As the important pathological section staining, Immunohistochemistry (IHC) staining uses labeled antibodies to highlight specific antigens, providing clearer results for malignancy identification compared with Hematoxylin and Eosin (H&E) staining. However, obtaining IHC manually is labor-intensive and costly. In this study, we attempt to automatically generate IHC staining image by style transferring from H&E, which is easy to get. Due to the unalignment between single evaluation metric and real generation quality, we propose a tailored metric-based model selection (TMMS) method for pathological section staining transferring (PSST). Our method can fuse multiple traditional metrics and multiple relevant losses to measure the quality of generated IHC and select a best-performed model. Extensive experiments and analysis demonstrate the effectiveness of our method TMMS.

Drones are integral to various applications, out of which traffic surveillance is an important application. However, their operational efficiency is limited by battery life, which restricts their capacity for extended critical missions. Additionally, in remote or high-interference areas, the bandwidth for drone communication is often limited, leading to a decrease in the quality of images transmitted to the base station. This paper aims to address such challenges by having drones transmit video data in real-time at lower resolutions for traffic monitoring. This approach conserves energy and optimizes transmission. However, it adversely affects object detection accuracy at the base station due to compromised data quality. To address this issue, we incorporate Generative Adversarial Networks (GANs) to improve LR images, restoring their quality for precise object detection. Results indicate that the accuracy of traffic analytics achieved with GAN-enhanced images is comparable to that obtained with high-resolution data transmission. Consequently, our approach allows a fundamental trade-off among drone energy consumption, transmission time, flight time, and object detection accuracy, enabling robust detection performance while conserving energy and enhancing operational capabilities.

Traditional breast cancer surgeries require collaboration between ultrasound (US) doctors and surgeons, making the procedure complex and treating physicians prone to fatigue. In leader-follower robotic surgery, a surgeon controls an US robotic arm and an instrument robotic arm with their left and right hands, enabling independent surgical performance. However, the lack of US scanning skills among surgeons, as well as the physical separation in leader-follower operations, can negatively impact both the scanning and surgical outcomes. This paper proposes a robot-assisted scheme based on dynamic arm impedance compensation (IC) that references expert arm stiffness to compensate for novice arm stiffness. The impedance compensator adjusts the compensation strategy according to the scanning area and scanning stage. The impedance force generator estimates the scanning direction via Kalman filtering and applies stiffness and damping forces in the vertical direction to suppress tremors and other involuntary movements. The experimental results revealed that during the coarse and fine scanning phases, the probe position variance decreased by 57.9% and 73.6%, the contact force variance decreased by 55.2% and 42.5%, and the US image confidence increased by 22.0% and 23.8%, respectively. Compared with traditional filtering compensation (FC) schemes, this approach reduces the average position variance and contact force variance by 32.0% and 25.3%, respectively, and increases confidence by 7.3%. In a no-compensation test, the IC training group outperformed the FC group. This scheme can assist leader-follower US scanning and rapidly improve surgical skills.

The untethered microrobots driven by multiple external physics fields have promising ability in minimally invasive disease treatments. One common type of the driving fields is gradient magnetic field, which can provide microrobots with adequate driving force in complicated environment. In this study, a control method of microrobot through gradient magnetic field system is presented, which is realized by moving the field free point (FFP) to produce an alterable magnetic driving force. A confirmatory experiment of the robot reciprocating motion control is undertaken in a 1D gradient magnetic robot system. The control method could be applied to further studies on in vivo applications of targeted microrobot drug delivery system.

Intrusion Detection System (IDS) is a crucial security mechanism for protecting computer networks from cyber-attacks. Deep learning models have the potential to detect attack types by leveraging their ability to learn and extract features from large volumes of data. In this study, we compare the performance of four different deep learning algorithms for IDS: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), bidirectional LSTM, and bidirectional GRU. We evaluate the attack prediction accuracy for three types of attacks: Denial of Service (DoS), Generic, and Exploits. We vary each algorithm's range parameter and epochs and determine the best parameter combination sets for achieving the highest accuracy. Our experimental results demonstrate that increased range parameters influence the accuracy of LSTM, bi-LSTM, and Bi-GRU models. Ultimately, GRU proved to have the most outstanding performance among the four algorithms tested.

