Muhammad Adnan Elahi

Real-time automatic license plate recognition for CCTV forensic applications

We propose an efficient real-time automatic license plate recognition (ALPR) framework, particularly designed to work on CCTV video footage obtained from cameras that are not dedicated to the use in ALPR. At present, in license plate detection, tracking and recognition are reasonably well-tackled problems with many successful commercial solutions being available. However, the existing ALPR algorithms are based on the assumption that the input video will be obtained via a dedicated, high-resolution, high-speed camera and is/or supported by a controlled capture environment, with appropriate camera height, focus, exposure/shutter speed and lighting settings. However, typical video forensic applications may require searching for a vehicle having a particular number plate on noisy CCTV video footage obtained via non-dedicated, medium-to-low resolution cameras, working under poor illumination conditions. ALPR in such video content faces severe challenges in license plate localization, tracking and recognition stages. This paper proposes a novel approach for efficient localization of license plates in video sequence and the use of a revised version of an existing technique for tracking and recognition. A special feature of the proposed approach is that it is intelligent enough to automatically adjust for varying camera distances and diverse lighting conditions, a requirement for a video forensic tool that may operate on videos obtained by a diverse set of unspecified, distributed CCTV cameras.

Hybrid Artifact Removal for Confocal Microwave Breast Imaging

Several factors determine the effectiveness of an early-stage artifact removal algorithm for the detection of breast cancer using confocal microwave imaging. These factors include the ability to select the correct time window containing the artifact, the ability to remove the artifact while being robust to normal variances, and ability to effectively preserve the tumor response in the resultant signal. Very few (if any) of the existing artifact removal algorithms incorporate all of these qualities. In this letter, a novel hybrid artifact removal algorithm for microwave breast imaging applications is presented, which combines the best attributes of two existing algorithms to effectively remove the early-stage artifact while preserving the tumor response. This algorithm is compared to existing algorithms using a range of appropriate performance metrics.

Artifact Removal Algorithms for Microwave Imaging of the Breast

One of the most important components of any Confocal Microwave Imaging (CMI) system for breast cancer detection is the early-stage artifact removal algorithm. The early-stage artifact is composed of the incident pulse combined with the re∞ection from the skin-breast interface and residual antenna reverberation, and must be removed from the received signal at each antenna before further processing can take place. If the early-stage artifacts are not removed, they could potentially mask energy re∞ected from shallow tumors located close to the surface of the skin, and also hinder the identiflcation of tumors located deeper within the breast. Many existing artifact removal algorithms are based on variants of the assumption that the artifact in a particular channel can be estimated and efiectively removed by creating a reference waveform. This reference waveform is typically based on the average of the artifact in all channels. The artifact in a particular channel is then removed by subtracting this reference waveform from the recorded signal. More sophisticated algorithms estimate the artifact in each channel as a flltered combination of all the artifacts, and have been shown to be more robust to normal variations in skin thickness. However, increased underlying dielectric heterogeneity, as highlighted by Lazebnik etal., could result in greater variation in the early-stage artifact, making the artifact removal process much more di-cult. In this paper, several existing artifact removal are examined in this context of increased dielectric heterogeneity, and based on these results, suggestions for future work are presented. More than 40,000 women die annually in the United States from breast cancer, making it the leading cause of death in American women. One of the most promising alternate breast imaging modalities is microwave imaging. The physical basis for microwave imaging is the dielectric contrast between the constituent tissues of the breast and cancerous tissue at microwave frequencies. The Confocal Microwave Imaging (CMI) approach involves illuminating the breast with a UWB pulse, recording the backscattered signals and then using these signals to identify and locate signiflcant dielectric scatterers within the breast. Regions of high energy within the resultant image may suggest the presence of tumours. However, recent studies have found the breast to be dielectrically heterogeneous. Signiflcantly, Lazebnik etal. (1) found a very signiflcant dielectric contrast between normal adipose and flbroglandular tissue within the breast. Comparison studies have examined the performance of several UWB beamforming algorithms in the dielectrically heterogeneous breast. However, no previous study has compared the performance of early-stage artifact removal algo- rithms. The early-stage artifact is composed of the input signal, the re∞ection from the skin-fat interface and any antenna reverberation. This artifact is typically several orders of magnitude greater than than the re∞ections from any tumours present within the breast. If the artifact is not removed efiectively, it could easily mask tumours present within the breast. In this paper sev- eral existing artifact removal algorithms are described and compared, before suggestions for future development are presented.

Design and implementation of real time vehicle tracking system

Tracking systems were first developed for the shipping industries to determine the position of ships and boats in the sea. Initially passive systems were developed to support in tracking and navigation for location-based applications. For the applications that require real time location information of the vehicle, these systems cannot be employed, because they store the location information in the internal storage that can only be accessed when vehicle is available. Recently, Automatic Vehicle Location (AVL) systems are developed and deployed in numerous environments. These systems are capable of transmitting vehicles location information in real time. In these systems, the device installed in the vehicle can transmit the location information in real time to a remote data centre, instead of storing into local storage, using some radio network. In this paper, we present the design and implementation of a real time AVL system that incorporates a hardware device installed in the vehicle a nd a remote Tracking Server (TS).

Detailed evaluation of artifact removal algorithms for radar-based microwave imaging of the breast

One of the most important components of radar-based microwave imaging systems for breast cancer detection is the early-stage artifact removal algorithm. The early-stage artifact is composed of the input signal, the reflection from the skin-fat interface and any antenna reverberation present. This artifact is typically several orders of magnitude greater than the reflections from any tumours present within the breast. If the early-stage artifact is not removed and the tumour response effectively preserved, the artifact could potentially mask energy reflected from shallow tumours located close to the surface of the skin, and also hinder the identification of tumours located deeper within the breast.

