelity (Wi-Fi) 6E/7 mobile applications. By analyzing the input impedance of open-slot antennas and planar monopole antennas using numerical calculations, the characteristics of the input impedance can be obtained. We find that combining the two antenna types in parallel can significantly enhance the bandwidth. Then, the four-dimensional image calculated by MATLAB based on the parallel impedance formula is processed to validate the methodology. Thus, a broad antenna element based on the impedance property analysis methodology is achieved, which operates ranging from 2.6 GHz to 7.46 GHz. Moreover, the equivalent circuit of the antenna element is established to further verify the validity of the methodology. Finally, a broadband MIMO antenna system consisting of eight antenna elements is designed, fabricated, and measured, and the isolation performance is better than 12 dB. Acceptable total efficiency higher than 45% is also obtained with envelope correlation coefficients (ECCs) lower than 0.05. The proposed impedance property analysis methodology innovatively proposes a new way to increase bandwidth, which can be widely applied in various antenna designs. Also, reasonable results show that the proposed MIMO antenna system is a good candidate for 5G and Wi-Fi 6E/7 mobile applications. [ABSTRACT FROM AUTHOR]

Simple Summary: Siganus oramin, as a commercial species, is a well-received table fish in southeastern China. However, valuable reproduction-related data are still scarce. In the present study, gonad transcriptome analysis was first performed using Illumina Novaseq technology. Comparative transcriptome analysis of adult male and female gonads identified a large number of differentially expressed genes. Among them, many reproduction-related genes that play important roles in gonad differentiation and development were found. These data may contribute greatly to the regulation of rabbitfish reproduction. The rabbitfish, Siganus oramin, is a commercially important table fish in southeastern China. However, there have been few studies on its gonad development and reproduction regulation. Comparative transcriptome analysis was first performed on adult male and female gonads of S. oramin. In total, 47,070 unigenes were successfully assembled and 22,737 unigenes were successfully annotated. Through comparative transcriptome analysis of male and female gonads, a total of 6722 differentially expressed genes were successfully identified, with 3528 upregulated genes and 3154 downregulated genes in the testes. In addition, 39 differentially expressed reproduction-related genes were identified. Finally, quantitative real-time PCR was used to validate the expression levels of several differentially expressed genes. These results provide important data for further studying the function of reproduction-related genes and the molecular mechanism regulating gonad development and reproduction in S. oramin. [ABSTRACT FROM AUTHOR]

Simple Summary: Hepatocellular carcinoma is the second leading cause of cancer-related deaths and the seventh most common cancer worldwide. Although there have been rapid developments in the treatment of hepatocellular carcinoma over the past decade, the incidence and mortality rates of hepatocellular carcinoma remain challenging. Only about 30% of patients can be treated with curative methods, while over 50% of patients require systemic treatment to prolong survival, with a limited benefit. Molecular targeted therapy and immunotherapy have brought about a revolution in hepatocellular carcinoma systemic treatment. Nevertheless, the treatment of hepatocellular carcinoma is still a challenge due to significant drug resistance, tumor heterogeneity, lack of druggable mutation targets, and lack of effective biomarkers. To improve outcomes of hepatocellular carcinoma patients, we need to gain a deeper understanding of the hepatocellular carcinoma genome and explore more combination treatment regimens. Hepatocellular carcinoma (HCC) is the most common form of liver cancer, accounting for ~90% of liver neoplasms. It is the second leading cause of cancer-related deaths and the seventh most common cancer worldwide. Although there have been rapid developments in the treatment of HCC over the past decade, the incidence and mortality rates of HCC remain a challenge. With the widespread use of the hepatitis B vaccine and antiviral therapy, the etiology of HCC is shifting more toward metabolic-associated steatohepatitis (MASH). Early-stage HCC can be treated with potentially curative strategies such as surgical resection, liver transplantation, and radiofrequency ablation, improving long-term survival. However, most HCC patients, when diagnosed, are already in the intermediate or advanced stages. Molecular targeted therapy, followed by immune checkpoint inhibitor immunotherapy, has been a revolution in HCC systemic treatment. Systemic treatment of HCC especially for patients with compromised liver function is still a challenge due to a significant resistance to immune checkpoint blockade, tumor heterogeneity, lack of oncogenic addiction, and lack of effective predictive and therapeutic biomarkers. [ABSTRACT FROM AUTHOR]

