Strategic positioning of manufacturing operations within global supply chains

In global environment, a company has to make many decisions that impact upon its position in global supply chain networks such as outsourcing, offshoring, joint venture, vertical/horizontal integration, etc. All these decisions impact on the company’s strategic position, and hence on competitive space and performance. Therefore, it is important for a company to carefully manage strategic positioning by making careful decisions about the adoption of alternative manufacturing and supply chain activities. Unfortunately, there is no complete process studied in strategic positioning of manufacturing operations within global supply chain. Therefore, the work presented in this paper has investigated leading research and industrial practices to create a formal and rational decision process. An analysis of previous literature, industrial practices, and the resulting decision process are all presented in this paper.

Success factors in a forming strategic positioning of manufacturing operations within global supply chains

Managing supply chains effectively has become a critical element in enhancing company profitability and has been identified as the new frontier of competitive advantage. An important element of effective supply chain management is the strategic positioning of the company. The strategic positioning process is concerned with the choice of production-centred activities a company carries out internally and those provided externally. Strategic positioning within manufacturing supply chains however is a relatively recent research topic with apparently few articles currently available that explicitly address associated issues directly. Moreover there is no previous research working strategic positioning of manufacturing operations in global context. Therefore the purpose of this paper is to explore strategic positioning within global supply chains. This paper is based on three cases drawn from the cross industry sector manufacturing companies. It describes an exploratory analysis which is aimed at gaining insight into the success factor to form a strategic positioning within global supply chains.

Strategic positioning of manufacturing operations within global supply chains

This paper describes research that has sought to create a structured and integrated methodology that guides manufacturers through the decision of strategic positioning within global supply chains. The position of a company is concerned with deciding a boundary and configuration of internal and external business activities to the company and is directly related to initiatives such as outsourcing, make or buy, and offshoring. This paper provides an in-depth description of this concept, describes work carried out to form a methodology for strategic positioning within the global supply chain, and presents the details of the methodology. This research has made a significant contribution to the knowledge on how manufacturing companies can form a strategic positioning within global supply chains.

Internet based VRS Code Positioning

Absolute positioning – the real time satellite based positioning technique that relies solely on global navigation satellite systems – lacks accuracy for several real time application domains. To provide increased positioning quality, ground or satellite based augmentation systems can be devised, depending on the extent of the area to cover. The underlying technique – multiple reference station differential positioning – can, in the case of ground systems, be further enhanced through the implementation of the virtual reference station concept. Our approach is a ground based system made of a small-sized network of three stations where the concept of virtual reference station was implemented. The stations provide code pseudorange corrections, which are combined using a measurement domain approach inversely proportional to the distance from source station to rover. All data links are established trough the Internet.

USING ATHLETE ENDORSEMENT IN BRAND POSITIONING – An insight from multinational sports goods companies

It has become a popular method in marketing campaigns to use well-known and admired personalities to promote products. A famous person can draw attention to the brand and change consumers’ perceptions of the brand. The research problem of this study was to offer guidelines on how to use athlete endorsement effectively in brand positioning. This research tried to illustrate how athlete endorsement is a vital brand building factor. Athletes have been found to have special credibility, so this research concentrated on the sports goods industry. Furthermore, athlete endorsement has become a natural part of communicating a sports brand. Athletes highlight the performance of the brand to the consumers. The study was conducted as a qualitative research applying case study method. The two case companies selected for this study were Adidas and Puma. The main findings of the empirical research corresponded to the theoretical framework of the study. Brand positioning strategies in the sports goods industry often include athlete endorsement to some extent. Large multinational companies try to attract the best athletes to promote their brand. The key to successful brand positioning is to have a good product with the right personalities to promote it. However, large multinational companies may face difficulties in localizing their marketing efforts. As a result, global campaigns may not be lucrative everywhere. The role of an expert in an athlete’s sport has resulted in increasing credibility in promoting brands. Creating a superior product in consumers’ minds creates competitive advantage. A top athlete may be linked with superior equipment. After considering the goals of the positioning strategy, companies need to plan their brand awareness process, brand image and brand values. Then, athletes need to be selected based on their characteristics to meet the criteria. Sport sponsorship is a way to support the message delivered through athlete endorsement. Athlete endorsement and sport sponsorship complement the marketing communications mix of a brand. Additionally, companies raise brand awareness and communicate brand image through the athlete. Thus, athletes may earn more money through the sponsorship deals and even feature the advertisements after their professional career. Key words

