Thesis (MTech (Retail Business Management))--Cape Peninsula University of Technology, 2007 / As the retail industry remains persistently competitive, progressive retailers tend to adopt multi-channel retailing (MGR) to gain competitive advantage and to achieve other potential benefits. Many experts argue that MGR widens the retailer's target market and operations to satisfy the needs of a wide range of customers and, subsequently, eam the retailer potential benefits (Dawson, 2002:5; Berman & Thelen, 2004:148-149; Ghanesh, 2004:140; Sotgiu & Ancarani,2004:128). According to Dawson (2002:5), MGR is being used extensively by large retail outlets worldwide to complement their traditional operations, thereby providing the most convenient means for customers to shop while the outlets gain competitive advantage. However, the benefits of MGR are not achieved by retail small, micro and medium enterprises (SMMEs), OWing to the fact that there are low levels of MGR adoption in retail SMMEs. This therefore limits their market opportunities. The research reported here examines how MGR could become a growth strategy for retail SMMEs. The study necessitated an extensive literature review of retailing and multi-channel retailing, multi-channel strategy and the multi-channel customer. Both qualitative and quantitative data was collected through personal interviews and questionnaires, and from large and small retail outlets and their customers in the Gape Town region. The stUdy found that the large retail outlets studied are doing well with MGR, without having to build concrete multi-channel strategies to integrate their different channels. What contributes to that achievement is their ability to harmonise their channels to achieve channel inter-dependency. The study also revealed that there is not much difference between large and small retailers when it comes to the potential benefits of MGR; therefore size is not a major issue. The differences lie in the different sectors, and with different target markets and different product categories, as these require different mixes of channels. Moreover, retail transactions in an MGR environment are found to follow a cycle of steps within the overall process, in which a customer's visit to one channel often becomes just one step in the purchase process. The study provides a useful guide for retail SMMEs to successfully adopt MeR for their profitability and growth.

Embedded systems have become more complex and it is hard to predict the behavior of networkdue to its dynamic nature. Several devices can interact to perform highly sophisticated real-timetasks while having the demand of interaction and sharing of resources. The interactingcomponents in these systems have strict timing constraints which require time criticalcommunication.We have designed the DCC-EDF protocol for wireless communication using the motivation fromthe research done in optical communication. We have chosen dedicated control channel protocolbecause it does not require time synchronization. To provide real-time services, we will use theEarly Deadline First scheduling algorithm (EDF) because of its optimality and efficiency. Theperformance for the soft real-time traffic is analyzed through simulation.

Increasing capacity in wireless mesh networks can be achieved by using multiple channels and radios. By using different channels, two nodes can send packets at the same time without interfering with each other. To utilize diversity of available frequency, typically cards use channel-switching, which implies significant overhead in terms of delay. Assignment of which channels to use needs to be coupled with routing decisions as routing influences topology and traffic demands, which in turn impacts the channel assignment. Routing algorithms for wireless mesh networks differ from routing algorithms that are used in wired networks. In wired networks, the number of hops is usually the only metric that matters. Wireless networks, on the other hand, must consider the quality of different links, as it is possible for a path with a larger amount of hops to be better than a path with fewer hops. Typical routing protocols for wireless mesh networks such as Optimized Link State Routing (OLSR) use a single path to send packets from source to destination. This path is precomputed based on link state information received through control packets. The consideration of more information than hop-count in the routing process has shown to be beneficial as for example link quality and physical layer data rate determines the quality of the end-to-end path. In multi-channel mesh networks, also channel switching overhead and channel diversity need to be considered as a routing metric. However, a major drawback of current approaches is that a path is precomputed and used as long as the path is available and shows a good enough metric. As a result, short term variations on link quality or channel switching are not considered. In this thesis, a new routing protocol is designed that provides a set of alternative forwarding candidates for each destination. To minimize delay (from both transmission and channel switching), a forwarding mechanism is developed to select one of the available forwarding candidates for each packet. The implementation was tested on an ARM based multi-radio platform, of which the results show that in a simple evaluation scenario the average delay was reduced by 22 % when compared to single path routing.