Indoor target localization is pivotal across various applications, encompassing security monitoring, behavioral analysis, and elderly care. This work proposes an advanced target localization algorithm that harnesses antennas endowed with sensing capabilities to capture the phase change in the signal ratio, derived from the signal amplitude. This phase change, indicative of the target's movement direction, is analyzed alongside the front and rear arrival angle information and signal amplitude characteristics obtained from LoRa signals. The algorithm, through a comprehensive examination of the phase change patterns, amalgamated with arrival angle data and signal amplitude characteristics, effectively estimates the precise location of the target. Experimental validations underscore the algorithm's efficacy in determining the target's location during continuous walking activity. Conducted within a 6 m × 12 m open platform, the algorithm achieves an average localization error of 48.5 cm, underscoring its superior performance compared to existing methodologies. [ABSTRACT FROM AUTHOR]

Simple Summary: In this study, transcriptome sequencing was first performed on the brain, liver, and muscle tissues of 3-month-old M. armatus individuals with different growth rates. A number of growth-related genes and biological pathways that related to skeletal muscle tissue development and insulin-like growth factor binding were screened. Our results suggest that IGFBP-1A, IGFBP-1B, GH, and GHR play an important role in the early developmental stage of M. armatus. These results contribute to providing a reference for further studies on the molecular mechanism of growth and development in teleost fish. In order to explore the main regulatory genes and related pathways of growth traits, transcriptome sequencing was first performed on the brain, liver, and muscle tissues of 3-month-old M. armatus with different growth rates. By comparative transcriptome analysis of fast-growing and slow-growing groups of M. armatus, a total of 2887 DEGs were screened, of which 59 up-regulated genes and 105 down-regulated genes were detected in the brain, 146 up-regulated genes and 202 down-regulated genes were detected in the liver, and 529 up-regulated genes and 1846 down-regulated genes were detected in muscle, including insulin-like growth factor binding protein 1a (IGFBP1A), insulin-like growth factor binding protein 1b (IGFBP1B), myosin, light chain 1 (MYL1), and myoglobin (MB). Through Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, we identified a total of 288 significantly enriched GO entries and 68 significantly enriched KEGG pathways related to growth, such as skeletal muscle tissue development, insulin-like growth factor binding, and the mitotic cell cycle. These key genes and signaling pathways may play a key role in regulating the growth of M. armatus. Digging into the regulatory mechanisms of these key genes will provide a theoretical basis for further exploration of the molecular mechanisms related to the growth and development of M. armatus, and help to breed new varieties of M. armatus with rapid growth. [ABSTRACT FROM AUTHOR]

Noncontact heart rate monitoring techniques based on millimeter-wave radar have advantages in unique medical scenarios. However, the accuracy of the existing noncontact heart rate estimation methods is still limited by interference, such as DC offsets, respiratory harmonics, and environmental noise. Additionally, these methods still require longer observation times. Most deep learning methods related to heart rate estimation still need to collect more heart rate marker data for training. To address the above problems, this paper introduces a radar signal-based heart rate estimation network named the "masked phase autoencoders with a vision transformer network" (MVN). This network is grounded on masked autoencoders (MAEs) for self-supervised pretraining and a vision transformer (ViT) for transfer learning. During the phase preprocessing stage, phase differencing and interpolation smoothing are performed on the input phase signal. In the self-supervised pretraining step, masked self-supervised training is performed on the phase signal using the MAE network. In the transfer learning stage, the encoder segment of the MAE network is integrated with the ViT network to enable transfer learning using labeled heart rate data. The innovative MVN offers a dual advantage—it not only reduces the cost associated with heart rate data acquisition but also adeptly addresses the issue of respiratory harmonic interference, which is an improvement over conventional signal processing methods. The experimental results show that the process in this paper improves the accuracy of heart rate estimation while reducing the requisite observation time. [ABSTRACT FROM AUTHOR]

