Pub Date : 2020-03-01DOI: 10.5455/jjcit.71-1567496518
Belal Asaati, A. Hudrouss
التعديل الفضائي المزدوج (DSM) عبارة عن تقنية إرسال تم اقتراحها حديثا لأنظمة اتصالات متعددة المخرجات تمتلك هذه التقنية فاعلية طيفية أعلى مقارنة مع التعديل الفضائي الكلاسيكي (SM) ، فهي تعمل على مضاعفة عدد هوائيات الإرسال الفعالة. في هذه الورقة يتم تطبيق اختيار الهوائي على الفضاء الهوائي المزدوج من أجل تحسين الأداء من حيث معدل خطأ البت (BER). و بشكل أكثر تحديدا نقوم بدمج خوارزميتين دون المستوى الأمثل مع التعديل الفضائي المزدوج ؛ وهما اختيار الهوائيات القائم على السعة المثالية(COAS) و اختيار الهوائي بناءا على الاتساع الارتباط بين الهوائيات (A-C-AS). وقد تم عرض نتائج محاكاة للخوارزميتين ومقارنتها مع اختيار الهوائيات الإقليدية المثالية (EDAS) باستخدام برمجية الماتلاب. تظهر النتائج وجود أفضلية للخوارزميتين من حيث درجة التعقيد الحسابي على الرغم من أن هناك خسارة لا تذكر في معدل خطأ البت ، فالخوارزميتين أقل تعقيدا بكثير من خوارزمية EDAS.
{"title":"TRANSMIT ANTENNA SELECTION SCHEMES FOR DOUBLE SPATIAL MODULATION","authors":"Belal Asaati, A. Hudrouss","doi":"10.5455/jjcit.71-1567496518","DOIUrl":"https://doi.org/10.5455/jjcit.71-1567496518","url":null,"abstract":"التعديل الفضائي المزدوج (DSM) عبارة عن تقنية إرسال تم اقتراحها حديثا لأنظمة اتصالات متعددة المخرجات تمتلك هذه التقنية فاعلية طيفية أعلى مقارنة مع التعديل الفضائي الكلاسيكي (SM) ، فهي تعمل على مضاعفة عدد هوائيات الإرسال الفعالة. \u0000في هذه الورقة يتم تطبيق اختيار الهوائي على الفضاء الهوائي المزدوج من أجل تحسين الأداء من حيث معدل خطأ البت (BER). و بشكل أكثر تحديدا نقوم بدمج خوارزميتين دون المستوى الأمثل مع التعديل الفضائي المزدوج ؛ وهما اختيار الهوائيات القائم على السعة المثالية(COAS) و اختيار الهوائي بناءا على الاتساع الارتباط بين الهوائيات (A-C-AS). \u0000وقد تم عرض نتائج محاكاة للخوارزميتين ومقارنتها مع اختيار الهوائيات الإقليدية المثالية (EDAS) \u0000باستخدام برمجية الماتلاب. تظهر النتائج وجود أفضلية للخوارزميتين من حيث درجة التعقيد الحسابي على الرغم من أن هناك خسارة لا تذكر في معدل خطأ البت ، فالخوارزميتين أقل تعقيدا بكثير من خوارزمية EDAS.","PeriodicalId":36757,"journal":{"name":"Jordanian Journal of Computers and Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2020-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41587148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.5455/jjcit.71-1592597688
Raed Alazaidah, F. Ahmad, M. Mohsin, F. Thabtah, W. Alzoubi
{"title":"MULTI-LABEL RANKING METHOD BASED ON POSITIVE CLASS CORRELATIONS","authors":"Raed Alazaidah, F. Ahmad, M. Mohsin, F. Thabtah, W. Alzoubi","doi":"10.5455/jjcit.71-1592597688","DOIUrl":"https://doi.org/10.5455/jjcit.71-1592597688","url":null,"abstract":"","PeriodicalId":36757,"journal":{"name":"Jordanian Journal of Computers and Information Technology","volume":"8 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70819906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.5455/jjcit.71-1588437727
Abdelhamid Riadi, M. Boulouird, hassani moha
{"title":"CHANNEL ESTIMATION AND DETECTION FOR OFDM MASSIVE-MIMO IN FLAT AND FREQUENCY SELECTIVE FADING CHANNELS","authors":"Abdelhamid Riadi, M. Boulouird, hassani moha","doi":"10.5455/jjcit.71-1588437727","DOIUrl":"https://doi.org/10.5455/jjcit.71-1588437727","url":null,"abstract":"","PeriodicalId":36757,"journal":{"name":"Jordanian Journal of Computers and Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70820274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.5455/jjcit.71-1587943974
Ahmad Hasasneh
One of the best ways of communication between deaf people and hearing people is based on sign language or so-called hand gestures. In the Arab society, only deaf people and specialists could deal with Arabic sign language, which makes the deaf community narrow and thus communicating with normal people difficult. In addition to that, studying the problem of Arabic sign language recognition (ArSLR) has been paid attention recently, which emphasizes the necessity of investigating other approaches for such a problem. This paper proposes a novel ArSLR scheme based on an unsupervised deep learning algorithm, a deep belief network (DBN) coupled with a direct use of tiny images, which has been used to recognize and classify Arabic alphabetical letters. The use of deep learning contributed to extracting the most important features that are sparsely represented and played an important role in simplifying the overall recognition task. In total, around 6,000 samples of the 28 Arabic alphabetic signs have been used after resizing and normalization for feature extraction. The classification process was investigated using a softmax regression and achieved an overall accuracy of 83.32%, showing high reliability of the DBN-based Arabic alphabetical character recognition model. This model also achieved a sensitivity and a specificity of 70.5% and 96.2%, respectively.
