Recent advancements in session-based recommendation models using deep learning techniques have demonstrated significant performance improvements. While they can enhance model sophistication and improve the relevance of recommendations, they also make it challenging to implement a scalable real-time solution. To addressing this challenge, we propose GRAINRec- a Graph and Attention Integrated session-based recommendation model that generates recommendations in real-time. Our scope of work is item recommendations in online retail where a session is defined as an ordered sequence of digital guest actions, such as page views or adds to cart. The proposed model generates recommendations by considering the importance of all items in the session together, letting us predict relevant recommendations dynamically as the session evolves. We also propose a heuristic approach to implement real-time inferencing that meets Target platform's service level agreement (SLA). The proposed architecture lets us predict relevant recommendations dynamically as the session evolves, rather than relying on pre-computed recommendations for each item. Evaluation results of the proposed model show an average improvement of 1.5% across all offline evaluation metrics. A/B tests done over a 2 week duration showed an increase of 10% in click through rate and 9% increase in attributable demand. Extensive ablation studies are also done to understand our model performance for different parameters.

Buy It Again (BIA) recommendations are a crucial component in enhancing the customer experience and site engagement for retailers. In this paper, we build a short (S) and long (L) term Hawkes (H) process for each item and use it to obtain BIA recommendations for each customer. The challenges of deploying into a production environment including model scalability, an evolving item catalog, and real-time inference are discussed along with solutions such as model compression, frequency-based item filtering, training data sampling, data parallelization, parallel execution and microservice-based real-time recommendations. We significantly reduced model training time from roughly 250 hours to about 3 hours by applying the solutions, while serving real-time inference with less than 70ms latency. We compare our BIA model against state-of-the-art baselines using three publicly available datasets and provide results from A/B tests with millions of live customers. On 3 public datasets, our model outperforms SOTA baseline models in recall and NDCG metrics by around 85% and 10%, respectively, and in live A/B testing it exhibited more than 30% increase in click-through rate and roughly 30% revenue increase compared to other state of the art models.

Many vehicular applications, especially safety-related ones, rely on spatial-temporal messages periodically broadcast by vehicles. In the absence of a secure authentication scheme, invalid spatial-temporal messages may be sent out by malicious vehicles. Meanwhile, malicious applications may also collect a lot of personal information from spatial-temporal messages. Since inter-vehicle connections are often deployed in high-moving traffic, any authentication must be implemented in real-time. To meet all these properties, we propose a Fast and Anonymous Spatial-Temporal Trust (FastTrust) scheme for inter-vehicle connections. In contrast to most authentication protocols which rely on fixed infrastructures, FastTrust is mostly designed on hash chains and an entropy-based commitment, and is able to secure periodic spatial-temporal messages. FastTrust also protects vehicles’ privacy by deploying a pseudonym-varying scheduling mechanism to satisfy the anonymity and unlinkability requirements. Finally, in order to efficiently isolate malicious vehicles, a lightweight certificate management scheme is proposed for the limited bandwidth of vehicular networks. We provide analytical evaluations to show that our FastTrust achieves the security and privacy properties. Extensive validations are done to show that FastTrust can authenticate dozens of times faster than the existing signature algorithms, and isolate malicious vehicles at a low cost in terms of communication and computational resources.

Buy It Again (BIA) recommendations are crucial to retailers to help improve user experience and site engagement by suggesting items that customers are likely to buy again based on their own repeat purchasing patterns. Most existing BIA studies analyze guests’ personalized behaviour at item granularity. This finer level of granularity might be appropriate for small businesses or small datasets for search purposes. However, this approach can be infeasible for big retailers which have hundreds of millions of guests and tens of millions of items. For such data sets, it is more practical to have a coarse-grained model that captures customer behaviour at the item category level. In addition, customers commonly explore variants of items within the same categories, e.g., trying different brands or flavors of yogurt. A category-based model may be more appropriate in such scenarios. We propose a recommendation system called a hierarchical PCIC model that consists of a personalized category model (PC model) and a personalized item model within categories (IC model). PC model generates a personalized list of categories that customers are likely to purchase again. IC model ranks items within categories that guests are likely to reconsume within a category. The hierarchical PCIC model captures the general consumption rate of products using survival models. Trends in consumption are captured using time series models. Features derived from these models are used in training a category-grained neural network. We compare PCIC to twelve existing baselines on four standard open datasets. PCIC improves NDCG up to 16% while improving recall by around 2%. We were able to scale and train (over 8 hours) PCIC on a large dataset of 100M guests and 3M items where repeat categories of a guest outnumber repeat items. PCIC was deployed and A/B tested on the site of a major retailer, leading to significant gains in guest engagement.

