This Licentiate thesis is divided into three parts corresponding to three different papers. There is one research report, one conference paper and one submitted journal paper. All three parts deal with acoustic echo and/or noise cancelling problems when using adaptive microphone arrays. In particular, the papers address the performance of an adaptive microphone array in a small enclosure such as the car cabin. A calibrating scheme is proposed which is independent of array geometry and channel matching, and which calibrates the adaptive array to the given acoustic environment and to the given electronic equipment. Results from real measurements in a car interior are included and compared with an analytical description of an adaptive microphone array. Part A gives an analytical description of an adaptive microphone array which facilitates a simple built-in calibration to the environment and instrumentation. Part B describes the method for performing acoustic echo cancelling with a dig-ital "on-site", "self-calibrating" microphone array system. The calibration process is a simple indirect calibration which continuously adapts to the actual environment and electronic equipment. There is a US patent based on this part and an international patent is currently under examination. Part C presents a neural network based microphone array system, which is capable to continuously perform speech enhancement and adaptation to nonuniform quantization, such as A-law and µ-law.

This Licentiate thesis is divided into three parts corresponding to three different papers. There is one research report, one conference paper and one submitted journal paper. All three parts deal with acoustic echo and/or noise cancelling problems when using adaptive microphone arrays. In particular, the papers address the performance of an adaptive microphone array in a small enclosure such as the car cabin. A calibrating scheme is proposed which is independent of array geometry and channel matching, and which calibrates the adaptive array to the given acoustic environment and to the given electronic equipment. Results from real measurements in a car interior are included and compared with an analytical description of an adaptive microphone array. Part A gives an analytical description of an adaptive microphone array which facilitates a simple built-in calibration to the environment and instrumentation. Part B describes the method for performing acoustic echo cancelling with a dig-ital "on-site", "self-calibrating" microphone array system. The calibration process is a simple indirect calibration which continuously adapts to the actual environment and electronic equipment. There is a US patent based on this part and an international patent is currently under examination. Part C presents a neural network based microphone array system, which is capable to continuously perform speech enhancement and adaptation to nonuniform quantization, such as A-law and µ-law.

This paper presents a neural network based microphone array system, which is capable to continuously perform speech enhancement and adaptation to nonuniform quantization, such as A-law and $mu@-law. Such a quantizer is designed to increase the Signal to Quantization Noise Ratio (SQNR) for small amplitudes in telecommunications systems. The proposed method primarily developed for hands-free mobile telephones, suppresses the ambient car noise with approximately 10 dB. The system is based upon a multi-layered nonlinear back-propagation trained network by using a built-in calibration technique.

This report describes a novel method to perform acoustic echo cancelling with microphone arrays. The method employs a digital self-calibrating microphone system. The on-site calibration process is a simple indirect calibration which adapts in each special case to the environment and electronic equipment. The method also continuously takes into account environmental disturbances such as car engine noise and fan noise. The method is primarily aimed at handsfree mobile telephones, by suppressing the handsfree loudspeaker and car noise simultaneously. The report also contains an extensive evaluation in a car.

A novel method of performing acoustic echo canceling using microphone arrays is presented. The method employs a digital self-calibrating microphone system. The calibration process is a simple indirect on-site calibration that adapts to the particulars of the acoustic environment and the electronic equipment in use. Primarily intended for handsfree telephones in automobiles, the method simultaneously suppresses the handsfree loudspeaker and car noise. The system also continuously takes into account disturbances such as fan noise. Examples from an extensive evaluation in a car are also included. Typical performance results demonstrate 20-dB echo cancellation and 10-dB noise reduction simultaneously.

This paper presents a new practical approach to complex Chebyshev approximation by semi-infinite linear programming. The approximation problem may be general with arbitrary complex basis functions. By the new front-end technique, the associated semi-infinite linear programming problem is solved exploiting the finiteness of the related Lagrange multipliers by adapting finite--dimensional linear programming to the dual semi--infinite problem, and thereby taking advantage of the numerical stability and efficiency of conventional linear programming software packages. Furthermore, the optimization procedure is simple to describe theoretically and straightforward to implement in computer coding. The new design technique is therefore highly accessible. The new algorithm is formally introduced as the linear Dual Nested Complex Approximation (DNCA) algorithm. The DNCA algorithm is versatile and can be applied to a variety of applications such as narrow-band as well as broad-band beamformers with any geometry, conventional Finite Impulse Response (FIR) filters, analog and digital Laguerre networks, and digital FIR equalizers. The proposed optimization technique is applied to several numerical examples dealing with the design of a narrow-band base-station antenna array for mobile communication. The flexibility and numerical efficiency of the proposed design technique are illustrated with these examples where hundreds of antenna elements are optimized without numerical difficulties.

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.