To overcome the limitations of traditional frequency selective surfaces in flexible state switching at the same frequency, this paper proposes a novel active frequency selective surface (AFSS) that simultaneously exhibits conversion between absorption and transmission modes at the same frequency band. By welding PIN diodes on the bottom structure of the AFSS, the conversion between band-pass (transmission) and band-stop (absorption) filters can be controlled electronically. In the common-frequency switch between absorption and transmission modes, the impedance matching of the AFSS is attained by altering the capacitance value of the varactors embedded on the top structure of the AFSS. The functionalities of the proposed AFSS design are investigated by full-wave simulations (in HFSS software) at 11.4 GHz. Furthermore, the operating principle is analyzed using an equivalent circuit model (in AWR software). To verify the concept, a prototype is manufactured, and the responses of mode switching are measured by adjusting the bias voltage. The measurement result is consistent with the simulation analysis. Owing to the tunability of the varactors, the structural asymmetry is compensated to achieve 80% absorptivity and transmissivity within a field of view of ±35°. The developed AFSS structure is highly valuable to be used in scenarios such as antenna domes, etc. [ABSTRACT FROM AUTHOR]

Since the dawn of the past century, landmark discoveries in cell-mediated immunity have led to a greater understanding of the innate and adaptive immune systems and revolutionised the treatment of countless diseases, including cancer. Today, precision immuno-oncology (I/O) involves not only targeting immune checkpoints that inhibit T-cell immunity but also harnessing immune cell therapies. The limited efficacy in some cancers results mainly from a complex tumour microenvironment (TME) that, in addition to adaptive immune cells, comprises innate myeloid and lymphoid cells, cancer-associated fibroblasts, and the tumour vasculature that contribute towards immune evasion. As the complexity of TME has called for more sophisticated human-based tumour models, organoids have allowed the dynamic study of spatiotemporal interactions between tumour cells and individual TME cell types. Here, we discuss how organoids can study the TME across cancers and how these features may improve precision I/O. We outline the approaches to preserve or recapitulate the TME in tumour organoids and discuss their potential, advantages, and limitations. We will discuss future directions of organoid research in understanding cancer immunology in-depth and identifying novel I/O targets and treatment strategies. [ABSTRACT FROM AUTHOR]

Legal judgments are generally very long, and relevant information is often scattered throughout the text. To complete a legal judgment summarization, capturing important, relevant information comprehensively from a lengthy text is crucial. The existing abstractive-summarization models based on pre-trained language have restrictions on the length of an input text. Another concern is that the generated summaries have not been well integrated with the legal judgment's technical terms and specific topics. In this paper, we used raw legal judgments as information of different granularities and proposed a two-stage text-summarization model to handle different granularities of information. Specifically, we treated the legal judgments as a sequence of sentences and selected key sentence sets from the full texts as an input corpus for summary generation. In addition, we extracted keywords related to technical terms and specific topics in the legal texts and introduced them into the summary-generation model as an attention mechanism. The experimental results on the CAIL2020 and the LCRD datasets showed that our model achieved an overall 0.19–0.41 improvement in its ROUGE score, as compared to the baseline models. Further analysis also showed that our method could comprehensively capture essential and relevant information from lengthy legal texts and generate better legal judgment summaries. [ABSTRACT FROM AUTHOR]

Simple Summary: Spinibarbus hollandi has been recognized as an economically important aquaculture species in southeastern China. However, the information on its reproductive regulation is scarce. The long maturity period and low egg laying amount remain a major challenge for large-scale breeding of S. hollandi. In the current study, the gonad transcriptomes of S. hollandi were first obtained through Illumina Novaseq technology. By transcriptome comparing of ovary and testis, a lot of differential expression genes were identified and most of them were supposed to participate in gonad formation, differentiation and gametogenesis. Moreover, the expression levels of some typic reproductive genes indicated they might play similar roles in gonad differentiation and development of S. hollandi. Finally, the gonad transcriptome information can help researchers understand the regulatory functions of sex-related genes in S. hollandi. Spinibarbus hollandi is an important commercial aquaculture species in southeastern China, but with long maturity period and low egg laying amount. However, there has been little study of its gonad development and reproductive regulation, which limits aquaculture production. Here, for the first time, gonadal transcriptomes of male and female S. hollandi were analyzed. A total of 167,152 unigenes were assembled, with only 48,275 annotated successfully. After comparison, a total of 21,903 differentially expressed genes were identified between male and female gonads, of which 16,395 were upregulated and 5508 were downregulated in the testis. In addition, a large number of differentially expressed genes participating in reproduction, gonad formation and differentiation, and gametogenesis were screened out and the differential expression profiles of partial genes were further validated using quantitative real-time PCR. These results will provide basic information for further research on gonad differentiation and development in S. hollandi. [ABSTRACT FROM AUTHOR]