This thesis presents a framework for automatic recognition of human actions in uncontrolled, realistic video data such as movies, internet and surveillance videos. In this thesis, the human action recognition problem is solved from the perspective of local spatio-temporal feature and bag-of-features representation. The bag-of-features model only contains statistics of unordered low-level primitives, and any information concerning temporal ordering and spatial structure is lost. To address this issue, we proposed a novel multiscale local part model on the purpose of maintaining both structure information and ordering of local events for action recognition. The method includes both a coarse primitive level root feature covering event-content statistics and higher resolution overlapping part features incorporating local structure and temporal relationships. To extract the local spatio-temporal features, we investigated a random sampling strategy for efficient action recognition. We also introduced the idea of using very high sampling density for efficient and accurate classification. We further explored the potential of the method with the joint optimization of two constraints: the classification accuracy and its efficiency. On the performance side, we proposed a new local descriptor, called GBH, based on spatial and temporal gradients. It significantly improved the performance of the pure spatial gradient-based HOG descriptor on action recognition while preserving high computational efficiency. We have also shown that the performance of the state-of-the-art MBH descriptor can be improved with a discontinuity-preserving optical flow algorithm. In addition, a new method based on histogram intersection kernel was introduced to combine multiple channels of different descriptors. This method has the advantages of improving recognition accuracy with multiple descriptors and speeding up the classification process. On the efficiency side, we applied PCA to reduce the feature dimension which resulted in fast bag-of-features matching. We also evaluated the FLANN method on real-time action recognition. We conducted extensive experiments on real-world videos from challenging public action datasets. We showed that our methods achieved the state-of-the-art with real-time computational potential, thus highlighting the effectiveness and efficiency of the proposed methods.

Branding has become increasingly important in order to distinguish a brand from numerous competitors in the fashion industry. An effective way to differentiate the brand from others has shown to be through the brand image, which is why managers should work on sustaining a positive brand image. Managing brand image through different sales channels has become even more important due to the rise of multi-channels. The integration and effort of offline and online channels can result in both enhanced purchase intention and brand image. Previous literature has shown how purchase intention can be derived from the level of congruity between the consumer’s self-image and the brand’s image. Despite this, most previous research is conducted from a company perspective, why this research intends to provide theoretical contributions from a consumer perspective. This is particularly important for fashion brands, as for the ever-changing and highly competitive characteristics of the fashion market. Furthermore, an understanding of consumers’ perception of brand image in different sales channels has been missing in previous research. Therefore, the aim of this research is to investigate how consumers perceive brand image of multi-channel fashion companies for the purpose of providing insight into how brand image should be managed through different channels. Using a qualitative perspective, this explorative research conducts ten semi-structured interviews with women and men between the ages of 18 and 63, that are experienced within fashion consumption in online and offline channels. Analysis of the research findings indicates that there are 13 attributes that consumers consider as most prominent for brand image; six offline attributes and seven online attributes. For the offline image, it emerges that the surrounding environment, products and collections, price and value, store personnel and service, reputation are prominent attributes. For online, easy access and navigation, design and aesthetics, merchandise description, price and value, communication, services and reputation are prominent. It is also found that interviewees find it highly important that the image is consistent through different channels so that the brand conveys a holistic image.