Analysis of precipitation climatology simulated by high resolution coupled global models over the South America

The results of coupled high resolution global models (CGCMs) over South America are discussed. HiGEM1.2 and HadGEM1.2 simulations, with horizontal resolution of ~90 and 135 km, respectively, are compared. Precipitation estimations from CMAP (Climate Prediction Center—Merged Analysis of Precipitation), CPC (Climate Prediction Center) and GPCP (Global Precipitation Climatology Project) are used for validation. HiGEM1.2 and HadGEM1.2 simulated seasonal mean precipitation spatial patterns similar to the CMAP. The positioning and migration of the Intertropical Convergence Zone and of the Pacific and Atlantic subtropical highs are correctly simulated by the models. In HiGEM1.2 and HadGEM1.2, the intensity and locations of the South Atlantic Convergence Zone are in agreement with the observed dataset. The simulated annual cycles are in phase with estimations of rainfall for most of the six regions considered. An important result is that HiGEM1.2 and HadGEM1.2 eliminate a common problem of coarse resolution CGCMs, which is the simulation of a semiannual cycle of precipitation due to the semiannual solar forcing. Comparatively, the use of high resolution in HiGEM1.2 reduces the dry biases in the central part of Brazil during austral winter and spring and in most part of the year over an oceanic box in eastern Uruguay.

GPS/VRS positioning using atmospheric modeling

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Higher order ionospheric effects in GNSS positioning in the European region

After removal of the Selective Availability in 2000, the ionosphere became the dominant error source for Global Navigation Satellite Systems (GNSS), especially for the high-accuracy (cm-mm) demanding applications like the Precise Point Positioning (PPP) and Real Time Kinematic (RTK) positioning.The common practice of eliminating the ionospheric error, e. g. by the ionosphere free (IF) observable, which is a linear combination of observables on two frequencies such as GPS L1 and L2, accounts for about 99% of the total ionospheric effect, known as the first order ionospheric effect (Ion1). The remaining 1% residual range errors (RREs) in the IF observable are due to the higher - second and third, order ionospheric effects, Ion2 and Ion3, respectively. Both terms are related with the electron content along the signal path; moreover Ion2 term is associated with the influence of the geomagnetic field on the ionospheric refractive index and Ion3 with the ray bending effect of the ionosphere, which can cause significant deviation in the ray trajectory (due to strong electron density gradients in the ionosphere) such that the error contribution of Ion3 can exceed that of Ion2 (Kim and Tinin, 2007).The higher order error terms do not cancel out in the (first order) ionospherically corrected observable and as such, when not accounted for, they can degrade the accuracy of GNSS positioning, depending on the level of the solar activity and geomagnetic and ionospheric conditions (Hoque and Jakowski, 2007). Simulation results from early 1990s show that Ion2 and Ion3 would contribute to the ionospheric error budget by less than 1% of the Ion1 term at GPS frequencies (Datta-Barua et al., 2008). Although the IF observable may provide sufficient accuracy for most GNSS applications, Ion2 and Ion3 need to be considered for higher accuracy demanding applications especially at times of higher solar activity.This paper investigates the higher order ionospheric effects (Ion2 and Ion3, however excluding the ray bending effects associated with Ion3) in the European region in the GNSS positioning considering the precise point positioning (PPP) method. For this purpose observations from four European stations were considered. These observations were taken in four time intervals corresponding to various geophysical conditions: the active and quiet periods of the solar cycle, 2001 and 2006, respectively, excluding the effects of disturbances in the geomagnetic field (i.e. geomagnetic storms), as well as the years of 2001 and 2003, this time including the impact of geomagnetic disturbances. The program RINEX_HO (Marques et al., 2011) was used to calculate the magnitudes of Ion2 and Ion3 on the range measurements as well as the total electron content (TEC) observed on each receiver-satellite link. The program also corrects the GPS observation files for Ion2 and Ion3; thereafter it is possible to perform PPP with both the original and corrected GPS observation files to analyze the impact of the higher order ionospheric error terms excluding the ray bending effect which may become significant especially at low elevation angles (Ioannides and Strangeways, 2002) on the estimated station coordinates.

