FPGA Implementation of Data Flow Graphs for Digital Signal Processing Applications

A rapid growth in digital signal processing applications has increased the requirement for high-speed digital systems. Multiprocessor systems are the best choice for these applications. A prior sequence of operations should be applied to the operations that described the nature of these applications before hardware implementation is produced. These operations should be scheduled and hardware allocated. This paper proposes a new scheduling technique for digital signal processing (DSP) applications has been represented by data flow graphs (DFGs). In addition, hardware allocation is implemented in the form of embedded system. A proposed scheduling technique also achieves the optimal scheduling of a DFG at design time. The optimality criteria considered in this algorithm are the maximum throughput within the available hardware resources. The maximum throughput is achieved by arranging the DFG nodes according to their inter-related data dependencies. Then, two nodes can be clustered into one compound task to reduce the overall execution time by minimizing the number of tasks to be executed that minimizing the number of cycles to execute them. Then each task is presented in form of instruction to be executed in the hardware system. A hardware system is composed of one or multiple homogenous pipelined processing elements and it is designed to meet the maximum-rate schedule.  Two implementations are proposed of the system architecture according to the number of the processing elements, namely:  the serial system and the parallel system. The serial system comprises one processing element where all tasks are processed sequentially, whilst the parallel system has four processing elements to execute tasks concurrently. These systems consist mainly of seven units: central shared memory, state table, multiway function unit buffer, execution array, processing element/s, instruction buffer and the address generation unit. The hardware components were built on an FPGA chip using Verilog HDL. In synthesis results, the parallel system has better system performance by 25.5% than the serial system. While the serial system requires smaller area size, which described by the number of slice registers and the number of the slice lookup tables (LUTs) than the parallel one. The relationship between the number of instructions that are executed in both systems, and the system area and the system performance that presented by system frequency, are studied. By increasing memories size in both systems, the system performance isn’t affected as in a serial system, and it is slightly decreased as the parallel system by 1.5% to 4.5%. In terms of the systems area, both serial system area and parallel system area are increased and in some cases are doubled. The proposed scheduling technique is shown to outperform the retaining technique, which we have chosen to compare with.  The serial system has better performance by 19.3% higher system frequency than a retiming technique. And the parallel system also outperforms the retaining technique by 51.2% higher system frequency in synthesis results

FPGA Implementation of Fast Binary Multiplication Based on Customized Basic Cells

Multiplication is considered one of the most time-consuming and a key operation in wide variety of embedded applications. Speeding up this operation has a significant impact on the overall performance of these applications. A vast number of multiplication approaches are found in the literature where the goal is always to achieve a higher performance. One of these approaches relies on using smaller multiplier blocks which are built based on direct Boolean algebra equations to build large multipliers. In this work, we present a methodology for designing binary multipliers where different sizes customized partial products generation (CPPG) cells are designed and used as smaller building blocks. The sizes of the designed CPPG cells are 2×2, 3×3, 4×4, 5×5, and 6×6. We use these cells to build 8×8, 16×16, 32×32, 64×64, and 128×128 binary multipliers. All of the CPPG cells and the binary multipliers are described using the VHDL language, tested, and implemented using XILINX ISE 14.6 tools targeting different FPGA families. The implementation results show that the best performance is achieved when cell 3×3 is used and Virtex-7 FPGA is targeted. The binary multipliers that are designed using the proposed CPPG cells achieve better performance when compared with the binary multipliers presented in the literature. As an application that utilizes the proposed multiplier, a Multiply-Accumulate (MAC) unit is designed and implemented in Spartan-3E. The implementation results of the MAC unit demonstrate the effectiveness of the proposed multiplier

Building a neural speech recognizer for quranic recitations

This work is an effort towards building Neural Speech Recognizers system for Quranic recitations that can be effectively used by anyone regardless of their gender and age. Despite having a lot of recitations available online, most of them are recorded by professional male adult reciters, which means that an ASR system trained on such datasets would not work for female/child reciters. We address this gap by adopting a benchmark dataset of audio records of Quranic recitations that consists of recitations by both genders from different ages. Using this dataset, we build several speaker-independent NSR systems based on the DeepSpeech model and use word error rate (WER) for evaluating them. The goal is to show how an NSR system trained and tuned on a dataset of a certain gender would perform on a test set from the other gender. Unfortunately, the number of female recitations in our dataset is rather small while the number of male recitations is much larger. In the first set of experiments, we avoid the imbalance issue between the two genders and down-sample the male part to match the female part. For this small subset of our dataset, the results are interesting with 0.968 WER when the system is trained on male recitations and tested on female recitations. The same system gives 0.406 WER when tested on male recitations. On the other hand, training the system on female recitations and testing it on male recitation gives 0.966 WER while testing it on female recitations gives 0.608 WER