Recent advancements in deep neural networks for graph-structured data have led to state-of-the-art performance on recommender system benchmarks. In this work, we present a Graph Convolutional Network (GCN) algorithm SWAG (Sample Weight and AGgregate), which combines efficient random walks and graph convolutions on weighted graphs to generate embeddings for nodes (items) that incorporate both graph structure as well as node feature information such as item-descriptions and item-images. The three important SWAG operations that enable us to efficiently generate node embeddings based on graph structures are (a) Sampling of graph to homogeneous structure, (b) Weighting the sampling, walks and convolution operations, and (c) using AGgregation functions for generating convolutions. The work is an adaptation of graphSAGE over weighted graphs. We deploy SWAG at Target and train it on a graph of more than 500K products sold online with over 50M edges. Offline and online evaluations reveal the benefit of using a graph-based approach and the benefits of weighing to produce high quality embeddings and product recommendations.

With the increasing volume of transactions taking place online, mobile fraud has also increased. Mobile applications often authenticate the user only at install time. The user may then remain logged in for hours or weeks. Any unauthorized access may lead to financial, criminal or privacy losses. In this work, we leverage currently available built-in motion sensors in smartphones to learn users' behavioral characteristics while interacting with the mobile device to provide an implicit re-authentication mechanism that enables a frictionless and secure user experience in the application. This approach improves the generality as well as power efficiency of the authentication mechanism compared to using the camera feed which involves (a) specific hardware, (b) higher battery usage and (c) privacy concerns. We present DeepAuth as a generic framework for re-authenticating users in a mobile app. In our approach, we use time and frequency domain features extracted from motion sensors and a long short-term memory (LSTM) model with negative sampling to build a re-authentication framework. The framework is able to re-authenticate a user with 96.70% accuracy in 20 seconds from a set of data collected from 47 volunteers.

Connected cars have received massive attention in Intelligent Transportation System. Many potential services, especially safety-related ones, rely on spatial-temporal messages periodically broadcast by cars. Without a secure authentication algorithm, malicious cars may send out invalid spatial-temporal messages and then deny creating them. Meanwhile, a lot of private information may be disclosed from these spatial-temporal messages. Since cars move on expressways at high speed, any authentication must be performed in real-time to prevent crashes. In this paper, we propose a Fast and Anonymous Spatial-Temporal Trust (FastTrust) mechanism to ensure these properties. In contrast to most authentication protocols which rely on fixed infrastructures, FastTrust is distributed and mostly designed on symmetric-key cryptography and an entropy-based commitment, and is able to fast authenticate spatial-temporal messages. FastTrust also ensures the anonymity and unlinkability of spatial-temporal messages by developing a pseudonym-varying scheduling scheme on cars. We provide both analytical and simulation evaluations to show that FastTrust achieves the security and privacy properties. FastTrust is low-cost in terms of communication and computational resources, authenticating 20 times faster than existing Elliptic Curve Digital Signature Algorithm.

Dramatic progress has been made in the usage of semantic word embeddings for solving word analogy tasks in recent years. Word embeddings or vector representation of words has been the key to many advances in natural language processing. This paper presents a novel application of Word-Embeddings for Anomaly Classification (WEAC), where we detect whether an event log entry is an anomalous one or not. Additionally, WEAC helps us classify the anomaly by identifying the anomalous feature(s) in the event log. For example, an unusual network activity such as a store transaction server logging into dropbox.com would be automatically flagged as anomalous because of the wrong feature associations for entries in the corresponding event log. WEAC works with two training models: Skip-Gram (SG) and Continuous Bag of Words (CBOW). Negative sampling is used to boost the training. The initial results on wikipedia text8 dataset, as well as investigation on enterprise HTTP logs are promising. The model achieved an average detection rate of 65-100% and classification accuracy of 85-100%. The detection rate was superior to state-of-the-art anomaly detection techniques.