{"title":"Arabic Sign Language Characters Recognition Based on Deep Learning Approach and a Simple Linear Classifier","authors":"Ahmad Hasasneh","doi":"10.5455/jjcit.71-1587943974","DOIUrl":"https://doi.org/10.5455/jjcit.71-1587943974","url":null,"abstract":"One of the best ways of communication between deaf people and hearing people is based on sign language or so-called hand gestures. In the Arab society, only deaf people and specialists could deal with Arabic sign language, which makes the deaf community narrow and thus communicating with normal people difficult. In addition to that, studying the problem of Arabic sign language recognition (ArSLR) has been paid attention recently, which emphasizes the necessity of investigating other approaches for such a problem. This paper proposes a novel ArSLR scheme based on an unsupervised deep learning algorithm, a deep belief network (DBN) coupled with a direct use of tiny images, which has been used to recognize and classify Arabic alphabetical letters. The use of deep learning contributed to extracting the most important features that are sparsely represented and played an important role in simplifying the overall recognition task. In total, around 6,000 samples of the 28 Arabic alphabetic signs have been used after resizing and normalization for feature extraction. The classification process was investigated using a softmax regression and achieved an overall accuracy of 83.32%, showing high reliability of the DBN-based Arabic alphabetical character recognition model. This model also achieved a sensitivity and a specificity of 70.5% and 96.2%, respectively.","PeriodicalId":36757,"journal":{"name":"Jordanian Journal of Computers and Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70820231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.5455/jjcit.71-1590557276
Elham Darbanian, Dadmehr Rahbari, Roghayeh Ghanizadeh, M. Nickray
{"title":"IMPROVING RESPONSE TIME OF TASK OFFLOADING BY RANDOM FOREST, EXTRA-TREES AND ADABOOST CLASSIFIERS IN MOBILE FOG COMPUTING","authors":"Elham Darbanian, Dadmehr Rahbari, Roghayeh Ghanizadeh, M. Nickray","doi":"10.5455/jjcit.71-1590557276","DOIUrl":"https://doi.org/10.5455/jjcit.71-1590557276","url":null,"abstract":"","PeriodicalId":36757,"journal":{"name":"Jordanian Journal of Computers and Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70819890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.5455/jjcit.71-1595835783
Yuvaraaj Velayutham, Nur Azaliah Abu Bakar, N. H. Hassan, Ganthan Narayana Samy
{"title":"IOT SECURITY FOR SMART GRID ENVIRONMENT: ISSUES AND SOLUTIONS","authors":"Yuvaraaj Velayutham, Nur Azaliah Abu Bakar, N. H. Hassan, Ganthan Narayana Samy","doi":"10.5455/jjcit.71-1595835783","DOIUrl":"https://doi.org/10.5455/jjcit.71-1595835783","url":null,"abstract":"","PeriodicalId":36757,"journal":{"name":"Jordanian Journal of Computers and Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70819938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.5455/jjcit.71-1587215553
R. Elmansouri, Said Meghzili, A. Chaoui, A. Belghiat, Omar Hedjazi
{"title":"INTEGRATING UML 2.0 ACTIVITY DIAGRAMS AND PI-CALCULUS FOR MODELING AND VERIFICATION OF SOFTWARE SYSTEMS USING TGG","authors":"R. Elmansouri, Said Meghzili, A. Chaoui, A. Belghiat, Omar Hedjazi","doi":"10.5455/jjcit.71-1587215553","DOIUrl":"https://doi.org/10.5455/jjcit.71-1587215553","url":null,"abstract":"","PeriodicalId":36757,"journal":{"name":"Jordanian Journal of Computers and Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70820219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.5455/jjcit.71-1593380662
K. Nahar, Raed khatib, Moyawiah Shannaq, Malek Barhoush
{"title":"AN EFFICIENT HOLY QURAN RECITATION RECOGNIZER BASED ON SVM LEARNING MODEL","authors":"K. Nahar, Raed khatib, Moyawiah Shannaq, Malek Barhoush","doi":"10.5455/jjcit.71-1593380662","DOIUrl":"https://doi.org/10.5455/jjcit.71-1593380662","url":null,"abstract":"","PeriodicalId":36757,"journal":{"name":"Jordanian Journal of Computers and Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70819929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.5455/jjcit.71-1571410322
Arwa Shannaq, Lamiaa A. Elrefaei
Nowadays, the engagement of deep neural networks in computer vision increases the ability to achieve higher accuracy in many learning tasks, such as face recognition and detection. However, the automatic estimation of human age is still considered as the most challenging facial task that demands extra efforts to obtain an accepted accuracy for real application. In this paper, we attempt to obtain a satisfied model that overcomes the overfitting problem, by fine-tuning CNN model which was pre-trained on face recognition task to estimate the real age. To make the model more robust, we evaluated the model for real age estimation on two types of datasets: on the constrained FG_NET dataset, we achieved 3.446 of MAE, while on the unconstrained UTKFace dataset, we achieved 4.867 of MAE. The experimental results of our approach outperform other state-of-the-art age estimation models on the benchmark datasets. We also fine-tuned the model for age group classification task on Adience dataset and our model achieved an accuracy of 61.4%.