Towards Building a Speech Recognition System for Quranic Recitations: A Pilot Study Involving Female Reciters

This paper is the first step in an effort toward building automatic speech recognition (ASR) system for Quranic recitations that caters specifically to female reciters. To function properly, ASR systems require a huge amount of data for training. Surprisingly, the data readily available for Quranic recitations suffer from major limitations. Specifically, the currently available audio recordings of Quran recitations have massive volume, but they are mostly done by male reciters (who have dedicated most of their lives to perfecting their recitation skills) using professional and expensive equipment. Such proficiency in the training data (along with the fact that the reciters come from a specific demographic group; adult males) will most likely lead to some bias in the resulting model and limit their ability to process input from other groups, such as non-/semi-professionals, females or children. This work aims at empirically exploring this shortcoming. To do so, we create a first-of-its-kind (to the best of our knowledge) benchmark dataset called the Quran recitations by females and males (QRFAM) dataset. QRFAM is a relatively big dataset of audio recordings made by male and female reciters from different age groups and proficiency levels. After creating the dataset, we experiment on it by building ASR systems based on one of the most popular open-source ASR models, which is the celebrated DeepSpeech model from Mozilla. The speaker-independent end-to-end models, that we produce, are evaluated using word error rate (WER). Despite DeepSpeech’s known flexibility and prowess (which is shown when trained and tested on recitations from the same group), the models trained on the recitations of one group could not recognize most of the recitations done by the other groups in the testing phase. This shows that there is still a long way to go in order to produce an ASR system that can be used by anyone and the first step is to build and expand the resources needed for this such as QRFAM. Hopefully, our work will be the first step in this direction and it will inspire the community to take more interest in this problem

Emotional Intelligence Among Medical Students in Palestine A Cross-Sectional Study

Background: Emotional intelligence (EI) is defined as a pro-social behavior that deals with recognizing, understanding, influencing and managing our own and other’s emotions. In medical education and clinical practice, EI has been related to improves the doctor-patient relationship. Objectives: Measure EI among Palestinian medical students in two stages of their studies, clinical and basic sciences, and assessing the factors that may affect it. Moreover, compare medical students of Al-Quds and Al-Najah Universities regarding EI score and detect possible differences. Methods: A quantitative, cross-sectional, questionnaire-based, online survey was conducted among 692 medical students in Al-Quds and Al-Najah universities in Palestine. Emotional intelligence was evaluated using a 33-item scale as an index introduced by Schutte et al. (1998). Data was analyzed in a quantitative manner using SPSS (VER.20). Results: 745 students filled the questionnaire with a response rate of 92.88%. A total of 692 were sampled which were representative of the student population. The mean score of EI is 3.83 (SD=0.41) out of a maximum possible score of 5 with 69.1% of the sample having high EI. Statistics showed that EI decreased significantly at α≤0.05 among basic and clinical stages of study with a negative correlation between EI and academic year (PCC= -0.086). This indicates that as the academic year increases, EI decreases (p=0.023). Moreover, EI is affected significantly at α≤0.05 in a positive manner by having a hobby or doing extracurricular activities. In addition, students who indicate they always regret studying medicine tend to relate to lower EI, this may reflect the lack of interest to study this field. Conclusion: Medical students, both male and female, have a relatively high level of emotional intelligence in the universities that were studied. Students in the clinical stage have lower EI than basic sciences medical students, which indicates that students have a conflict between objectivity and humanity while training clinically. Therefore, emotional support during clinical years would serve in improving EI. Moreover, EI is affected by having a hobby or extracurricular activities, indicating that EI can be modulated through the encouragement of such activities

Ransomware-Resilient Self-Healing XML Documents

In recent years, various platforms have witnessed an unprecedented increase in the number of ransomware attacks targeting hospitals, governments, enterprises, and end-users. The purpose of this is to maliciously encrypt documents and files on infected machines, depriving victims of access to their data, whereupon attackers would seek some sort of a ransom in return for restoring access to the legitimate owners; hence the name. This cybersecurity threat would inherently cause substantial financial losses and time wastage for affected organizations and users. A great deal of research has taken place across academia and around the industry to combat this threat and mitigate its danger. These ongoing endeavors have resulted in several detection and prevention schemas. Nonetheless, these approaches do not cover all possible risks of losing data. In this paper, we address this facet and provide an efficient solution that would ensure an efficient recovery of XML documents from ransomware attacks. This paper proposes a self-healing version-aware ransomware recovery (SH-VARR) framework for XML documents. The proposed framework is based on the novel idea of using the link concept to maintain file versions in a distributed manner while applying access-control mechanisms to protect these versions from being encrypted or deleted. The proposed SH-VARR framework is experimentally evaluated in terms of storage overhead, time requirement, CPU utilization, and memory usage. Results show that the snapshot size increases proportionately with the original size; the time required is less than 120 ms for files that are less than 1 MB in size; and the highest CPU utilization occurs when using the bzip2. Moreover, when the zip and gzip are used, the memory usage is almost fixed (around 6.8 KBs). In contrast, it increases to around 28 KBs when the bzip2 is used