Large numbers of neutrophils infiltrate tumors and comprise a notable component of the inflammatory tumor microenvironment. While it is established that tumor cells exhibit the Warburg effect for energy production, the contribution of the neutrophil metabolic state to tumorigenesis is unknown. Here, we investigated whether neutrophil infiltration and metabolic status promotes tumor progression in an orthotopic mouse model of pancreatic ductal adenocarcinoma (PDAC). We observed a large increase in the proportion of neutrophils in the blood and tumor upon orthotopic transplantation. Intriguingly, these tumor-infiltrating neutrophils up-regulated glycolytic factors and hypoxia-inducible factor 1-alpha (HIF-1α) expression compared to neutrophils from the bone marrow and blood of the same mouse. This enhanced glycolytic signature was also observed in human PDAC tissue samples. Strikingly, neutrophil-specific deletion of HIF-1α (HIF-1αΔNφ) significantly reduced tumor burden and improved overall survival in orthotopic transplanted mice, by converting the pro-tumorigenic neutrophil phenotype to an anti-tumorigenic phenotype. This outcome was associated with elevated reactive oxygen species production and activated natural killer cells and CD8+ cytotoxic T cells compared to littermate control mice. These data suggest a role for HIF-1α in neutrophil metabolism, which could be exploited as a target for metabolic modulation in cancer. [ABSTRACT FROM AUTHOR]

Low Earth orbit (LEO) satellites that can be used as computing nodes are an important part of future communication networks. However, growing user demands, scarce channel resources and unstable satellite–ground links result in the challenge to design an efficient channel allocation algorithm for the LEO satellite network. Edge computing (EC) provides sufficient computing power for LEO satellite networks and makes the application of reinforcement learning possible. In this paper, an adaptive dynamic channel allocation algorithm based on a temporal–spatial correlation analysis for LEO satellite networks is proposed. First, according to the user mobility model, the temporal–spatial correlation of handoff calls is analyzed. Second, the dynamic channel allocation process in the LEO satellite network is formally described as a Markov decision process. Third, according to the temporal–spatial correlation, a policy for different call events is designed and online reinforcement learning is used to solve the channel allocation problem. Finally, the simulation results under different traffic distributions and different traffic intensities show that the proposed algorithm can greatly reduce the rejection probability of the handoff call and then improve the total performance of the LEO satellite network. [ABSTRACT FROM AUTHOR]

Harvesting energy from solar radiation has emerged as an effective approach to prolong the lifetime of outdoor energy harvesting sensor networks. The harvested energy must be carefully managed to ensure that sufficient energy is available when solar energy is scarce. For the prediction problem of solar energy power harvesting, this paper proposes an adaptive seasonal auto-regressive integrated moving average model (ASARIMA) for solar energy harvesting prediction. A training set can be adaptively adjusted by the similarity of historical data, and then we conduct seasonal difference data fitting based on the adjusted training set to obtain the optimal prediction model parameters. Experimental results show that this ASARIMA model performs better than other existing power prediction algorithms. If the weather conditions are stable, the prediction error of the ASARIMA decreases by more than 70%. If the weather conditions change sharply, the prediction error decreases by more than 20% in comparison with those of other algorithms. [ABSTRACT FROM AUTHOR]