{"title":"AGE ESTIMATION USING SPECIFIC DOMAIN TRANSFER LEARNING","authors":"Arwa Shannaq, Lamiaa A. Elrefaei","doi":"10.5455/jjcit.71-1571410322","DOIUrl":"https://doi.org/10.5455/jjcit.71-1571410322","url":null,"abstract":"Nowadays, the engagement of deep neural networks in computer vision increases the ability to achieve higher accuracy in many learning tasks, such as face recognition and detection. However, the automatic estimation of human age is still considered as the most challenging facial task that demands extra efforts to obtain an accepted accuracy for real application. In this paper, we attempt to obtain a satisfied model that overcomes the overfitting problem, by fine-tuning CNN model which was pre-trained on face recognition task to estimate the real age. To make the model more robust, we evaluated the model for real age estimation on two types of datasets: on the constrained FG_NET dataset, we achieved 3.446 of MAE, while on the unconstrained UTKFace dataset, we achieved 4.867 of MAE. The experimental results of our approach outperform other state-of-the-art age estimation models on the benchmark datasets. We also fine-tuned the model for age group classification task on Adience dataset and our model achieved an accuracy of 61.4%.","PeriodicalId":36757,"journal":{"name":"Jordanian Journal of Computers and Information Technology","volume":"1 1","pages":""},"PeriodicalIF":1.2,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70820352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-08-01DOI: 10.5455/JJCIT.71-1554982934
Ashraf Y. A. Maghari
لقد شاع استخدام تقنيات ضغط الصور لتخزين البيانات ونقلها، الأمر الذي يتطلب حيزا تخزينيا كبيرا وسرعة نقل عالية. ويؤدي النمو السريع للصور عالية الجودة إلى تنامي الطلب على تقنيات فعالة لتخزين البيانات وتبادلها عبر الانترنت. في هذه الورقة البحثية، نقدم دراسة مقارنة بين خوارزميات تقنيتي DCT و DWT مع استخدام ترميز هوفمان. والمقارنة في هذه الدراسة مبنية على خسمة عوامل: معدل الضغط، ومتوسط مربع الخطأ (MSE)، وأعلى نسب الأشارة إلى الضجيج (PSNR)، ومقياس عامل التشابه البنيوي (SSIM)، وزمن الضغط/ازالة الضغط.
{"title":"A Comparative Study of DCT and DWT Image Compression Techniques combined with Huffman coding","authors":"Ashraf Y. A. Maghari","doi":"10.5455/JJCIT.71-1554982934","DOIUrl":"https://doi.org/10.5455/JJCIT.71-1554982934","url":null,"abstract":"لقد شاع استخدام تقنيات ضغط الصور لتخزين البيانات ونقلها، الأمر الذي يتطلب حيزا تخزينيا كبيرا وسرعة نقل عالية. ويؤدي النمو السريع للصور عالية الجودة إلى تنامي الطلب على تقنيات فعالة لتخزين البيانات وتبادلها عبر الانترنت. في هذه الورقة البحثية، نقدم دراسة مقارنة بين خوارزميات تقنيتي DCT و DWT مع استخدام ترميز هوفمان. والمقارنة في هذه الدراسة مبنية على خسمة عوامل: معدل الضغط، ومتوسط مربع الخطأ (MSE)، وأعلى نسب الأشارة إلى الضجيج (PSNR)، ومقياس عامل التشابه البنيوي (SSIM)، وزمن الضغط/ازالة الضغط.","PeriodicalId":36757,"journal":{"name":"Jordanian Journal of Computers and Information Technology","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48512201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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