This paper presents a new method to simultaneously perform 20 dB acoustic echo cancellation and 10 dB speech enhancement utilizing an adaptive microphone array. The system is based on a fast and efficient on-site calibration. Primarily intended for handsfree telephones in automobiles, the microphone array system simultaneously emphasizes the near-end talker and suppresses the handsfree loudspeaker and car noise. The method can also be used in other situations such as conventional speaker phones.

Traffic flow estimates play a key role in traffic network management and planning of transportation networks. Commonly it is the average daily traffic (ADT) flow for different road segments that constitute the data. This paper shows how an advanced and detailed analysis based on hourly flow measurements over the day can contribute to a deeper understanding of how hourly flows together reflect the vehicles' routes. The proposed method identifies the shortest travel time paths between all possible origins and destinations in a transportation network, and thereafter it identifies the most significant routes in the network by performing statistical tests. For this purpose, the paper presents a mathematical model, a vehicle simulator based on this model, and a statistical framework that is able to find the most probable underlying routes. The paper contains a real test scenario based on 24-hour traffic flows (hour by hour) to demonstrate the applicability of the method. © 2020 IEEE.

 Traffic analyses, particularly speed measurements, are highly valuable in terms of road safety and traffic management. In this paper, an analytical model is presented to measure the speed of a moving vehicle using an off-the-shelf video camera. The method utilizes the temporal sampling rate of the camera and several intrusion lines in order to estimate the probability density function (PDF) of a vehicle’s speed. The proposed model provides not only an accurate estimate of the speed, but also the possibility of being able to study the performance boundaries with respect to the camera framerate as well as the placement and number of intrusion lines in advance. This analytical modelis verified by comparing its PDF outputs with the results obtained via a simulation of the corresponding movements. In addition,as aproof-of-concept, the proposed model is implemented for avideo-based vehicle speed measurement system. The experimental results demonstrate the model’s capability in terms of taking accurate measurements of the speed via a consideration of the temporal sampling rate and lowering the deviation by utilizing more intrusion lines. The analytical model is highly versatile and can be used as the core of various video-based speed measurement systems in transportation and surveillance applications.

This paper presents a new practical approach to complex quadratic programming which solves the broad class of complex approximation problems employing finitization of semi-infinite formulations. The approximation problem may be general with arbitrarily complex basis functions. By using a new technique, the associated semi-infinite quadratic programming problem can be solved taking advantage of the numerical stability and efficiency of conventional quadratic programming software packages. Furthermore, the optimization procedure is simple to describe theoretically and straightforward to implement in computer coding. The new design technique is therefore highly accessible. The complex approximation algorithm is versatile and can be applied to a variety of applications such as narrow-band as well as broad-band beamformers with any geometry, conventional FIR filters, digital Laguerre networks, and digital FIR equalizers. The new algorithm is formally introduced as the quadratic Dual Nested Complex Approximation (DNCA) algorithm. The essence of the new technique, justified by the Caratheodory's dimensionality theorem, is to exploit the finiteness of the related Lagrange multipliers by adapting conventional finite-dimensional quadratic programming to the semi-infinite quadratic programming re-formulation of complex approximation problems. The design criterion in our application is to minimize the side-lobe energy of an antenna array when subjected to a specified bound on the peak side-lobe level. Additional linear constraints are used to form the main-lobe. The design problem is formulated as a semi-infinite quadratic program and solved by using the new front-end applied on top of a software package for conventional finite-dimensional quadratic programming. The proposed optimization technique is applied to several numerical examples dealing with the design of a narrow-band base-station antenna array for mobile communication. The flexibility and numerical efficiency of the proposed design technique are illustrated with these examples where even hundreds of antenna elements are optimized without numerical difficulties.

This paper presents a new practical approach to semi-infinite complex Chebyshev approximation. By using a new technique, the general complex Chebyshev approximation problem can be solved with arbitrary base functions taking advantage of the numerical stability and efficiency of conventional linear programming software packages. Furthermore, the optimization procedure is simple to describe theoretically and straightforward to implement in computer coding. The new design technique is therefore highly accessible. The complex approximation algorithm is general and can be applied to a variety of applications such as conventional FIR filters, narrow-band as well as broad-band beamformers with any geometry, the digital Laguerre networks, and digital FIR equalizers. The new algorithm is formally introduced as the Dual Nested Complex Approximation (DNCA) linear programming algorithm. The design example in limelight is array pattern synthesis of a mobile base-station antenna array. The corresponding design formulation is general and facilitates treatment of the solution of problems with arbitrary array geometry and side-lobe weighting. The complex approximation problem is formulated as a semi-infinite linear program and solved by using a front-end applied on top of a software package for conventional finite-dimensional linear programming. The essence of the new technique, justified by the Caratheodory dimensionality theorem, is to exploit the finiteness of the related Lagrange multipliers by adapting conventional finite-dimensional linear programming to the semi-infinite linear programming problem. The proposed optimization technique is applied to several numerical examples dealing with the design of a narrow-band base-station antenna array for mobile communication. The flexibility and numerical efficiency of the proposed design technique are illustrated with these examples where even hundreds of antenna elements are optimized without numerical difficulties.