In view of the magnetically actuated capsule robot applications in diagnoses of human stomach disease, the challenges that are associated with degrees-of-freedom (DOF), environmental adaptability, and the size of the entire system must be addressed. In this study, a new electromagnetic coil system that is based on adaptive magnetic levitation is presented; it is mainly composed of an independent orientation control electromagnetic coil and a magnetic levitation control electromagnetic coil. The system is designed to perform motion control of the capsule, including 3-DOF levitating translational motion control, and pitch and yaw motion control in levitation. In this manner, it compensates for the lack of previous magnetic levitation systems that were based on electromagnetic coils for the control of the tilt angle of the capsule. With torque-based actuation control, the tilt angle of the capsule can be controlled continuously with an angle increment of no more than 2° (within 30°) during levitation. The capsule under the magnetic levitation control, which is based on the fuzzy proportional-integral -derivative controller, can maintain the stability of levitation; the error between the actual capsule position and the required position did not exceed 0.1 mm when the capsule was out of water or completely immersed in it. Moreover, the experiment of the capsule levitating from air to water further verifies the adaptability of the system with regard to the environment; this provides a new examination method for capsule endoscopy. Note to Practitioners—This work is caused by the motion manipulation problem of a magnetically driven capsule robot. Most of the existing magnetically driven capsule robots use a rolling or sliding method that fits tightly against the stomach wall, and these forms of motion make it possible to risk missing diagnoses in the examination of gastric diseases, and the maneuverability limits its further application. This paper proposes an adaptive control strategy for a magnetically driven capsule robot based on magnetic levitation motion. It allows the magnetically driven capsule robot to levitate to any position in the stomach for photography and observation without external mechanical support, and this levitation motion is sufficiently stable. In addition, the proposed capsule robot has independent orientation and position control, which has the potential to achieve automated inspection.

This study proposes a visualization framework for bottlenose dolphin (Tursiops truncatus) whistle classification using statistical signal properties such as slope, standard deviation, kurtosis, skewness, minimum and maximum frequency, frequency range, absolute value, and duration. For each whistle, all statistical properties are stored in a MySQL database, one of the relational databases, so that users can classify whistles. Moreover, correlation values are provided in a heat map to examine the similarity of whistles among the population. This framework will allow marine mammal researchers to determine the whistle properties of individual bottlenose dolphins. Researchers can then investigate behavioral and ecological questions such as (i) does whistle structure differs among dolphin populations (ii) and does anthropogenic noise affects whistle structure.

ABSTRACT This paper presents the design, implementation and evaluation of the FreeMobility localization system—a distributed system that allows access points (APs) or cellular towers to dynamically determine the location of a mobile user using only received signal strength (SS) and mobility information (direction and traveled distance) from the mobile. FreeMobility does not require special infrastructure or installation, and it is readily applicable to commodity devices like laptops, PDAs and cell phones using existing Wi‐Fi and/or cellular networks. This paper introduces mathematical methodology that dynamically predicts the location of the mobile; the paper also presents detailed analysis and experimental results that explore practical issues in implementing the system. FreeMobility was evaluated using: (1) measurements from IEEE 802.11 networks at both the University of South Carolina, Beaufort (USC Beaufort) and a residential neighborhood in Columbia, SC; and (2) online data resources from war‐driving community and other research communities. The results demonstrate that FreeMobility provides reliable location estimates, and the localization error is comparable with other existing systems. With the assistance of the geographic information system (GIS), FreeMobility does not require dense tower deployment, and it is able to provide location attributes in addition to pure coordinates from most other systems. Copyright © 2009 John Wiley & Sons, Ltd.