Inflammation in the tumor microenvironment has been shown to promote disease progression in pancreatic ductal adenocarcinoma (PDAC); however, the role of macrophage metabolism in promoting inflammation is unclear. Using an orthotopic mouse model of PDAC, we demonstrate that macrophages from tumor-bearing mice exhibit elevated glycolysis. Macrophage-specific deletion of Glucose Transporter 1 (GLUT1) significantly reduced tumor burden, which was accompanied by increased Natural Killer and CD8+ T cell activity and suppression of the NLRP3-IL1β inflammasome axis. Administration of mice with a GLUT1-specific inhibitor reduced tumor burden, comparable with gemcitabine, the current standard-of-care. In addition, we observe that intra-tumoral macrophages from human PDAC patients exhibit a pronounced glycolytic signature, which reliably predicts poor survival. Our data support a key role for macrophage metabolism in tumor immunity, which could be exploited to improve patient outcomes. [ABSTRACT FROM AUTHOR]

Data missing is a common problem in wireless sensor networks. Currently, to ensure the performance of data processing, making imputation for the missing data is the most common method before getting into sensor data analysis. In this paper, the temporal and spatial nearest neighbor values-based missing data imputation (TSNN), a new imputation based on the temporal and spatial nearest neighbor values has been presented. First, four nearest neighbor values have been defined from the perspective of space and time dimensions as well as the geometrical and data distances, which are the bases of the algorithm that help to exploit the correlations among sensor data on the nodes with the regression tool. Next, the algorithm has been elaborated as well as two parameters, the best number of neighbors and spatial–temporal coefficient. Finally, the algorithm has been tested on an indoor and an outdoor wireless sensor network, and the result shows that TSNN is able to improve the accuracy of imputation and increase the number of cases that can be imputed effectively. [ABSTRACT FROM AUTHOR]

Simple Summary: In this review, we discuss treatment strategies in biliary tract cancers (gallbladder cancer, intrahepatic cholangiocarcinoma and extrahepatic cholangiocarcinoma). In particular, we will describe advances in cellular therapies and cancer vaccines for biliary tract cancers, followed by our local experience with combining a melanoma-associated antigen (MAGE)-positive cell lysate-based autologous dendritic cell vaccine and anti-angiogenic therapy (bevacizumab) in a case of stage IV gallbladder cancer. Biliary tract cancer (BTC) is a rare, but aggressive, disease that comprises of gallbladder carcinoma, intrahepatic cholangiocarcinoma and extrahepatic cholangiocarcinoma, with heterogeneous molecular profiles. Advanced disease has limited therapeutic options beyond first-line platinum-based chemotherapy. Immunotherapy has emerged as a viable option for many cancers with a similar unmet need. Therefore, we reviewed current understanding of the tumor immune microenvironment and recent advances in cellular immunotherapy and therapeutic cancer vaccines against BTC. We illustrated the efficacy of dendritic cell vaccination in one patient with advanced, chemorefractory, melanoma-associated antigen (MAGE)-positive gallbladder carcinoma, who was given multiple injections of an allogenic MAGE antigen-positive melanoma cell lysate (MCL)-based autologous dendritic cell vaccine combined with sequential anti-angiogenic therapy. This resulted in good radiological and tumor marker response and an overall survival of 3 years from diagnosis. We postulate the potential synergism of adding anti-angiogenic therapy, such as bevacizumab, to immunotherapy in BTC, as a rational scientific principle to positively modulate the tumor microenvironment to augment antitumor immunity. [ABSTRACT FROM AUTHOR]

As an emerging technology, edge computing will enable traditional sensor networks to be effective and motivate a series of new applications. Meanwhile, limited battery power directly affects the performance and survival time of sensor networks. As an extension application for traditional sensor networks, the energy consumption of Wireless Multimedia Sensor Networks (WMSNs) is more prominent. For the image compression and transmission in WMSNs, consider using solar energy as the replenishment of node energy; a distributed image compression scheme based on solar energy harvesting is proposed. Two level clustering management is adopted. The camera node-normal node cluster enables camera nodes to gather and send collected raw images to the corresponding normal nodes for compression, and the normal node cluster enables the normal nodes to send the compressed images to the corresponding cluster head node. The re-clustering and dynamic adjustment methods for normal nodes are proposed to adjust adaptively the operation mode in the working chain. Simulation results show that the proposed distributed image compression scheme can effectively balance the energy consumption of the network. Compared with the existing image transmission schemes, the proposed scheme can transmit more and higher quality images and ensure the survival of the network. [ABSTRACT FROM AUTHOR]