Inom ARENA, den svenska kunskapsplattformen för vägavgifter,har vi tidigare föreslagit satellitbase-rad Syntetisk Apertur Radar (SAR) för att göra effektutvärderingsmätningar av lastbilstrafik över stora områden av Sverige. Denna nya mätmetod är mycket lovande och syftet med detta delprojekt har varit att studera hur ett urval avmätområden kan ske om man avser att genomföra trafikmätningar ur ett effektutvärderingsperspektiv. Som utgångspunkt för rapporten används resultatetfrån de mätningar som inom ramen för ARENA tidigarehar genomförts och avrapporterats, det vill sägaenserie avmätning över södra Sverige där satellitsystemetTerraSAR-X användes för att mäta trafik. Motiveringen till att använda ett satellitsy-stemmed SAR är kostnadseffektivitetenoch förmåganatt mäta trafik på samma sättöver hela Sve-rige och dessutomunder allaåretsdagaroavsett väderlek. Beräkningarna för var man skall eller bör mäta baserar sig på underlag från Nationell vägdata-bas(NVDB) som ärTrafikverketsdatabas över vägnätet i Sverige. Detta underlagi form av trafikflö-denhar i sin tur fått utgöra ett underlag tillhur satellitmätningar kan skei olika landsändar det vill sägaförmågan att observeralastbilstrafiki de aktuella områdena.Avsikten med en mätning av detta slag är att den härledda informationen ska levereras till slutanvän-darevars syfte är att utvärdera effekterna av en Vägslitageskatt. Idenna rapport presenteras kartor ur vilka intressanta områden ur olika aspekter kan väljas ur och inte minst var man i sådant fall skall eller bör mäta.Rapporten avslutas med ett mer teoretiskt resonemang kring hur effektiva skatt-ningar av medeltrafikur ett satellitperspektiv skulle kunna betraktas ochi någon meninghur mät-ningar och den efterföljande statistiska databehandlingendärigenomblir så effektiv som möjligt.

Avståndsbaserade vägavgiftssystem införs successivt i många länder världen över. Effekten av ett systems implementering kan i många delar vara svårt att analysera, inte minst ur ett mer övergri-pande perspektiv. Denna rapport är en studierörande en helt ny typ av satellitbaserad,yttäckande mätteknik som har genomförts inom ARENA, den svenska kunskapsplattformen för brukaravgifter i transportsektorn. Den yttäckande trafikmätningen sker med avancerad radarteknik som blivit tillgänglig under de sen-aste åren och där vi idag befinner oss i ett stort skifte på så sätt att tillgängligheten av data kommer att öka betydande de närmsta åren. Rapporten innehåller resultat från ett fältförsök över södra Sverige där det tyska satellitsystemet TerraSAR-X användes. Syftet är också att få en uppfattning över hur metodiken, som krävs vid en fullskalig satellitmätning, ser ut. Det vill säga hur man skall välja ut mätområdet, beställning av mät-ning över mätområdet, nedladdning av satellitdatauttag samt en verifiering av hur mätningarna på olika sätt kan bidra till före-och efteranalyser vid ett eventuellt införande av ett vägavgiftssystem.Rapporten har ett övergripande mål att knyta an till de inom ARENAidentifierade domäner, faktorer och nyckeltal som i sin tur ansetts vara relevanta för att utvärdera effekterna av ett införande av en vägslitageskatt för tung trafik. Rapportens huvudsakliga fokus är vad som möjligt att mäta från satel-litbaserade system men även i någon mening översiktligt uppskatta förmågan hos andra yttäckande system såsom drönare och flygburna system. Det vill säga vad systemen kan förväntas leverera uti-från en analys kring tillgänglighet, kvalitet, kostnad och användbarhet.

This paper presents a new practical approach to complex Chebyshev approximation by semi-infinite linear programming. By the new front-end technique, the associated semi-infinite linear programming problem is solved exploiting the finiteness of the related Lagrange multipliers by adapting finite-dimensional linear programming to the dual semi-infinite problem, and thereby taking advantage of the numerical siability and efficiency of conventional linear programming software packages. Furthermore, the optimization procedure is simple to describe theoretically and straightforward to implement in computer coding. The new design technique is therefore. highly accessible. The algorithm is formally introduced as the linear Dual Nested Complex Approximation (DNCA) algorithm. The DNCA algorithm is versatile and can be applied to a variety of applications such as narrow-band as well as broad-band beamformers with any geometry, conventional Finite Impulse Response (FIR) filters, analog and digital Laguerre networks. and digital FIR equalizers. The proposed optimization technique is applied to several numerical examples dealing with the design of a narrow-band base-station antenna array for mobile communication.