Desempenho do modelo global da ionosfera do IGS: avaliação no posicionamento por ponto na região sul do Brasil em período de alta atividade solar

One of the main drawbacks of the GPS accuracy for L1 users is the error due to ionosphere. This error depends on the total electron content presents in the ionosphere, as well as of the carrier frequency. Some models have been developed to correct GPS observables of the systematic error due to the ionosphere. The model more known and used is the Klobuchar model, which corrected 50-60% of the ionospheric error approximately. Alternatively, IGS (International GNSS Service) also has developed a model called Global Ionospheric Map (GIM). These maps, in format IONEX, are available in the site of the IGS, and one of the applications of them is to correct the GPS observables of the error due to ionosphere. This work aims at evaluating the quality of GPS point positioning using the IGS ionospheric model in the southerm region of Brazil. Tests carried out had shown an average improvement in the horizontal and vertical determination of 44% and 77%, respectively, when GIM is used in the point positioning.

Trade, investment and fragmentation of the global market: is Latin America lagging behind?

Includes bibliography

Connecting and Educating Hindus: Hinduism Today and the Mediatization of a Global Hinduism

Hinduism Today is a quarterly magazine that appears in roughly 15.000 copies, shipped to nearly 60 countries worldwide. The majority of readers are Hindus in diverse diaspora countries, mainly Singapur, Malaysia, Mauritius, Trinidad und the USA. Its editors are monks of Kauai Adheenam, belonging to the Śaiva Siddhānta Church, situated in Kauai, Hawai’i, USA. One of the magazine’s declared goals is to foster global Hindu solidarity and educate Hindus worldwide about their religion. In this paper, I want to take a look at the history of this magazine in connection with the Śaiva Siddhānta Church, and at the development of the expressed aims behind its publication. For this, I draw on fieldwork done in Kauai in January, 2014. After a brief introduction to some theoretical and methodological preliminaries of my work, I shall, give an overview of the history of the Śaiva Siddhānta Church, founded by Satguru Sivaya Subramuniyaswami. Following this, I will deal in more detail with the origins and development of the magazine and the websites connected with it. I will focus especially on the role the magazine was intended to play for global Hindu diasporas. A fourth chapter will analyze the modes of definition employed in order to depict Hinduism as a unified global religion. In conclusion, I shall briefly reflect upon the specific agenda of “Global Hinduism” and the strategies of positioning as followed by the publishers of Hinduism Today.

From Connecting Hindus to Uniting Global Hinduism: History and Guiding Principles of Hinduism Today

Hinduism Today is a quarterly magazine that appears in roughly 15.000 copies, shipped to nearly 60 countries worldwide. The majority of readers are Hindus in diverse diaspora countries, mainly Singapur, Malaysia, Mauritius, Trinidad und the USA. Its editors are monks of Kauai Adheenam, belonging to the Śaiva Siddhānta Church, situated in Kauai, Hawai’i, USA. One of the magazine’s declared goals is to foster global Hindu solidarity and educate Hindus worldwide about their religion. In this paper, I want to take a look at the history of this magazine in connection with the Śaiva Siddhānta Church, and at the development of the expressed aims behind its publication. For this, I draw on fieldwork done in Kauai in January, 2014. After a brief introduction to some theoretical and methodological preliminaries of my work, I shall, give an overview of the history of the Śaiva Siddhānta Church, founded by Satguru Sivaya Subramuniyaswami. Following this, I will deal in more detail with the origins and development of the magazine and the websites connected with it. I will focus especially on the role the magazine was intended to play for global Hindu diasporas. A fourth chapter will analyze the modes of definition employed in order to depict Hinduism as a unified global religion. In conclusion, I shall briefly reflect upon the specific agenda of “Global Hinduism” and the strategies of positioning as followed by the publishers of Hinduism Today.