360-degree video is on the cusp of going mainstream. Such videos have a large size (4-5⌉ the size of a regular one). Since each viewer has to wear Head-Mounted Display (HMD), screen sharing is impossible when a group is watching the same content. Multiple parallel video-streams will be required to serve a group of viewers along the last mile (think of a family or a group of friends watching Super Bowl in their HMDs). This would end up quickly choking the entire network. In this paper, we present a scheme to optimize the network bandwidth using motion-prediction-based multicast to serve concurrent viewers. Based on empirical evaluation of more than 150 viewers watching our pool of sixteen 360-degree videos, we observe that most viewers follow similar motion patterns when watching the same video. We present a data- driven scheme for temporal prediction of viewer motion from previous states, and hence optimize the multicast bandwidth consumption by sending only the portion likely to be watched by a group of viewers. Our evaluations with real viewer motion traces show a bandwidth saving of over 50%, compared to full frame video multicast, and significant bandwidth reduction compared to unicast.

Privacy and authenticity of data pushed by or into wearable devices are of important concerns. Wearable devices equipped with various sensors can capture user's activity in fine-grained level. In this work, we investigate the possibility of using user's activity information to develop an implicit authentication approach for wearable devices. We design and implement a framework that does continuous and implicit authentication based on ambulatory activities performed by the user. The system is validated using data collected from 30 participants with wearable devices worn across various regions of the body. The evaluation results show that the proposed approach can achieve as high as 97% accuracy rate with less than 1% false positive rate to authenticate a user using a single wearable device. And the accuracy rate can go up to 99.6% when we use the fusion of multiple wearable devices.

Synchronizing videos over file-hosting services on personal cloud such as Dropbox, Box or Onedrive leads to wastage in bandwidth and storage, which can be critical, while using mobile devices. Users can alternatively download the video on-the-go, but that leads to high latency, depending on network bandwidth and video file size. In contrast, adaptive video streaming allows near-real-time viewing by streaming the best possible quality in a given network condition. This feature is achieved by keeping multiple versions of video in cloud, leading to additional costs in cloud storage. Moreover, current solutions can only support a small set of bitrates, leading to abrupt switches in video resolution especially when the network condition is unstable, as often experienced by mobile users. This paper introduces Vsync, a framework for cloud based video synchronization for mobile devices. A video content is streamed using a cloud-based real-time transcoding and transmission framework to provide smooth video quality. Built over prediction models for video transcoding sessions and a QoE based adaptive video streaming protocol, Vsync is able to obtain the improvements of 37 ~ 80% than other compared schemes. The dataset and evaluation was done on a pool of 220K video clips.

With the prevalence of mobile computing, lots of wireless devices need to establish secure communication on the fly without pre-shared secrets. Device pairing is critical for bootstrapping secure communication between two previously unassociated devices over the wireless channel. Using auxiliary out-of-band channels involving visual, acoustic, tactile, or vibrational sensors has been proposed as a feasible option to facilitate device pairing. However, these methods usually require users to perform additional tasks, such as copying, comparing, and shaking. It is preferable to have a natural and intuitive pairing method with minimal user tasks. In this paper, we introduce a new method, called MagPairing, for pairing smartphones in close proximity by exploiting correlated magnetometer readings. In MagPairing, users only need to naturally tap the smartphones together for a few seconds without performing any additional operations in authentication and key establishment. Our method exploits the fact that smartphones are equipped with tiny magnets. Highly correlated magnetic field patterns are produced when two smartphones are close to each other. We design MagPairing protocol and implement it on Android smartphones. We conduct extensive simulations and real-world experiments to evaluate MagPairing. Experiments verify that the captured sensor data on which MagPairing is based has high entropy and sufficient length, and is nondisclosure to attackers more than few centimeters away. Usability tests on various kinds of smartphones by totally untrained users show that the whole pairing process needs only 4.5 s on average with more than 90% success rate.