Electric vehicles (EVs) are environmentally friendly and are considered to be a promising approach toward a green transportation infrastructure with lower greenhouse gas emissions. However, the limited driving range of EVs demands a strategic allocation of charging facilities, hence providing recharging opportunities that help reduce EV owners' anxiety about their vehicles' range. In this paper, we study a set covering method where self-avoiding walks are utilized to find the most significant locations for charging stations. In the corresponding optimization problem, we derive a lower bound of the number of charging stations in a transportation network to obtain full coverage of the most probable routes. The proposed method is applied to a transportation network of the southern part of Sweden.

This paper presents a new practical approach to optimally allocate charging stations in large-scale transportation networks for electric vehicles (EVs). The problem is of particular importance to meet the charging demand of the growing fleet of alternative fuel vehicles. Considering the limited driving range of EVs, there is need to supply EV owners with accessible charging stations to reduce their range anxiety. The aim of the Route Node Coverage (RNC) problem, which is considered in the current paper, is to find the minimum number of charging stations, and their locations in order to cover the most probable routes in a transportation network. We propose an iterative approximation technique for RNC, where the associated Integer Problem (IP) is solved by exploiting a probabilistic random walk route selection, and thereby taking advantage of the numerical stability and efficiency of the standard IP software packages. Furthermore, our iterative RNC optimization procedure is both pertinent and straightforward to implement in computer coding and the design technique is therefore highly applicable. The proposed optimization technique is applied on the Sioux-Falls test transportation network, and in a large-scale case study covering the southern part of Sweden, where the focus is on reaching the maximum coverage with a minimum number of charging stations. The results are promising and show that the flexibility, smart route selection, and numerical efficiency of the proposed design technique, can pick out strategic locations for charging stations from thousands of possible locations w ithout numerical difficulties. ©2019 Hie Authors. Published by Elsevier B.V.

The emergence of electric vehicles offers a promising approach to achieving a more sustainable transportation system, given their lower production of direct emissions. However, the limited driving range and insufficient public recharging infrastructure in some areas hinder their competitiveness against traditional vehicles with internal combustion engines. To address these issues, this paper introduces an ``iterative route cover optimization method'' to suggest  charging station locations in high-demand regions. The method samples routes from a route choice set and optimally locates at least one charging station along each  route. Through iterative resampling and optimal allocation of charging stations, the method identifies the potential recharging demand in a location or a region. We demonstrate the method's applicability to a transportation network of the southern part of Sweden. The results show that the proposed method is capable to suggest locations and geographical regions where the recharging demand is potentially high. 

Modern vehicles are to a large extent connected today, either directly by built-in navigation systems in the vehicles or indirectly by other devices such as mobile phones and GPS units. This enables the possibility to continuously collect traffic data in a cost-effective way. The increased access to detailed data allows practitioners and researchers to analyze the transportation system from various perspectives. The travel speed is a common descriptor of the traffic state, and it can be extracted from GPS data. By analyzing how the travel speed vary over time and detect anomalies among the measured travel speeds, it is possible to detect potential deficiencies in the transportation system, e.g., insufficient road capacity which may cause bottlenecks. Often, a weakness in the infrastructure is detected in a very late stage which means that extensive investments may be required to resolve the deficiency.

The purpose of the pilot study is to develop methods and models to detect deficiency in the transportation system and to identity travel speeds that deviates from the normal state, i.e., travel speeds that are considered as very low or very high with respect to the normal behavior. Thus, the starting point of the pilot study is to find appropriate ways to model the traffic state along the studied road segments by using measured travel speeds from a general point of view. Analysis of the traffic state allows the study of how the normal state of the road segments change of time to detect deficiency related to road capacity and road access which may occur if no changes are made, or to detect road segments where the normal state is unchanged.

Typically, slower travel speeds may be an indicator of that a deficiency along a road segment exists. Thus, we present a method to systematically partition measured travel speeds in low, normal, and high travel speeds. The method is robust and enable the possibility to compare different road segment with different attributes, such as number of lanes and free-flow travel speed, with each other. Furthermore, we present a new measurement to describe how the low travel speeds relates to the free flow travel speed, e.g., the speed limit. Existing measurements and indicators used today utilize travel speeds which range from low to high. Our proposed measurement uses low travel speed and free flow travel speed exclusively and aims to quantify the accessibility and condition of a road segment.