In this paper, our main concern is to study the influence of the number of edges on the convergence rate and the total communication cost in distributed average consensus problems. We begin with the case of regular networks, i.e. networks for which the associated graph (vertices corresponding to sensors and edges being defined by communicating sensor pairs) has the same vertex degree for all vertices. In regular graphs, the number of edges is effectively determined by the common vertex degree. Therefore the problem is converted to one of analyzing the influence of the common vertex degree on the convergence rate and total communication cost. To evaluate the convergence rate we use the ratio of two eigenvalues of the Laplacian matrix of the graph, for which we obtain lower and upper bounds in terms of the common vertex degree. Using the bounds, we can illustrate the intuitively reasonable property that the convergence rate will increase with increase in the common vertex degree. However the increment in the convergence rate drops dramatically as the common vertex degree becomes progressively large. At the same time the total communication cost of the consensus process will become large. Based on these two observations we define a type of `Magic Number' to help analyze the value or otherwise of adding more links to the network. The Monte Carlo simulation results are consistent with the theoretical analysis and demonstrate the existence of the magic number. Further we present the simulation results on irregular graphs in which the degree distribution is subject to a Poisson distribution. From the simulation results we observe that the magic number also exists in the irregular graphs. In general the magic number for irregular graphs is larger than the one for regular graphs.

In previous work we proposed a method to determine the bias in localization algorithms using 2 or 3 sensors, whose location have been already identified, for targets in 2-dimensional space by mixing Taylor series and Jacobian matrices. In this paper we extend the bias-correction method to n-dimensional space with N sensors. To illustrate this approach, we analyze the proposed method in three situations using localization algorithms. Monte Carlo simulation results demonstrate the proposed bias-correction method can correct the bias very well in most situations.

Location and mobility management are critical issues for the growing worldwide market in location based services (LBS). Existing research generally requires either special hardware to measure time (thus the distance) and angle or manually constructing database maps using one or a combination of techniques such as lateration, triangulation, database mapping or dead reckoning. In contrast, pathways that uniquely exploit sharing spaces among collaborating objects for the purpose of navigation are far less studied. This paper first analyzes the FreeMobility, a simpler but effective location determination technique, that exploits wireless communications abilities under a broad environmental context; then detailed comparison with database mapping based methods is conducted using measurement data from both GSM and Wi-Fi networks. Results indicate that the adoption of environmental constraints will transform wireless localization research from straightforward hardware dominated approaches to a more independent system approach and thus it opens a new window of opportunity valuable to the research community.

In this paper we introduce a new approach to determine the bias in localization algorithms by mixing Taylor series and Jacobian matrices, which results in an easily calculated analytical expression for the bias. To illustrate this approach, we analyze the proposed method in two situations using localization algorithms based on distance measurements. Monte Carlo simulations verify that the proposed method is consistent with the performance of localization algorithms, which means the bias-correction method can correct the bias in most situations except when there is a collinearity problem. Although the method is analyzed in distance-based localization algorithms, it can be extended to other kinds of localization algorithms.

Routing metrics are critical to select paths with maximal throughput in multi-rate multi-hop wireless networks. Simple path selection based on minimal hop count often leads to poor performance due to the fact that paths with low hop count often have higher packet loss rates. Better paths can be obtained by characterizing the actual quality of wireless link. A number of link quality aware routing metrics such as per hop round trip time (RTT), expected transmission count (ETX), expected transmission time (ETT) have been developed and explored. This study highlights some shortcomings of these routing metrics and proposes the design of a novel metric called improved ETT metric, or iETT, that addresses the discussed weaknesses and works more efficiently under various link quality conditions. The performance of the iETT metric is compared against the normal ETT metric over a DSR-based routing protocol. In extensive simulations, iETT metric outperforms the normal ETT metric in terms of network throughput and average packet delay.