Location-based services are quickly becoming immensely popular. In addition to services based on users' current location, many potential services rely on users' location history, or their spatial-temporal provenance. Malicious users may lie about their spatial-temporal provenance without a carefully designed security system for users to prove their past locations. In this paper, we present the Spatial-Temporal provenance Assurance with Mutual Proofs (STAMP) scheme. STAMP is designed for ad-hoc mobile users generating location proofs for each other in a distributed setting. However, it can easily accommodate trusted mobile users and wireless access points. STAMP ensures the integrity and non-transferability of the location proofs and protects users' privacy. A semi-trusted Certification Authority is used to distribute cryptographic keys as well as guard users against collusion by a light-weight entropy-based trust evaluation approach. Our prototype implementation on the Android platform shows that STAMP is low-cost in terms of computational and storage resources. Extensive simulation experiments show that our entropy-based trust model is able to achieve high ( > 0.9) collusion detection accuracy.

Energy Expenditure (EE) Estimation is an important step in tracking personal activity and preventing chronic diseases such as obesity, diabetes and cardiovascular diseases. Accurate and online EE estimation using small wearable sensors is a difficult task, primarily because most existing schemes work offline or using heuristics. In this work, we focus on accurate EE estimation for tracking ambulatory activities (walking, standing, climbing upstairs or downstairs) of individuals wearing mobile sensors. We use Convolution Neural Networks (CNNs) to automatically detect important features from data collected from triaxial accelerometer and heart rate sensors. Using CNNs, we find a significant improvement in EE estimation compared to other state-of-the-art models. We compare our results against state-of-the-art Activity-Specific Linear Regression as well as Artificial Neural Networks (ANN) based models. Using a universal CNN model, we obtain an overall low Root Mean Square Error (RMSE) of 1.12 which is 30% and 35% lower than existing models. The results were calibrated against a COSMED K4b2 indirect calorimeter readings.

The quality of mobile videos is usually quantified through the Quality of Experience (QoE), which is usually based on network QoS measurements, user engagement, or post-view subjective scores. Such quantifications are not adequate for real-time evaluation. They cannot provide on-line feedback for improvement of visual acuity, which represents the actual viewing experience of the end user. We present a visual acuity framework which makes fast online computations in a mobile device and provide an accurate estimate of mobile video QoE. We identify and study the three main causes that impact visual acuity in mobile videos: spatial distortions, types of buffering and resolution changes. Each of them can be accurately modeled using our framework. We use machine learning techniques to build a prediction model for visual acuity, which depicts more than 78% accuracy. We present an experimental implementation on iPhone 4 and 5s to show that the proposed visual acuity framework is feasible to deploy in mobile devices. Using a data corpus of over 2852 mobile video clips for the experiments, we validate the proposed framework.

Many location-based services require a mobile user to continuously prove his location. In absence of a secure mechanism, malicious users may lie about their locations to get these services. Mobility trace, a sequence of past mobility points, provides evidence for the user's locations. In this paper, we propose a Continuous Location Integrity and Provenance (CLIP) Scheme to provide authentication for mobility trace, and protect users' privacy. CLIP uses low-power inertial accelerometer sensor with a light-weight entropy-based commitment mechanism and is able to authenticate the user's mobility trace without any cost of trusted hardware. CLIP maintains the user's privacy, allowing the user to submit a portion of his mobility trace with which the commitment can be also verified. Wireless Access Points (APs) or colocated mobile devices are used to generate the location proofs. We also propose a light-weight spatial-temporal trust model to detect fake location proofs from collusion attacks. The prototype implementation on Android demonstrates that CLIP requires low computational and storage resources. Our extensive simulations show that the spatial-temporal trust model can achieve high (> 0.9) detection accuracy against collusion attacks.

Video source identification is very important in validating video evidence, tracking down video piracy crimes, and regulating individual video sources. With the prevalence of wireless communication, wireless video cameras continue to replace their wired counterparts in security/surveillance systems and tactical networks. However, wirelessly streamed videos usually suffer from blocking and blurring due to inevitable packet loss in wireless transmissions. The existing source identification methods experience significant performance degradation or even fail to work when identifying videos with blocking and blurring. In this paper, we propose a method that is effective and efficient in identifying such wirelessly streamed videos. In addition, we also propose to incorporate wireless channel signatures and selective frame processing into source identification, which significantly improve the identification speed. We conduct extensive real-world experiments to validate our method. The results show that the source identification accuracy of the proposed scheme largely outperforms the existing methods in the presence of video blocking and blurring. Moreover, our method is able to identify the video source in a near-real-time fashion, which can be used to detect the wireless camera spoofing attack.