The pilot study also includes an initial attempt to apply cluster analysis to detect recurrent patterns along the studied road segments. Cluster analysis is in several contexts an effective method to group time series to detect recurrent patterns among the speed profiles. The purpose of using cluster analysis is to evaluate if speed profiles with similar behavior is related to, for instance, weekday or time of the day. Thus, cluster analysis may be used to detect road segments with recurring low travel speeds, and potentially be used to forecast when congestion or queues may occur.

The pilot study is mainly limited to travel speed data. The proposed methods and models show that it is possibly to solely use travel speed data to detect deficiencies in the transportation system. In particular, the pilot study shows the potential to detect deficiencies in the transportation system without additional data sources such as link flow data.

Traffic flows play a very important role in transportation engineering. In particular, link flows are a source of information about the traffic state, which is usually available from the authorities that manage road networks. Link flows are commonly used in both short-term and long-term planning models for operation and maintenance, and to forecast the future needs of transportation infrastructure. In this paper, we propose a model to study how traffic flow in one location can be expected to reflect the traffic flow in a nearby region. The statistical basis of the model is derived from link flows to find estimates of the distribution of traffic flows in junctions. The model is evaluated in a numerical study, which uses real link flow data from a transportation network in southern Sweden. The results indicate that the model may be useful for studying how large departing flows from a node reflect the link flows in a neighboring geographic region. 

Travel time reliability is vital for both road agencies and road users. Expected travel time reliability can be used by road agencies to assess the state of a transportation system, and by road users, to schedule their trips. Road network deficiencies, such as insufficient traffic flow capacity of a road segment or poor road design, have a negative impact on the reliability of travel times. Thus, to maintain robust and reliable travel times, the detection of road network deficiencies is vital. By continuously analyzing travel times and using appropriate travel time reliability measurements, it is possible to detect existing deficiencies or deficiencies that may eventually occur unless necessary actions are taken. In many cases, indices and measurements of travel time reliability are related to the distribution of the travel times, specifically the skewness and width of the distribution. The current paper introduces a median-based misery index for travel time reliability. The index is robust and handles travel times that follow a skewed distribution well. The index measures the relative difference between the slow travel speeds and the free-flow travel speed. The index is inspired by the median absolute deviation, and its primary application is to detect routes or road segments with potential road network deficiencies. To demonstrate the applicability of the index, we conducted an empirical case study using real travel speed data from the European route E4 in Sweden. The results from the empirical case study indicate that the index is capable of detecting road segments with slow travel speeds regardless of the travel speed distribution.

Travel times and travel time reliability are important indicators of the traffic state in a transportation system. Analyzing travel times and their reliability and how they vary over time makes it possible to identify existing road network deficiencies or deficiencies that may eventually occur unless necessary actions are taken. Road network deficiencies, such as inadequate road capacity or substandard road design, typically lead to congestion and trip delays for road users. A novel median-based misery index is proposed to highlight potential road network deficiencies in the transportation system. The proposed index offers a way to gauge travel time reliability, providing valuable insights into roads where improvements may be needed. The newly developed median-based misery index operates by computing the relative disparity between slow travel speeds and free-flow travel speeds. The median-based misery index is more robust to skewed distributions of travel times than the ordinary misery index. Our empirical case study includes a spatiotemporal analysis of travel speed data from a section of the European route E4 in Sweden. The case study results show that the new index may be used to detect peak periods when the traffic conditions for road users have deteriorated.

This report presents a method to achieve acoustic echo canceling and noise suppression using microphone arrays. The method employs a digital self-calibrating microphone system. The on-site calibration process is a simple indirect calibration which adapts in each specific case to the environment and the electronic equipment used. The method also continuously reduces environmental disturbances such as car engine noise and fan noise. The method is primarily aimed at hands free mobile telephones by suppressing the hands free loudspeaker and car cabin noise simultaneously. The report also contains an evaluation of the impact of echo and noise suppression on a real conversation, accomplished in a car using a microphone array.

Presents a microphone array system for use in handsfree mobile telephone equipment. The array is based on a fast and efficient “on-site” and “self- calibration” scheme. The performance in suppressing the interior car cabin noise and the far-end speech is approximately 17 dB, respectively, while maintaining the near-end speaker level. The near-end signal is almost undistorted. The performance of two different algorithms, normalized least-mean-square (NLMS) and fully connected backpropagation supervised neural network (MLP-NN) are evaluated. The proposed microphone array calibration scheme can also be used in other situations such as speech recognition devices.

This study addresses the issue of black-grass, a herbicide-resistant weed that threatens wheat yields in Western Europe, through the use of high- resolution Unmanned Aerial Vehicles (UAVs) and synthetic data augmentation in precision agriculture. We mitigate challenges such as the need for large labeled datasets and environmental variability by employing synthetic data augmentations in training a Mask R-CNN model. Using a minimal dataset of 43 black-grass and 12 wheat field images, we achieved a 37% increase in Area Under the Curve (AUC) over the non-augmented baseline, with scaling as the most effective augmentation. The best model attained a recall of 53% at a precision of 64%, offering a promising approach for future precision agriculture applications. © NLDL 2024. All rights reserved.