Emotional Intelligence Among Medical Students in Palestine A Cross-Sectional Study

Background: Emotional intelligence (EI) is defined as a pro-social behavior that deals with recognizing, understanding, influencing and managing our own and other’s emotions. In medical education and clinical practice, EI has been related to improves the doctor-patient relationship. Objectives: Measure EI among Palestinian medical students in two stages of their studies, clinical and basic sciences, and assessing the factors that may affect it. Moreover, compare medical students of Al-Quds and Al-Najah Universities regarding EI score and detect possible differences. Methods: A quantitative, cross-sectional, questionnaire-based, online survey was conducted among 692 medical students in Al-Quds and Al-Najah universities in Palestine. Emotional intelligence was evaluated using a 33-item scale as an index introduced by Schutte et al. (1998). Data was analyzed in a quantitative manner using SPSS (VER.20). Results: 745 students filled the questionnaire with a response rate of 92.88%. A total of 692 were sampled which were representative of the student population. The mean score of EI is 3.83 PalStudent Journal Correspondence concerning this article should be addressed tothe mentioned authors at the mentioned institutes. Copyright © 2020 Al-Quds University, Deanship of Scientific Research. All rights reserved. E-mail: [email protected] Palestine, Abu Dis, Al-Quds University (SD=0.41) out of a maximum possible score of 5 with 69.1% of the sample having high EI. Statistics showed that EI decreased significantly at α≤0.05 among basic and clinical stages of study with a negative correlation between EI and academic year (PCC= -0.086). This indicates that as the academic year increases, EI decreases (p=0.023). Moreover, EI is affected significantly at α≤0.05 in a positive manner by having a hobby or doing extracurricular activities. In addition, students who indicate they always regret studying medicine tend to relate to lower EI, this may reflect the lack of interest to study this field. Conclusion: Medical students, both male and female, have a relatively high level of emotional intelligence in the universities that were studied. Students in the clinical stage have lower EI than basic sciences medical students, which indicates that students have a conflict between objectivity and humanity while training clinically. Therefore, emotional support during clinical years would serve in improving EI. Moreover, EI is affected by having a hobby or extracurricular activities, indicating that EI can be modulated through the encouragement of such activities

Emotional Intelligence Among Medical Students in Palestine A Cross-Sectional Study

Background: Emotional intelligence (EI) is defined as a pro-social behavior that deals with recognizing, understanding, influencing and managing our own and other’s emotions. In medical education and clinical practice, EI has been related to improves the doctor-patient relationship. Objectives: Measure EI among Palestinian medical students in two stages of their studies, clinical and basic sciences, and assessing the factors that may affect it. Moreover, compare medical students of Al-Quds and Al-Najah Universities regarding EI score and detect possible differences. Methods: A quantitative, cross-sectional, questionnaire-based, online survey was conducted among 692 medical students in Al-Quds and Al-Najah universities in Palestine. Emotional intelligence was evaluated using a 33-item scale as an index introduced by Schutte et al. (1998). Data was analyzed in a quantitative manner using SPSS (VER.20). Results: 745 students filled the questionnaire with a response rate of 92.88%. A total of 692 were sampled which were representative of the student population. The mean score of EI is 3.83 PalStudent Journal Correspondence concerning this article should be addressed tothe mentioned authors at the mentioned institutes. Copyright © 2020 Al-Quds University, Deanship of Scientific Research. All rights reserved. E-mail: [email protected] Palestine, Abu Dis, Al-Quds University (SD=0.41) out of a maximum possible score of 5 with 69.1% of the sample having high EI. Statistics showed that EI decreased significantly at α≤0.05 among basic and clinical stages of study with a negative correlation between EI and academic year (PCC= -0.086). This indicates that as the academic year increases, EI decreases (p=0.023). Moreover, EI is affected significantly at α≤0.05 in a positive manner by having a hobby or doing extracurricular activities. In addition, students who indicate they always regret studying medicine tend to relate to lower EI, this may reflect the lack of interest to study this field. Conclusion: Medical students, both male and female, have a relatively high level of emotional intelligence in the universities that were studied. Students in the clinical stage have lower EI than basic sciences medical students, which indicates that students have a conflict between objectivity and humanity while training clinically. Therefore, emotional support during clinical years would serve in improving EI. Moreover, EI is affected by having a hobby or extracurricular activities, indicating that EI can be modulated through the encouragement of such activities