Wireless communication systems are highly prone to channel errors. With video being a major player in Internet traffic and undergoing exponential growth in wireless domain, we argue for the need of a Video-aware MAC (VMAC) to significantly improve the throughput and delay performance of real-time video streaming service. VMAC makes two changes to optimize wireless LAN for video traffic: (a) It incorporates a Perceptual-Error-Tolerance (PET) to the MAC frames by reducing MAC retransmissions while minimizing any impact on perceptual video quality; and (b) It uses a group NACK-based Adaptive Window (NAW) of MAC frames to improve both throughput and delay performance in varying channel conditions. Through simulations and experiments, we observe 56--89% improvement in throughput and 34--48% improvement in delay performance over legacy DCF and 802.11e schemes. VMAC also shows 15--78% improvement over legacy schemes with multiple clients.

Facial recognition is a popular biometric authentica-tion technique, but it is rarely used in practice for de-vice unlock or website / app login in smartphones, alt-hough most of them are equipped with a front-facing camera. Security issues (e.g. 2D media attack and vir-tual camera attack) and ease of use are two important factors that impede the prevalence of facial authentica-tion in mobile devices. In this paper, we propose a new sensor-assisted facial authentication method to over-come these limitations. Our system uses motion and light sensors to defend against 2D media attacks and virtual camera attacks without the penalty of authenti-cation speed. We conduct experiments to validate our method. Results show 95-97% detection rate and 2-3% false alarm rate over 450 trials in real-settings, indicat-ing high security obtained by the scheme ten times faster than existing 3D facial authentications (3 sec-onds compared to 30 seconds).

Video telephony is becoming popular over smart-phones and tablets. Unlike the Desktop era, smartphone users are often `mobile' and this impacts how the video is processed and transmitted over the network. The significant increase in the motion content in such videos change the composition of video frames. Coupled with wireless packet losses, it often leads to poor quality of video received by the end user. In this work, we propose RVD, a framework for Reliable Video Delivery in mobile telephony by accounting for video object motion comprising foreground end-user motion and background scene changes in the network transmission of video. Multilayer perceptron (MLP) based non-linear regression model is used to analyze the impact of redundancy on received video quality under network variations and different degrees of video motion. RVD achieves 17-25% bandwidth savings for a target video quality, and 50-56% quality improvement over video-oblivious approaches.

Digital camera identification can be accomplished based on sensor pattern noise, which is unique to a device, and serves as a distinct identification fingerprint. Camera identification and authentication have formed the basis of image/video forensics in legal proceedings. Unfortunately, real-time video source identification is a computationally heavy task, and does not scale well to conventional software implementations on typical embedded devices. In this paper, we propose a hardware architecture for source identification in networked cameras. The underlying algorithms, an orthogonal forward and inverse discrete wavelet transform and minimum mean square error-based estimation, have been optimized for 2-D frame sequences in terms of area and throughput performance. We exploit parallelism, pipelining, and hardware reuse techniques to minimize hardware resource utilization and increase the achievable throughput of the design. A prototype implementation on a Xilinx Virtex-6 FPGA device was optimized with a resulting throughput of 167 MB/s, processing 30 640 × 480 video frames in 0.17 s.

We characterize the performance of both video and network layer properties of Skype, the most popular video telephony application. The performance in both mobile and stationary scenarios is investigated; considering network characteristics such as packet loss, propagation delay, available bandwidth and their effects on the perceptual video quality, measured using spatial and temporal no-reference video metrics. Based on 200+ live traces, we study the performance of this mobile video telephony application. We model video quality as a function of input network parameters and derive a feed-forward Artificial-Neural-Network that accurately predicts video quality given network conditions (0.0206 ≤ MSE ≤ 0.570). The accuracy of this model improves significantly by incorporating end-user mobility as an input to the model.