This work investigates the impact of the neural networks architecture when performing fingerprint recognition. Three networks are studied; a Triplet network and two Siamese networks. They are evaluated on datasets with specified amounts of relative translation between fingerprints. The results show that the Siamese model based on contrastive loss performed best in all evaluated metrics. Moreover, the results indicate that the network with a categorical scheme performed inferior to the other models, especially in recognizing images with high confidence. The Equal Error Rate (EER) of the best model ranged between 4%−11% which was on average 6.5 percentage points lower than the categorical schemed model. When increasing the translation between images, the networks were predominantly affected once the translation reached a fourth of the image. Our work concludes that architectures designed to cluster data have an advantage when designing an authentication system based on neural networks.

Urinsikten om behovet av brukaravgifter inom transportsektornföddes idén att skapa en arena för den samlade kompetenseni Sverige. Detta blev ARENA-projektet, vars första fas,2006-2008,hade som mål attutveckla ett koncept för ett vägavgiftssystem för lastbilar.Den andra fasen avslutades2011 med syftet att verifiera det utvecklade vägavgiftskonceptet genom praktiska demonstrationer. Projektet lade även grunden för en innovationsmiljö inom ITS, som är knuten till NetPort Science Park och Blekinge Tekniska Högskola.I den tredje fasen breddades anslaget till att stödja utveckl-ingen av samverkan och interoperabilitet mellan olika system för exempelvis trängselskatt, infra-strukturavgifter eller kilometerskatt, såväl i Sverige som internationellt.Från 2015 har ARENA övergått till att vara en långsiktig nationell kunskapsplattform för brukaravgif-ter inom transportsektorn. Inom ARENA drivs olika projekt beroende på de problemställningar som är aktuella i samhället. Ett av dessa handlar om att utveckla metodik för effektutvärdering av kilome-terskatt för tunga fordonDetta projekt har löpt parallellt med Vägslitageskattekommitténarbete och som ARENAsamverkat medoch stöttat.Arbetet inom ARENA fokuserarpå attutreda vilka effekterav en kilometerskatt som är troliga/möj-liga och vilka av dessa som i sin tur är möjliga att observeraoch hur de kan observeras. Detta har gjorts genom bl.a. inventeringar av andra liknande initiativ och hur de har utvärderats, litteraturge-nomgångar och workshops. Rapporter från detta arbete återfinns på projektets hemsida (http://www.arena-ruc.se/). Denna avslutande rapport fokuserar framförallt på övergripande slutsat-ser baserat på arbetet inom ARENA,samt de rekommendationer om datainsamling och utvärdering som projekt-och styrgruppen för ARENA anser är relevanta att nu gå vidare med, baserat på att ett beslut om införande av kilometerskatt inte har tagits och det är oklart huruvida beslut kommer att tas inom de närmaste åren. ARENA anser, med utgångspunkt i ovanstående, att en systematisk föremätning(ex-ante)bör ge-nomföras först när beslut om att införa skatten har tagits och det finns detaljerad information om hur skatten är utformad.ARENA anser dock att kunskapsnivån om godstransporter på väg generellt sett är bristfällig varför det är motiverat att inleda datafångst inom vissa utpekade områden för att stärka kunskapen. Den data som bör samlas in är värdefull också i samband med utvärdering av ef-fekterna av t.ex. förändrade cabotageregler, ändrade gränser för lastbilars mått och vikt, ändrad energi-och koldioxidbeskattning etc.Det pågår idag flera nationella initiativ kring förbättrat kunskapsunderlag om yrkestrafiken och gods-transporter på väg. ARENAs rekommendationer ligger i linje med dessa och arbetet kring att öka kun-skapsnivån bör fortgå.

We present TAPAS-Z, which is an agent-based freight transport analysis model for simulation of decision-making and transport activities. TAPAS-Z is a further development of a simulation model called TAPAS, and it has improved support for simulation of transport in large geographical regions. It is based on the principles that shipments are simulated for chosen supplier-consumer relations in a geographic region, and that the geographic locations of suppliers and consumers are randomly varied for each shipment. In TAPAS-Z, one supplier represents all real-world suppliers in a geographic zone, and one consumer represents all real-world consumers in a zone. In that way, TAPAS-Z is able to capture some of the diversity in freight transport that is caused by the varying geographic locations of senders and receivers, and which is important when assessing the impact of transport policy and infrastructural measures.