Bayesian scene analysis for multi-camera 3D tracking and camera positioning

Los sistemas de seguimiento mono-cámara han demostrado su notable capacidad para el análisis de trajectorias de objectos móviles y para monitorización de escenas de interés; sin embargo, tanto su robustez como sus posibilidades en cuanto a comprensión semántica de la escena están fuertemente limitadas por su naturaleza local y monocular, lo que los hace insuficientes para aplicaciones realistas de videovigilancia. El objetivo de esta tesis es la extensión de las posibilidades de los sistemas de seguimiento de objetos móviles para lograr un mayor grado de robustez y comprensión de la escena. La extensión propuesta se divide en dos direcciones separadas. La primera puede considerarse local, ya que está orientada a la mejora y enriquecimiento de las posiciones estimadas para los objetos móviles observados directamente por las cámaras del sistema; dicha extensión se logra mediante el desarrollo de un sistema multi-cámara de seguimiento 3D, capaz de proporcionar consistentemente las posiciones 3D de múltiples objetos a partir de las observaciones capturadas por un conjunto de sensores calibrados y con campos de visión solapados. La segunda extensión puede considerarse global, dado que su objetivo consiste en proporcionar un contexto global para relacionar las observaciones locales realizadas por una cámara con una escena de mucho mayor tamaño; para ello se propone un sistema automático de localización de cámaras basado en las trayectorias observadas de varios objetos móviles y en un mapa esquemático de la escena global monitorizada. Ambas líneas de investigación se tratan utilizando, como marco común, técnicas de estimación bayesiana: esta elección está justificada por la versatilidad y flexibilidad proporcionada por dicho marco estadístico, que permite la combinación natural de múltiples fuentes de información sobre los parámetros a estimar, así como un tratamiento riguroso de la incertidumbre asociada a las mismas mediante la inclusión de modelos de observación específicamente diseñados. Además, el marco seleccionado abre grandes posibilidades operacionales, puesto que permite la creación de diferentes métodos numéricos adaptados a las necesidades y características específicas de distintos problemas tratados. El sistema de seguimiento 3D con múltiples cámaras propuesto está específicamente diseñado para permitir descripciones esquemáticas de las medidas realizadas individualmente por cada una de las cámaras del sistema: esta elección de diseño, por tanto, no asume ningún algoritmo específico de detección o seguimiento 2D en ninguno de los sensores de la red, y hace que el sistema propuesto sea aplicable a redes reales de vigilancia con capacidades limitadas tanto en términos de procesamiento como de transmision. La combinación robusta de las observaciones capturadas individualmente por las cámaras, ruidosas, incompletas y probablemente contaminadas por falsas detecciones, se basa en un metodo de asociación bayesiana basado en geometría y color: los resultados de dicha asociación permiten el seguimiento 3D de los objetos de la escena mediante el uso de un filtro de partículas. El sistema de fusión de observaciones propuesto tiene, como principales características, una gran precisión en términos de localización 3D de objetos, y una destacable capacidad de recuperación tras eventuales errores debidos a un número insuficiente de datos de entrada. El sistema automático de localización de cámaras se basa en la observación de múltiples objetos móviles y un mapa esquemático de las áreas transitables del entorno monitorizado para inferir la posición absoluta de dicho sensor. Para este propósito, se propone un novedoso marco bayesiano que combina modelos dinámicos inducidos por el mapa en los objetos móviles presentes en la escena con las trayectorias observadas por la cámara, lo que representa un enfoque nunca utilizado en la literatura existente. El sistema de localización se divide en dos sub-tareas diferenciadas, debido a que cada una de estas tareas requiere del diseño de algoritmos específicos de muestreo para explotar en profundidad las características