Design and implementation of real time video streaming and ROI transmission system using RTP on an embedded digital signal processing (DSP) platform

Recently, video streaming has been extensively used for information broadcasting. Availability of improved and enhanced transmission facilities make it possible to use video streaming in fast real time applications. Real time video transmission is widely used in surveillance, conferencing, media broadcasting and applications that include remote assistance. Though a lot of work has already been done in the field of video streaming and many systems are already available, most of them either use a dedicated physical medium for data transmission or rely on general purpose computer systems as servers; which make these systems less efficient and un-suitable for such real time applications that require high speed dedicated video servers. This issue can be resolved by deploying special purpose high speed dedicated embedded processors. In this paper, we present the design and implementation of a real-time video streaming system by using an embedded media processing platform. The system deals with the issues of real-time protocol implementation on captured video and serving it to a client system via Ethernet which plays it in real-time. Further, in this piece of work we also deal with ‘Region of Interest’ (ROI) transmission to improve processing efficiency of the system, which allows video fragmentation and communication together in real-time.

Differential Evolution Optimization of the SAR Distribution for Head and Neck Hyperthermia

Hyperthermia is an emerging cancer treatment modality, which involves applying heat to the malignant tumor. The heating can be delivered using electromagnetic (EM) energy, mostly in the radiofrequency (RF) or microwave range. Accurate patient-specific hyperthermia treatment planning (HTP) is essential for effective and safe treatments, in particular, for deep and loco-regional hyperthermia. An important aspect of HTP is the ability to focus microwave energy into the tumor and reduce the occurrence of hot spots in healthy tissue. This paper presents a method for optimizing the specific absorption rate (SAR) distribution for the head and neck cancer hyperthermia treatment. The SAR quantifies the rate at which localized RF or microwave energy is absorbed by the biological tissue when exposed to an EM field. A differential evolution (DE) optimization algorithm is proposed in order to improve the SAR coverage of the target region. The efficacy of the proposed algorithm is demonstrated by testing with the Erasmus MC patient dataset. DE is compared to the particle swarm optimization (PSO) method, in terms of average performance and standard deviation and across various clinical metrics, such as the hot-spot-tumor SAR quotient (HTQ), treatment quantifiers, and temperature parameters. While hot spots in the SAR distribution remain a problem with current approaches, DE enhances focusing microwave energy absorption to the target region during hyperthermia treatment. In particular, DE offers improved performance compared to the PSO algorithm currently deployed in the clinic, reporting a range of improvement of HTQ standard deviation of between 40.1–96.8% across six patients.

Adaptive artifact removal for selective multistatic microwave breast imaging signals

This work is supported by Science Foundation Ireland (Grant Numbers: 11/SIRG/I2120 and 12/IP/1523) and has been developed in the framework of COST Action TD1301, MiMed.

Evaluation of Image Reconstruction Algorithms for Confocal Microwave Imaging: Application to Patient Data

Confocal Microwave Imaging (CMI) for the early detection of breast cancer has been under development for over two decades and is currently going through early-phase clinical evaluation. The image reconstruction algorithm is a key signal processing component of any CMI-based breast imaging system and impacts the efficacy of CMI in detecting breast cancer. Several image reconstruction algorithms for CMI have been developed since its inception. These image reconstruction algorithms have been previously evaluated and compared, using both numerical and physical breast models, and healthy volunteer data. However, no study has been performed to evaluate the performance of image reconstruction algorithms using clinical patient data. In this study, a variety of imaging algorithms, including both data-independent and data-adaptive algorithms, were evaluated using data obtained from a small-scale patient study conducted at the University of Calgary. Six imaging algorithms were applied to reconstruct 3D images of five clinical patients. Reconstructed images for each algorithm and each patient were compared to the available clinical reports, in terms of abnormality detection and localisation. The imaging quality of each algorithm was evaluated using appropriate quality metrics. The results of the conventional Delay-and-Sum algorithm and the Delay-Multiply-and-Sum (DMAS) algorithm were found to be consistent with the clinical information, with DMAS producing better quality images compared to all other algorithms.

Dielectric properties of bones for the monitoring of osteoporosis

AbstractOsteoporosis is one of the most common diseases that leads to bone fractures. Dual-energy X-ray absorptiometry is currently employed to measure the bone mineral density and to diagnose osteoporosis. Alternatively, the dielectric properties of bones are found to be influenced by bone mineral density; hence, dielectric properties of bones may potentially be used to diagnose osteoporosis. Microwave tomographic imaging is currently in development to potentially measure in vivo dielectric properties of bone. Therefore, the foci of this work are to summarize all available dielectric data of bone in the microwave frequency range and to analyze the confounders that may have resulted in variations in reported data. This study also compares the relationship between the dielectric properties and bone quality reported across different studies. The review suggests that variations exist in the dielectric properties of bone and the relationship between bone volume fraction and dielectric properties is in agreement across all studies. Conversely, the evidence of a relationship between bone mineral density and dielectric properties is inconsistent across the studies. This summary of dielectric data of bone along with a comparison of the relationship between the dielectric properties and bone quality will accelerate the development of microwave tomographic imaging devices for the monitoring of osteoporosis. Graphical abstractᅟ