The process of collecting traffic data is a key component to evaluate the current state of a transportation network and to analyze movements of vehicles. In this paper, we argue that both active stationary and mobile measurement devices should be taken into account for high-quality traffic data with sufficient geographic coverage. Stationary devices are able to collect data over time at certain locations in the network and mobile devices are able to gather data over large geographic regions. Hence, the two types of measurement devices have complementary properties and should be used in conjunction with each other in the data collection process. To evaluate the complementary characteristics of stationary and mobile devices for traffic data collection, we present a traffic simulation model, which we use to study the share of successfully identified vehicles when using both types of devices with varying identification rate. The results from our simulation study, using freight transport in southern Sweden, shows that the share of successfully identified vehicles can be significantly improved by using both stationary and mobile measurement devices.

In this paper, we study the complementary characteristics of stationary and mobile devices for traffic data collection. Since stationary devices continuously collect traffic data at fixed locations in a network, they can give insight of the traffic at particular locations over a longer period of time. Mobile devices have wider range and are able to collect traffic data over a larger geographic region.Thus, we argue that both types of technology should be considered to obtain high-quality information about vehicle movements .We present a traffic simulation model, which we use to study the share of successfully identified vehicles when considering both stationary and mobile technologies with varying identification rate. The results of our study, where we focus on freight transport insouthern Sweden, confirms that it is possible to identify the majority of vehicles, even when the identification rate is low, and thatthe share of identified vehicles can be increased by using both stationary and mobile measurement devices

Headsets for speech communication are used in a wide range of applications. The basic idea is to allow hands-free speech communication, leaving both hands available for other tasks. One typical headset application is aircraft pilot communication. The pilot must be able to communicate with personnel on the ground and at the same time use both hands to control the aircraft. Communication headsets usually consist of a pair of headphones and a microphone attached with an adjustable boom. Headphone design varies widely between different manufacturers and models. In its simplest form, the headphone has an open construction providing little or no attenuation of the environmental noise. In headsets designed for noisy environments, the headphones are mounted in ear cups with cushions that provide some attenuation. The microphone is primarily designed to pick up the speech signal, but if the headset is used in a noisy environment, the background noise will also be picked up and transmitted together with the speech. As a consequence, speech intelligibility at the receive end will be reduced, possibly to zero. To increase the speech-to-noise ratio, it is common to use a directional microphone that has a lower sensitivity to sound incident from other directions than the frontal direction. In addition to this, the microphone electronics are usually equipped with a gate function that completely shuts off the microphone signal if its level drops below a threshold value. The purpose of the gate is to open the channel for transmission only when a speech signal is present. Headsets are frequently used in noisy environments where they suffer from problems of speech intelligibility. Even if an ear-cup type headset is used, the attenuation is relatively poor for low frequencies. Low frequency noise has a masking effect on speech, which significantly reduces the speech intelligibility. Several cases have been reported in which the sound level of the communication signal was increased to hazardous levels by the user to overcome this low frequency masking effect [1,2]. Ear exposure to the communication system resulted in hearing damage, such as hearing loss, tinnitus and hyperacusis.

Histogram equalization is a technique to enhance the contrast of the image by redistributing the histogram. In this paper, a fast piecewise linear histogram equalization method is introduced based on an adjustable degree of enhancement and piecewise continuous transformation functions using frequencies of different grey-levels. This method aims to address and maximize the contrast enhancement of the image by stretching the entire spectrum. For this purpose, particular nodes (bins) on the histogram are simultaneously detected that in comparison with recursive methods, it requires less computational time. Then, the particular nodes are stretched using transformation functions to align with the reference nodes. The experimental results indicate that the performance of the proposed method is promising in terms of contrast enhancement. Moreover, this method preserves the texture of various regions in the image very well through the equalization process by using the degree of enhancement. © 2020 Owner/Author.

Change detection in aerial images is the core of many remote sensing applications to analyze the dynamics of a wide area on the ground. In this paper, a remote sensing method is proposed based on viewpoint transformation and a modified Kendall rank correlation measure to detect changes in oblique aerial images. First, the different viewpoints of the aerial images are compromised and then, a local pattern descriptor based on Kendall rank correlation coefficient is introduced. A new distance measure referred to as Kendall's Tau-d (Tau distance) coefficient is presented to determine the changed regions. The developed system is applied on oblique aerial images with very low aspect angles that obtained using an unmanned aerial vehicle in two different days with drastic change in illumination and weather conditions. The experimental results indicate the robustness of the proposed method to variant illumination, shadows and multiple viewpoints for change detection in aerial images.