Recently, metal-framed handset antennas have aroused much attention due to their fantastic appearance and superior robustness. In this paper, a T-shaped antenna suitable for integration with a USB connector is proposed for metal-framed handset application by utilizing the theory of characteristic modes (TCM). Five eigenmodes, one for the lower band and four for the higher band, were merged by a T-shaped metal frame, a feed point, and two shorting pins, as well as three radio frequency (RF) switches so as to broaden the antenna's operating bandwidth. A prototype was fabricated in order to verify the design concept. Experimental results showed that the proposed antenna was able to cover bandwidths of 0.824–0.960 GHz (GSM band) and 1.710–2.690 GHz (DCS, PCS, UMTS, and LTE bands) with an acceptable radiation efficiency of up to 50% and satisfactory radiation patterns. [ABSTRACT FROM AUTHOR]

Limited battery power directly affects the performance and survival time of sensor networks. In this study, solar energy was used to provide additional energy for sensor nodes, and a clustering algorithm for heterogeneous wireless sensor networks (WSNs) based on a solar energy supply is proposed. A cluster head is selected on the basis of the self-replenishment state and the remaining energy of the nodes. A cluster-head selection mechanism and network layer multi-hop routing are adopted to balance the energy consumption of the network. A set of experiments showed that the proposed algorithm can effectively prolong the network lifetime, improve the stability and efficiency of the network, and balance the energy consumption compared with other algorithms under the same environmental energy supply. [ABSTRACT FROM AUTHOR]

A satellite network’s multiple access protocol must be well designed to efficiently occupy channel resources and meet various qualities of service requirements. In this paper, a combined free/demand assignment multiple access protocol based on gray forecast is proposed. In terms of access, the adoption of the gray forecast method allows the user to book the number of timeslots to have more than the existing number of burst of packets. In terms of bandwidth allocation, a user priority allocation method is used to prioritize users with high needs and focus on the allocation of satellite resources. Experimental results show that the proposed protocol can effectively improve the channel utilization of the entire system. Thus, this protocol has some advantages over existing protocols in network throughput and end-to-end delay. [ABSTRACT FROM AUTHOR]

Passive indoor personnel detection technology is now a hot topic. Existing methods have been greatly influenced by environmental changes, and there are problems with the accuracy and robustness of detection. Passive personnel detection based on Wi-Fi not only solves the above problems, but also has the advantages of being low cost and easy to implement, and can be better applied to elderly care and safety monitoring. In this paper, we propose a passive indoor personnel detection method based on Wi-Fi, which we call FDF-PIHD (Frequency Domain Fingerprint-based Passive Indoor Human Detection). Through this method, fine-grained physical layer Channel State Information (CSI) can be extracted to generate feature fingerprints so as to help determine the state in the scene by matching online fingerprints with offline fingerprints. In order to improve accuracy, we combine the detection results of three receiving antennas to obtain the final test result. The experimental results show that the detection rates of our proposed scheme all reach above 90%, no matter whether the scene is human-free, stationary or a moving human presence. In addition, it can not only detect whether there is a target indoors, but also determine the current state of the target. [ABSTRACT FROM AUTHOR]

The error bound is a typical measure of the limiting performance of all filters for the given sensor measurement setting. This is of practical importance in guiding the design and management of sensors to improve target tracking performance. Within the random finite set (RFS) framework, an error bound for joint detection and estimation (JDE) of multiple targets using a single sensor with clutter and missed detection is developed by using multi-Bernoulli or Poisson approximation to multi-target Bayes recursion. Here, JDE refers to jointly estimating the number and states of targets from a sequence of sensor measurements. In order to obtain the results of this paper, all detectors and estimators are restricted to maximum a posteriori (MAP) detectors and unbiased estimators, and the second-order optimal sub-pattern assignment (OSPA) distance is used to measure the error metric between the true and estimated state sets. The simulation results show that clutter density and detection probability have significant impact on the error bound, and the effectiveness of the proposed bound is verified by indicating the performance limitations of the single-sensor probability hypothesis density (PHD) and cardinalized PHD (CPHD) filters for various clutter densities and detection probabilities.