This paper presents the design, implementation and evaluation of the FreeMobility location determination system. FreeMobility is a distributed system that allows Access Points (AP) or cellular towers to dynamically determine the location of a mobile user using the received signal strength and the mobility information (the direction and traveled distance) from the mobile. FreeMobility does not require the database calibration nor does it require special infrastructure or installation, and it is readily applicable to commodity mobile devices like laptops, PDAs and cell phones. Using both actual measurements (from the University of South Carolina Beaufort and a residential neighborhood in Columbia, South Carolina) and online data resources (from the war-driving community and other research groups), this paper presents detailed analysis and experimental results that explore practical issues in implementing the FreeMobility system. The results demonstrated that FreeMobility provides reliable location estimates and the best localization error is to within 1.0 meter. With the assistance of the Geographic Information System (GIS), FreeMobility is able to provide meaningful location attributes in addition to basic coordinates from most other systems. Also with GIS, FreeMobility does not require dense tower deployment in order to pinpoint a mobile's location.

Location determination of indoor mobile users is challenging due the complex and volatile indoor radio propagation signals. A radio-frequency (RF) based indoor localization system, like RADAR or ARIADNE, typically operates by first constructing a lookup table mapping the radio signal strength at different known locations in the building, and then a mobile user's location at an arbitrary point in the building is determined by measuring the signal strength at the location in question and searching the corresponding location from the above lookup table. Usually, the mobile's signal strength is measured by three or more sniffers deployed inside the building. Obviously, the number of sniffers and their positions greatly affect the localization performance. This paper presents a detailed analysis and experimental results that explore the impact of the sniffers deployment on the performance of the indoor localization. The results demonstrate that the best localization performance is obtained when the center of gravity of the equilateral triangular (formed by three sniffers) coincides with that of the floor plan; and in order to provide optimal localization for all positions of a large floor, it is necessary to deploy more than three sniffers in a semi-mesh style such that any position in the building is always covered by three nearby sniffers.

Location determination of mobile users within a building has attracted much attention lately due to its many applications in mobile networking including network intrusion detection problems. However, it is challenging due to the complexities of the indoor radio propagation characteristics exacerbated by the mobility of the user. A common practice is to mechanically generate a table showing the radio signal strength at different known locations in the building. A mobile user's location at an arbitrary point in the building is determined by measuring the signal strength at the location in question and determining the location by referring to the above table using a LMSE (least mean square error) criterion. Obviously, this is a very tedious and time consuming task. This paper proposes a novel and automated location determination method called ARIADNE. Using a two dimensional construction floor plan and only a single actual signal strength measurement, ARIADNE generates an estimated signal strength map comparable to those generated manually by actual measurements. Given the signal measurements for a mobile, a proposed clustering algorithm searches that signal strength map to determine the current mobile's location. The results from ARIADNE are comparable and may even be superior to those from existing localization schemes.

The demand for location based applications including network intrusion detection has grown tremendously lately. However, location determination of indoor mobile users is challenging because of the complexities of the indoor radio propagation characteristics. A radio-frequency (RF) based indoor localization system, like ARIADNE, typically operates by first constructing a lookup table mapping the radio signal strength at different known locations in the building, and then a mobile user's location at an arbitrary point in the building is determined by measuring the signal strength at the location in question and searching the corresponding location from the above lookup table. Usually, the mobile user's signal strength is measured by three or more sniffers mechanically deployed inside the building. Obviously, the sniffers position configuration and the number of available sniffers greatly affect the localization performance. This paper presents experimental results that explore the impact of the sniffers deployment on the localization based on the ARIADNE system. The results demonstrate that the triangular deployment and extra available sniffers generally generate better localization performance.

We propose reliable and precise distributed localization algorithms for wireless networks, iterative multidimensional scaling (IT-MDS) and simulated annealing multidimensional scaling (SA-MDS). We use only radio communication constraints to infer a node's distances, and uniquely combine a multidimensional scaling algorithm (MDS) with a lateration algorithm. We analytically establish the upperbound on the estimation error. The proposed techniques can estimate all nodes' positions, even with limited and imprecise network knowledge. Analysis and test runs show that the proposed methods are independent of the topology randomness and the range measurement errors. We attempt to bound the localization imprecision. Simulation results yield an average estimation error of about 25% of radio transmission range. The highest estimation error observed is 40% radio range for most networks.