Interest in aerial image analysis has increased owing to recent developments in and availabilityofaerialimagingtechnologies,likeunmannedaerialvehicles(UAVs),aswellasagrowing need for autonomous surveillance systems. Variant illumination, intensity noise, and different viewpointsareamongthemainchallengestoovercomeinordertodeterminechangesinaerialimages. In this paper, we present a robust method for change detection in aerial images. To accomplish this, the method extracts three-dimensional (3D) features for segmentation of objects above a defined reference surface at each instant. The acquired 3D feature maps, with two measurements, are then used to determine changes in a scene over time. In addition, the important parameters that affect measurement, such as the camera’s sampling rate, image resolution, the height of the drone, and the pixel’sheightinformation,areinvestigatedthroughamathematicalmodel. Toexhibititsapplicability, the proposed method has been evaluated on aerial images of various real-world locations and the results are promising. The performance indicates the robustness of the method in addressing the problems of conventional change detection methods, such as intensity differences and shadows.

Advanced analysis of road traffic data is an essential component of today's intelligent transportation systems. This paper presents a video-based vehicle speed measurement system based on a proposed mathematical model using a movement pattern vector as an input variable. The system uses the intrusion line technique to measure the movement pattern vector with low computational complexity. Further, the mathematical model introduced to generate the pdf (probability density function) of a vehicle's speed that improves the speed estimate. As a result, the presented model provides a reliable framework with which to optically measure the speeds of passing vehicles with high accuracy. As a proof of concept, the proposed method was tested on a busy highway under realistic circumstances. The results were validated by a GPS (Global Positioning System)-equipped car and the traffic regulations at the measurement site. The experimental results are promising, with an average error of 1.77 % in challenging scenarios.

Speed measurement is one of the key components of intelligent transportation systems. It provides suitable information for traffic management and law enforcement. This paper presents a versatile and analytical model for a video-based speed measurement in form of the probability density function (PDF). In the proposed model, the main factors contributing to the uncertainties of the measurement are considered. Furthermore, a guideline is introduced in order to design a video-based speed measurement system based on the traffic and other requirements. As a proof of concept, the model has been simulated and tested for various speeds. An evaluation validates the strength of the model for accurate speed measurement under realistic circumstances.

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.

Object detection in aerial images, particularly of vehicles, is highly important in remote sensing applications including traffic management, urban planning, parking space utilization, surveillance, and search and rescue. In this paper, we investigate the ability of three-dimensional (3D) feature maps to improve the performance of deep neural network (DNN) for vehicle detection. First, we propose a DNN based on YOLOv3 with various base networks, including DarkNet-53, SqueezeNet, MobileNet-v2, and DenseNet-201. We assessed the base networks and their performance in combination with YOLOv3 on efficiency, processing time, and the memory that each architecture required. In the second part, 3D depth maps were generated using pairs of aerial images and their parallax displacement. Next, a fully connected neural network (fcNN) was trained on 3D feature maps of trucks, semi-trailers and trailers. A cascade of these networks was then proposed to detect vehicles in aerial images. Upon the DNN detecting a region, coordinates and confidence levels were used to extract the corresponding 3D features. The fcNN used 3D features as the input to improve the DNN performance. The data set used in this work was acquired from numerous flights of an unmanned aerial vehicle (UAV) across two industrial harbors over two years. The experimental results show that 3D features improved the precision of DNNs from 88.23 % to 96.43 % and from 97.10 % to 100 % when using DNN confidence thresholds of 0.01 and 0.05, respectively. Accordingly, the proposed system was able to successfully remove 72.22 % to 100 % of false positives from the DNN outputs. These results indicate the importance of 3D features utilization to improve object detection in aerial images for future research. CCBY

This work confronts the complex issue of cross-interference in Frequency Modulated Continuous Wave (FMCW) radars, a critical concern that has become more pronounced with the proliferation of automotive radar systems. The study intro-duces a two-dimensional autoregressive (AR) modeling technique for signal reconstruction in the time domain, tailored specifically for the textured nature of FMCW radar frames composed of fast- time (Range bin) and slow-time (Doppler bin) signals. According to the simulations conducted in this study, the proposed 2-D AR model (of order 3) exhibits superior performance compared to its 1-D counterpart (of order 5). This is evidenced by a slightly lower Mean Absolute Percentage Error (MAPE) during model training and a higher Signal-to-Interference-plus-Noise Ratio (SINR) for the reconstructed signal, suggesting that the 2-D model requires less frequent temporal sampling. The study further investigates different sampling strategies and evaluates the influence of model order on signal reconstruction. Based on these assessments, a third-order 2-D AR is recommended as a suitable trade-off model for interference mitigation of FMCW radars for the evaluated scenarios. This paper is structured as follows: Section I defines the interference problem in FM CW radars and the latest solutions to this problem are discussed. Sections II and III include the working principles of FMCW radar and theoretical backgrounds about multi-dimension auto-regressive modeling, respectively. Eventually, the mitigation techniques and numerical evaluations of the proposed approach are presented in Sections IV and V. © 2024 Warsaw University of Technology.