Les communications numériques ont évolué pour répondre à la demande des consommateurs pour accroître l'accès à la navigation Internet, TV, vidéo à la demande, jeux interactifs et de réseaux sociaux. Cette augmentation de débit est obtenue en utilisant des techniques avancées de traitement du signal, des modulations complexes, et des bandes passantes larges. Par conséquent, des récepteurs hautes performances, capables de traiter des signaux large bande, sont nécessaires pour les équipements d'infrastructure et de communication grand-public hauts de gamme. Les récepteurs à numérisation directe RF sont attrayants pour ces applications à large bande, mais plusieurs aspects doivent être étudiés afin de fournir des solutions performantes intégrées. Les principales contributions de cette thèse sont les suivantes: - l’analyse et la conception au niveau système des récepteurs à échantillonnage et numérisation directs RF : - l'analyse théorique de la distorsion non-linéaire large-bande, pour les stratégies d'échantillonnage passe-bas et passe-bande - l'analyse théorique des défauts des convertisseurs analogique-numérique haute-vitesse dans un contexte de réception large bande - la conception d'un conditionneur de signal RF optimisé pour une application câble, incluant: - un égaliseur RF programmable multi-pente, utilisant une seule inductance, avec son algorithme de contrôle - une boucle de contrôle de gain mixte combinant un détecteur RMS et un détecteur crête - contribution à la réalisation d'un produit récepteur RF multi-canaux, à numérisation directe, compétitif en consommation d'énergie, coût, et performances RF / The Holy Grail radio receiver architecture for Software Radio makes uses of direct RF digitization. The early RF signal digitization theoretically provides maximum re-configurability of the radio front-end to multiple bands and standards, as opposed to analog-extensive front-ends. In addition, in applications for which a large portion of the RF input signal spectrum is required to be received simultaneously, the RF direct digitization architecture could provide the most power-and-cost-effective front-end solution. This is typically the case in centralized architectures, for which a single receiver is used in a multi-user environment (data and video gateways) or in re-multiplexing systems. In these situations, this highly-digitized architecture could dramatically simplify the radio front-end, as it has the potential to replace most of the analog processing. In this Ph.D thesis, we study the trade-offs, from RF to DSP domains, which are being involved in direct RF digitization receivers. The developed system-level framework is applied to the design of a cable multi-channel RF direct digitization receiver. Special focus is provided on the design of an optimum RF signal conditioning, on the specification of time-interleaved analog-to-digital converter impairments, including clock quality, and on some algorithmic aspects (automatic gain control loop, RF front-end amplitude equalization control loop). The two-chip implementation is presented, using BiCMOS and 65nm CMOS processes, together with the block and system-level measurement results. The solution is highly competitive, both in terms of area and RF performance, while it drastically reduces power consumption.

abstract: Eigenvalues of the Gram matrix formed from received data frequently appear in sufficient detection statistics for multi-channel detection with Generalized Likelihood Ratio (GLRT) and Bayesian tests. In a frequently presented model for passive radar, in which the null hypothesis is that the channels are independent and contain only complex white Gaussian noise and the alternative hypothesis is that the channels contain a common rank-one signal in the mean, the GLRT statistic is the largest eigenvalue $\lambda_1$ of the Gram matrix formed from data. This Gram matrix has a Wishart distribution. Although exact expressions for the distribution of $\lambda_1$ are known under both hypotheses, numerically calculating values of these distribution functions presents difficulties in cases where the dimension of the data vectors is large. This dissertation presents tractable methods for computing the distribution of $\lambda_1$ under both the null and alternative hypotheses through a technique of expanding known expressions for the distribution of $\lambda_1$ as inner products of orthogonal polynomials. These newly presented expressions for the distribution allow for computation of detection thresholds and receiver operating characteristic curves to arbitrary precision in floating point arithmetic. This represents a significant advancement over the state of the art in a problem that could previously only be addressed by Monte Carlo methods. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2019

In this paper, a cooperative underwater multi-channel MAC (CUMAC) protocol has been proposed with both delay mapping and channel allocation assessment in order to improve network performance and handle triple hidden terminal (THT) problems in underwater sensor networks. A novel channel allocation matrix (CAM) was developed for estimating propagation delay and increasing utilization of channel. In the proposed scheme, every node maintains a database for delay mapping, based on which the sender runs a scheduling algorithm prior to transmitting any data. This delay mapping database assists a node in predicting packet collision probability. The overall objectives are—first, to increase the rate of successful transmission through mitigation of THT problems in multi-channel underwater sensor networks; and second, to increase channel utilization leveraging the database of delay mapping and channel allocation assessment. Results from performance evaluation demonstrate the efficiency of the proposed CUMAC-CAM protocol in terms of packet delivery ratio, energy consumption, end-to-end delay, network throughput, collision probability, packet loss ratio and fairness index compared to the contemporary CUMAC protocol and RTS/CTS based multi-channel MAC protocols.

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