del marco desarrollado: por un lado, análisis de la ambigüedad del caso específicamente tratado y estimación aproximada de la localización de la cámara, y por otro, refinado de la localización de la cámara. El sistema completo, diseñado y probado para el caso específico de localización de cámaras en entornos de tráfico urbano, podría tener aplicación también en otros entornos y sensores de diferentes modalidades tras ciertas adaptaciones. ABSTRACT Mono-camera tracking systems have proved their capabilities for moving object trajectory analysis and scene monitoring, but their robustness and semantic possibilities are strongly limited by their local and monocular nature and are often insufficient for realistic surveillance applications. This thesis is aimed at extending the possibilities of moving object tracking systems to a higher level of scene understanding. The proposed extension comprises two separate directions. The first one is local, since is aimed at enriching the inferred positions of the moving objects within the area of the monitored scene directly covered by the cameras of the system; this task is achieved through the development of a multi-camera system for robust 3D tracking, able to provide 3D tracking information of multiple simultaneous moving objects from the observations reported by a set of calibrated cameras with semi-overlapping fields of view. The second extension is global, as is aimed at providing local observations performed within the field of view of one camera with a global context relating them to a much larger scene; to this end, an automatic camera positioning system relying only on observed object trajectories and a scene map is designed. The two lines of research in this thesis are addressed using Bayesian estimation as a general unifying framework. Its suitability for these two applications is justified by the flexibility and versatility of that stochastic framework, which allows the combination of multiple sources of information about the parameters to estimate in a natural and elegant way, addressing at the same time the uncertainty associated to those sources through the inclusion of models designed to this end. In addition, it opens multiple possibilities for the creation of different numerical methods for achieving satisfactory and efficient practical solutions to each addressed application. The proposed multi-camera 3D tracking method is specifically designed to work on schematic descriptions of the observations performed by each camera of the system: this choice allows the use of unspecific off-the-shelf 2D detection and/or tracking subsystems running independently at each sensor, and makes the proposal suitable for real surveillance networks with moderate computational and transmission capabilities. The robust combination of such noisy, incomplete and possibly unreliable schematic descriptors relies on a Bayesian association method, based on geometry and color, whose results allow the tracking of the targets in the scene with a particle filter. The main features exhibited by the proposal are, first, a remarkable accuracy in terms of target 3D positioning, and second, a great recovery ability after tracking losses due to insufficient input data. The proposed system for visual-based camera self-positioning uses the observations of moving objects and a schematic map of the passable areas of the environment to infer the absolute sensor position. To this end, a new Bayesian framework combining trajectory observations and map-induced dynamic models for moving objects is designed, which represents an approach to camera positioning never addressed before in the literature. This task is divided into two different sub-tasks, setting ambiguity analysis and approximate position estimation, on the one hand, and position refining, on the other, since they require the design of specific sampling algorithms to correctly exploit the discriminative features of the developed framework. This system, designed for camera positioning and demonstrated in urban traffic environments, can also be applied to different environments and sensors of other modalities after certain required adaptations.