Location-based services are quickly becoming immensely popular. In addition to services based on users' current location, many potential services rely on users' location history, or their spatial-temporal provenance. Malicious users may lie about their spatial-temporal provenance without a carefully designed security system for users to prove their past locations. In this paper, we present the Spatial-Temporal provenance Assurance with Mutual Proofs (STAMP) scheme. In contrast to most existing location proof systems which rely on infrastructure like wireless APs, STAMP is based on co-located mobile devices mutually generating location proofs for each other. This makes STAMP desirable for a wider range of applications. STAMP ensures the integrity and non-transferability of the location proofs and protects users' privacy. We also examine different collusion scenarios and propose a light-weight entropy-based trust evaluation approach to detect fake proofs resulting from collusion attacks. Our prototype implementation on the Android platform shows that STAMP is low-cost in terms of computational and storage resources. Extensive simulation experiments show that our entropy-based trust model is able to achieve high (> 0.9) collusion detection accuracy.

Video source identification is very important in validating video evidence, tracking down video piracy crimes and regulating individual video sources. With the prevalence of wireless communication, wireless video cameras continue to replace their wired counterparts in security/surveillance systems and tactical networks. However, wirelessly streamed videos usually suffer from blocking and blurring due to inevitable packet loss in wireless transmissions. The existing source identification methods experience significant performance degradation or even fail to work when identifying videos with blocking and blurring. In this paper, we propose a method which is effective and efficient in identifying such wirelessly streamed videos. In addition, we also propose to incorporate wireless channel signatures and selective frame processing into source identification, which significantly improve the identification speed.

Evolved Multimedia Broadcast Multicast Services (eMBMS) in LTE standards provides Raptor code as Forward Error Correction (FEC) scheme in application layer. Hybrid automatic repeat request (HARQ) is also used to increase reliability at MAC layer for packet recovery. The two mechanisms, with no interactions between them, may either lead to more redundancy in download link (DL) network resource or meaningless drops of recovery data at application layer. In this paper, we first analyze tradeoff between two recovery mechanisms and then present a probabilistic model to find optimal Raptor encoding rate and number of HARQ retransmissions for a given network condition. This can achieve a saving of upto 13-15% in DL network resources compared to existing schemes while ensuring reliable file delivery. It was also found to reduce the transmission delay (by minimizing the number of re-transmissions). The model was evaluated using LTE-A simulation framework.

Video quality assessment in mobile devices, for instances smart phones and tablets, raises unique challenges such as unavailability of original videos, the limited computation power of mobile devices and inherent characteristics of wireless networks (packet loss and delay). In this paper, we present a metric, Temporal Variation Metric (TVM), to measure the temporal information of videos. Despite its simplicity, it shows a high correlation coefficient of 0.875 to optical flow which captures all motion information in a video. We use the TVM values to derive a reduced-reference temporal quality assessment metric, Temporal Variation Index (TVI), which quantifies the quality degradation incurred in network transmission. Subjective assessments demonstrate that TVI is a very good predictor of users' Quality of Experience (QoE). Its prediction shows a 92.5% of correlation to subjective Mean Opinion Score (MOS) ratings. Through video streaming experiments, we show that TVI can also estimate the network conditions such as packet loss and delay. It depicts an accuracy of almost 95% in extensive tests on 183 video traces.

LTE-Advanced (LTE-A) systems support wider transmission bandwidths and hence, higher data rates for bulk traffic, as a result of Carrier Aggregation (CA). However, existing literature lacks efforts on channel-aware CA, especially in the uplink. The cell-edge users particularly suffer from exhaustion of resources, higher fading losses, lower SINR values (hence, requiring a higher power consumption) due to lossy channels that their traffic requirements are least-satisfied by channel-blind CA. This paper addresses the above concern by proposing an edge-prioritized channel- and traffic-aware uplink CA comprising Component Carrier (CC) assignment and resource scheduling. The LTE-A UEs are spatially-grouped and the under-represented edge UE groups, having the least assignable resources (good CCs), are prioritized for CA. This results in assigning the best channels to the edge groups. The frequency resources are scheduled to the groups based on inter-group and intra-group Proportional Fair Packet Scheduling (PFPS) in the time and frequency domains respectively, to resolve resource contention. The proposed approach outperforms the existing channel-blind Round-Robin and channel-aware Opportunistic CA, in terms of overall uplink throughput, by 33% in CC assignment and 21% in PFPS, in addition to significant throughput improvements for the edge UEs.

