This paper presents a methodology for predicting remaining train arrival times along a predefined railway path using sectionally sampled GPS-based observations collected between consecutive stations. Station-level remaining arrival times are derived from multiple train trips and analyzed using a linear regression model, enabling assessment of both prediction accuracy and temporal consistency. The approach provides continuous predictions of remaining arrival times at each station along the path rather than focusing solely on the final destination. Results demonstrate that median remaining arrival time errors decrease with increasing sampling density, an effect attributed to greater sample availability that captures a larger proportion of the underlying train dynamics and timetable adherence. Under these conditions, the proposed methodology achieves a station-based average median remaining arrival time error of less than 91 seconds. Across 100 repeated out-of-sample evaluations, the results indicate reproducible performance and acceptable coarseness even when the sampling density is substantially reduced. This continuous, station-level prediction capability enables early detection of delays and potential timetable conflicts, providing minute-level predictive accuracy suitable for decision support for train dispatchers.

This paper presents a crucial multispectral image registration and sensor calibration method for an agricultural application. The multispectral images are obtained using a special drone equipped with multiple cameras flying at low altitudes. However, the distance between lenses, the lens distortions and the low-altitude flights lead to a lack of alignment in the built-in normalized difference vegetation index (NDVI). This lack of alignment results in a very poor performance in further analysis, especially for image segmentation and target detection to distinguish crops from invasive plants. In this work, we point out the importance of reducing this misalignment. To do so, the near-infrared and red sensors are first calibrated to remove the lens distortions. Then, the corresponding keypoints are utilized to calculate the transformation matrix and to minimize the back-projection error. The registered near-infrared and red images are then used to compute NDVI. The experimental results show higher alignment and F1-score of 0.73 which is a significant improvement in the performance of a trained deep neural network using NDVI in the detection of invasive plants. This is particularly a challenging task as the invasive plants resemble the desired crops.

The typical activities in a harbor mainly concern logistics, traffic and transportation that need to be monitored or optimized. This paper reports a measurement campaign whose goal is to evaluate the performance of activity surveillance in a harbor using satellite SAR systems. The measurements were conducted in the southern Baltic sea, around the Verkö harbor in Karlskrona, Sweden in the middle of 2018. The SAR data acquisition was with TerraSAR-X/TanDEM-X operating in staring spotlight mode as high resolution was desired. For an accurate evaluation, all activities in the Verkö harbor were video recorded by a drone during the satellite measurement time. Some radar corner reflectors were also deployed on the ground that are available for co-registration. The data associated with two measurements in the end of August and in the beginning of September is presented in this paper. The SAR change detection processing and the initial analysis show the possibility to monitor activities concerning logistics, traffic and transportation using satellite SAR.

Vehicle classification has a significant use in traffic surveillance and management. There are many methods proposed to accomplish this task using variety of sensors. In this paper, a method based on fuzzy c-means (FCM) clustering is introduced that uses dimensions and speed features of each vehicle. This method exploits the distinction in dimensions features and traffic regulations for each class of vehicles by using multiple FCM clusterings and initializing the partition matrices of the respective classifiers. The experimental results demonstrate that the proposed approach is successful in clustering vehicles from different classes with similar appearances. In addition, it is fast and efficient for big data analysis.

This paper reports the measurement campaign Blekingetraffic that was performed by Blekinge Institute of Technology with collaboration with the partners. The measurements are part of a case study to evaluate the feasibility of spaceborne SAR for measuring traffic flow change before and after toll charge introduced in Sweden. The radar measurements were conducted around Pukavik, western of Karlshamn, Sweden in the end of 2015 and in the beginning of 2016. In this paper, the results of one of the controlled measurements performed in the first week of February 2016 is presented. In the ground scene, some trucks tracked with GPS were deployed in European route E22 and local roads. The trucks have either cabinets or food tankers resulting in different radar cross sections. The data acquisition is with TanDEM-X using two satellites giving up to 6 data channels. Since the measurements were performed at 5:00 pm there were unknown traffic i.e. trucks and cars that were not controlled. Therefore, the traffic on the roads was recorded by cameras next to the road and on a drone. The initial analysis of the measured data shows the possibilities to apply available processing techniques such as ground moving target indication and change detection to obtain the knowledge of traffic flow change.

This paper presents the successive mean quantization transform (SMQT). The transform reveals the organization or structure of the data and removes properties such as gain and bias. The transform is described and applied in speech processing and image processing. The SMQT is considered as an extra processing step for the mel frequency cepstral coefficients commonly used in speech recognition. In image processing the transform is applied in automatic image enhancement and dynamic range compression.

This paper explores the successive mean quantization transform (SMQT) for automatic enhancement of gray-scale images. The transform is in the paper presented using set theory. The image enhancement capabilities and properties of the transform are analyzed. The transform is capable to perform both a nonlinear and a shape preserving stretch of the image histogram. Experiments and comparisons to histogram equalization are conducted.

This paper gives an analytical description of an adaptive microphone array that facilitates a simple built-in calibration to the environment and instrumentation. This method, suggested for use in hands-free mobile telephones and speech recognition systems for cars, provides speech enhancement and acoustic echo-cancellation. The scheme offers several advantages, such as a simple calibration procedure, suppression of directional sources, versatile robust beamforming, and reduced target signal distortion. The analysis employs noncausal Wiener filters yielding compact and effective theoretical suppression limits.

This paper presents a method to simultaneously perform 20 dB acoustic echo cancellation and 15-20 dB speech enhancement using an adaptive microphone array combined with spectral subtraction. Primarily intended for handsfree telephones in automobiles, the microphone array system simultaneously emphasizes the near-end talker and suppresses the handsfree loudspeaker and the broadband car noise. The array system is based on a fast and efficient on-site calibration and can be used in other situations such as conventional speaker phones.