Bayesian scene analysis for multi-camera 3D tracking and camera positioning

Los sistemas de seguimiento mono-cámara han demostrado su notable capacidad para el análisis de trajectorias de objectos móviles y para monitorización de escenas de interés; sin embargo, tanto su robustez como sus posibilidades en cuanto a comprensión semántica de la escena están fuertemente limitadas por su naturaleza local y monocular, lo que los hace insuficientes para aplicaciones realistas de videovigilancia. El objetivo de esta tesis es la extensión de las posibilidades de los sistemas de seguimiento de objetos móviles para lograr un mayor grado de robustez y comprensión de la escena. La extensión propuesta se divide en dos direcciones separadas. La primera puede considerarse local, ya que está orientada a la mejora y enriquecimiento de las posiciones estimadas para los objetos móviles observados directamente por las cámaras del sistema; dicha extensión se logra mediante el desarrollo de un sistema multi-cámara de seguimiento 3D, capaz de proporcionar consistentemente las posiciones 3D de múltiples objetos a partir de las observaciones capturadas por un conjunto de sensores calibrados y con campos de visión solapados. La segunda extensión puede considerarse global, dado que su objetivo consiste en proporcionar un contexto global para relacionar las observaciones locales realizadas por una cámara con una escena de mucho mayor tamaño; para ello se propone un sistema automático de localización de cámaras basado en las trayectorias observadas de varios objetos móviles y en un mapa esquemático de la escena global monitorizada. Ambas líneas de investigación se tratan utilizando, como marco común, técnicas de estimación bayesiana: esta elección está justificada por la versatilidad y flexibilidad proporcionada por dicho marco estadístico, que permite la combinación natural de múltiples fuentes de información sobre los parámetros a estimar, así como un tratamiento riguroso de la incertidumbre asociada a las mismas mediante la inclusión de modelos de observación específicamente diseñados. Además, el marco seleccionado abre grandes posibilidades operacionales, puesto que permite la creación de diferentes métodos numéricos adaptados a las necesidades y características específicas de distintos problemas tratados. El sistema de seguimiento 3D con múltiples cámaras propuesto está específicamente diseñado para permitir descripciones esquemáticas de las medidas realizadas individualmente por cada una de las cámaras del sistema: esta elección de diseño, por tanto, no asume ningún algoritmo específico de detección o seguimiento 2D en ninguno de los sensores de la red, y hace que el sistema propuesto sea aplicable a redes reales de vigilancia con capacidades limitadas tanto en términos de procesamiento como de transmision. La combinación robusta de las observaciones capturadas individualmente por las cámaras, ruidosas, incompletas y probablemente contaminadas por falsas detecciones, se basa en un metodo de asociación bayesiana basado en geometría y color: los resultados de dicha asociación permiten el seguimiento 3D de los objetos de la escena mediante el uso de un filtro de partículas. El sistema de fusión de observaciones propuesto tiene, como principales características, una gran precisión en términos de localización 3D de objetos, y una destacable capacidad de recuperación tras eventuales errores debidos a un número insuficiente de datos de entrada. El sistema automático de localización de cámaras se basa en la observación de múltiples objetos móviles y un mapa esquemático de las áreas transitables del entorno monitorizado para inferir la posición absoluta de dicho sensor. Para este propósito, se propone un novedoso marco bayesiano que combina modelos dinámicos inducidos por el mapa en los objetos móviles presentes en la escena con las trayectorias observadas por la cámara, lo que representa un enfoque nunca utilizado en la literatura existente. El sistema de localización se divide en dos sub-tareas diferenciadas, debido a que cada una de estas tareas requiere del diseño de algoritmos específicos de muestreo para explotar en profundidad las características del marco desarrollado: por un lado, análisis de la ambigüedad del caso específicamente tratado y estimación aproximada de la localización de la cámara, y por otro, refinado de la localización de la cámara. El sistema completo, diseñado y probado para el caso específico de localización de cámaras en entornos de tráfico urbano, podría tener aplicación también en otros entornos y sensores de diferentes modalidades tras ciertas adaptaciones. ABSTRACT Mono-camera tracking systems have proved their capabilities for moving object trajectory analysis and scene monitoring, but their robustness and semantic possibilities are strongly limited by their local and monocular nature and are often insufficient for realistic surveillance applications. This thesis is aimed at extending the possibilities of moving object tracking systems to a higher level of scene understanding. The proposed extension comprises two separate directions. The first one is local, since is aimed at enriching the inferred positions of the moving objects within the area of the monitored scene directly covered by the cameras of the system; this task is achieved through the development of a multi-camera system for robust 3D tracking, able to provide 3D tracking information of multiple simultaneous moving objects from the observations reported by a set of calibrated cameras with semi-overlapping fields of view. The second extension is global, as is aimed at providing local observations performed within the field of view of one camera with a global context relating them to a much larger scene; to this end, an automatic camera positioning system relying only on observed object trajectories and a scene map is designed. The two lines of research in this thesis are addressed using Bayesian estimation as a general unifying framework. Its suitability for these two applications is justified by the flexibility and versatility of that stochastic framework, which allows the combination of multiple sources of information about the parameters to estimate in a natural and elegant way, addressing at the same time the uncertainty associated to those sources through the inclusion of models designed to this end. In addition, it opens multiple possibilities for the creation of different numerical methods for achieving satisfactory and efficient practical solutions to each addressed application. The proposed multi-camera 3D tracking method is specifically designed to work on schematic descriptions of the observations performed by each camera of the system: this choice allows the use of unspecific off-the-shelf 2D detection and/or tracking subsystems running independently at each sensor, and makes the proposal suitable for real surveillance networks with moderate computational and transmission capabilities. The robust combination of such noisy, incomplete and possibly unreliable schematic descriptors relies on a Bayesian association method, based on geometry and color, whose results allow the tracking of the targets in the scene with a particle filter. The main features exhibited by the proposal are, first, a remarkable accuracy in terms of target 3D positioning, and second, a great recovery ability after tracking losses due to insufficient input data. The proposed system for visual-based camera self-positioning uses the observations of moving objects and a schematic map of the passable areas of the environment to infer the absolute sensor position. To this end, a new Bayesian framework combining trajectory observations and map-induced dynamic models for moving objects is designed, which represents an approach to camera positioning never addressed before in the literature. This task is divided into two different sub-tasks, setting ambiguity analysis and approximate position estimation, on the one hand, and position refining, on the other, since they require the design of specific sampling algorithms to correctly exploit the discriminative features of the developed framework. This system, designed for camera positioning and demonstrated in urban traffic environments, can also be applied to different environments and sensors of other modalities after certain required adaptations.