Many modern computing applications have been enabled through the use of real-time multimedia processing. While several hardware architectures have been proposed in the research literature to support such primitives, these fail to address applications whose performance and resource requirements have a dynamic aspect. Embedded multimedia systems typically need a power and computation efficient design in addition to good compression performance. In this article, we introduce a Polymorphic Wavelet Architecture (Poly-DWT) as a crucial building block towards the development of embedded systems to address such challenges. We illustrate how our Poly-DWT architecture can potentially make dynamic resource allocation decisions, such as the internal bit representation and the processing kernel, according to the application requirements. We introduce a filter switching architecture that allows for dynamic switching between 5/3 and 9/7 wavelet filters and leads to a more power efficient design. Further, a multiplier-free design with a low adder requirement demonstrates the potential of Poly-DWT for embedded systems. Through an FPGA prototype, we perform a quantitative analysis of our Poly-DWT architecture, and compare our filter to existing approaches to illustrate the area and performance benefits inherent in our approach.

Long Term Evolution (LTE) is emerging as a major candidate for 4G cellular networks to satisfy the increasing demands for mobile broadband services, particularly multimedia delivery. MIMO (Multiple Input Multiple Output) technology combined with OFDMA and more efficient modulation/coding schemes (MCS) are key physical layer technologies in LTE networks. However, in order to fully utilize the benefits of the advances in physical layer technologies MIMO configuration and MCS need to be dynamically adjusted to derive the promised gains of 4G at the application level. This paper provides a performance evaluation of video traffic with variations in the physical layer transmission parameters to suit the varying channel conditions. A quantitative analysis is provided using the perceived video quality (evaluated using no-reference blocking and blurring metrics) along with transmission delay, as video quality measures. Experiments are performed to measure performance with changes in modulation as well as code rates in poor and good channel conditions. We discuss how an adaptive scheme can optimize the performance over a varying channel.

Arithmetic Coding (AC) is widely used for the entropy coding of text and multimedia data. It involves recursive partitioning of the range [0,1) in accordance with the relative probabilities of occurrence of the input symbols. In this paper, we present a data (image or video) encryption scheme based on arithmetic coding, which we refer to as Chaotic Arithmetic Coding (CAC). In CAC, a large number of chaotic maps can be used to perform coding, each achieving Shannon optimal compression performance. The exact choice of map is governed by a key. CAC has the effect of scrambling the intervals without making any changes to the width of interval in which the codeword must lie, thereby allowing encryption without sacrificing any coding efficiency. We next describe Binary CAC (BCAC) with some simple Security Enhancement (SE) modes which can alleviate the security of scheme against known cryptanalysis against AC-based encryption techniques. These modes, namely Plaintext Modulation (PM), Pair-Wise Independent Keys (PWIK), and Key and cipher text Mixing (MIX) modes have insignificant computational overhead, while BCAC decoder has lower hardware requirements than BAC coder itself, making BCAC with SE as excellent choice for deployment in secure embedded multimedia systems. A bit sensitivity analysis for key and plaintext is presented along with experimental tests for compression performance.

Abstract Multi‐core processors can deliver significant performance benefits for multi‐threaded software by adding processing power with minimal latency, given the proximity of the processors. Cryptographic applications are inherently complex and involve large computations. Most cryptographic operations can be translated into logical operations, shift operations, and table look‐ups. In this paper we design a novel processor (called mu‐core) with a reconfigurable Arithmetic Logic Unit, and design custom two‐dimensional multi‐core architectures on top of it to accelerate cryptographic kernels. We propose an efficient mapping of instructions from the multi‐core grid to the individual processor cores and illustrate the performance of AES‐128E algorithm over custom‐sized grids. The model was developed using Simulink and the performance analysis suggests a positive trend towards development of large multi‐core (or multi‐ µ‐core) architectures to achieve high throughputs in cryptographic operations. Copyright © 2010 John Wiley & Sons, Ltd.

Traditional microprocessor-based solutions are insufficient to serve the dynamic throughput demands of real-time scalable multimedia processing systems. This paper introduces a Polymorphic Architecture for the Discrete Wavelet Transform (Poly-DWT) as a building block of reconfigurable systems to address these needs. We illustrate how our Poly-DWT architecture can dynamically make resource allocation decisions according to application requirements. We perform a quantitative analysis of our Poly-DWT architecture using an FPGA prototype, and compare our filters to existing approaches to illustrate the area and performance benefits inherent in our approachrdquo.